consumer adoption of digital technologies for lifestyle ... · the main studies and results already...
TRANSCRIPT
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POLITECNICO DI MILANO
School of Industrial & Information Engineering
Master of Science in Management Engineering
Academic Year 2016-2017
SUPERVISORS:
Professor Luca Gastaldi
Professor Emanuele Lettieri
MASTER GRADUATION THESIS:
Pellegrini Lisa 852480
Pisati Alessandra 850835
Consumer adoption of digital technologies for
lifestyle monitoring:
a theoretical and empirical analysis with SEM
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ACKNOWLEDGEMENTS
We would like to thank the Observatory of Digital Innovation in Healthcare and in particular Professor Luca
Gastaldi for giving us the possibility to work on a current and interesting topic. During these months, he has
always been present not only to help and support us with his competences, but also to encourage and
motivate us.
Another acknowledgement goes to Professor Emanuele Lettieri, who was able to transfer us his knowledge
on the healthcare sector with precious advices.
We thank Marta Pinzone for helping us in applying the SEM methodology and for being always available for
clarifications.
Finally, we would like to thank all those people that supported us in these years and helped us in achieving
such an important goal: our families, our friends and our colleagues.
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Index
INDEX OF TABLES........................................................................................................................................................... 5
INDEX OF FIGURES ......................................................................................................................................................... 6
EXECUTIVE SUMMARY ................................................................................................................................................ 7
RIEPILOGO .................................................................................................................................................................14
1. INTRODUCTION .................................................................................................................................................22
1.1 RESEARCH CONTEXT AND OBJECTIVES ............................................................................................................ 22
2. LITERATURE ANALYSIS .......................................................................................................................................26
2.1 DIGITAL HEALTH .............................................................................................................................................. 26
2.1.1 Definition..................................................................................................................................................... 26
2.1.2 The current scenario ................................................................................................................................... 29
2.2 QUANTIFIED SELF ............................................................................................................................................ 33
2.2.1 The context ................................................................................................................................................. 35
2.2.2 Motivations ................................................................................................................................................. 38
2.2.3 Limitations and challenges ......................................................................................................................... 40
2.2.4 Tools ............................................................................................................................................................ 42
2.2.5 The wearables’ market ............................................................................................................................... 46
2.2.6 The current usage ....................................................................................................................................... 48
2.3 IMPACT ON THE PHYSICIAN-PATIENT RELATIONSHIP ...................................................................................... 49
2.3.1 Reasons behind the change ........................................................................................................................ 49
2.3.2 Evolution of the physician-patient relationship .......................................................................................... 50
2.3.3 Empirical evidences ..................................................................................................................................... 53
2.4 THE BEHAVIOUR ADOPTION THEORIES ........................................................................................................... 53
2.4.1 Attitudes...................................................................................................................................................... 55
2.4.2 Theory of reasoned action .......................................................................................................................... 56
2.4.3 Theory of planned behaviour ...................................................................................................................... 57
2.4.4 Technology acceptance model .................................................................................................................... 59
2.4.5 Habitual and repetitive behaviour .............................................................................................................. 59
2.5 THE RESEARCH ON WEARABLE TECHNOLOGIES .............................................................................................. 61
2.5.1 Technology features and utility ................................................................................................................... 66
2.5.2 Context ........................................................................................................................................................ 71
2.5.3 User ............................................................................................................................................................. 74
2.6 THE CURRENT CUSTOMER JOURNEY ............................................................................................................... 76
2.7 CONTRIBUTIONS AND GAPS ............................................................................................................................ 79
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3. METHODOLOGY ................................................................................................................................................82
3.1 GAPS IDENTIFICATION ..................................................................................................................................... 82
3.2 LITERATURE ANALYSIS ..................................................................................................................................... 84
3.3 EMPIRICAL ANALYSIS ....................................................................................................................................... 88
3.3.1 Construct creation ....................................................................................................................................... 88
3.3.2 Hypotheses definition ................................................................................................................................. 93
3.3.3 Questionnaire analysis ................................................................................................................................ 98
3.3.4 Data analysis ............................................................................................................................................. 101
3.3.5 Result analysis ........................................................................................................................................... 109
3.3.6 Wrap up on the process ............................................................................................................................ 110
4. EMPIRICAL RESEARCH ..................................................................................................................................... 112
4.1 THE QUESTIONAIRRE ..................................................................................................................................... 112
4.1.1 Results from the survey ............................................................................................................................. 112
4.1.2 Constructs analysis .................................................................................................................................... 116
4.2 DATA ANALYSIS ............................................................................................................................................. 117
4.2.1 Model analysis with SEM .......................................................................................................................... 117
5. RESULTS DISCUSSION ...................................................................................................................................... 123
6. CONCLUSIONS ................................................................................................................................................. 132
6.1 CONTRIBUTIONS............................................................................................................................................ 132
6.2 LIMITS AND FUTURE RESEARCH .................................................................................................................... 135
7. REFERENCES .................................................................................................................................................... 139
7.1 BIBLIOGRAPHY............................................................................................................................................... 139
7.2 WEB SITES ...................................................................................................................................................... 143
ATTACHED: SURVEY ................................................................................................................................................. 146
Index of tables
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Index of tables
Table 1 Construct name and definition ........................................................................................................... 11
Table 2 Supported hypothesis ......................................................................................................................... 12
Table 3 Nome e definizione dei costrutti ........................................................................................................ 18
Table 4 Ipotesi supportate ............................................................................................................................... 20
Table 5 Digital health components .................................................................................................................. 28
Table 6 Stakeholder map ................................................................................................................................. 29
Table 7 Context of monitoring......................................................................................................................... 34
Table 8 The behaviour adoption theories ....................................................................................................... 55
Table 9 Investigated Hypothesis ...................................................................................................................... 65
Table 10 Investigated factors .......................................................................................................................... 65
Table 11 Customer journey ............................................................................................................................. 78
Table 12 Literature gaps .................................................................................................................................. 81
Table 13 Keywords .......................................................................................................................................... 85
Table 14 Group of keywords and selected articles ......................................................................................... 86
Table 15 Constructs name and definition ....................................................................................................... 90
Table 16 Items and associated questions ........................................................................................................ 93
Table 17 Distribution of education, residence and residence size .................................................................. 99
Table 18 Constructs mean and standard deviation ....................................................................................... 113
Table 19 Control variables ............................................................................................................................. 116
Table 20 Cronbach’s Alpha ............................................................................................................................ 117
Table 21 Measurement model indicators ..................................................................................................... 119
Table 22 Discriminant validity ....................................................................................................................... 120
Table 23 Supported hypothesis ..................................................................................................................... 120
Table 24 Structural model, fit indices ............................................................................................................ 121
Table 25 R-squared ........................................................................................................................................ 122
Table 26 Control variables, supported hypothesis ........................................................................................ 122
Table 27 Contributions .................................................................................................................................. 135
Table 28 Limits and future research .............................................................................................................. 136
Index of figures
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Index of figures Figure 1 Hypotheses ........................................................................................................................................ 10
Figure 2 Path coefficient .................................................................................................................................. 11
Figure 3 Ipotesi supportate ............................................................................................................................. 18
Figure 4 Ipotesi con coefficienti ...................................................................................................................... 19
Figure 5 Ownership of digital health categories by numbers ......................................................................... 32
Figure 6 Digital healthcare system .................................................................................................................. 33
Figure 7 Device ownership in 2014 ................................................................................................................. 36
Figure 8 Google trends for smartwatch and wearable .................................................................................... 37
Figure 9 Number of connected wearable ........................................................................................................ 38
Figure 10 Market share for wearable devices ................................................................................................. 46
Figure 11 Wearable positioning map .............................................................................................................. 47
Figure 12 Wearable Unit Shipments................................................................................................................ 48
Figure 13 Factors contribute to the shift in the role and self-perception of patients .................................... 50
Figure 14 Evolution of the physician-patient relationship .............................................................................. 52
Figure 15 Theory of reasoned action ............................................................................................................... 57
Figure 16 Theory of planned behaviour .......................................................................................................... 58
Figure 17 Technology acceptance model ........................................................................................................ 59
Figure 18 Research question identification ..................................................................................................... 83
Figure 19 Process of article selection .............................................................................................................. 87
Figure 20 Hypotheses representation ............................................................................................................. 94
Figure 21 Distribution in different geographic areas ...................................................................................... 99
Figure 22 Population by chronic disease ....................................................................................................... 100
Figure 23 Population by sport attitude ......................................................................................................... 100
Figure 24 Approaches for handling common method variance .................................................................... 103
Figure 25 Path Analysis and Confirmatory Factor Analysis ........................................................................... 106
Figure 26 Mathematical model ..................................................................................................................... 109
Figure 27 SEM model results ......................................................................................................................... 118
Figure 28 Online health literacy influence .................................................................................................... 124
Figure 29 Doctor opinion influence ............................................................................................................... 126
Figure 30 Theories' hypothesis ...................................................................................................................... 129
Figure 31 Impact of pbc on pu ....................................................................................................................... 130
Figure 32 Overall model ................................................................................................................................ 131
EXECUTIVE SUMMARY
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EXECUTIVE SUMMARY
The healthcare sector is facing new challenges. Global healthcare is under increasing pressure due to the
aging of populations, the rising costs of chronic diseases and the progressive contraction of government
budgets. Digital technologies can play a relevant role in facing these challenges, but at the same time can
represent a challenge themselves. Nowadays we can observe just the first development and diffusion of
these technologies in the healthcare context. Digital technologies play a crucial role in empowering people
to take charge of their own health and wellness, by providing them timely and ubiquitously support and
control with personalized information. This could be a way to limit the rising cost of healthcare and, at the
same time, to improve life quality. Among the solutions that can bring many benefits to healthcare it is
possible to find wearable devices, which can instantaneously monitor health parameters such as heartrate,
calories consumed, walked steps and slept hours.
It is recognized that most diseases are partly caused by lifestyle choices. Unwholesome diets, tobacco use
and sedentary conducts, among other unhealthy habits, potentially contribute to develop several illnesses,
and limit the effectiveness of medical treatments. As a consequence, the constant monitoring of health and
lifestyle can prevent diseases. Moreover, trackers increase patient empowerment and experience. At the
same time, the analysis of the vast amount of data derived by these devices improve the ability to prevent
and diagnose illnesses for the overall community.
Wearable devices seem to have impacted the market: in 2015 the five major 5 players (Fitbit, Apple,
Garmin, Samsung, Xiaomi) sold 51,4 million units over the 81,9 of the total sales worldwide up from 28.8
million in 2014. Forecasts predicted this market to grow at the same rate of 64% for at least one year, but
this result did not happen. In fact, 2016 recorded an increase in the unit shipped but at a lower rate.
Moreover, researches observed that, after a period of 6 month, at least one third of wearable owners stop
using them.
These two issues highlight how, despite the fact that wearable technologies are quite known, their
adoption is still relative low. Due to importance of optimizing the investment on digital technologies, and at
the same time of increasing as much as possible the returns on these investments, the main goal of this
study is to investigate the factors that can lead individual to use digital technologies, and understand how
to leverage on them to promote their use. In particular, the focus is on the monitoring of the lifestyle to
benefit of the advantages generated by the new system. From the theoretic perspective, the aim is to
discuss the variables that influence the use of health monitoring technologies, while on the practical
perspective, the objective is to give tangible suggestion on how implementing and exploiting the
opportunities offered by these technologies.
EXECUTIVE SUMMARY
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In order to accomplish these goals, a model was designed and tested statistically thanks to the data
gathered through a survey on the topic. The survey was delivered by DoxaPharma and commissioned by
The Observatory of Digital Innovation in Healthcare of Politecnico di Milano in 2016.
To draw the model, a literature analysis has been conducted. The analysis aimed at understanding the
digital healthcare context, deepening the behavioural theories behind technology adoption, and identifying
the main studies and results already achieved on lifestyle monitoring topic.
An important contemporary trend is the quantified self, which can be defined as individual engaged in the
self-tracking of any kind of biological, physical, behavioral, or environmental information. This phenomenon
comprehends the monitoring of health parameters, which gives patient the possibility to instant monitor
their activities. This makes people understand what they should change in their lifestyle in order to be
healthier. The collection of data coming from all the different quantified self devices can be analyzed and,
thanks also to machine learning algorithms, can open the doors to predictive medicine solutions.
Of course, there are also some points of weakness that limit the success and diffusion of the new digital
system. At the base of many services offered through digital technologies there is data collection and data
sharing. Building an aggregate health database with reliable data is very difficult. In addition, there are
issues like data management and privacy protection. Moreover, getting clinicians to change their workflow
and patients to actually use the new systems is not trivial. It is also a matter of culture and engagement.
Another interesting finding coming from the literature analysis regards the relation between patients and
physicians. The doctor-patient relationship is subject to a progressive change due to the development of
information and services that encourage consumers to become more responsible for their own health.
They are always more inclined to actively participate in the health-related decisions affecting them.
Historically, physicians have taken an authoritarian role, in which the patient is left uninformed in a dark
hole of ignorance. In a more modern approach, patients are seen as partners that should be educated.
Since the implementation and use of these technologies is quite complex, each actor should be provided
not only with the right tool to collaborate, but the promotion should leverage on different factors. The
theories of behaviour identified those factors that can be stimulated in a different set of contexts. There
are three main theories that studied the controlled behaviour: Theory of Reasoned Action (TRA),
Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). These theories are based on
social psychologists’ belief that human behaviour is guided by social attitudes and intention. In addition to
the core variables (attitude, intention and behaviour), TRA investigated subjective norms, TAM introduced
the variable of perceived usefulness and perceived easy-of-use, and TPB tested perceived behavioural
control.
EXECUTIVE SUMMARY
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Finally, the last part of the literature analysis was lead analyzing the different studies conducted on the
adoption of digital technologies for health care purposes monitoring. It emerged that the main topics of
investigation until now have been some of the traditional variables of the adoption theories (attitude,
intention, behaviour, subjective norms, perceived usefulness and perceived easy-of-use), together with
peculiar aspects related to the two main souls of wearables: the fashionable one and the technological one.
There is a clear evidence that the aesthetic form, the design of the wearable as well as the compatibility of
the technology with consumer lifestyle are important determinants of the adoption. At the same time,
there are issues that have not been investigated yet.
One of the main gaps of the literature review is that, until now, no research has investigated the effect of
the construct “perceived behavioural control”. This construct is related to the resources available in term of
time, money and knowledge to perform the behaviour. Concerning this last issue, there is still no study that
investigated “online health literacy”, defined as the level of competences and confidences an individual has
on searching online, evaluating and making sense out of health information. Moreover, since the subject
fall in the healthcare context, the role of the doctor could influence the behaviour. Despite the relevance,
there aren’t studies that tried to investigate this issue. In particular, could be interesting to investigate this
element to understand how the change of the doctor-patient relationship, emerged from the literature
analysis, has impacted the adoption of digital technologies for lifestyle monitoring.
These three main gaps, defined the three main research questions.
- Research question N°1: How the perception of the individual concerning the easiness of control
over the behaviour is related to the adoption of the behaviour itself?
- Research question N°2: Are there some specific resources needed for the adoption of the studies
behaviour? In addition to time and money, is some peculiar knowledge needed to develop
interest in the behaviour?
- Research question N°3: How the doctor opinion and the doctor-patient relationship can affect the
use of digital health monitoring technologies?
Once defined the issue to address, the model could be drawn. In order to formulate the model, it was
necessary to define:
- Name of the construct and their definition, in order to give a clear explanation of the measurement
purpose of the construct, presented in the following table;
EXECUTIVE SUMMARY
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- Proposition: relationship among constructs, shown in the following figure.
CONSTRUCT NAME DEFINITION
PERCEIVED USEFULNESS The perceived utility an individual has over monitoring
the lifestyle with digital technologies
ONLINE HEALTH LITERACY The confidence an individual has on searching health
information on Internet
PERCEIVED BEHAVIOURAL CONTROL The perception on the control and easiness over the use
of a digital technologies
ATTITUDE The positive evaluation of the behaviour of using digital
technologies to monitor lifestyle
INTENTION The intention to use digital technologies to monitor daily
activities
PERCEIVED DOCTOR OPINION The perceived level of interest of the doctor in promoting
the monitoring of activities tracking
BEHAVIOUR The use of digital technologies with the aim of
monitoring heart rate; steps; trainings or calories
Table 1 Construct name and definition
Figure 1 Hypothesis
EXECUTIVE SUMMARY
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In addition to the constructs, derived from the literature analysis, some control variables have been
introduced on the behaviour. Examples of control variables are the presence of chronic diseases, the age,
the level of sportiveness and of instruction.
The model was analyzed using the Structural Equation Modeling (SEM) technique and results are shown in
Figure 2 and in Table 2. SEM analysis consists of two parts: measurement model and structural model. On
one hand the measurement model aims at answering at the question “how are the constructs related to
measurable variables?”; on the other hand, the structural model answers the question “what are the
relationship between the constructs?”. The quality of the measurement model is determined by the
convergent validity and the discriminant validity. The convergent validity defines how well the items of a
construct converge on the construct itself and in the present study were used as indicators the Average
Variance Extracted (AVE) and the Construct Reliability (CR). The discriminant validity defines how well the
constructs of the model are different among them. For the structural model, a good fit is obtained when
the Chi-square statistic is not significant, which by convention is taken to happen for p-values ≥ 0,05. In
addition, for the SEM it is important to consider the fit indices. There are two types of fit indices: absolute
and incremental. The first ones indicate the degree to which the hypothesized model reproduces the
sample data, while the second measure the proportional improvement in fit when the hypothesized model
is compared with two reference models: a worst case or null model, and an ideal model that perfectly
represents the modeled phenomena in the studied population. In this case were used indicators as the
Root Mean Square Error of Approximation (RMSEA); the Root Mean Square Residual (RMR or SRMR) and
the Comparative Fit Index (CFI).
Figure 2 Path coefficient
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HYPOTHESIS PATH COEFFICIENT P-VALUE STATISTIC VALIDITY
H1: OHL PBC 0,4382 0,000 Supported
H2: PDO PBC 0,6187 0,000 Supported
H3: PDO PU 0,4367 0,000 Supported
H4: PDO ATT - 0,3185 0,000 Supported
H5: PDO INT 0,3203 0,000 Supported
H6: PDO BVH - 0,3028 0,000 Supported
H7: PU ATT 0,8999 0,000 Supported
H8: ATT INT 0,0889 0,019 Supported
H9: PBC PU 0,5627 0,000 Supported
H10: PBC INT 0,5766 0,000 Supported
H11: PBC BHV 0,2433 0,008 Supported
H12: INT BHV 0,6099 0,000 Supported
Table 2 Supported hypothesis
After analyzing the measurement and the structural model, the main theoretical contributions are:
- Online health literacy has a positive significant influence on perceived behavioural control.
- Perceived doctor opinion influences perceived behavioural control in a strong way, intention and
perceived usefulness with a medium strength and has a negative impact on attitude and behaviour
- Perceived usefulness has a positive influence on attitude
- Attitude has a very weak impact on intention
- Perceived behavioural control affects intention and perceived usefulness in a strong way, and in a
weaker one behaviour
- Intention positively influences behaviour
- The higher the age of responders, the lower the inclination to adopt digital technologies
- The lower the level of sportiness of responders, the lower the inclination to adopt digital
technologies
- The control variables chronic diseases and level of instruction are not statistically supported by the
model (p-value > 0,05)
On the practical side, the study was able to demonstrate that, in order to promote the diffusion of digital
health monitoring technologies, there are some sensible factors over which it is convenient to focus since
they have a higher impact on behaviour. For example, it could be useful leveraging on perceived
behavioural control, by empowering the individual with the necessary resources to adopt the studied
behaviour. Some practical examples of the actions aimed at increasing perceived behavioural control and
so the behaviour adoption could be give the device for free to people that can’t afford it, or give a discount
EXECUTIVE SUMMARY
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for a segment of the population. An important resource that should be stimulated is the knowledge on the
use of the technology. This is testified by the result on online health literacy, which should be encouraged.
At the same time, the model suggests to leverage on the doctor influencing power over intention. The
suggestion considering the overall results is that, in order to promote the adoption of digital technologies
for the monitoring of lifestyle, individuals should be educated more than pushed toward a purchase. In this
perspective, the doctor role should be the one of an expert advisor. This new figure is coherent with the
change in the doctor-patient relationship. So, a character that in a way loses some authority traits to level
the past distance present in the relationship with the patient and does not give direct orders that can
negative influence behaviour.
These results are based on a model, which is defined as the representation or simplified version of an
aspect of the real world. By definition a model has to make some simplifications. Thus, is clear that every
model has some limitations — also the one that has been developed in this study. Some examples of
limitations are the lack of investigation of the antecedents of perceived doctor opinion, the lack of
healthcare professionals involved in the survey and the absence of variables related to motivations and
habitual behaviour.
To conclude it is possible to summarize that the thesis has contributed to the literature with results
validated through a base of a quite large amount of data (1000 responders) and through a deep empirical
research developed with the use of the SEM technique. The main contributions are: the investigation of
perceived behavioural control in the context of digital technologies for lifestyle tracking, the better
understanding of the role of the doctor and the impact of online health literacy to encourage and promote
the adoption of the studied behaviour.
RIEPILOGO
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RIEPILOGO
Il settore della sanità sta affrontando e dovrà affrontare nuove sfide. La sanità a livello globale è messa
sotto grande pressione dall’invecchiamento della popolazione, dall’aumento dei costi associati alle malattie
croniche e dagli scarsi fondi istituzionali disponibili. Le tecnologie digitali possono assumere un ruolo
rilevante nell’affrontare queste sfide, ma allo stesso tempo rappresentano una sfida essa stessa. Oggigiorno
è possibile osservare solo i primi sviluppi e le prime implementazioni delle nuove tecnologie digitali in
ambito sanitario. Le tecnologie digitali giocano un ruolo da protagoniste nella nuova epoca della sanità,
coinvolgendo i pazienti nel farsi carico della propria salute e del proprio benessere, fornendo loro
informazioni personalizzate, supporto e controllo tempestivi e costanti. Questo può essere un modo per
limitare i crescenti costi della sanità e allo stesso tempo migliorare lo stile di vita. Alcune delle nuove
tecnologie che possono essere di grande utilità alla sanità, sono le applicazioni mobile e i dispositivi
wearable che monitorano istantaneamente parametri vitali come il battito cardiaco, le calorie consumate e
le ore di sonno.
È risaputo che una considerevole maggioranza delle malattie è in parte causata da scelte nello stile di vita.
Seguire una dieta malsana, fumare tabacco e non praticare attività sportiva, oltre ad altre cattive abitudini;
possono potenzialmente contribuire a sviluppare diverse malattie e a limitare l’efficacia di alcuni
trattamenti medici. Di conseguenza, il costante monitoraggio della salute e dello stile di vita può prevenire
delle malattie. Inoltre, tali tecnologie possono aumentare l’esperienza del consumatore grazie a un’attiva
partecipazione. Allo stesso tempo l’analisi di una grande mole di dati, collezionati da questi dispositivi, può
migliorare la capacità di prevenire e diagnosticare malattie da parte dei medici con conseguenti benefici
per l’intera comunità.
I dispositivi wearable sembrano aver influenzato il mercato: nel 2015 i cinque principali attori (Fitbit, Apple,
Garmin, Samsung, Xiaomi) hanno venduto 51,4 milioni di unità rispetto alle 81,9 del totale delle vendite in
tutto il mondo, con un forte aumento nei confronti delle 28,8 milioni di unità del 2014. Le previsioni
prevedevano una crescita del mercato costante (64%) per almeno un anno, tuttavia ciò non è accaduto.
Infatti, sebbene il 2016 abbia registrato un aumento delle unità vendute, il tasso di crescita è stato inferiore
rispetto alle stime. Inoltre, diversi studi e ricerche hanno osservato che dopo un periodo di 6 mesi, almeno
un terzo dei proprietari di dispositivi wearable smette di usarli.
Questi due temi evidenziano come, nonostante le tecnologie indossabili siano ben note, la loro adozione è
ancora relativamente bassa. A causa dell'importanza di ottimizzare gli investimenti sulle tecnologie digitali
e al contempo aumentare il più possibile i rendimenti di tali investimenti, l'obiettivo principale di questo
studio è stato quello di indagare i fattori che possono indurre l'individuo a utilizzare le tecnologie digitali e
capire come far leva su di essi per promuoverne l’adozione. In particolare il focus è sul monitoraggio dello
RIEPILOGO
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stile di vita al fine di sfruttare i benefici generati dai nuovi sistemi. Dal punto di vista teorico, lo scopo è
discutere le variabili che influenzano l’uso delle tecnologie per il monitoraggio della salute; mentre, dal
punto di vista pratico, l’obiettivo è di fornire dei consigli tangibili su cui sviluppare e implementare le
opportunità offerte dai nuovi dispositivi.
Per fare ciò è stato deciso di disegnare un modello e di testarlo statisticamente grazie ai dati raccolti
attraverso un sondaggio sul tema. L'indagine è stata sviluppata da DoxaPharma e commissionata
dall'Osservatorio di Innovazione Digitale in Sanità del Politecnico di Milano nel 2016.
In primo luogo, per disegnare il modello è stata condotta un'analisi della letteratura. La revisione della
letteratura mira a comprendere il contesto sanitario digitale, approfondendo le teorie comportamentali e
individuando i principali studi e risultati già ottenuti su questi temi.
Un importante trend legato al monitoraggio è quello che vede l’evoluzione del quantified self, che può
essere definito come un individuo impegnato nell'automonitoraggio di qualsiasi tipo di informazione
biologica, fisica, comportamentale o ambientale. Questo fenomeno comprende la rilevazione dei parametri
sanitari; che dà al paziente la possibilità di monitorare immediatamente le proprie attività e quindi di capire
cosa dovrebbe cambiare nel suo stile di vita per renderlo più sano. La raccolta dei dati può essere analizzata
e, grazie anche agli algoritmi di machine learning, è possibile formulare soluzioni di medicina preventiva.
Naturalmente, ci sono anche alcuni punti di debolezza che limitano il successo e la diffusione del nuovo
sistema digitale. Alla base dei molti servizi offerti attraverso le tecnologie digitali ci sono la raccolta di dati
personali e la loro condivisione. La costruzione di un’affidabile base di dati in ambito sanitario è molto
difficile. In aggiunta ci sono problemi come la gestione dei dati e la protezione della privacy. Inoltre, portare
i medici a cambiare il loro modo di lavorare e portare i pazienti a usare effettivamente i nuovi sistemi, non è
banale. È anche una questione di cultura e coinvolgimento.
Un altro risultato interessante proveniente dall’analisi della letteratura riguarda la relazione medico-
paziente. Tale relazione è soggetta a un cambiamento progressivo dovuto allo sviluppo d’informazioni e
servizi che incoraggiano i consumatori a diventare più responsabili della propria salute. I pazienti sono
sempre più propensi a partecipare attivamente alla presa di decisioni che si riferiscono alla loro salute.
Storicamente, i medici hanno sempre assunto un ruolo autoritario, in cui il paziente è lasciato in un buio di
ignoranza. In un approccio più moderno, i pazienti sono considerati come partner che devono essere
istruiti.
Poiché l'implementazione e l'uso di queste tecnologie è abbastanza complesso, ogni attore non dovrebbe
essere solo fornito con lo strumento giusto per collaborare, ma la promozione dovrebbe far leva su diversi
fattori. Le teorie del comportamento identificano quei fattori che possono essere stimolati in un diverso
insieme di contesti. Ci sono tre teorie principali che hanno studiato il comportamento controllato: Theory
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of Reasoned Action (TRA), Technology Acceptance Model (TAM) e Theory of Planned Behavior (TPB).
Queste teorie si basano sulla convinzione degli psicologi sociali che il comportamento umano è guidato da
attitudini e intenzioni. Oltre alle variabili di base (attitudine, intenzione e comportamento), TRA ha studiato
le norme soggettive; TAM ha introdotto la variabile di utilità percepita e della facilità di utilizzo percepita e
TPB ha testato il controllo comportamentale percepito.
Infine, l’analisi della letteratura ha portato anche all’analisi dei diversi studi condotti sull'adozione di
tecnologie digitali per il monitoraggio dei parametri vitali. Da ciò è emerso che i principali argomenti
d’indagine finora sviluppati sono stati alcune delle tradizionali variabili delle teorie dell'adozione
(attitudine, intenzione, comportamento, norme soggettive, utilità percepita e facilità di utilizzo percepita),
insieme a particolari aspetti legati alle due principali anime dei wearables: quella della moda e quella
tecnologica. Ci sono evidenti prove che la forma estetica e il design dei dispositivi, così come la
compatibilità della tecnologia con lo stile di vita dei consumatori, sono determinanti importanti per
l'adozione. Allo stesso tempo, ci sono problemi che non sono ancora stati investigati.
Una delle principali lacune, emerse dall’analisi della letteratura, è che fino ad ora nessuna ricerca ha
esaminato l'effetto del controllo comportamentale percepito. Questo costrutto è legato alle risorse
disponibili in termini di tempo, denaro e conoscenza per svolgere il comportamento. Riguardo a
quest’ultimo problema, non ci sono ancora studi che hanno approfondito il concetto di alfabetizzazione
sanitaria online, definita come il livello di competenze e confidenze che un individuo ha sulla ricerca in
internet, la valutazione e la comprensione di informazioni sulla salute. Inoltre, dal momento che il contesto
di ricerca di questa tesi ricade in ambito sanitario, il ruolo del dottore può influenzare il comportamento.
Nonostante la rilevanza, non ci sono studi che cercano di comprendere maggiormente questo aspetto. In
particolare può essere interessante analizzarlo al fine di capire come il cambiamento della relazione del
paziente con il medico, emersa dalla revisione della letteratura, impatta sull’adozione delle tecnologie
digitali per monitorare lo stile di vita.
Queste tre principali lacune hanno definito le tre domande di ricerca.
- Domanda di ricerca n ° 1: come è legata la percezione dell'individuo riguardante la facilità del controllo
sul comportamento all'adozione del comportamento stesso?
- Domanda di ricerca n ° 2: ci sono alcune risorse specifiche necessarie per l'adozione del comportamento
in analisi? Oltre al tempo e al denaro, ci sono alcune conoscenze peculiari necessarie per sviluppare
l'interesse verso il comportamento?
- Domanda di ricerca n ° 3: come l'opinione del medico e la relazione medico-paziente possono
influenzare l'utilizzo delle tecnologie di monitoraggio sanitario digitale?
RIEPILOGO
17
Una volta definito il problema da affrontare, è possibile definire il modello. Per formularlo è stato
necessario definire:
- Nome dei costrutti e loro definizione, al fine di fornire una chiara spiegazione dello scopo di
misurare i costrutti analizzati, i quali sono presentati nella tabella seguente;
NOME DEL COSTRUTTO DEFINIZIONE
UTILITÀ PERCEPITA L’utilità percepita che un individuo ha sul monitoraggio dello stile
di vita tramite le tecnologie digitali.
ALFABETIZZAZIONE SANITARIA
ONLINE
La confidenza che un individuo ha nel cercare informazioni su
Internet riguardo alla propria salute.
CONTROLLO COMPORTAMENTALE
PERCEPITO
La percezione del controllo e della facilità che un individuo ha
sull’utilizzo di una tecnologia digitale per monitorare lo stile di
vita.
ATTITUDINE La positiva valutazione che un individuo ha sul comportamento di
tecnologie digitali per monitorare lo stile di vita.
INTENZIONE L’intenzione che un individuo ha di utilizzare tecnologie digitali
per monitorare le attività quotidiane e i parametri sanitari.
OPINIONE PERCEPITA DEL
DOTTORE
Il livello d’interesse, percepito da un individuo, che il proprio
dottore ha nel promuovere il monitoraggio delle attività.
COMPORTAMENTO L’utilizzo da parte di un individuo di tecnologie digitali al fine di
monitorare parametri vitali quali il battito cardiaco, i passi, gli
allenamenti o le calorie consumate.
Table 3 Nome e definizione dei costrutti
RIEPILOGO
18
- Proposizione: relazione tra i costrutti, mostrata nella figura sottostante.
In aggiunta ai costrutti derivanti dall’analisi della letteratura sono state inserite nel modello anche delle
variabili di controllo sul comportamento come ad esempio l’età, l’attività fisica praticata dall’individuo,
l’istruzione e la presenza di malattie croniche.
Il modello è stato analizzato tramite la tecnica Structural Equation Modeling (SEM) e i risultati sono riportati
nella Figure 4 e nella Table 4. La SEM si sviluppa in due parti: modello di misura e modello strutturale. Da un
lato il modello di misura si pone l’obiettivo di rispondere alla domanda “come sono legati i costrutti alle
variabili osservate?”; dall’altro lato il modello strutturale risponde alla domanda “quali sono le relazioni tra
i diversi costrutti?”. La qualità del modello di misura è determinata dalla convergent validity e dalla
discriminant validity. La convergent validity definisce quanto gli item di un costrutto convergono sul
costrutto stesso e, nel presente lavoro di tesi, gli indicatori utilizzati per verificare questa parte sono
Average Variance Extracted (AVE) e Construct Reliability (CR). La discriminant validity definisce quanto i
costrutti di un modello sono differenti tra loro. Per il modello strutturale, un buon risultato si ottiene
quando l’indice Chi-quadro non è significativo, che per convenzione si verifica per valori del p-value
maggiori di 0,05. Inoltre, per un appropriato uso della SEM è importante considerare i cosiddetti fit indices.
Esistono due tipi di fit indices: assoluti o incrementali. I primi indicano il grado in cui le ipotesi del modello
rappresentano effettivamente il campione di dati; mentre i secondi confrontano il modello in analisi con
due modelli di riferimento: un caso peggiore o modello nullo e un modello ideale che rappresenta
perfettamente il fenomeno e la popolazione in esame. Per questo lavoro di tesi gli indicatori utilizzati sono
Figure 3 Ipotesi supportate
RIEPILOGO
19
stati Root Mean Square Error of Approximation (RMSEA); Root Mean Square Residual (RMR or SRMR) e
Comparative Fit Index (CFI).
IPOTESI PATH COEFFICIENT P-VALUE VALIDITÀ STATISTICA
H1: OHL PBC 0,4382 0,000 Supportata
H2: PDO PBC 0,6187 0,000 Supportata
H3: PDO PU 0,4367 0,000 Supportata
H4: PDO ATT - 0,3185 0,000 Supportata
H5: PDO INT 0,3203 0,000 Supportata
H6: PDO BVH - 0,3028 0,000 Supportata
H7: PU ATT 0,8999 0,000 Supportata
H8: ATT INT 0,0889 0,019 Supportata
H9: PBC PU 0,5627 0,000 Supportata
H10: PBC INT 0,5766 0,000 Supportata
H11: PBC BHV 0,2433 0,008 Supportata
H12: INT BHV 0,6099 0,000 Supportata
Table 4 Ipotesi supportate
In seguito all’analisi del modello di misura e del modello strutturale, i principali contributi teorici sono:
- L’alfabetizzazione sanitaria online ha un positivo e significativo impatto sul controllo
comportamentale percepito.
Figure 4 Ipotesi con coefficienti
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20
- L’opinione percepita del dottore influenza in gran misura il controllo comportamentale percepito, e
più modestamente l’intenzione e l’utilità percepita. Al contrario ha un impatto negativo
sull’attitudine e sul comportamento.
- L’utilità percepita influenza positivamente l’attitudine.
- L’attitudine ha un impatto molto basso sull’intenzione.
- Il controllo comportamentale percepito influenza in maniera considerevole l’intenzione e l’utilità
percepita, mentre impatta relativamente poco sul comportamento.
- L’intenzione influenza positivamente il comportamento.
- Maggiore è l’età dei partecipanti al questionario, minore è l’inclinazione di adottare tecnologie
digitali per monitorare lo stile di vita.
- Minore è l’attività fisica praticata regolarmente dagli intervistati, minore è l’inclinazione verso il
comportamento.
- Le variabili di controllo malattie croniche e livello di istruzione non sono supportate statisticamente
dal modello (p-value > 0,05)
Dal punto di vista pratico, lo studio svolto è stato in grado di dimostrare che, al fine di promuovere la
diffusione di tecnologie digitali per il monitoraggio dello stato di salute, esistono dei fattori critici su cui è
più conveniente focalizzare l’attenzione poiché tali variabili hanno un maggiore impatto sul
comportamento. Per esempio, sarebbe utile agire sul controllo comportamentale percepito fornendo
l’individuo con tutte le risorse necessarie al fine di adottare il comportamento sotto studio. Alcuni esempi
pratici di azioni intente ad aumentare il controllo comportamentale percepito, e quindi l’adozione del
comportamento, sono il fornire gratuitamente i dispositivi a tutti coloro che non possono sostenere
l’investimento economico necessario per l’acquisto delle tecnologie, oppure il fornire sconti a determinate
fasce della popolazione. Un’altra importante risorsa che bisognerebbe stimolare è la conoscenza sull’uso
della tecnologia. Ciò è provato dal risultato riguardante l’alfabetizzazione sanitaria online, la quale
dovrebbe essere incoraggiata. Allo stesso tempo, il modello suggerisce di far leva sulla figra del dottore che
impatta positivamente sull’intenzione. Al fine di promuovere l’adozione dei dispositivi digitali per il
monitoraggio dello stile di vita, gli individui dovrebbero essere educati più che direttamente spinti verso
l’acquisto. Sotto questa prospettiva, il ruolo del dottore dovrebbe essere quello di un consigliere esperto.
La nuova figura è coerente con il cambiamento nella relazione medico-paziente, investigata nell’analisi
della letteratura. Di conseguenza, il dottore dovrebbe in un certo senso perdere parte di quella autorità che
ha caratterizzato il suo ruolo nel passato e non dovrebbe dare ordini diretti al paziente in quanto un simile
atteggiamento andrebbe a impattare negativamente sul comportamento.
RIEPILOGO
21
I risultati riportati sono stati generati sulla base di un modello, che è definito come la rappresentazione in
forma semplificata di un aspetto del mondo reale. Per definizione un modello è caratterizzato da una serie
di semplificazioni. Di conseguenza è ovvio che anche il modello proposto in questo lavoro di tesi abbia delle
limitazioni. Alcuni esempi sono la mancanza di analisi di possibili antecedenti del costrutto opinione
percepita del dottore, il mancato coinvolgimento di operatori sanitari tra gli intervistati del questionario e
l’assenza di variabili che tengono in considerazione motivazione e comportamenti abituali.
Per concludere, è possibile sostenere che la tesi ha contribuito alla letteratura con risultati validati
attraverso una base di dati considerevole (1000 intervistati) e attraverso una ricerca empirica sviluppata
tramite l’uso della tecnica SEM. i maggiori contributi sono: lo studio dell’impatto del controllo
comportamentale percepito in un contesto di tecnologie digitali per il monitoraggio dello stile di vita, la
miglior comprensione del ruolo del dottore e l’impatto dell’alfabetizzazione sanitaria online per
incoraggiare e promuovere l’adozione del comportamento in esame.
INTRODUCTION
22
1. INTRODUCTION
1.1 RESEARCH CONTEXT AND OBJECTIVES
Global healthcare is under increasing pressure due to the aging of populations, the rising costs of chronic
illness and the lower budgets for the health sector. Despite this, there is the hope that technology could
provide an efficient and cost-effective solution to many of these challenges. In particular, the possible new
technologies are the followings:
- Intelligent automation is making care delivery and administration more seamless across the health
ecosystem. From robots performing housekeeping duties to avatars streamlining the patient intake
process. It’s not about replacing people; it’s about allowing people to work more efficiently and
where they are needed most.
- Digital has generated a more fluid workforce that can go where help is needed without boundaries.
Digital services can allow you to Skype with a doctor. Virtual technology can allow a specialist in
New York treating a patient in New Mexico.
- Platforms are enabling smart cities, connected machines and more. In healthcare, they provide the
underlying technology that can make healthcare experiences more connected, linking the entire
ecosystem: from patients to providers, to health plans.
- Internet can provide an immense amount of knowledge and information on healthcare
- Wearable devices and fitness apps can now collect a big amount of personal information of
individual health parameter.
- The new technologies, and algorithms enable the possibility to manage the data collected and
formulate a more personalized health plan.
The benefits that digital technologies can have on the healthcare system are quite impactful. Nowadays we
can observe just the first development and diffusion of these technologies, without completely capturing
the advantages. In this first step of the digitalization, where investments in new technologies and
infrastructure are quite heavy, it is important to make these investments profitable in the future.
As already mentioned Healthcare systems are facing financial limitations while demand for their services is
rising. World report on aging and health from World Health Organization (WHO)1 in 2015 shows that the
problem of global population ageing is becoming more serious. The proportion of population aged over 60
1 The World Health Organization (WHO) is a specialized agency of the United Nations that is concerned with
international public health.
INTRODUCTION
23
years old will increase from 12% in 2015 to 22% in 20502. With a twice growing speed, the number of
elderly people aged 60 and over will reach 2 billion during next 35 years. By midcentury, ageing population
in many countries will rise significantly, which causes a series of problems all around the world. The medical
systems in many countries are taking heavy burdens, while the quantity of medical facilities and personnel
is seriously inadequate. The magnitude of these constrains makes utterly necessary to change current care
models in a bold manner, from late disease management to preventive personalized health, involving a
major shift in when, where and how care and support is delivered to each patient and service user. In fact,
it is generally recognized that most prevalent diseases are partly caused or aggravated by lifestyle choices
that people make in their everyday life. Unwholesome diets, tobacco use and sedentary conducts, among
other unhealthy habits, potentially contribute to develop severe illnesses and limit the effectiveness of
medical treatments (Oresti Banos, 2015). For example, the risk of lung cancer is 25 times higher for
smokers and according to the statistics smoke generates 82% of lung cancer3. Those who follow a healthy
lifestyle have a risk of being affected by cancer lower that 18% respect to the others4. Thus, enabling
people to make healthier choices, to be more resilient, and to deal more effectively with illness and
disability when it arises, turns to be a fundamental part of this necessary new health perspective.
Information and communication technology (ICT) is called upon to be a cornerstone of the new health era,
playing a crucial role in empowering people to take charge of their own health and wellness, by providing
them timely and ubiquitously with personalized information, support and control. One possible solution is
incorporation of both wearable computing and Internet of Things (IoT) technology into health.
The European commissions recognized the importance of digital technologies to improve the life quality.
The global mHealth5 market is growing rapidly; it can create jobs and economic grow by combining the ICT,
medical devices, pharmaceuticals, biotechnology and healthcare sector. The European commission
recognizes that in Europe, healthcare is behind almost every other sector in implementing ICT. There are
several reasons for this, like, the lack of common European standards, legal and privacy issues, on which
the institutions are working.
The constant monitoring of health parameters could play a crucial role in the prevention of disease for the
single individual. At the same time the analysis of the vast amount of data can improve the ability to
prevent and diagnose illnesses for the overall community. Thanks to diffusion of smartphones, mobile apps
2 Who, Ageing and health Fact Sheet N°404 (2015) http://www.who.int/mediacentre/factsheets/fs404/en/
3 https://cancer-code-europe.iarc.fr/index.php/it/12-modi/tabacco/682-l-consumo-di-tabacco-provoca-il-cancro-
quale-percentuale-dei-casi-di-cancro-e-causata-dal-fumo
4 https://cancer-code-europe.iarc.fr/index.php/it/12-modi/attivita-fisica
5 mHealth is an abbreviation for mobile health, a term used for the practice of medicine and public health supported
by mobile devices.
INTRODUCTION
24
for health monitoring, as step and calories tracking have had a lot of success. With the development of new
technologies, wearable devices emerged. Wearable devices seem to have impacted the market: in 2015 the
five major 5 players (Fitbit, Apple, Garmin, Samsung, Xiaomi) sold 51,4 million units worldwide up from 28.8
million in 2014. The forecast for 2020 predict the wearable market to reach 190 million units shipped.
Moreover, due to the importance of wearable technologies and the increasing number of researches
around this topic, a dedicated journal was created: International Journal of Wearable Devices (IJWD). It
provides a chance for academic and industry professionals to discuss recent progresses in the area of
wearables devices.
Many technology companies are revealing new wearables that could be used in patient care. Earlier in
2016, Philips introduced a wearable biosensor that would continuously measure vitals such as heart rate,
respiratory rate, skin temperature, posture, physical activity, and a single-lead ECG6. This biosensor would
be connected to software that would send notifications to the clinician or caregiver, which could help with
early detection and intervention to improve patient outcomes7.
The Biodesign Institute of Arizona State University is conducting a research project called Project
HoneyBee8. Project HoneyBee is researching how and which wearable biosensors can be used to drive
better patient outcomes while reducing healthcare costs. This project is aiming to validate biosensors as an
inexpensive technology that can be useful in a clinical setting and for reducing the costs of chronic disease.
Some disease areas already being studied include heart disease, chronic obstructive pulmonary disease
(COPD), atrial fibrillation, and diabetes.
It is evident that more research is needed to determine the best way to incorporate wearables into
healthcare to drive better outcomes. Many still believe there is a lot of potential so companies are
continuing to research and develop new products. In this context, we can understand how important is to
conduct more studies on the topic and in particular on the drivers of adoption of these technologies.
Due to importance of optimizing the investment on digital technologies, and at the same time of increasing
as much as possible the returns on these investments, the main goal of this study is to investigate the
motivation that can lead individual to use digital technologies, with a focus on the monitoring of the
lifestyle, to benefit of the advantages generated by the new system. Understand how the new digitalized
model can be implemented in the most effective way, is fundamental for the success of the project.
6 electrocardiogram
7 https://www.philips.com/a-w/about/news/archive/standard/news/press/2016/20160222-Philips-to-introduce-next-
generation-monitoring-solution-enabled-by-wearable-biosensors.html
8 http://rss.ubabenefits.com/tabid/2835/Default.aspx?art=OzT8%2Fg76NAM%3D&mfid=XecEv5Wrckw%3D
INTRODUCTION
25
Starting from the analysis of the current scenario of the digital healthcare system, we would like to
highlight some research hints and gaps, trying to investigate the determinants of adoption of the studied
technologies.
The objective of this study is to understand what are the factors behind the use of digital technologies for
lifestyle tracking and to understand how to leverage on these factors to promote their use.
LITERATURE ANALYSIS
26
2. LITERATURE ANALYSIS
As defined in the introduction, the context of research is the healthcare sector with attention on the
implementation on digital technologies in this context. The following chapters aim at describing the overall
digital health system, providing a definition of the so called Digital Health. More in detail, the focus is
shifted on the quantified self-phenomenon concerning in particular the health monitoring. Consequently,
since our objective is investigating the drivers that lead to the adoption of digital technologies to track the
lifestyle, we analyzed the theories that describe the behaviour formation. The attention was put on the
Theory of Reasoned Action, the Theory of Planned Behaviour and the Technology Acceptance Model.
Hence, we analyzed previous works with the same objective of our thesis. In fact, we tried to recap and list
all the factors that previous studies have considered in the adoption of digital technologies for lifestyle
monitoring. This analysis allowed us to draw the current customer journey, and identify the main
unaddressed issues.
2.1 DIGITAL HEALTH
2.1.1 Definition
Different definitions can be found into the literature for Digital Health. One of the most exhaustive could be
“digital health is an improvement in the way healthcare provision is conceived and delivered by healthcare
providers through the use of ICTs to monitor and improve the wellbeing and health of patients and to
empower patients in the management of their health and that of their families”. (Iyawaa, Herselmana, &
Bothaa, 2016)
Consequently, the concept of digital health is quite broad, and it can include different components. Table 5
represents the components identified on digital health literature (Iyawaa, Herselmana, & Bothaa, 2016).
COMPONENTS/SOURCES DESCRIPTION
E-health E-health refers to the use of Internet and web technologies in the
provision of healthcare delivery services.
M-health M-health refers to the use of mobile devices in administering healthcare
services.
Health 2.0/Medicine 2.0 Health 2.0/Medicine 2.0 refers to “the integration of Web 2.0 in the
utilization of healthcare and medicine to enable and facilitate specifically
social networking, participation, apomediation, collaboration, and
openness within and between these user groups”.
Telemedicine/telecare Telemedicine/telecare refers to the use of different information and
LITERATURE ANALYSIS
27
communication technologies (ICTs) by physicians to remotely connect
with patients.
Public health
surveillance
Public health surveillance is used in gathering health information of a
specific population to facilitate “decision making” regarding the health of
the population in a setting.
Personalized
medicine/patient
engagement
Personalized medicine refers to the provision of unique treatment to
patients based on their genetic and genomic components.
Health and medical
platforms
Health and medical platforms include online platforms such as online
forums that help foster interaction between patients and experts.
Health promotion
strategies
Health promotion strategies refer to “the process of enabling people to
increase control over their health and its determinants, and thereby
improve their health”.
Self-tracking Quantified self-tracking enables patients to monitor their health status by
adopting a wide range of technologies that facilitate the process.
Wireless
health/Wireless
sensors
Wireless sensors refer to the use of different wireless monitoring devices
situated in a wireless network used for monitoring patients’ health by a
physician.
Genomics Genomics emphasizes how patients uniquely react to diseases based on
their genomic components.
Medical imaging Imaging/medical imaging refers to “techniques and processes used to
create images of various parts of the human body for diagnostic and
treatment purposes within digital health.
Information system Information systems in healthcare refer to health information systems.
Mobile connectivity and
bandwidth
Mobile connectivity and bandwidth facilitate the connectivity of different
digital health technologies for physicians to remain digitally connected to
patients.
Internet In healthcare specifically, the use of the Internet facilitates information
sharing.
Social networking Social networking platforms on which health professionals and patients
can share information.
Computing power
and data universe
Digital health facilitates the management of patient health information by
medical practitioners, patients and their families. Therefore, digital health
will require information that can be accessed at different places and at
LITERATURE ANALYSIS
28
different times, hence the need for high computing power and the data
universe. Digital health requires high computing power and storage.
Interoperability The “ability of two or more systems or components to exchange
information and to use the information that has been exchanged”.
Sensors and wearables Wearable technologies are devices that inform the user when they are
worn.
Health and wellness apps Health and wellness apps refer to mobile applications used for
disseminating health information to patients to facilitate the management
of health by the patient.
Gamification Gamification in healthcare facilitates patients into performing certain
activities in relation to health practices.
Electronic health
records
Electronic health records (EHRs) consist of all the combinations of patient
health information from past and previous visits to a health institution,
which can be presented to a medical practitioner to make decisions
regarding a patient’s health.
Electronic medical records Electronic medical records (EMRs) are “computerized medical information
systems that collect, store and display patient information”.
Big data Snijders, Matzat and Reips define big data as a “term used to describe
data sets so large and complex that they become awkward to work with
using standard statistical software”.
Health information
technology
Health information technology refers to the “application of Information
and Communication Technologies (ICT) involving both computer
hardware and software that deal with the processing, storage, retrieval,
sharing and use of healthcare information, data, and knowledge for
communication and decision making”.
Health analytics The “software solutions and analytical capabilities needed to assimilate
big data”.
Digitized health
systems
The “storage and exchange of digitized patient medical records”.
Privacy and security Privacy and security are measures taken to ensure that patients’ health
information is well protected.
Cloud computing The use of cloud computing in deploying healthcare services to patients.
Table 5 Digital health components
LITERATURE ANALYSIS
29
2.1.2 The current scenario
The digital health revolution engages different stakeholders that need to collaborate and communicate
together to reach all the benefits that the new system enabled by digital technologies can provide to the
society (Petersen, Adams, & DeMuro). The main stakeholders are health providers and patients, together
with technology manufacturers. Healthcare is concerned with institutional organizations in different ways
among the different countries, consequently also the government could be an active stakeholder that can
benefit of the advantages related to this phenomenon and so can promote it. Digital health technologies
encompass a wide variety of tools, ranging from wearable sensors and portable diagnostic equipment to
data-driven software platforms, telemedicine tools, and mobile healthcare apps. Together, they have the
potential to help the healthcare system achieving five important goals:
- helping patients become more engaged in their own care
- closing communication gaps
- identifying patients' needs and tailoring services to meet them
- enabling consumers to get care in convenient, cost-effective ways
- improving decision-making by consumers and providers (predictive diagnosis)
We could see in Table 6 the stakeholder map with the related advantages that each actor can get by the
new digital healthcare system.
Table 6 Stakeholder map
LITERATURE ANALYSIS
30
As we can understand from the different components of digital health, data plays an extremely relevant
role, that is key also in determining an increasingly personalized form of services and products, and in
which the user is always engaged and can self-manage part of the data system that was accessible only by
the doctor. MyTomorrows9 is one example of the changing look of business models, in this case, directly
connecting customers and pharma. This platform makes more transparent and simpler, for both physicians
as well as their patients, the access to developmental drugs.
In this new age, devices and apps will be used to create a “health selfie”. For example:
- The Myo10, originally a motion controller for games, is now being used in orthopedics for patients
who need to exercise after a fracture. With the aid of the Myo, patients can monitor their progress
and doctors can measure the angle of movement (Dimitrov, 2016).
- The Zio Patch11 measures heart rate and electrocardiogram and has the US Food and Drug
Administration approval.
A number of mobile apps, which support device handling, have emerged, including myDario12 and
MySleepBot13 among others. It has been predicted that in the near future we will look at our phone or
smartwatch to check health outcomes more often than we do now to check our mail or WhatsApp
(Dimitrov, 2016).
The driver behind all these new technologies is the data that is generated, and various parties are trying to
bundle the data streams and obtain control. In the Netherlands, the Radboud University Medical Center
collaborated with Philips and Salesforce on HereIsMyData, a database where patients can store their health
9 MyTomorrows aims at lowering the barriers to accessing pre-approval medicines. The MyTomorrows digital
platform simplifies the process to find and learn about medicines in development in order to facilitate access for those
people who might be eligible to access them. In this way myTomorrows ensures that physician and patients don’t miss
out on treatment options available to them due to a lack of information. https://mytomorrows.com/en/
10 The Myo is an armband that allows the control of digital devices. https://www.myo.com/
11 Zio Patch is a continuous cardiac monitoring system that is proven to help identify and rule-out arrhythmias earlier
in the diagnostic pathway to accelerate patient care. http://irhythmtech.com/
12 Dario is a personalized, pocket-sized, all-in-one glucose meter coupled with a robust real time mobile app to
manage diabetes quickly, efficiently and accurately https://mydario.com/
13 MySleepBot is not just an application that monitors your sleep keeping a detailed sleep history, but it is also a smart
alarm that makes you wake up at the right time. Moreover it also provides suggestions and insights of how the
number of hours of sleep affects the rest of your week. https://mysleepbot.com/
LITERATURE ANALYSIS
31
data and determine who can access them14. “Big data” is a pervasively trend that has been used by the
media in the last several years. While many definitions have been proposed, the common denominator
seems to include the “four V’s”—Volume (vast amounts of data), Variety (significant heterogeneity in the
type of data available in the set), Velocity (speed at which a data scientist or user can access and analyze
the data), Veracity (quality of captured data that can vary greatly, affecting accurate analysis). Defined as
such, healthcare has become one of the key emerging users of big data. For example, Fitbit and Apple’s
ResearchKit can provide researchers access to vast shares of biometric data on users, which can then be
used to test hypotheses on nutrition, fitness, disease progression and treatment success. Most complex
high dimensional data sets include imaging (photos, X-rays, MRIs, and slides), wave analysis such as EEG15
and ECG16, audio files with associated transcripts, free text notes with natural language processing (NLP)
outputs, and mappings between structured concepts such as lab tests and the Logical Observation
Identifiers Names and Codes (LOINC) codes or the International Classification of Diseases-9 (ICD9) and
ICD10 codes.
Some market analysis estimates that by 2020, 40% of IoT-related technology will be health-related, more
than any other category, making up a $117 billion (Dimitrov, 2016). But what are the current scenario and
the current adoption level?
In Figure 5, we can see market findings of Rock Health’s second annual Digital Health Consumer Adoption
report17, which surveyed more than 4,000 respondents across the U.S., indicated that 46 percent of
consumers are now considered active digital health adopters, having used three or more tools in categories
such as telemedicine and wearables over the course of 12 months. The adoption of a different number of
digital health technologies is shifting to a more smash shape, due to the fact that a higher percentage of
people is using an increasing number of digital health categories.
14 Philips and Dutch Radboud university medical center debut wearable diagnostic prototype for chronic illness at
Dreamforce 2014 event (2014) https://www.philips.com/a-
w/about/news/archive/standard/news/press/2014/20141014-Philips-and-Dutch-Radboud-university-medical-center-
debut-wearable-diagnostic-prototype-for-chronic-illness-at-Dreamforce-2014-event.html
15 Electroencephalogram
16 Electrocardiogram
17 Rockhealth (2016) https://rockhealth.com/reports/digital-health-consumer-adoption-2016/
LITERATURE ANALYSIS
32
Figure 5 Ownership of digital health categories by numbers
Of course, there are also some points of weakness that limit the success and diffusion of the new digital
system. At the base of many services offered through digital technologies there is data collection and data
sharing. Building an aggregate health database with reliable data is very difficult. Then, one of the main
challenges is the data management and the protection of data from privacy and security issues. This last
issue can also affect the adoption of these new technologies and it is another relevant problem that
prevents the accomplishment of this digital health revolution. Getting clinicians to change their workflow to
something better and getting patients to actually use the systems is not trivial. In particular, the last point
can also be highlighted by the inequality in the access of technologies by the population. This is an
important risky element that can, not only prevent the diffusion of digital technologies, but can also create
a situation of inequality in the healthcare system.
Figure 6 illustrates how this healthcare revolution will look in practice. A patient with wearable sensors that
collect data about the health state of the individual and that are stored directly to a secure cloud. The
patient will have an ID card (associated to credentials) that, when scanned, links to the secure cloud which
stores not only the data collected by the wearable but also their electronic health record vitals and lab
results, medical and prescription histories. Physicians and nurses can easily access this record on a tablet or
desktop computer. It sounds pretty basic, but the adoption of Electronic Health Records18 (EHRs) is a game
changer. In less than a decade, an ink-and-paper system of managing records that goes back thousands of
years will be digitized and replaced. The advantages are obvious and many. Paper records, often written in
questionable handwriting, can get archived in filing cabinets, out of the reach of researchers or other
healthcare providers. Instead, by keeping all the important information in one place, and easily sharable,
18 EHRs is the systematized collection of patient and population electronically-stored health information in a digital
format.
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33
EHRs will eliminate many inefficiencies, and save lives. One of the major challenges to implement the IoT
has to do with communication; although many devices now have sensors to collect data, they often talk
with the server in their own language. Manufacturers each have their own proprietary protocols, which
means sensors by different makers can’t necessarily speak with each other. This fragmented software
environment, coupled with privacy concerns can undermine the whole idea of the IoT.
2.2 QUANTIFIED SELF
An important contemporary trend is the quantified self, which can be defined as individual engaged in the
self-tracking of any kind of biological, physical, behavioral, or environmental information (Swan, 2013).
There is a proactive stance toward obtaining information and acting on it. Quantified self-embodies self-
knowledge through self-tracking.
In some way, most everyone could be defined a self-tracker since many individuals measure something
about themselves or have things measured about them regularly, but also because inside humans there are
innate curiosity, tinkering and problem-solving capabilities.
There are different types of areas that can be tracked and analyzed (Swan, 2013), we can see them in Table
7.
Figure 6 Digital healthcare system
LITERATURE ANALYSIS
34
MAIN AREAS MAIN PARAMETERS
Physical activities Heartbeats; steps; calories; repetitions; sets; METs (metabolic
equivalents)
Diet Calories consumed; carbs; fat; protein; specific ingredients;
glycemic index; satiety; portions; supplement doses; tastiness;
cost; location
Psychological states and traits Mood; happiness; irritation; emotions; anxiety; self-esteem;
depression; confidence; sleep quality
Mental and cognitive states and
traits
IQ; alertness; focus; selective/sustained/divided attention;
reaction; memory; verbal fluency; patience; creativity;
reasoning; psychomotor vigilance
Environmental variables Location; architecture; weather; noise; pollution; clutter; light;
season
Situational variables Context; situation; gratification of situation; time of day; day
of week
Social variables Influence; trust; charisma; karma; current role/status in the
group or social network
Table 7 Context of monitoring
As it is possible to see not only health parameters are tracked; objectives may range from general tracking
to pathology resolution, to physical and mental performance enhancement. In our study, we will focus our
attention only on those trackers that, using digital technologies, aims at monitoring behavior in order to
live following a healthy lifestyle. Hence quantified self can be seen as the use of personal data to improve
people health and well-being and the members of the quantified self movement believe that collecting and
tracking data on their lifestyle behavior helps in making better decisions and at the same time provides
actionable insights into their personal health.
As a consequence, environmental situational and social variables are not part of our analysis.
The terms “quantified self” and “self-tracker” are labels, contemporary formalization belonging to the
general progression in human history of using measurement, science and technology to bring order,
understanding, manipulation and control to natural world, including human body. The concept of
quantified self begun at the individual level, considering just one self-tracking per time. Nowadays the term
has been extended including other permutations like “group data”: the idea of aggregated data from
multiple self-trackers that share and work collaboratively with their data (Swan, 2013).
LITERATURE ANALYSIS
35
The origins of quantified self can be traced back to the early digital pioneers in the 70s, but the term
“quantified self” was introduced for the first time in San Francisco by Wired Magazine editors Gary Wolf
and Kevin Kelly in 2007 as "a collaboration of users and tool makers who share an interest in self-knowledge
through self-tracking." The main idea was investigating what the new tools of self-tracking can do and the
objective was creating an environment to better explore the previous question19. In 2010, Wolf spoke
about the movement at TED, and in May 2011, the first international conference was held in California.
Gary Wolf said that almost everything we do generates data and these data can be used to give people new
ways to deal with medical problems, help sleep patterns, and improve diet. Also some philosophers, like
Michel Foucault, are recognized as members of the foundations in the ideas behind the quantified
movement. Foucault focuses on the idea of “care of the self”, emphasizing the importance of self-
knowledge for personal development. He explains that everything is about looking inside oneself and
emphasizes self-reflection.
Early 2011, Martijn Aslander, a Dutch social hacker, introduced the quantified self movement to Joan
Jansens (Dean School of Sport Studies at Hanze UAS) and Martijn de Groot (Researcher at Hanze UAS). They
both remained intrigued and in November 2011 they visited the first European Quantified Self conference
in Amsterdam. In particular Joan Jansens and Martijn de Groot discussed a lot about the possible
applications of quantified self for Healthy Ageing, one of the focus themes at Hanze UAS. The result was the
decision of creating a Quantified Self Institute with the aim of bridging the quantified self community and
higher education. Quantified Self Institute was set up as a multidisciplinary network organization, gathering
knowledge about personalized health, generating new knowledge about self tracking through applied
scientific research and translating all this to education and entrepreneurship. Nearly a year later Gary Wolf
gave his support to the creation of a Quantified Self Institute and in the same month, on 28 September
2012, the Quantified Self Institute was officially founded. Since then, lecturers, researchers and students of
various schools and research groups have been working on projects concerning self tracking and health
with the mission of improving the quality of life by generating and sharing knowledge on quantified self.
2.2.1 The context
The quantified self is starting to be a mainstream phenomenon. While businesses, military forces and
medical professionals have been using digital technologies to monitor behavior for decades, private
consumer market has start only recently to adopt this kind of technologies. A similar change is the
consequence of the increasing computer power and the plunging cost of electronics that made digital
19 Quantified Self Institute, self-knowledge through numbers (2016) https://qsinstitute.com/about/what-is-
quantified-self/
LITERATURE ANALYSIS
36
technologies available to everyone (Majmudar, Colucci, & Landman, 2015). From the moment in which
personal computers (PCs) where introduced, computers have become closer to human beings both
physically and psychologically. In the early stages of PCs, computers were mainly used for operational
purposes in the organizations. After entering homes and being used in daily life, they became more familiar
to people. The diffusion of smaller, lighter and networked computers was led by the advances in mobile
systems and ICTs and now most everyone has his own personal devices. Over 90% of the world’s
population owns a mobile phone device and more or less half of the world’s population uses mobile
broadband services (Jung, Kim, & Choi, 2016). Furthermore, wearable computing devices, which are closer
to our bodies, have undergone experimentation and have recently begun to be diffused. In fact, as we can
see from Figure 720 below, in 2014, 16% of Internet users owned a smartwatch or a smart wristband. At the
same time also the rising of the healthcare costs and the rising of the number of people that are affected by
diseases whose causes are related to the lifestyle, can be considered important drivers behind the success
of the quantified self phenomena. For example, the number of people affected by chronic diseases, such as
heart or respiratory diseases, cancer and diabetes, are growing and researcher estimated that in 2020, 44
million of individuals will die because of similar illnesses21.
Figure 7 Device ownership in 2014
20 Techcrunch (2015). 80% Of All Online Adults Now Own A Smartphone, Less Than 10% Use Wearables
https://techcrunch.com/2015/01/12/80-of-all-online-adults-now-own-a-smartphone-less-than-10-use-wearables/
21 http://www.who.int/nmh/publications/ncd_report_full_en.pdf
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37
The global market for self-monitoring health technologies reached $1.1 billion in 2013 and $3.2 billion in
2014 and this number is expected to grow to $18.8 billion in 201922. According to a survey, 31% of
consumers identify themselves as self-trackers, those who monitor health via apps, smart watches,
wearable fitness trackers and website. In addition 25% of non-users say they are interested in using self-
trackers and 20% live with someone who is currently using a wearable self-tracker23. A Google trend-
analysis, represented in Figure 8, reported a relevant increase in searches for smartwatches and
wearables24. In the graph, the light red line represents wearables searches while the dark red one the
smartwatch searches.
Figure 8 Google trends for smartwatch and wearable
In particular, the number of connected wearable devices worldwide is predicted to continuously increase:
in 2016 it amounted to 325 million and the forecast for the year 2021 is 929 million25. A more detailed
statistic is presented in Figure 9.
If we focus our attention on the U.S. market in 2015 over two thirds of adults track at least one health
indicator, such as weight, food intake, exercise routine, blood pressure, or blood sugar levels, either in their
heads, on paper, or with a sensor or mobile app. Moreover 46% of these quantified patients report that
self-monitoring has changed their approach to maintain their health. With over 85% of healthcare
consumers owning cell phones, and 53% owning smartphones, health sensors coupled with mobile
22 Cision PR Newswire (2015). Health Self-Monitoring: Technologies and Global Markets.
http://www.prnewswire.com/news-releases/health-self-monitoring-technologies-and-global-markets-
300102733.html
23 eMarketer. (2015). Retrieved from What Do Fitness Self-Trackers Care About? Here's the Skinny:
https://www.emarketer.com/Article/What-Do-Fitness-Self-Trackers-Care-About-Heres-Skinny/1011900
24 Google Trends. (2017). Retrieved from https://trends.google.it/trends/explore?date=2012-10-07%202017-08-
10&q=wearable,smartwatches
25 Statista. (2017). Retrieved from Number of connected wearable devices worldwide from 2016 to 2021 (in millions):
https://www.statista.com/statistics/487291/global-connected-wearable-devices/
LITERATURE ANALYSIS
38
communication technology allow for accurate, automated, and convenient record keeping (Majmudar,
Colucci, & Landman, 2015).
Figure 9 Number of connected wearable
2.2.2 Motivations
The benefits of the quantified self in healthcare are copious and involve different actors: the trackers and
the health institutions. Starting from the trackers the main benefits are the real time monitoring and
patient empowerment; while for the health institutions the advantage is the redefinition of the population
medicine (Majmudar, Colucci, & Landman, 2015).
1. Real time monitoring
Quantified self technologies allow to continuous monitoring the lifestyle of people and the health
parameters. The data collected in this way are obtained in real time. As a consequence a continuous
monitoring encourage people to achieve their goals of living following a healthy lifestyle because they can
see in real time the improvements. At the same time, real time monitoring is extremely important if we
consider patients affected by chronic diseases because the measured parameters are critical for them. In
this case a continuous monitoring can help both patients and doctors. Patients are helped in better
understanding and living with their diseases; while doctors can have more detailed information to cure
them.
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39
2. Patient empowerment
The principal reason individuals decide to monitor their lifestyle is to resolve or optimize a specific lifestyle
issue, like for example sleep quality. Continuous monitoring makes people understand what they have to
change in their behavior in order to follow a healthier lifestyle. Indeed quantified self devices are
thoughtfully designed to greatly improve patient experience, engagement and health outcomes. At their
core, these systems close the feedback loop between a patient's choices, actions, and overall health. For
example there are some initial studies that have shown how activity monitoring improves metabolic
profiles of inactive older adults and also improves time to recovery and shortens hospital stay after cardiac
surgery.
According to researchers another finding is the presence of a pragmatic attitude toward having had a
problem that need solving. A significant benefit is the greater velocity of question asking and experiment
iterating compared to traditional methods (Majmudar, Colucci, & Landman, 2015).
3. Redefinition of population medicine
The third main advantage in monitoring people lifestyle is the redefinition of the population medicine:
having large samples is a way to allow new methods and discovery. As Google has demonstrated, large data
sets are essential for progress; simple machine learning algorithms can be run over a great population to
produce significant results. The possibility to analyze these data with patient consent opens the door to
confirming previously established associations using massive sample sizes (e.g. resting heart rate and
mortality), as well as studying complex relationships between risk factors and outcomes (e.g. resting heart
rate, fitness level, cigarette use, hypertension, and mortality). Furthermore, novel associations could be
used to generate new hypotheses that can then be prospectively tested in controlled cohorts. A predictive
maintenance approach to health, based on quantitative data computed on large populations, allows early
warning signals to be more readily produced. Having information ahead on time would be invaluable in
deploying predictive medicine solutions. Quantified self data could discover trends, cyclicality, episodic
triggers and other elements that are not clear in traditional time-linear data.
Acting in this way, health institutions cannot only improve the health research and outcomes, but they can
also reduce the cost of healthcare and the number of healthcare visits (Majmudar, Colucci, & Landman,
2015).
To sum up quantified self activities support continuous health monitoring at both individual and population
level encouraging healthy behavior and improving people lifestyle so that is possible to prevent or reduce
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40
health problems. Quantified self devises provide personalized, localized and on-demand interventions in
ways previously unimaginable.
Nevertheless the benefits we have previously described can be reached only if healthcare sensors are
widely adopted and used by both patients and healthcare providers.
2.2.3 Limitations and challenges
The development of digital technologies to monitor people lifestyle behavior is affected by some limitations
and challenges. The most important ones are the reliability of the data collected, safety and privacy issues;
the integration of the data into the clinical workflow and the system measurement monitoring (Majmudar,
Colucci, & Landman, 2015). The above-mentioned problems are deeper analyzed in the following pages.
1. Reliability
Digital solutions that collect data on people lifestyle and health must be safe, mostly because they should
be considered medical devices. The risk on patient safety is present if the digital health solution doesn’t
function as intended and hence if reliability and validity are not respected. On one hand reliability could be
defined as the consistency of the measure: a measure is said to have a high reliability if it produces
consistent results under consistent conditions. Especially is relevant the degree to which values are
consistent when repeated because in this case a possible challenge is present when the goal is capturing
temporal variability. On the other hand, validity is the degree to which a measured value really measures
what it claims to measure. Notably are important: concurrent validity, the degree to which different
measure of the same phenomena should produce similar results; convergent validity, the degree of
agreement between a new assessment method and a gold standard; divergent validity, how well new
measures diverge from measures of other phenomena; and finally predictive validity: how well a future
outcome can be predicted from the measures (Majmudar, Colucci, & Landman, 2015).
2. Privacy issues
Health sensors and associated software must also protect patient's health information. Privacy, security
and confidentiality are crucial since the data collected from these technologies contains highly personal
information such as social interaction, location, emotion and potentially sensitive health conditions. Health
data streams would have attendant rights and responsibilities. For example, attendant rights include the
LITERATURE ANALYSIS
41
contributor’s right to decide how and with who share the data. On the other hand, responsibilities mean
that is the contributor’s responsibility to share data in any venue in which the individual is comfortable.
Health institutions are required to protect the privacy of their data and they are trying to develop methods
to preserve users’ privacy and confidentiality while maintaining the main advantages digital technologies
for monitoring lifestyle behavior provide to research institutes. The situation is different if we consider
individuals because they are free to share their own data and post it publicly. Lot of individuals are not
comfortable in sharing their data, but those that are can contribute their data to create a valuable public
good that is usable by all. In this way, they can increase the population medicine and the associated
benefits.
3. Integration into clinical workflow
The third challenge is associated to the fact that health sensors have the potential to generate too much
data and quickly overwhelm clinicians. For example, a patient who checks his blood pressure twice a day as
instructed, might have 180 readings for his primary care provider at his three-month follow-up. We can say
that quantified self is becoming an interesting challenge for big data science.
Big data are defined as data set too large, fast growing, heterogeneous and complex to process with on-
hand database management tools. Big data are characterized by the so-called 4V: volume; variety; veracity
and velocity. In particular, big health data can be divided into three categories: traditional medical data
(personal and familiar health history; prescription history; lab reports; demographic data; etc.); “omic” data
(genomics; microbiomics; proteomics; metabolomics; etc.) and quantified self tracking data (self-reported
data; mobile application data; quantified self devices data; etc.).
Both traditional institutional health professionals and quantified self individuals are dealing with this new
era of data and have to face the challenge of employing these data towards pathology resolution and
wellness outcomes. Companies and service providers need to include automated analytics that can
translate information into knowledge and generate insights that can be acted upon by providers or
integrated into a care management plan. Furthermore, the health sensor data should be interoperable
between vendor products, be able to integrate into the patient's electronic health record, and be easily
accessible to the provider during the patient visit for active interrogation and intervention.
4. System measurement monitoring - demonstrating value
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42
As sensor technologies evolve, we need continued rigorous evaluation in the form of prospective clinical
trials to guide the appropriate use and coverage of digital health solutions, similar to the regulatory
compliance and clinical evidence requirements for drugs and medical devices. Health systems, and
specifically, healthcare providers, can play an active role in accelerating the dissemination of innovative
technologies by assuming the role of early adopters and early majority, and working with industry to
perform clinical validation.
Given regulatory and compliance requirements around safety and privacy, innovators and industry will be
primarily responsible for technical validation of sensor technologies. Healthcare organizations and
providers will play a dominant role in assessing the impact of these solutions on quality of care and health
outcomes, by engaging in implementation pilots and/or well-designed clinical trials. Indeed evidence
requirements for new intervention in health are well established and as a consequence, before launching
them on the market, quantified self devices should be analyzed and valued with some experiments in order
to understand their effectiveness and efficacy in monitoring people lifestyle.
At the same time, we have to remember that these kinds of devices are technologies and as a consequence
are affected by all the problems of the traditional technologies. In particular, the time needed for making
the experiments is critical because the technology may be obsolete before the trial is completed. The
rapidity of the technology evolution requires that some components of the quantified self devices need to
continuously improve during the trial. In fact, to address the problem of technologies becoming obsolete
before they are fully tested, researchers wish to provide upgrades on a regular basis.
2.2.4 Tools
Developments in technologies have made healthcare management easier. Smartphone apps have emerged,
we can identify three different categories of mobile health applications from the patient point of view:
- Clinical assistance apps, to support the doctor and the patient in their communication and service
delivery
- Monitoring apps, to keep an eye on the behaviour and conditions of the patient
- Health life apps, to follow some suggestions on diet and fitness to be healthy
Apps for monitoring lifestyle and health are easy to use for individuals, anytime, anywhere and on any
device. Behavior change support features and persuasion strategies are integrated into mobile health
applications to provide more efficient healthcare for patients. Concerning monitoring health apps there is a
LITERATURE ANALYSIS
43
huge variety. From calories assumption monitoring with MyFitnessPal26 to calories consumed monitoring,
Icardio27 to record heart rate, MySleepBot28 to monitor to sleep, or the new instant blood pressure app to
measure and track blood pressure.
Then an important innovation in the field has been the wearable technology, a category of devices that can
be worn by consumers. It is a technology that can be incorporated into things that people wear on a day-to
day basis. The idea is that technology can be part of our daily lives since it can become less intrusive, as it
can be part of our clothing or sometimes even part of our bodies. Wearable tech is part of the wider
Internet of Things movement, where everyday objects become “smart” also thanks to sensors. The device
is considered wearables if it:
- Can be worn for extended period of time
- Provide user inputs, enabling user control
- Enhances of user experience
It is possible to divide wearables into three categories: (1) notifiers; (2) glasses; (3) trackers (Lunney,
Cunningham, & Eastin, 2016). Notifiers are wearable technologies that provide information about the world
around us, for example smart watches; glasses use eyeglasses to create augmented virtual reality. Finally,
trackers are those wearable devices that record data thanks to some sensors. Each category is deeper
analyzed in the following paragraphs.
Smartwatches are an example of wearable devices, belonging to the category of notifiers. They are one of
the latest developments in the information technologies and all the leading ICT industry players (Samsung
Electronics; Sony and Apple) have released diverse styles of smartwatches (Jung, Kim, & Choi, 2016). They
provide numerous functions in addition to just simply showing the time. Indeed, the primary purpose of
smartwatches is the collection of data, which are analyzed by the users on different devices such as laptop
computers or mobile applications, as it occurs in the majority of the cases. We can conclude that since
smartphone use is increasing a lot, the market of complementary services like mobile applications is
increasing too and this trend is evident also when we talk about wearable devices. There are now more
26 MyFitnessPal is an application that allows you to track all the foods you eat and the calories you introduce in order
to lose weight more effectively. https://www.myfitnesspal.com/it
27 iCardio is a free workout tracking app with in addition heart rate training features.
https://itunes.apple.com/us/app/icardio-workout-tracker-heart-rate-trainer/id314841648?mt=8
28 MySleepBot is not just an application that monitors your sleep keeping a detailed sleep history, but it is also a smart
alarm that makes you wake up at the right time. Moreover, it also provides suggestions and insights of how the
number of hours of sleep affects the rest of your week. https://mysleepbot.com/
LITERATURE ANALYSIS
44
than 165,000 mobile health apps available on the Apple iTunes and Android app stores29. On the market
are offered a huge number of applications for smartwatches; now are available more than 10,000 apps for
“Apple Watch” and more than 4,000 apps for “Android Wear” (Chuah, Rauschnabel, Krey, & Nguyen, 2016).
An interesting cite about smartwatches is the following: “The Apple Watch will play with your attention –
increasing it in some cases and reducing it in others”, Jeff Carlson, Technology Journalist, 2015. The quote is
about the idea that smartwatches can reduce people attention to other devices: less time is dedicated to
smartphone because lots of functions are provided by the wearable itself and are easily available for the
consumer wrist. On the other hand is also true that smartwatches increase people attention to technology
since they give access to emails, messaging and much more (Chuah, Rauschnabel, Krey, & Nguyen, 2016).
Finally is important to remember that wearables are for definition technological devices that people wear.
As a consequence visibility is crucial, above all in the case of smartwatches because they have the same
visibility of traditional watches. For this reason smartwatches devices are becoming more and more a
luxury good. Instead of hiding technology, it merges with fashion.
The second category of wearable is glasses. Historically they have been used for decades in military and
aviation domains to quicker deliver primary flights or mission information, but with the introduction of
Google Glass, these technologies are now marketed to the general population. Researchers have
investigated a lot on Google Glasses functions and on their pros and cons. In particular Google Glass
projects images onto the visual field of the user and has the ability to deliver lot of smartphone’s features
in a hands-free wearable unit. As a consequence, the main advantage of this typology of wearable is that
they make information easily accessible, reducing the amount of time users spend looking down to scan
information on instruments. Hence theoretically similar configuration should allow users to view
information on the display while at the same time being able to scan the environment. Practically even if
users are capable of seeing both the information on the display and the outside world, humans are
generally not capable of seeing both at the same time. A potential drawback is the so-called attention
capture phenomenon: from an attentional point of view, the information displayed can capture user
attention making him miss elements of the outside scene and leading to a range of undesirable
visual/perceptual and cognitive effects (Young, Stephens, Stephan, & Stuart, 2015).
29MedScape Ken Terry (2015). Number of Health Apps Soars, but Use Does Not Always Follow
http://www.medscape.com/viewarticle/851226
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Finally, the last category of wearables is made by trackers and hence devices that record data thanks to
some sensors. To this group belong also all the wearables used by patients affected by chronic disease to
constantly monitor the critical parameters. The last examples of innovation in this field are Cycardia30 and
Embrace31. The first is a bra that allow to perform monthly screening on the health of the breast in
autonomy. The second is a watch, a medical device with technology developed with partners such as the
Massachusetts Institute of Technology, developed to improve the lives of people living with epilepsy. The
epileptic will then be able to monitor the potentially triggering situations of a crisis, thus preventing it. At
the same time, also wearable fitness trackers fall into the present class. We would like to focus our
attention on the latter devices because they are the most popular: 61% of the wearable technology market
is attributed to sport or activity trackers32. They also enjoy the higher level of awareness among consumers.
Wearable fitness devices track physical activity (steps taken, calories burned, workout intensity, etc.)
through a device that in most of the cases is worn on the wrist. The data collected form the wearable
fitness trackers are transferred to a mobile application through Bluetooth or plugging the device into the
phone. Only by transferring the data into the application, consumer can provide a meaning to them and
analyse trainings, set the goals and evaluate the progresses. The most popular companies producing
wearable fitness devices are Garmin and Fitbit.
Related to fitness trackers wearable, there is another digital solution: Online Fitness Communities33 (OFCs).
They are platforms that translate data gathered by a wearable device or mobile app into feedback, both of
informational and social nature. OFCs thus generate meaningful information about the user’s performance
and/or health. Popular examples of OFC’s are Strava, RunKeeper, Fitbit and Endomondo. OFCs enable users
to either manually add activities to their profile or to upload sessions logged through wearable devices or
dedicated smartphone applications which use the sensors and GPS of the smartphone to automatically log
a user’s activities once a session is started. After completion of the activity, data is transferred to the user
30 The solution, the iTBra™, consists of two wearable breast patches which detect circadian temperature changes
within breast tissue. Through your PC or mobile device, anonymized data obtained from the iTBra is communicated
directly to the Cyrcadia Health core lab for analysis. In this way women with the earliest detection have more
treatment options and the best treatment results. http://cyrcadiahealth.com/
31 Embrace is a nervous system monitoring, a sleep monitoring and an activity tracker.
https://www.empatica.com/product-embrace
32 ITWeb (2013). Wearable device market set to explode.
http://www.itweb.co.za/index.php?option=com_content&view=article&id=61952
33 Online platforms that connect people and professionals to get support and motivation from the community
LITERATURE ANALYSIS
46
profile using Internet or WiFi connection, where users can analyze their performance. Users can view other
athletes’ activities and can allow others to view theirs. Furthermore, users can interact among each others
based on the activities they share. On Strava34, for example, they can give ‘kudos’, which is the Strava
equivalent of a ‘Facebook like’, to activities posted by a Strava user as a means of endorsing each other’s
achievements. (Stragier, Abeele, Mechant, & Marez, 2016)
2.2.5 The wearables’ market
One of the first factors to be analyzed in the market is competition. It is quite evident that in the market of
wearables, the competition sees different players. There are two different types of wearable, smart band
on one side for fitness trackers and activity monitoring, that are targeting one aim; smartwatches on the
other side, that thanks to the Internet connection are multifunctional devices. On the first side, Fitbit is the
major player. It had a quite strong market power compared to other competitors until Apple introduced
the apple watch in the market. Another important actor to be considered is Xiaomi that acquired a good
market share. From
Figure 1035 we can notice that Samsung seems not to be able to increase sales for wearables, while Garmin
maintain a constant market share.
34 With Starva you can enter in contact with athletes of all over the world. Publishing your trainings you can get
feedbacks and in this way improve yourself. https://www.strava.com/
35 Statista (2017). Market share of wearables unit shipments worldwide by vendor from 1Q'14 to 1Q'17
https://www.statista.com/statistics/435944/quarterly-wearables-shipments-worldwide-market-share-by-vendor/
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Figure 10 Market share for wearable devices
In order to better understand the performances obtained by the players, it is important to identify their
positioning on the market. In Figure 11, as axes of the positioning map we decided to use:
- the purpose of the wearable distinguishing mainly from fitness trackers and smartphones
- the focus on the fashion aspect or on the technological one
What we can observe is that Fitbit and Apple represent the high-end segment of respectively fitness
trackers and smartwatches. Xiaomi strategy is a follower one that is trying to offer a similar product at the
lower price. Xiaomi started offering fitness band, but has recently launched a smartwatch very similar to
the apple watch but cheaper. While Fitbit and Apple implemented a differentiation strategy, Xiaomi is
fighting on a cost leadership basis.
Figure 11 Wearable positioning map
The real question is if smartwatches are going to substitute fitness band or they can still cohabitate.
Recently, we can notice a transformation of smart band into smartwatches thanks to the adjunct of the
Internet connection.
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In the law left angle, there are some niche players. In fact, Garmin is offering advanced devices for sporty
people, while BBK is focusing on the child segment. Lifesense main focus is on health for women.
What we can notice is that the adoption by a big number of people requires for sure a good and fashion
design. Now that prices are shrinking, due to the increasing competition, the expectation is to see an
increase in the number of users. An interesting undressed point in this perspective is the willingness to pay
for such devices.
2.2.6 The current usage
Despite all the trends we have reported and the increased interest of quantified self devices for the future,
current sales are still relative low. Comparing the total sales of the last three years showed in
Figure 1236, we can notice a growing demand, but at a decreasing rate. From 2014 to 2015 there has been
an increase of almost 65%, while the following year recorded only a 20% increase.
Figure 12 Wearable Unit Shipments
Moreover, 1/3 of customers who purchase similar devices after a while stop using them37. Little is known
about the reasons behind those behaviors; researchers should better investigate the adoption of quantified
self devices. In particular, what is still unanswered is the question of what drives consumers to become
quantified self trackers and monitor their lifestyle in the long term.
It is important to understand this phenomenon because even if it is on a first stage of diffusion, it is already
moving toward a new direction. In fact, one interesting aspect of quantified self is the link between the
quantitative and the qualitative in the sense that quantified self-activities include both the collection of
objective metrics data and the subjective experience of the impact of these data.
36 The Global Wearables Market in 2015 by Felix Richter (2016) https://www.statista.com/chart/8420/wearable-
device-shipments/
37 Wearables: one-third of consumers abandoning devices (2014)
https://www.theguardian.com/technology/2014/apr/01/wearables-consumers-abandoning-devices-galaxy-gear
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Self-tracking 1.0 could be defined as the tracking of basic easily measurable quantitative phenomena like
steps walked; hour slept; nutrition and exercise regimens. Self-tracking 2.0 is becoming the tracking of
qualitative phenomena such as mood, emotion, happiness and productivity.
Moreover, while collecting data may be quantitative, the use of these data is often qualitative. There is no
purpose in collecting data if there is no feedback loop connecting it back to real life problem solving and
behavior change. Quantitative data collection is just the first step of a bigger process that uses the data to
generate useful insights that engage an improvement in the lifestyle.
To conclude since most individuals are not good in thinking quantitative, but are more comfortable in
thinking in stories, and hence in a qualitative way, some of the most effective quantified self devices are
those that combine these two dimensions: quantitative accuracy and qualitative meaning-making
functionality.
2.3 IMPACT ON THE PHYSICIAN-PATIENT RELATIONSHIP
The adoption of digital technologies to monitor lifestyle is changing the exclusive focus of medicine from
curing disease to prevention and enhancing health status. A critical feature of this change is the
development of information, services and products that assist consumers to assume more responsibility for
their own health and to actively participate in healthcare decisions. As a consequence, a technological
revolution is in progress and it is reshaping the way healthcare is organized and delivered. Furthermore, his
revolution impacts the relationship between physicians and patients. Despite the importance and the
increasing relevance of this reality, few efforts have been dedicated in researching the evolution of
physician-patient relationship due to the adoption of digital technologies to track health parameters. On
the other hand lot has been said considering e-health. The expression “e-health” refers to health services
and easily updated health-related information enhanced or provided by the Internet and related
technologies (De Rosis & Barsanti, 2016). Although the research context is a little different, we decided in
any case to investigate what has been already discovered since we believe the doctor-patient relationship
is a very important aspect to consider for analyzing the studied behaviour.
2.3.1 Reasons behind the change
The main motivation behind the evolution in the physician-patient relationship is the development of
information and services that encourage consumers to become more responsible for their own health, and
to actively participate in health-related decisions that affect them. A similar behaviour has been made
possible by the diffusion of Internet, which has increased the availability of information. Internet surpassed
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other media in its ability to be “consumer centric”: the needs and desires of consumers are accurately and
timely documented online.
Several factors contribute to the shift in the role and self-perception of patients from passive recipients of
medical care to active consumers of health services and increase a demand for health information
(Anderson, Rainey, & Eysenbach, 2003).
Figure 13 Factors contribute to the shift in the role and self-perception of patients
In detail the main reasons, summarized in Figure 13, are the following:
- Rapid technological advantages that lead patients to don’t accept anymore their physicians’
statements that there is no therapy available;
- Lack of time available to physicians for visiting their patients;
- Increased cost-saving pressures of the healthcare systems of the main industrialized countries;
- Cultural shift in the relationship between patients and physicians, which makes consumers more
critical and aware that doctors are not “gods,” but human beings who may make errors;
- Increasing trend of preventive medicine;
- Higher general education level.
All the above-cited factors have contributed to generate consumer dissatisfaction, power and knowledge
and hence increase of information demand. Consequently, a positive feedback loop is evident because
more patients are choosing to become “e-patients” who gather more information by using the Web
becoming more self-reliant. In this way patients, can alert important symptoms at an earlier stage. In
addition, there is a shift in the role of patients from passive to active consumers, pushing the balance of
power from physician to patients. In fact, under ideal conditions, patients would arrive at the medical visit
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empowered with information, leading to a greater partnership between patients and physicians.
2.3.2 Evolution of the physician-patient relationship
As previously said the shift in the balance of power turns into a change in the role of doctors (Anderson,
Rainey, & Eysenbach, 2003). Historically, physicians have taken an authoritarian role, in which the patient is
left uninformed in a dark hole of ignorance (Figure 14(a)). Doctors’ opinions were always considered the
true and never put under discussion: healthcare professionals are the patients’ most reliable source of
health-related information. In a more modern approach, patients are seen as partners that should be
educated. Physicians maintain an active role and medical information is provided to patients at the
physician’s discretion (Figure 14(b)). Today however, physicians are increasingly confronted with patients
who empower and educate themselves using sources as Internet (Figure 14(c)). A challenge for the future is
to encourage patient responsibility for care by facilitating their ability to locate and interpret authoritative
medical and health information. At the same time, tools must be provided to inform and protect patients
against the risks associated with inappropriate information and self-diagnosis. The most important thing is
ensuring a balanced relationship (Figure 14(d)).
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Figure 14 Evolution of the physician-patient relationship
Moving the power in the middle between physician and patient could be problematic because of the way in
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which doctors can reply to a similar technological revolution. In particular researchers identify three main
possible ways in which physicians can respond to the more “Internet informed” patients (McMullan, 2005):
- In the first scenario, we have a health professional-centered relationship. Health professionals,
especially those with poor information technology skills, may feel their medical authority being
threatened and may respond defensively by asserting their expert opinion.
- In the second scenario, the relationship is more patient-centered. Many patients have the time and
the motivation to search for information regarding their health problems. On the other hand health
professionals do not have as much time to search for every clinical condition they might encounter,
but they do have the skill and knowledge to analyze the information and assess the relevance to
the particular patient. In this scenario patients and healthcare providers collaborate.
- The third scenario is the one in which healthcare providers are committed in educating patients.
For example doctors can recommend websites or training patients on how to filter information.
Doctor assumes the role of consultant and becomes a ‘‘net-friendly clinician’’ helping the patient
navigate through the wealth of healthcare information (Wald, Dube, & Anthony, 2007).
The last scenario is the most difficult to reach since it requires significant efforts from both patients and
physicians, but at the same time is the one in which the benefits of the new solutions are better exploited.
In order to move in this direction is important to educate physician in the use of information technologies
and to educate them communicating in the proper way the potentials of Internet. Benefits should be
recognized and fully understood.
2.3.3 Empirical evidences
Empirical evidences support the findings of the change in the physician-patient relationship. De Rosis
investigated the use of Internet for collecting health-related information and the sharing of this information
with the doctor. One of the main findings was that the use of e-health is, in general, significantly
determined only by the satisfaction with the healthcare system: less satisfied patients are more used to
search for information online. On the contrary neither satisfactory nor unsatisfactory relationships with the
doctor are necessarily associated with the decision of whether or not to use the Internet. Although this
result, the role of the physician remains significantly important in the patient behaviour after e-health
experience: a productive partnership with the doctor could support a better health literacy (preventing the
e-health risk related to inaccurate information), and a more appropriate empowerment (filling the gap
between patient health education achieved online and positive health-related decisions). To sum up
patients are more inclined of sharing the information discovered on Internet, if they feel more involved by
their doctor in the decision-making processes.
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2.4 THE BEHAVIOUR ADOPTION THEORIES
In order to understand what are the main drivers that lead the path toward the adoption of health
monitoring technologies, we applied the technology adoption theories, summarized in Table 8. These
theories are based on social psychologists’ belief that human behaviour is guided by social attitudes. Some
early writers even defined the field of social psychology as the scientific study of attitudes. Gordon Allport
(1953) expressed his enthusiasm in his famous opening statement to his classic handbook chapter on
attitudes when he stated that the “concept of attitude is probably the most distinctive and indispensable
concept in contemporary social psychology”. This opinion triggered a series of studies about the attitude-
behaviour relationship aimed at investigating the type of connection there is among these two elements.
THEORY OF REASONED ACTION CORE CONSTRUCTS DEFINITIONS
Drawn from social psychology, TRA is one of
the most fundamental and influential theories
of human behavior. It has been used to
predict different types of behaviours.
Attitude toward
behaviour
“An individual’s positive or
negative feelings about
performing the target
behaviour”
Subjective norm “The person’s perception
that most people who are
important to him think he
should or not perform the
behaviour in question
THEORY OF PLANNED BEHAVIOUR CORE CONSTRUCTS DEFINITIONS
TPB extended TRA by adding the construct of
perceived behavioral control. In TPB,
perceived behavioral control is theorized to be
an additional determinant of intention and
behavior.
Attitude toward
behaviour
Adapted from TRA
Subjective norms
Adapted from TRA
Perceived Behavioural
Control
“The perceived ease or
difficulty of performing the
behaviour”
TECHNOLOGY ACCEPTANCE MODEL CORE CONSTRUCTS DEFINITIONS
TAM is design to predict information
technology acceptance and usage on the job.
Unlike, TRA, the final conceptualization of
Perceived usefulness
“The degree to which a
person believes that using a
particular system would
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TAM excludes the attitude construct in order
to better explain intention parsimoniously.
enhance his or her job
performance”
Perceived ease of use “The degree to which a
person believes that using a
particular system would be
free of effort”
Subjective norms Adapted from TRA/TPB
COMBINED CORE CONSTRUCTS DEFINITIONS
This model combines the predictors of TPB
with perceived usefulness from TAM to
provide a hybrid model
Attitude toward
behaviour
Adapted from TRA/TPB
Subjective norms Adapted from TRA/TPB
Perceived Behavioural
Control
Adapted from TRA/TPB
Perceived usefulness Adapted from TAM
Table 8 The behaviour adoption theories
2.4.1 Attitudes
Attitudes are believed to be one of the major determinants of consumer choice and buying decisions. The
research on attitudes and behaviour belongs to the psychology area. Different studies were focused on this
topic; one of them by Wicker on predicting job performances, absenteeism and turnover from job
satisfaction or to predict cheating in the classroom from attitudes towards cheating, reached the
conclusion that attitudes were not related to overt behaviours. The issue was resolved when less than a
decade later, Ajzen and Fishbein published their classic analysis of research on the attitude-behaviour
relation. They argued that Wicker had asked the wrong question. The important question was not whether
attitudes were related to behaviour, but when they were related (Fishbein & Ajzen, 1975).
Before deepening the analysis on attitudes, we want to focus on three aspects of the definition on which
most social psychologists agree, namely that attitudes are evaluative responses, that they are directed
towards some attitude object and that they derive from, or are based on, three classes of information
(cognitive, affective and behavioral). People’s attitudes reflect the way they evaluate the world around
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them, their likes and dislike. Attitude objects may be abstract or concrete. They may be individuals or
categories. The assumption that attitudes are always directed towards an attitude object distinguishes
them from other concepts such as mood, which involve more diffuse evaluative reactions.
The discrepancy often observed between verbal expressions of attitudes and overt actions was further
challenged by the research conducted on implicit attitudes. Implicit attitudes are evaluations of which the
individual is typically not aware and that influence reactions or actions over which the individual has little
or no control. In contrast, explicit attitudes are evaluations of which the individual is consciously aware and
that can be expressed using self-report measures.
Some attitudes exert a powerful impact on thinking and on behaviour whereas others have little or no
effect. This distinction has been referred to as attitude strength. Krosnick and Petty (1995) suggest that
stronger attitudes are characterized by four attributes: higher stability over time, greater impact on
behaviour, greater influence on information processing and greater resistance to persuasion.
Attitudes serve an important function in helping use to adopt to our physical and social environment. Every
day we are confronted with a multitude of stimuli. Categorization and attitude formation are the two basic
processes that enable us to bring order into this chaos. Attitudes as stored evaluations help us to separate
the good from the bad, to approach those stimuli that contribute to our survival and increase our well-
being. Katz (1960) distinguished four functions of attitudes:
- Adjustment function: maximize our rewards and minimize penalties in interactions with physical
and social environment
- Value-expressive function: express and reflect values that are central to their self-concept or
because they hope that expressing these attitudes might help them to maintain relationships with
important groups
- Ego defensive function: protect our self-esteem by avoiding having to acknowledge harsh truths
about ourselves
- Knowledge function: provide frames or reference for understanding the world
2.4.2 Theory of reasoned action
From the studies of Ajzen and Fishbein, the theory of reasoned action (TRA) born. As represented in Figure
15, at the base of the theory there is the principle of compatibility, according to which the measures of
attitudes will only be related to measures of behavior if both constructs are assessed at the same level of
generality. The TRA sustains that behaviours are influenced by intention and the intention to perform a
specific behaviour is determinated by a person’s attitude towards that behaviour and by subjective norms.
A person attitude towards a specific behaviour will be determined by the individual’s belief that performing
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that
behavi
our
will
result
in
certain
positiv
e or
negati
ve
consequences and the evaluation of these consequences.
Subjective norms combine two components, namely normative beliefs and motivation to comply.
Normative beliefs are our beliefs about how people who are important to us expect us to behave; while
motivation to comply is the willingness of the individual to comply with the expectancy of others.
2.4.3 Theory of planned behaviour
The Theory of Planned Behavior (TPB) was born as a general theory to predict behavior by considering
certain factors as predictors and influencers. The concept was proposed by Icek Ajzen to improve on the
predictive power of the TRA by including perceived behavioural control. The theory aim was to predict and
explain human behaviour in specific context, and that’s why it considers some behaviour specific elements.
As in the original TRA, a central factor in TPB is the individual’s intention to perform a given behaviour.
Intentions are assumed to capture the motivational factors that influence a behavior; they are indications
of how hard people
are willing to try, of
how much of an
effort they are planning to exert, to perform the behavior. Generally, the stronger the intention to engage
Figure 15 Theory of reasoned action
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in a behavior, the more likely should be its performance. According to Ajzen the performance depends not
only on motivation (intentions) but also on ability (behavioural control) that is to the extent that a person
has the required opportunities and resources and intends to perform a behaviour. The assumption is
usually made that motivation and ability interact in their effects on behavioral achievement. Thus,
intentions would be expected to influence performance to the extent that the person has behavioral
control, and performance should increase with behavioral control to the extent that the person is
motivated to try. The original derivation of the theory of planned behavior is defined in terms of trying to
perform a given behavior rather than in relation to actual performance. However, early work with the
model showed strong correlations between measures of the model’s variables that asked about trying to
perform a given behavior and measures that dealt with actual performance of the behavior (Netemeyer et
al., 1991).
The TPB represented in Figure 16 considers not only attitudes and subjective norms as intention influencers
but adds another determinant, the perceived behavioural control. This component is determinated by
control beliefs that may be based in part on experience with the behavior, but they will usually also be
influenced by second-hand information about the behavior, by the experiences of acquaintances and
friends, and by other factors that increase or reduce the perceived difficulty of performing the behavior in
question. The more resources and opportunities individuals believe they possess, and the fewer obstacles
or impediments they anticipate, the greater should be their perceived control over the behavior.
As we said before, the perceived behavioural control affect behaviour indirectly through intention but has
also a direct link to behaviour that is not mediated by intentions. People who lack the ability or opportunity
to achieve some goal will adjust their intentions accordingly because intentions are partly determinated by
the perception of the probability that a goal can be reached. The direct link depends on the accuracy of the
individual’s perception of behavioral control. Many studies on the TRA and on the TPB have clearly
established the utility of the distinctions by showing that the different constructs stand in predictable
relations to intentions and behavior. At the same time the relative importance of attitude, subjective norm,
and perceived behavioral control in the prediction of intention is expected to vary across behaviors and
situations. Thus, in some applications it may be found that only attitudes have a significant impact on
intentions, while in others that attitudes and perceived behavioral control are sufficient to account for
intentions, and in still others that all three predictors make independent contributions. In particular, the
gap between intention and behaviour remains large enough to have motivated researches to develop
strategies that would reduce it. One strategy has been to extent the standard model by adding components
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that would improve predictions in specific areas of behaviour.
Figure 16 Theory of planned behaviour
2.4.4 Technology acceptance model
The technology acceptance model (TAM) was proposed by Davis in 1985 in his doctoral thesis at the MIT
School of Management. TAM is an adaptation of the TRA by Fishbein and Ajzen (1975) and mainly designed
for modeling user acceptance of information technology. Similar to TRA, TAM postulates that computer
usage is determinated by behavioural intentions. Differently from TRA, firstly TAM did not take into
account subjective norm in the prediction of actual behavior and secondly, instead of consider salient
beliefs as determinants of attitude toward a behaviour, identifies only two distinct belief: perceived
usefulness and perceived ease of use. This model hypothesizes that system use is directly determined by
behavioral intention to use, which is in turn influenced by user’s attitude toward using the system and
perceived usefulness (PU) of the system. As represented in Figure 17 attitude and PU are also affected by
perceived ease of use (PEOU). PU, reflecting a person’s salient belief in using the technology, will be helpful
in improving performance. PEOU, explaining a person’s salient beliefs in using the technology, will be free
of any effort (Taylor and Todd, 1995). In addition to that PU has a direct influence on behavior, based on
the ideas that within organizational settings, people form intentions toward behaviours that they believe
will increase their job performance. These determinants are also easy to understand for system developers
and can be specifically considered during system requirement analysis and other system development
stages. These factors are common in technology-usage settings and can be applied widely to solve the
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acceptance problem. TAM is considerably less general than TRA and TPB, designed to apply only to the
computer usage behavior.
2.4.5 Habitual and repetitive behaviour
The theories of TAM and TPB, discussed so far, all assume that the impact of attitude on behaviour is
mediated by intention, according to the TRA and the TPB, attitudes, subjective norms and perceived
behavioural control result in the formation of a behavioural intention. It is this intention that is assumed to
be the most direct cause of behaviour. In the last few decades, social and consumer psychologists have
become increasingly interested in the automaticity of many higher mental processes. Automatic processes
are processes that occur without intention, effort or awareness and do not interfere with other concurrent
cognitive processes. Thus, there is more and more research that demonstrates that attitudes, norms and
even goals can be primed by people’s social or physical environment, and influence behaviour without
them, being aware of being influenced (Fennis & Stroebe, 2015).
As discussed earlier, implicit attitudes typically reflect people’s automatic evaluative response to a stimulus
object, whereas explicit attitudes reflect processes that can be cognitively controlled. Although implicit and
explicit measures of attitudes often converge, there are certain conditions under which they diverge. One
such domain is the area of prejudice, where some people react with prejudice on an implicit level, but try
to consciously control such responses. But prejudice is not the only domain where people’s explicit and
implicit attitudes diverge. For example, a dieter may feel attracted to chocolate or ice cream, but on further
deliberation will reject these foods because of their high-calorie content. And yet when his motivational or
cognitive resources are depleted, he might find himself buying an eating a large portion of ice cream.
Figure 17 Technology acceptance model
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Many researches have been conducted to understand when implicit attitudes are more predictive than
explicit attitudes respect to behaviour. What emerged from these researches is that when individuals are
either not motivated or unable to exert control, implicit attitudes are better predictor. Also subjective
norms might often guide behaviour automatically without individual being aware of their influence. Norms
are knowledge-based belief shaped by social influence and triggered by situational cues. They are if-then
rules that state that in certain situations individuals should behave in certain ways, the behaviour being
specified is the social norm. Theories of unconscious goal pursuit make the assumptions that goal can be
unconsciously activated and pursued without the individual having formed a conscious intention.
There are several different ways to stimulate automatic behaviour
- address implicit attitudes
- triggering social norms
- past experience
- goal priming
Some studies tested how the relationship between intention and behavior varies by habit strength. What
emerged is that intention is a significant predictor of behavior when habit is weak. While, when habit is
very strong, the predictive power of intention decreases. In that last scenario, measures of past behavior
have been proved to be better predictors than intention (Fennis & Stroebe, 2015).
2.5 THE RESEARCH ON WEARABLE TECHNOLOGIES
The quantified self, often called personal informatics, refers to technologies that help people collect,
monitor, and display information about their daily activities through intelligent devices, services, and
systems (Swan, 2013). The rise of personal informatics poses new challenges for HCI (human computer
interaction) and generates opportunities for applications in various domains related to quality of life.
Quantified self applications promote healthy behavior through the design and evaluation of various
technologies, often with embedded self-monitoring components. Specifically, data collection, data analysis,
and data sharing are the means through which individuals can assess, become aware of, and self-reflect on
their behavior. These forms of data can influence individual decisions and the social mind. With quantified
self, people record and trace their own chosen target behavior, including both subjective information (e.g.,
emotion, affection, situation, symptom, or disturbance that symptoms may produce, as well as inner
thoughts or feelings) and objective information (e.g., frequency or intensity of a behavior under
observation). While the proliferation of personal informatics has simplified the collection of personal data,
helping people to engage with these systems over a long period, remains an open question.
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Within the IoT field, wearables have achieved a state of public awareness and integration into society,
particularly those that support healthy lifestyles. There is, now, a wide spectrum of devices, and associated
applications, that offer training plans, assist with activity tracking, and generally collect and process health
and fitness related data (Swan, 2013). This category of wearables also promise to counteract large
demographic trends such as population ageing or the increase in chronic diseases as well as rising
healthcare costs. However, in order to reach and realize the benefits that this category of wearables
promises to individual, business and society, it is necessary for these products to be widely adopted and
used. For the moment, adoption is still relatively low (Sultan, 2015), even though prices are decreasing. In
addition, about half of consumers abandon their wearables within the first 6 months (Canhoto & Arp,
2016). This pattern means that businesses cannot harvest the data on which the valuation of the IoT
industry is premised, and cannot recover their development and marketing costs. In addition, individuals
may not reap the promised health and fitness benefits, while society is unable to constrain widespread
health problems such as rising obesity levels. Therefore, research that develops understanding of the
drivers of adoption, and sustained use, of health and fitness related wearables can have a significant
positive impact on managerial practice and society.
In order to define the main new research points, and so the uninvestigated factors that can affect the
development of the quantified self behaviour, it is necessary to understand the current motivations that
lead users to form their quantified self and what are the characteristics that wearables should have in order
to fit users’ expectations and the right context to promote the adoption of such technologies.
The theories of behavior investigate some different variables as behaviour, intention, attitude, perceived
usefulness, perceived bahavioural control, perceived ease of use, subjective norms. Different studies until
now have used these theories to understand the use of digital technologies to monitor the daily activities;
trying to add some specific construct, related to the studied technology. Many of them have as focus the
use of wearable devices, with particular attention to smartwatches, but there are others with focus on
online fitness communities or health apps.
In Table 9, there is a synthesis of the empirical studies that tried to test different and modified models
based on the adoption theories.
ARTICOLI FACTORS INVESTIGATED
Exploring consumers’
intention to accept
smartwatch (Wu, Wu, &
Chang, 2016)
H1: PERCEIVED RELATIVE ADVANTAGE to ATT
H2: PEOU to ATT REJECTED
H3: PERCEIVED COMPATIBILITY to ATT REJECTED
H4: RESULT DEMOSTRABILITY to ATT
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H5: PERCEIVED ENJOYMENT to ATT
H6: PERCEIVED RELATIVE ADVANTAGE to INT REJECTED
H7: PERCEIVED SOCIAL INFLUENCE to INT
H8: ATT to INT
Perceptions of a Wearable
Ubiquitous Monitoring Device
(Moran, Nishida, & Nakata,
2013)
H1: APPLICATION SPACE to APPLICATION PERCEPTIONS
H2: TECHNOLOGY SPACE to TECHNOLOGY PERCEPTIONS
H3: APPLICATION PERCEPTIONS to ATT TOWARD APPLICATION
H4: APPLICATION PERCEPTIONS to SOCIAL INFLUENCE
H5: APPLICATION PERCEPTIONS to FACILITATING CONDITIONS
H6: TECHNOLOGY PERCEPTIONS to FACILITATING CONDITIONS
H7: TECHNOLOGY PERCEPTIONS to ATT TOWARD TECH
H8: ATT TOWARD APP AND TECH to ATT
H9: SOCIAL INFLUENCE to ATT
H10: SOCIAL INFLUENCE to BEHAVIOUR
H11: FACILITATING CONDITIONS to ATT
H12: FACILITATING CONDITIONS to BEHAVIOUR
H13: ATT to BEHAVIOUR
The effect of consumer
innovativeness on perceived
value and continuance
intention to use smartwatch
(Hong, Lin, & Hsieh, 2016)
H1: CONSUMER INNOVATIVENESS to HEDONIC VALUE
H2: CONSUMER INNOVATIVENESS to UTILITARIAN VALUE
H3: HEDONIC VALUE to INT
H4: UTILITARIAN VALUE to INT
Examining individuals’
adoption of healthcare
wearable devices: An
empirical study from privacy
calculus perspective (Lia,
Wub, Gaob, & Shi, 2015)
H1: ADOPTION INTENTION to ADOPTION
H2: PPR to ADOPTION INTENTION
H3: PB to ADOPTION INTENTION
H4: INFORMATION SENSITIVITY to PPR
H5: PERSONAL INNOVATIVENESS to PPR
H6: LEGISLATIVE PROTECTION to PPR
H7: PERCEIVED PRESTIGE to PPR
H8: PERCEIVED INFORMATIVENESS to PPR REJECTED
H9: PERCEIVED INFORMATIVENESS to PB
H10: FUNCTIONAL CONGRUENCE to PB
*PPR= PERCEIVED PRIVACY RISK
Domain-specific
innovativeness and new
H1: PPI to RELATIVE ADV REJECTED
H2: IPI to RELATIVE ADV
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product adoption: A case of
wearable devices (Jeong, Kim,
Park, & Choi, 2016)
H3: PPI to SOCIAL IMAGE
H4: IPI to SOCIAL IMAGE
H5: PPI to AESTHETICS REJECTED
H6: IPI to AESTHETICS
H7: PPI to NOVELTY
H8: IPI to NOVELTY
H9: RELATIVE ADV to INT
H10: SOCIAL IMAGE to INT
H11: AESTHETICS to INT
H12: NOVELTY to INT
*PPI: PRODUCT POSSESSING INNOVATIVENESS
*IPI: INFORMATION POSSESSING INNOVATIVENESS
Wearable technologies: The
role of usefulness and visibility
in smartwatch adoption
(Chuah, Rauschnabel, Krey, &
Nguyen, 2016)
H1: PU to AT
H2: PU to INT REJECTED
H3: PEOU to AT
H4: PEOU to PU
H5: AT to INT
H6: VISIBILITY to AT
H7: VISIBILITY to INT
User acceptance of wearable
devices: An extended
perspective of perceived value
(Yang, Yu, Zo, & Choi, 2015)
H1: PV to INT
H2: PU to PERCEIVED VALUE
H3: PERCEIVED ENJOYMENT to PERCEIVED VALUE
H4: SOCIAL IMAGE to PERCEIVED VALUE
H5: PERFORMANCE RISK to PERCEIVED VALUE
H6: FINANCIAL RISK to PERCEIVED VALUE
H7: FUNCTIONALITY to PU
H8: COMPATIBILITY to PU
H9: VISUAL ATTRACTIVENESS to PERCEIVED ENJOYMENT
H10: VISUAL ATTRACTIVENESS to SOCIAL IMAGE
H11: BRAND to SOCIAL IMAGE
Is the smartwatch an IT
product or a fashion product?
A study on factors affecting
the intention to use
smartwatches (Choi & Kim,
H1: ATT to INT
H2: PU to ATT
H3: PEOU to PU
H4: PEOU to ATT REJECTED
H5: PERCEIVED ENJOYMENT to ATT
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2016) H6: PERCEIVED SELF EXPRESSIVENESS to ATT REJECTED
H7: PERCEIVED SELF EXPRESSIVENESS to INT
H8a1: COMPATIBILITY to PU
H8a2: INNOVATIVENESS to PU REJECTED
H8b1: COMPATIBILITY to PEOU
H8b1: INNOVATIVENESS to PEOU
H9a1: VANITY to PERCEIVED ENJOYMENT
H9a2: NEED OF UNIQUENESS to PERCEIVED ENJOYMENT
H9b1: VANITY to PERCEIVED SELF REJECTED EXPRESIVENESS
REJECTED
H9b2: NEED OF UNIQUENESS to PERCEIVED SELF EXPRESIVENESS
Understanding the Adoption
of Smart Wearable Devices to
Assist Healthcare in China
(Gao, Zhang, & Peng, 2016)
H1: PEOU to PU
H2: PU to ATT
H3: PEOU to ATT
H4: PU to INT
H5: ATT to INT
H6: COMPATIBILITY to ATT
H7: PERSONAL CHARACTERISTICS to ATT REJECTED
H8: PERSONAL CHARACTERISTICS to INT REJECTED
H9: TRUST to ATT
H10: PERCEIVED RISK to ATT REJECTED
Wearable fitness technology:
A structural investigation into
acceptance and perceived
fitness outcomes (Lunney,
Cunningham, & Eastin, 2016)
H1a: PU to USE
H1b: PU to ATT
H2a: PEOU to USE
H2b: PEOU to ATT REJECTED
H3: ATT to USE
H4: SN to USE
Table 9 Investigated Hypothesis
Considering the different studies, we can identify the main investigated variables that refer to the
measurement of attitudes toward these technologies, the intention to use them and the actual behaviour,
use or purchase. Then we can observe the investigation on three groups of variables that can affect the first
main group of construct based on the theories, namely: the technology features and utility, the context of
use and the technology user. In the following paragraph, there is a deepening for each variable of the
groups that are summarized in Table 10. These factors are specific and peculiar to the subject of
investigation, and have been defined by previous researchers.
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2.5.1 Technology features and utility
2.5.1.1 Functional congruence
To monitor personal physical conditions in real-time, the sensor should be worn on the body 24 h a day.
Thus, an overall quality that involves the product comfort, function, and battery duration, etc., plays an
important role in individuals’ assessment of perceived benefit for healthcare wearable devices. We can use
functional congruence to represent the overall quality of the device. In which we can include different
variables as:
- User interface: the system should incorporate a friendly, ease-to-use user interface. And the
functions, which are supported by the system, should involve as little user interaction as possible. It
means the number of operations to complete a task should be minimized.
- Processing: The system should provide the ability of real-time processing. The user prefers
measurement feedback without delay and emergencies detected by the system could be handled
in time.
GROUP VARIABLES
Technology features and utility Functional congruence
Placement
Physical appearance
Informativeness
Perceived usefulness, perceived ease of use
Results demonstrability
Perceived enjoyment
Perceived risk
Context Compatibility
Social influence & Subjective norms
Company image
Privacy and security issues
Validation
User Trust
Innovativeness
Health literacy
Table 10 Investigated factors
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- Intelligence: the system should have a degree of intelligence to provide more extensive and
convenient services to the user. Medical decision algorithms should be developed to extract and
integrate useful parameters from collected data from bio-sensors.
- Robustness: the system is required to work well under different circumstances, performing robustly
in real-life situation.
- Effectiveness: the measured bio-signals should achieve high accuracy and low distortion. The
results generated by the system should have enough reliability to be trusted by the medical
professionals.
- Power supply: the power consumption of the system should be low to support long operation time.
Wearable sensors have the potential to generate big databases: dealing with wearable sensor
systems means dealing with a big data challenge. As a consequence, the battery power is the most
substantial factor limiting the volume of data generated by wearable sensor systems. There is a
trade-off between transmitting all data to a server and preserving battery life. On one hand, pre-
processing data on the sensor to extract salient information before transmission will preserve
battery life, but on the other hand, since the amount of data is reduced, this imply that some
information is discarded in the process. In this case, it is present the risk of losing the advantages of
continuous monitoring. Anyway, it is still feasible to wirelessly retrieve all the data if the devices
are charged on a daily base, but should be noted that having to recharge frequently may reduce the
users’ acceptance. What customers demand are devices smaller in size, which can record more
data and last longer than even before. Customers are used to have all these expectations since they
are surrounded by rapid developments in technologies. (Redmond, et al., 2014)
- Scalability: the ability of supporting the addition / removal of system components (e.g., sensors) is
useful in adapting the changeability of user needs. (Meng & Kim, 2011)
2.5.1.2 Placement
The system should ensure a non-invasive and stable placement on the user’s body. Under the premise of
secure and fitted attachment, which would contribute to artefacts prevention, the body movement of the
user should not be hindered by using the system (Meng & Kim, 2011). In this perspective, the system
should have small size and low weight to be easily worn by the user. The article “Wearable and Implantable
Sensors: The Patient’s Perspective” studied this topic. The research objective was identifying trends in user
preferences for medical wearable sensing devices from the users’ perspective. Chiefly they define wearable
sensing device for medical applications as any system that is connected to the body and measures clinical
relevant information. A self-completed online questionnaire was developed to define what the system
should measure, how the device should look and which are the preferences on the device features. Also
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open-ended questions were introduced because according to the researchers this typology provides richer
insight. In the end 299 questionnaires were completed and the sample population was mainly female with
a mean age of 54 years, living in the United Kingdom and affected by chronic diseases (arthritis;
hypertension; asthma and diabetes). As reported by responders, devices should be small, discreet and
unobtrusive. The majority of people (85%) were in favour of sensors to be worn externally and hence non-
invasive; in general the wrist and the arms were the preferred locations. Despite this devices should be not
visible to others, concealed in the clothes or incorporated in everyday objects. This is a critical aspect
because the sample population was affected by chronic diseases and their desire is not revealing to
everyone their pathologies. To sum up a medical sensing device should be comfortable, easy to attach to
the body, simple to operate and that doesn’t affect the normal daily behaviour. Under this conditions all
the subjects were willing to wear the device for more than 20 hours a day, but they did want the placement
of the device that take less than 5 minutes and the technology should have a running life of more than six
months. The result of this research was that the authors promoted that developers should consider the
target user at the early stages of the design process. Current trend in patients’ preferences should be
incorporated since the beginning to obtain a reduction of the overall research and development costs and
an increase of the ecological utility (Bergmann, Chandaria, & McGregor, 2012).
2.5.1.3 Physical appearance
From the literature analysis, we can identify a group of papers that tested the role of physical appearance
of the wearable device. In the case of wearable technologies, the visibility of the device seems to play a
relevant role in its adoption. To highlight the importance of this factor, the article “Is the smartwatch an IT
product or a fashion product? A study on factors affecting the intention to use smartwatches” (Choi & Kim,
2016) aimed at understanding the role played by the aesthetical part of the device on the intention to use
smartwatches. Utilizing the TAM as the base framework, the current study extended the model by
incorporating perceived enjoyment and perceived self-expressiveness, which are influenced by an
individual’s vanity and need for uniqueness. The findings from 562 Korean respondents indicated that the
characteristics of smartwatches as fashion products significantly explain the intention to use a smartwatch,
particularly the individual’s desire for uniqueness. A limited effect of vanity on self-expressiveness implies
that the smartwatch is not yet deemed a luxury commodity. On the one hand, a smart ICT device is a type
of product with a short life-cycle. The hardware and software of these gadgets constantly improve, and
customers get satisfaction from acquiring the most up-to-date devices. On the other hand, people seek
values such as aesthetic pleasure and brand reputation when buying a wristwatch. The study “User
acceptance of wearable devices: An extended perspective of perceived value” (Yang, Yu, Zo, & Choi, 2015)
takes into account visual attractiveness and brand name as factors that influence perceived enjoyment and
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social image that have an indirect impact on the intention to use a wearable thought the construct
perceived value. Visual attractiveness and brand name are elements to differentiate some devices from
others, and can be a driver not only in the purchase but can also result in an increased use. In fact, if we
think about wearable devices mainly focused on the monitoring of sporty activities, the design could not
represent a significant driver, since the user will mainly wear it while doing sports. When the use of these
devices is extended beyond the sport context, then design has an increasing relevant role. Social image
defined as the extent to which peers in a user’s social network respect and admire the user because of IT
usage, becomes an important factor because people want to improve their social status or differentiate it
from those of others in their social system. In the article “Wearable technologies: The role of usefulness
and visibility in smartwatch adoption” (Chuah, Rauschnabel, Krey, & Nguyen, 2016) we can find the word
fashnology, to indicate a mix between fashion and technology proposed by Ruashnabel et al. (2016). Also in
this case visibility is significantly related both to attitude toward using smartwatch and to adoption
intention, with a stronger link when the user perceive the device also as a fashion product (43% of the
cases). As demonstrated by these studies the aesthetic side of devices for the parameters monitoring is an
important element that affects the adoption. The importance of the aesthetic side has a different shade for
elderly people. To investigate this issue Fang and Chang produced three different prototypes that should be
attached to three different part of the human body: neck; wrist and arm. Questionnaire survey and
interview were conducted to 24 participants aged over 50 years living in Taiwan. Results show how the
wrist device is the one with the highest acceptance. Participants imagined the wrist device as something
similar to an accessory like a watch or a bracelet and hence something that could be integrated with the
daily life. On the contrary the main problem of the other typologies of devices was the low easiness to read
the data. Moreover is interesting to specify that the participants who preferred the wrist device were those
who did not visit a hospital regularly. Participants who did need to visit a hospital regularly showed higher
acceptance of wrist devices, but lower acceptance of neck devices. The participants who did not need to
visit hospital regularly showed low acceptance of all types of devices, and tended to be negative towards
the devices (Fang & Chang, 2016).
2.5.1.4 Informativeness
Since healthcare wearable devices deliver real-time health information through a sensor worn on the body,
the product of healthcare wearable device can be considered as both, the delivered health information and
the hardware of sensor. Different from the definition of informativeness in marketing and website interface
designing fields, a new concept of informativeness in healthcare wearable device context can be defined as
the richness or proportion of healthcare information provided by wearable devices. Theoretically, the
informativeness of a healthcare monitoring digital technologies is also a dimension of the product quality,
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since it represents the information quality dimension, so the accuracy of the information. There are several
factors to take into account to deliver useful information. On the one hand, it must be understandable by
the end user, in order that he can make sense of the info. An example can be the Fitbit one where the user
is presented with information about his fitness in two ways: through an interpretation of the quantified
data input and manual data entry in the form of infographics and text; and through the game features,
which come in the form of badges and levels (Fotopoulou & O’Riordan, 2016). On the other hand, if the
healthcare wearable devices provide too much personal health information for users, they are more likely
to feel uncomfortable about their privacy protection, which finally may lead to a higher level of perceived
privacy risk (Lia, Wub, Gaob, & Shi, 2015).
2.5.1.5 Perceived usefulness, perceived ease of use
Many other studies tested in a more traditional way the perceived usefulness present in the TAM. All of
them demonstrated that perceived usefulness is a good predictor of attitudes toward using smartwatch.
Some of these studies also tried to verify the relationship among perceived ease of use, present in TAM,
with attitudes. In this case, not all researches verified the relationship with a significant one. Given the
results of the present studies, it is not clear if the perception of how easy the technology is to use, is an
influencer or not. In this case, we could make some hypotheses on the reasons of these results: for
example, the difference between novel and expert users can be a determinant. Regarding this topic in the
paper “Perceptions of a wearable ubiquitous monitoring device” (Moran, Nishida, & Nakata, 2013) the past
experience and the computer skill level are taken into consideration as control variables. These variables
are assumed to influence the wearable perception influencing then the attitude.
2.5.1.6 Results demonstrability
In the paper “Exploring consumers’ intention to accept smartwatch” we can find some variables being
tested on attitudes toward the use of smartwatches. Results demonstrability and relative advantage are
two variables considered to measure different shades of the perceived usefulness (Wu, Wu, & Chang,
2016). The relative advantage considers the benefits the use of a smartwatch can have compared to other
technologies, and the results demonstrability expresses if these advantages are apparent and easible
demonstrable. Relative advantage was tested to be a direct determinant of behavioral intention, while
results demonstrability was tested to be a significant influencer of attitudes. The communication of a clear
value proposition to the user, and at the same time the right communication of data to the user, is a way to
make him easily understand what are the direct advantages of data collection and elaboration.
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2.5.1.7 Perceived enjoyment
As already mentioned some articles (Yang, Yu, Zo, & Choi, 2015) (Choi & Kim, 2016) (Wu, Wu, & Chang,
2016) used and verified the perceived enjoyment as influencing factor on attitudes. These studies define
perceived enjoyment as the extent to which using wearable devices is perceived as enjoyable in its own
right, apart from any performance consequence that may be anticipated. They identified ‘perceived
enjoyment’ as a significant intrinsic motivational factor affecting behavioral intention. This intrinsic
motivational factor was found to be more influential for hedonic-oriented IT than utilitarian systems. A
confirmation of this phenomenon is provided in the article “Exploring the factors that support adoption and
sustained use of health and fitness wearables” (Canhoto & Arp, 2016), that performed a questionnaire on
the topic. Participants mentioned the importance of enjoyment while using the wearable, as exemplified by
this quote: ‘(If) I don’t have fun then I am not convinced, and it’s not good to me’. In terms of what made it
fun to use wearables, the interviewees mentioned features such as supportive messages, new features,
games and badges. For many participants, such a fun and enjoyable experience was achieved through the
community of users around the application or device. For instance, they arranged to meet with others using
a fitness application to exercise together. Some liked to compete against others, be real friends or virtual
ones.
2.5.1.8 Perceived risk
While perceived enjoyment is one of the components of perceived benefit, some researchers investigated
the variable perceived risk. Perceived risk is considered as felt uncertainty regarding possible negative
consequences of using a product or service. People are often anxious about the diverse types of risks
presented when engaging in activities or functions involved in a new technology. Liebermann and Paroush
proved that adoption rates of newly offered goods depend crucially on the marketer’s ability to mitigate
perceived risk involved with new goods offered (Gao, Zhang, & Peng, 2016). Trying any new product or
service involves some risk, as all actions have unanticipated consequences, some of which are likely to be
disagreeable. Risk may cause consumers to delay or cancel the purchase of a new product. Researchers
suggested that customers perceive the decision to purchase new high-tech products as risky because these
products and their industries exhibit pervasive technological and market uncertainties. Wearable devices
are new high-tech products, and their performance, price, and maintenance expenditure may be critical
factors in estimations of their value as useful computing devices. Thus, perceived performance risk is
defined as the possibility that the wearable devices will not function as expected, and perceived financial
risk is defined as the probability of monetary loss incurred from buying or maintaining wearable devices.
Result from the article “User acceptance of wearable devices: An extended perspective of perceived value”
(Yang, Yu, Zo, & Choi, 2015) supports the finding that early adopters are more willing to take risks (Rogers,
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1995) and are less price-sensitive than later adopters (Golder and Tellis, 2004). For potential users,
performance risk seems to have a greater impact than financial risk on perceived value. Even if investing in
unfamiliar devices entails financial risk, the latter is less critical than the malfunction risk. However,
potential customers will not hazard adopting wearable devices without having some assurance of their
performance. The achievement of the performance expected of wearable devices should dilute perceived
performance risk and lead to their widespread adoption.
2.5.2 Context
2.5.2.1 Compatibility
From the technological perspective, a variable that was tested by different studies as determinant factor on
the behavioural intention to use a wearable is the compatibility. It is defined as the extent to which a
potential customer’s value, self-demand and precious experiment are matching with a particular system. In
the study “Is the smartwatch an IT product or a fashion product?” perceived compatibility was considered
in addition to the before mentioned core variables of TAM, as one of the determinants of ‘perceived
usefulness’, ‘attitudes towards usage’, and ‘intention to use’ (Choi & Kim, 2016). The compatibility of a
technology is how well the technology or service fits into one’s daily behavioral patterns, lifestyle, or
experiences. Given that Schierz et al (Choi & Kim, 2016), found that compatibility had the most powerful
impact for all three variables in the context of mobile payment service, also other researchers wanted to
test if this variable was relevant also for wearables. The results of these researches demonstrate that
compatibility has a significant relationship with perceived usefulness (Jeong, Kim, Park, & Choi, 2016) and a
direct one with attitudes (Gao, Zhang, & Peng, 2016). In the other case (Wu, Wu, & Chang, 2016), the
hypothesis of a relationship between compatibility and attitude was not supported at the 95% confidence
level used in this study, but at the 90% confidence level (t statistics greater than 1.645). This is an
interesting finding indicating that the behavioral pattern change may be subtle. Users accept smartwatches
that may have small differences from their original usage patterns, habits, and experiences, while a
dramatic change causes resistance to smartwatches.
2.5.2.2 Social influence & Subjective norms
Related to the visibility aspect, social influence variable can be considered as a determinant of intentions
(Wu, Wu, & Chang, 2016). In the article “Exploring consumers’ intention to accept smartwatch” the variable
aims at defining if the use of the wearable is related to the individual social status, and how he is judge by
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others. What emerged from the study is that this variable has a significant influence on behavioral
intentions. That means that since the wearable is something that can be easily shown to others, people
care about what others think about them using it. Subjective norms represent what others expect us to do
and so, on what factors others judge us. In the article “Wearable fitness technology: A structural
investigation into acceptance and perceived fitness outcomes” (Lunney, Cunningham, & Eastin, 2016)
subjective norms serve a basic human social function, which allow individuals to distinguish who is in the
group and who is an outsider. Behaving in ways the group considers appropriate is a demonstration that
the individual belongs in the group. In terms of WFT devices, this supports to idea of social gamification.
2.5.2.3 Company image
Brand is a highly influential factor affecting consumer choices, and its power is even more prominent in
circumstances involving uncertain product qualities. In other words, brand is used as a device for mitigating
risks related to choosing and using a product. Therefore, brand could have a significant influence on
consumers’ choice of smartwatch, which is regarded as a novel product that combines a familiar object
(i.e., wristwatch) with computing (Jung, Kim, & Choi, 2016). A firm’s prestige is an overall assessment of its
product and service quality, customer experience, credible communication, and social character of its
abilities to satisfy consumers. It provides critical cues of how the firm handles customer affairs, including
privacy issues such as the way to collect and use personal information. Existent privacy studies have shown
that prestige has direct influence on individuals’ information privacy concerns and moderating effect on
trust on organizations. In addition, a firm with higher prestige would enjoy the ‘halo effect’ since
consumers are more likely to believe that the firm can also do better on privacy protection if it is doing
excellent jobs in other aspects. Individuals thus have fewer perceptions on privacy risks when purchasing
from a healthcare wearable device provider with higher prestige (Lia, Wub, Gaob, & Shi, 2015).
2.5.2.4 Privacy and security issues
First of all the system should guarantee the user safe in utilization. The sensors should not cause any skin
irritation or allergies to the user. And there should not be any unwanted radiation or infection concerns
when the system is used in long term.
Second, many patients understand the value but state concerns about open-data sharing. Patients are
increasingly amenable to sharing their data with peers. 2014 State of the Internet of Things Study found
that more than half of consumers are willing to share their wearable data with physicians. On the other
hand, a California Institute for Telecommunications and Information Technology survey, which found that
90% of respondents are seeking data anonymity. These concerns are realistic, especially in an era of GPS
technology, which could potentially reveal sensitive personal activities. Research suggests that individual
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openness to sharing will be dependent on the nature, use, user(s), legal protections, and potential
compensation associated with the data. Taken together, greater transparency by device companies and
researchers will be critical to patient engagement with these technologies (Chiauzzi, Rodarte, &
DasMahapatra, 2015).
Considering the concept of health information sensitivity as an individual’s information attribute that
informs the degree of perceived discomfort when disclosing health information to an external agent (a
healthcare wearable device provider in our case). When other things being equal, individuals will have
more perceptions for privacy risk when disclosing more sensitive information, because certain domains of
life are regarded more private than others (Lia, Wub, Gaob, & Shi, 2015).
Currently, organizations are implementing different strategies, such as building prestige, conducting privacy
policies, and third-party assurance, to attract more consumers and reduce their privacy concerns, since
they are aware that consumers are more likely to strike back on improper treatment of their personal
information.
2.5.2.5 Validation
Clinical or real-life validation is necessary to test the system and convince the user. However, complete
validation is not cost-effective. Therefore, the trade-off of validation breadth, depth and type should be
considered carefully (Meng & Kim, 2011). This confirmation should be continued in time, and can be
implemented by a new generation of digital health advisors who need to be to make these AI-derived
recommendations useful. They need to be easy-to-use, consumer-orientated persons who can connect to
the aggregated data store and the AI analytics engines that sit on top of that. They can empower
consumers/patients, and reduce the demand burden on clinicians. They will not replace clinicians but they
will help filter the demand to those who truly need to be seen, while empowering patients with real-time,
believable and personalized guidance for the more common things in day-to-day life (Dimitrov, 2016).
2.5.3 User
2.5.3.1 Trust
A potential adopter usually wants to maximize benefits and minimize risks. Trust can help reduce the
uncertainties a user faces when using SWD. As additional variable, the paper “Understanding the Adoption
of Smart Wearable Devices to Assist Healthcare in China” (Gao, Zhang, & Peng, 2016) considers trust. Trust
is defined as the extent to which a person believes that using a particular system would be safe and high
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quality. Trust explains 50.6 % of the observed variance in users’ attitudes toward SWD. The influence of
trust on users’ attitudes towards SWD was the most significant which is followed by compatibility, PU and
PEOU. The devices should be able to provide precious measurements and send some essential health-
related messages timely. Users also expected that the primary care units are able to provide services to
patients by taking advantage of SWD.
Previous research has found that trust is one of the important factors to the adoption of online information
services, starting from online shopping acceptance. Then trust was also investigated as determinant of
attitude toward the use of smartwatches. Thus, when considering the customers’ attitude towards SWD,
the companies should pay more attention the trust construct (Gao, Zhang, & Peng, 2016). To better
understand the findings of this study, the authors did an interview with a doctor from local primary care
unit was carried out in February 2016. The interviewee said that primary care services (e.g., home care)
were provided in theory. Most doctors and nurses have too many patients in their units. Therefore, they
did not get time to visit patients’ homes. The interviewee also indicated that collecting citizens’ daily health
data like heartbeats and heart rate do help doctors a lot when making a definite diagnosis in the long term.
However, this requires nearly all citizens to use SWD in appropriate ways. In addition to this, a sound
database must be constructed to collect and analyze all information that come from the patients.
Otherwise, the collected data will become a string of numbers, which is meaningless to the doctors. They
are often too busy to find out the meanings behind the numbers. Lastly, the doctor indicated that another
essential precondition for the widespread of SWD to assist healthcare in China is the support from the
government. Therefore, it is believed that the government plays an important role in the success of the
promotion of using SWD to assist healthcare in China and also to support trust of patients in the
organizations.
2.5.3.2 Innovativeness
Much research on technology adoption of wearable devices has focused on the utilization of TAM and its
extensions. While useful, this approach tends to neglect the role of individual characteristics, which is
considered to be critical in new product adoption. Past research identified consumer’s innovativeness as a
critical determinant for the adoption of new products. The last articles from Jeong, Kim, Park, Choi and
Hong, Lin, Hsieh tried to fill the gap in the literature by examining the consumer innovativeness. In one
study consumer’s domain specific innovativeness is conceptualized to be of two dimensions namely,
product possessing innovativeness and information possessing innovativeness (Jeong, Kim, Park, & Choi,
2016). While in the second article it was considered as a unique construct (Hong, Lin, & Hsieh, 2016). In
both cases the influence of this determinant on the purchase and on the continuance intention to use
smartwatch was mediated by some variables pertaining to both hedonic value and utilitarian value. What
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emerged from the researches was that innovativeness has a significant and positive relationship with the
purchase and use of a wearable, validating the theory of innovation diffusion within the context of
wearable devices. Moreover, both models confirmed that both utilitarian and hedonic values can influence
the purchase intention and the continued use of such technologies.
Personal innovativeness in IT refers to an individual’s willingness to try out a new kind of IT. Researchers
have made common sense that individuals would react differently due to their differences in characteristics
associated with IT innovativeness. Generally, the innovative users are more likely to adopt a new IT even if
there is a high level of uncertainty of the adoption. This is due to the fact that individual’s personal
innovativeness in IT decreases perceived privacy risk. (Lia, Wub, Gaob, & Shi, 2015)
2.5.3.3 Health literacy
Health literacy is a user characteristics that aims at defining the level of competences and confidence an
individual has on searching, evaluating and make sense out of health information. Greater health literacy
was significantly associated with greater perceived ease of use and perceived usefulness across all Health
Information Technology tools. (Mackert, MabryFlynn, Champlin, Donovan, & Pounders, 2016)
All the previous mentioned factors are factors that have been tested to influence the use of digital
technologies, from the purchase to the actual use of them. We can notice that the elements to take into
considerations are several. What can be interesting to understand is if the different elements affect
different target customers. For that reason a research on the customer journey has been taken into
consideration.
2.6 THE CURRENT CUSTOMER JOURNEY According to a study, 1/3 of wearable device owners stop using them within 6 months38. While considering
the overall population of wearable owners, over half of consumers no longer use it. Moreover, a
commercial study by Endeavour Partners (2014), reports that 50% of new users of wearables and 74% of
new users of health apps, stop using them within two weeks. This suggests that only a minority of users
succeed in making a habit out of using their wearable or app. (Stragier, Abeele, Mechant, & Marez, 2016)
Based on the literature analysis, it is possible to draft a customer journey than explains what consumer
consider before stopping the usage for a wearable user. In there is a synthesis of it. With the previous
38The Guardian, Charles Arthur (2014). Wearables: one-third of consumers abandoning devices
https://www.theguardian.com/technology/2014/apr/01/wearables-consumers-abandoning-devices-galaxy-gear
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explained aim of defining factors that affect different target customers, we identified two segments. The
first one more interested to the technological side, and the second one more interested to the aesthetic
side.
1. DISCOVER:
The first point of contact with the product category, so the first moment of awareness about this
technology can generate a first impression in the potential consumer. From the literature review, it was
proven that wearable devices in particular can be perceived both as a fashion item or as a technological
device. People who perceive them as a technology give more importance to the usefulness and so the first
impression could be related to the utility, while in the case of fashion interest the first impression would be
more related to the social image related to the device, and the its coolness (Choi & Kim, 2016). The first
discover can happen in different ways, by online advertising or by word of mouth or by personal
experience.
2. SEARCH:
Once triggered a little bit of interested about the product in the individual, he could deepen the knowledge
about it and search for more information. In particular, we can identify as main source of information,
Internet. So technology oriented individuals will be more interested in the devices performances, while
fashion driven ones will be more attentive to the design. The motivational issue driving the device adoption
will be monitor the lifestyle with a difference. In the first case people are more concerned about the
accuracy of data, while in the second case the aim is not only to improve health conditions and fitness
performances but also to be recognized as technological and active person.
3. PURCHASE:
Driven by these motivations, users will buy or not the devices. Based on the strength of their intention
formation. By collecting information and opinion of the product, and forming their own opinion on that
matched with the possibility to comply with intentions, the individual will be guided or not toward the
purchase.
4. USE:
Once bought the product, users will start using it. At the very beginning they will have to learn how to use
it. The usage of the product, will allow the user to experience it and compare the actual experience with
the expected one, generating satisfaction or not.
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5. STOP:
Usually after a 6 months time period, the user abandon the use of the wearable. So even if the product was
purchased 6 months before, the buyers are no more users. The motivations for the discard of the product
can be that it is difficult for the user to understand the utility of the data collect, and in particular make use
of them in a practical way, in order to see real benefits. In the case of fashion users, there could be also the
problem of a no more fashionable design, but this should led to the purchase of a new model.
What emerged from the recent researches on the use of digital technologies is that even once bought the
wearable for example or download the app, is difficult to create a habit. The stability of the behaviour over
time and context seems to be poor. According to the theories analyzed in the literature review this could be
due to the weakness of attitudes and others components affecting behaviour. In order to create a strong
habit, the components that affect behaviour both directly and not (through intention) should be reinforced.
On the one hand, the beliefs that form the attitudes should be reinforced. It could be possible to leverage
on a stronger communication of the benefits and advantages that the use of these technologies can
support. On the other hand, there could be the possibility to act on the formation of an automatic
behaviour, by addressing some factor that can directly influence behaviour, without the intention and
attitude formation. An intervention is needed in order to promote the purchase and to disincentive the
stop of the use. Something should intervene to make the customer experience more involved and better
transmit the usefulness of the data gathered to the user.
Table 11 Customer journey
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The possible ways to do that are different: the development of a counseling advisor (Dimitrov, 2016), the
use of the gamification approach, frequent updating. Last but not least researchers decided to involve from
the early stages the stakeholders of an instrumented footwear design process. Instrumented footwear
refers to any custom-made insole or foot-wear which incorporates electronic circuit used to capture
measurements such as physical activity, push-off and contact forces, gait data or health metrics. The
methodology adopted was designed using the principles of human-centred design. Knowledge of the users
and their capabilities and characteristics has been highlighted as important considerations. The main users’
contribution was about the comfort and human factor of the technological device; including ease of use
and material utilized. From the beginning of the project users were involved and hence also the first
prototype was developed according to their preferences (Harte, et al., 2015). The human centred design
methodology allows a reduction of the number of iterations in the process or at least an anticipation of the
same in the first phases, where changes are less expensive.
2.7 CONTRIBUTIONS AND GAPS
Summarizing what emerged from the literature analysis, it is important to highlight that digital technologies
and in particular self-tracking ones can collect health information in a big amount, that can be processed
and elaborated in order to make predictions and support the decision-making system in the healthcare and
wellbeing context. The adoption of these technologies is essential to benefit from the advantages of big
data manipulation. Behavioural theories aim at explaining the adoption and use of these technologies.
There are two different main pathways: the controlled behaviour and the automatic one. The researches
done on the topics are mainly related to wearables. These researches tested in an explicit way the factors
that influence controlled behaviour. There are many variables to take into account for the design of a
wearable. In order to set these variables in the right way a target customer should be defined. Then it is
really important to involve the users since the beginning of the design process.
The main topics of investigation until now have been the traditional variables of the adoption theories,
together with peculiar aspects related to the two main souls of wearables: the fashionable one and the
technological one. There is clear evidence that the aesthetic form, the design of the wearable as well as the
compatibility of the technology with consumer lifestyle are important determinants of the adoption. At the
same time, there are issues that have not been investigated yet, presented in Table 12.
One of the main gaps of the review literature is that until now there are no researches that investigated the
construct perceived behavioural control. In the research context, the main framework adopted as
inspiration has been the TAM, instead of TPB. In some cases, the two theories have been integrated, with
the introduction of subjective norms (Lunney, Cunningham, & Eastin, 2016). In the TAM models, the
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construct perceived easy of use can be classified as a subcategory of perceived behavioural control. In fact
perceived behavioral control consider also how much simple the individual thinks the behaviour is to
perform. But it considers also some additional elements, like time and money available.
Considering the impact that the digital revolution can have on the healthcare system, from the literature
analysis emerges the change on the doctor patient relationship. The relationship is an aspect that can
influence the studied behaviour. Despite this, there aren’t studies that tried to investigate this issue. The
present models tried to test how subjective norms act on the individual choice of using a digital technology
to monitor the lifestyle, considering as main stakeholders family and friends or the society in general. For
example, one of the used question was “Define how much you agree with the following statement: most
people who are important to me think I should use a wearable fitness device” (Lunney, Cunningham, &
Eastin, 2016). Always concerning the involvement of other stakeholders, it would be interesting to
investigate also the role played by the institutions as governments and healthcare providers. One salient
point could be understanding if the support of public institutions can promote the use of such technologies,
by influencing the perceived usefulness for example.
On the individual level, the willingness to pay could be another relevant factor of the studied behaviour. In
particular, due to the health-related aim of adoption of the technologies, willingness to pay can result in
some particular behaviours.
From the technological point of view what it still poor in analysis is how the level of compatibility of the
wearable with other technologies can affect their adoption.
Another missing investigation has been the consideration of habitual behaviour, why people stop using the
devices. This issue has been found particularly relevant, due to the observed stop in the use of wearable
devices after a 6-month time period from the purchase. Finally, another gap regards a deeper research on
online health literacy, defined as the level of competences and confidences an individual has on searching
online, evaluating and making sense out of health information. The focus on the online context can be
important due to the fact that only digital technologies allow the access to the online.
GAPS BENEFITS
Perceived behavioural control Test if this construct from the TPB model can represent
a significant determinant in the context of health
monitoring digital technologies.
Doctor role Evaluate if the doctor opinion and suggestion on using
digital technologies can influence the adoption.
Institutions and health providers’ role Evaluate if institutions and health providers’ advices on
using digital technologies can influence the adoption.
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Willingness to pay Understanding if the intentions of the customers are
moderated by the willingness to pay.
Compatibility with other technologies Test if the creation of a wearable compatible with
different types of working system related to the
technologies to with the wearable could be connected
can influence the adoption.
Habitual behaviour Investigate the determinants of habitual behaviour, in
order to prevent the stop of usage of the technology.
Online health literacy Analyse if online health literacy could encourage and
facilitate the adoption of digital technologies for
tracking lifestyle.
Table 12 Literature gaps
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3. METHODOLOGY The aim of this chapter is to describe the steps followed for the research and the instruments used to
implement it. Initially a research topic was defined and then the following steps were applied:
1. Literature analysis, identification of gaps and definition of the main research questions
2. Empirical analysis
a. Model formulation based on the research questions
b. Questionnaire analysis
c. Results discussion
3.1 GAPS IDENTIFICATION From the literature analysis, we were able to find the main gaps and the main unaddressed issues, which
could be interesting research questions for this thesis.
In the decision of the gap to address in our research we had to consider and comply with the relevance of
the topic.
We identified 3 main research questions to solve the main gaps and to better investigate the factors that
affect adoption of digital health monitoring technologies.
We didn’t explore all the gaps we identified from the literature research. In particular, we were not able to
investigate the willingness to pay, the role played by institutions and healthcare providers and the
compatibility with other technologies.
The process of selection of the most relevant gaps to fill can be summarized in Figure 18. The adopted
parameters are the feasibility and the impact of the gap. The feasibility regards the way in which the gaps
can be measured and hence the fact that all the constructs are built trough questions generated in a
survey. This is the reason why automatic behaviour has been excluded from our research, because it should
be addressed and evaluated with different kind of tools from the questionnaire. For example, a way to
measure automatic behaviour is the implicit association test. On the other hand, we considered as
parameter the impact of the gap in terms of importance and possible future contribution to theory and
practice.
Despite some gaps were believed to have an interesting impact, we were not able to measure them with
the available instruments. At the end three main research questions were formulated.
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Figure 18 Research question identification
Research question N°1: How the perception of the individual concerning the easiness of control over the
behaviour is related to the adoption of the behaviour itself?
First of all, we decided to include perceived behavioural control in order to investigate if the available
resources can have an impact on the studied behaviour. TPB is a theory that has been tested and validated
in different contexts and many times perceived behavioural control ended to be a significant determinant
of behaviour. Right now researchers are introducing the TPB also in the healthcare sector. Despite this, the
TPB has not yet been applied in our context of research. In fact, in our article selection we tried to connect
keywords as wearable, app and trackers with keywords as TPB and perceived behavioural control, but we
didn’t find any article. As a consequence, research question number 1 was formulated with the aim to
investigate the impact of perceived behavioural control.
Research question N°2: Are there some specific resources needed for the adoption of the studies
behaviour? In addition to time and money, is some peculiar knowledge needed to develop interest in the
behaviour?
Considering this research question, we included the online health literacy as peculiar knowledge that can
impact the adoption of digital technologies for lifestyle monitoring. Due to the increasing amount of data
and information available on the Internet, an increasing number of patients look for health explanations
and suggestions online. In order to be able to differentiate valuable researches from not trustable ones,
they should develop the capability to understand and evaluate the resources. That competence is better
defined as online health literacy. It includes both medical knowledge and website surfing one. This ability
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could influence also the interpretation of the data gathered through digital technologies and so their
adoption.
Research question N°3: How the doctor opinion and the doctor-patient relationship can affect the use of
digital health monitoring technologies?
We agreed on better investigate the influencing role of the doctor opinion on the adoption of digital
technologies for health monitoring. The research context is the healthcare, which is traditionally considered
a sector where competences are required in order to take decisions due to the relevance of the issues. The
suggestions of specialized and prepared professionals matter. For this reason, we wanted to test how the
authority of the doctor can put some pressure on the individual in terms of adoption. We decided to
investigate this issue in order to understand if the doctor is still perceived as an authority able to influence
and persuade individual, in particular in absence of control and knowledge over the behaviour. In the case
of digital technologies, the example could be the one of an elderly person who is not really able to use that
kind of technologies but will use them in any case because the doctor told him to do it. Leveraging on the
doctor authority to create a norm can be a way to implement an automatic behaviour. In spite of this, we
have to recognize that the world is changing. Nowadays the doctor opinion matters, but is no more always
considered the absolute true. Sometimes what the doctor says is put under discussion and different
medical opinions are listened before taking a decision. A reason behind this change is the increasing
availability of information that everyone can consult in order to become more educated patients. As a
consequence, the real research question we would like to address is the following: the doctor–patient
relationship has always been a keystone of care, but is this still true with the disruption of digital
technologies?
3.2 LITERATURE ANALYSIS The main objective of the study is to identify factors that encourage, support and motivate individuals to
use digital technologies with the aim of monitoring data about their activity parameters.
The literature analysis was conducted at an international level. The time period considered for the research
of articles has been from 2010 to 2017, focusing on peer-reviewed journals listed in Scopus and PubMed
databases. A constrains on the document typology limited our research: only articles and book chapters
were selected. Hence no conference papers, reviews, conference reviews were included in the literature
analysis. The reason behind this choice is related to the higher numbers of controls and reviews done by
experts applied to articles and book chaprters rather than on the others typologies of documents.
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For the articles selection, specific keywords were defined on the base of the research objectives. First of all,
we considered some general aspects as digital health, big data, self-tracking, monitoring, health literacy,
machine learning and quantified self related to the collection of data thanks to digital technologies. At the
same time, we defined which types of digital technologies take into consideration, so we included
keywords as wearables, app, trackers and digital technologies, keeping always in mind the context in which
these technologies should be adopted. Therefore, we included keywords as health and lifestyle. Moreover,
we tried to understand the current state of this market considering keywords as business model and
innovativeness. While another group of keywords is related to the theories considered, as the Theory of
Planned Behavior, the Theory of Reasoned Action and the Technology Acceptance Model. So, we used
keywords as consumer behavior, usefulness, attitude, trust, TAM, TPB, adoption, behavior, perception,
diffusion, acceptance, accept. In addition, with the aim of understanding the relationship between
intention and behavior we considered some factors as price and willingness to pay. Finally, the last group of
keywordS is about the impact of the doctor in the adoption of digital technologies for lifestyle tracking
since we are dealing with technologies that are used in the healthcare sector, where clinicians traditionally
played a relevant role. The above-mentioned keywords are summarized in Table 13.
CONTEXT KEYWORDS
Research environment Health, lifestyle, digital health, doctors, big data,
self-tracking, monitoring, health literacy,
machine learning and quantified self
Type of digital technologies Wearables, app and trackers
Market consideration Business model and innovativeness
Behavioural theories Consumer behavior, usefulness, attitude, trust,
TAM, TPB, adoption, behavior, perception,
diffusion, acceptance, accept
Willingness to pay Price, willingness to pay
Doctor impact Doctor-patient relationship
Table 13 Keywords
On the other hand Table 14 reports how we combined the different keywords in order to consider all the
aspects of our research and so to connect the research environment with the typology of digital
technologies, the behavioural theories and finally the willingness to pay. For every combination of
keywords we made the research considering all the types of digital technologies: wearables, apps and
trackers. Table 14 is also useful to understand how many articles were found for each group of keywords
and also how many of them were actually adopted in our thesis.
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KEYWORDS N° OF DOWNLOADED
ARTICLES
N° OF USEFUL
ARTICLES
Wearable/app/trackers and healthy life style 1 0
Wearable/app/tracker and digital health 2 0
Big data and customer wearable/app/trackers 1 1
Quantified self and wearable/app/trackers 10 4
Big data and health wearable/app/trackers 8 1
Wearable/app/trackers and machine learning and
big data
3 0
Wearable/app/trackers and health literacy 2 1
Wearable/app/trackers and innovativeness 2 2
Wearable/app/trackers and business model 2 1
Wearable/app/trackers and health and acceptance 9 3
Wearable/app/trackers and adoption and attitude 2 1
Wearable/app/trackers and trust 1 0
Wearable/app/trackers and health and consumer
behaviour
5 3
Wearable/app/trackers and perception 9 5
Wearable/app/trackers and usefulness 7 5
Wearable/app/trackers and diffusion 3 2
Wearable/app/trackers and accept 5 4
Wearable/app/trackers and TAM 4 4
Wearable/app/trackers and perceived behavioural
control
0 0
Wearable/app/trackers and TPB 0 0
Wearable/app/trackers and willingness to pay 0 0
Wearable/app/trackers and price 4 1
Wearable/app/trackers and doctor-patient
relationship
5 4
85 42
Minus
duplicates=12
30
Table 14 Group of keywords and selected articles
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Considering all these keywords and both Scopus and PubMed, 1683 results have been identified. The Figure
19 summarizes the process of article selection. Among those 1683 only 570 were articles and not
conference papers, reviews, conference reviews. Some others constrains have been defined in the
selection of articles, which has been conducted looking at the abstract analysis. We considered all articles
related to health and fitness parameters focusing on general activities without considering specific illness,
mental problems or safety delocalization issues. Since our focus was on the use of digital technologies to
collect health data, we did not consider the use of these technologies in other health stages as diagnosis or
treatment support. After this initial process of skimming 141 articles were considered of possible interest
for our research. Despite this, some articles were not available and we couldn’t download them because of
limitations. In the end 85 articles were downloaded for the full text reading. After duplicates removed and
after full text review 30 articles were included in the work as considered more appropriate for the research
objectives. In addition to these 30 articles there are also 16 articles and 2 book chapters that have been
used to better understand the behavioural theories and the methodologies used to analyse the data of the
survey.
Figure 19 Process of article selection
In order to analyze and track the content of each article, we created a dataset on Excel, containing a
framework of information for each article including: title, author, year, keywords, dataset and model or
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summary of the content. In particular, nine articles contained relevant models regarding the use of digital
technologies for activity monitoring.
Thanks to the literature analysis we were able to define the art of the state of the research on digital
technologies and on the investigated consumers’ behavior drivers.
In addition thanks to a deep investigation on all the researches already done on digital technologies for
tracking lifestyle, we were able to identify the points of weakness that should be better explored to
understand the drivers behind the adoption of such technologies.
3.3 EMPIRICAL ANALYSIS
3.3.1 Construct creation
Once defined the issue to address we could draw the model. In order to formulate the model it was
necessary to define:
- Name of the construct and their definition, in order to give a clear explanation of the measurement
purpose of the construct;
- Proposition: definition and justification of the construct role and the relationship among constructs.
The constructs and the items were defined not just on the base of the literature analysis, but also on the
base of the data available from the survey.
After the literature review conducted, the idea of the proposed model attempts to incorporate insights
coming from technology acceptance field and user experience, in order to have a deep understanding on
how consumers accept technologies in an experiential perspective. The conceptual framework includes the
impact of personal and social aspects on customers’ acceptance of new technologies that could lead their
usage. Likewise, it attempts to offer firms additional tools for testing new technologies before being
launched. Given the large investments that companies incur when developing new products, it is ideal to
have an estimate of user acceptance as early as possible. In this case, what emerged from the literature
analysis is that investments in digital technologies have already been done but still the use of these
technologies is limited. In order to optimize the investment it is important to understand what are the
relevant determinants to leverage on.
A large number of studies evidence the validity of the TAM (Davis, 1989; Davis et al., 1989; Davis, 1993)
others point out some limitations (Bagozzi, 2007; Legris et al., 2003; Venkatesh et al., 2003) that make us
think that the original TAM variables may not be sufficient to capture all beliefs influencing consumer
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toward using technology. The present model incorporates TAM and TPB variables already proved to be
significant including perceived usefulness, perceived behavioral control, attitude, behavioral intention and
behavior.
In addition lot of research has been done concerning the importance of the functional characteristics, the
design and the aesthetic, the engagement of the user and the context of use. Since all these elements are
already consolidated and quite solid, we decided to investigate what seems to be a relevant determinant in
the monitoring behavior adoption, but still has not been tested in a proper way and remains a gap in the
literature: the perceived doctor opinion. This construct is an ad hoc construct for the studied behavior, and
expresses if the individual perceives the doctor interested in the promotion of digital technologies for
lifestyle tracking. Since the technologies under study are used for health monitoring, the opinion of the
doctor could be quite determinant.
Furthermore, TAM and TPB models lack of the presence of variables related to personal characteristics and
hence we would like to test them. Reading the paper coming from the literature analysis, we recognized as
an important construct the level of innovativeness of the user. In particular with level of innovativeness we
mean how the individual is concerned in having new technological devices in the early stages of the
product lifecycle. At this point we analyzed the survey questions, but unfortunately we realized that there
weren’t proper questions on this topic. Afterwards we considered a similar personal characteristic, which is
online health literacy: the confidence an individual has on searching health information through the digital
channels. This variable includes aspects of both knowledge and propensity to use information coming from
technology.
To sum up we decided to test if the combination of the TAM and the TPB remain valid if integrated with the
new constructs of perceived doctor opinion and online health literacy.
In Table 15 there is the definition of each construct in order to better understand what it should measure.
CONSTRUCT NAME DEFINITION
PERCEIVED USEFULNESS The perceived utility an individual has over monitoring
the lifestyle with digital technologies
ONLINE HEALTH LITERACY The confidence an individual has on searching health
information on Internet
PERCEIVED BEHAVIOURAL CONTROL The perception on the control and easiness over the use
of a digital technologies
ATTITUDE The positive evaluation of the behaviour of using digital
technologies to monitor lifestyle
INTENTION The intention to use digital technologies to monitor daily
activities
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PERCEIVED DOCTOR OPINION The perceived level of interest of the doctor in promoting
the monitoring of activities tracking
BEHAVIOUR The use of digital technologies with the aim of
monitoring heart rate; steps; trainings or calories
Table 15 Constructs name and definition
We defined the questions associated to each construct and hence the items. Table 16 reports all the
constructs with their associated items, the question text and the question number of each item.
CONSTRUCT ITEM QUESTION
NUMBER
QUESTION TEXT
PERCEIVED
USEFULNESS
USEFULL FOR
HEALTH
PU_1 On a scale from 1 to 10 how much do
you agree with the following statement
“Monitoring my lifestyle with digital
devices would improve my health
conditions”
PERCEIVED
USEFULNESS
USEFULL FOR
HEALTH
MAINTENANCE
PU_2 On a scale from 1 to 10 how much do
you agree with the following statement
“Monitoring my lifestyle with digital
devices would enable me to maintain
healthy”
PERCEIVED
USEFULNESS
USEFULL PU_3 On a scale from 1 to 10 how much do
you agree with the following statement
“Monitoring my lifestyle with digital
devices is useful”
ONLINE HEALTH
LITERACY
SEARCH OHL_1 On a scale from 1 to 10 how much do
you agree with the following statement
“I know how to use Internet to answer
to questions about my heath conditions”
ONLINE HEALTH
LITERACY
SELECT OHL_2 On a scale from 1 to 10 how much do
you agree with the following statement
“I am able to distinguish valuable
resources from low quality resources in
the health field on Internet”
ATTITUDE HEART RATE ATT_1 On a scale from 1 to 10 how much
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relevant do you consider the hearth rate
monitoring?
ATTITUDE STEPS ATT_2 On a scale from 1 to 10 how much
relevant do you consider the steps
monitoring?
ATTITUDE TRAININGS ATT_3 On a scale from 1 to 10 how much
relevant do you consider the trainings
monitoring?
PERCEIVED DOCTOR
OPINION
THOUGH PDO_1 On a scale from 1 to 10 how much do
you agree with the following statement
“My personal doctor think that I should
monitor my lifestyle with digital devices”
PERCEIVED DOCTOR
OPINION
EXPECTATION PDO_2 On a scale from 1 to 10 how much do
you agree with the following statement
“My personal doctor expect me to
monitor my lifestyle with digital devices”
PERCEIVED
BEHAVIORAL
CONTROL
TIME PBC_1 On a scale from 1 to 10 how much do
you agree with the following statement
“I have enough time to monitor my
lifestyle with digital devices”
PERCEIVED
BEHAVIORAL
CONTROL
ECONOMIC
RESOURCES
PBC_2 On a scale from 1 to 10 how much do
you agree with the following statement
“I have enough economic resources to
monitor my lifestyle with digital devices”
PERCEIVED
BEHAVIORAL
CONTROL
EASINESS PBC_3 On a scale from 1 to 10 how much do
you agree with the following statement
“Monitoring my lifestyle with digital
devices is easy”
INTENTION FORECAST INT_1 On a scale from 1 to 10 how much do
you agree with the following statement
“I plan to monitor my lifestyle with
digital devices in the next months”
INTENTION PROBABILITY INT_2 On a scale from 1 to 10 how much do
you agree with the following statement
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“It is probable that I’ll monitor my
lifestyle with digital devices in the next
months”
INTENTION INTENTIONED INT_3 On a scale from 1 to 10 how much do
you agree with the following statement
“I intended to monitor my lifestyle with
digital devices in the next months”
BEHAVIOUR HEART RATE BHV_1 On a scale from 1 to 3 indicate if you use
an app to monitor hearth rate.
1 indicate that you do not use it and are
not interested
2 indicate that you do not use it but you
are interested
3 indicate that you use it
BEHAVIOUR STEPS BHV_2 On a scale from 1 to 3 indicate if you use
an app to monitor steps
1 indicate that you do not use it and are
not interested
2 indicate that you do not use it but you
are interested
3 indicate that you use it
BEHAVIOUR TRAININGS BHV_3 On a scale from 1 to 3 indicate if you use
an app to monitor trainings
1 indicate that you do not use it and are
not interested
2 indicate that you do not use it but you
are interested
3 indicate that you use it
BEHAVIOUR CALORIES BHV_4 On a scale from 1 to 3 indicate if you use
an app to monitor calories
1 indicate that you do not use it and are
not interested
2 indicate that you do not use it but you
are interested
3 indicate that you use it
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Table 16 Items and associated questions
In addition to the constructs, four control variables were considered: the sportiness, the age, the education
level and the presence of chronic diseases. The selection of these control variables was driven by the
literature analysis and by an investigation done on the survey. In fact, considering the demographic
characteristics available in the survey, we look at the way in which the different interviewees segments
answered at the questions. Only in case of variations of the results, the demographic characteristic was
introduced in the model as control variable. At the end sex, geographic area, residence and employment
were rejected.
3.3.2 Hypotheses definition
Finally, to define the model the following research hypotheses have been defined based on the literature
analysis and the gaps identified.
HYPOTHESIS 1: ONLINE HEALTH LITERACY PERCEIVED BEHAVIORAL CONTROL
The confidence an individual has on searching for health information on Internet has a positive influence on
the perception of the control over using digital technologies to monitor the lifestyle.
HYPOTHESIS 2: PERCEIVED DOCTOR OPINION PERCEIVED BEHAVIORAL CONTROL
The perceived doctor interest in promoting the monitoring of daily activities with a digital device has a
positive influence on the perceived behavioral control of using a wearable technology.
HYPOTHESIS 3: PERCEIVED DOCTOR OPINION PERCEIVED USEFULNESS
The perceived doctor interest in promoting the monitoring of daily activities with a digital device has a
positive influence on the perceived usefulness of using a wearable technology.
HYPOTHESIS 4: PERCEIVED DOCTOR OPINION ATTITUDE
The perceived doctor opinion on the monitoring of daily activities with a digital device has a positive
influence on the attitude towards such technology.
HYPOTHESIS 5: PERCEIVED DOCTOR OPINION INTENTION
The perceived doctor opinion on the monitoring of daily activities with a digital device has a positive
influence on the behavioral intention.
HYPOTHESIS 6: PERCEIVED DOCTOR OPINION BEHAVIOUR
The perceived doctor interest in promoting the monitoring of daily activities with a digital device has a
positive influence on the use of the wearable technology.
HYPOTHESI 7: PERCEIVED USEFULNESS ATTITUDE
The perceived utility of using digital technologies to monitor lifestyle has a positive influence on the
formation of a positive attitude toward this behavior.
HYPOTHESIS 8: ATTITUDE INTENTION
The attitude to use of digital technologies to monitor lifestyle has a positive influence over the behavioural
intention.
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HYPOTHESIS 9: PERCEIVED BEHAVIORAL CONTROL PU
The perceived control over the use of digital technologies to monitor lifestyle has a positive influence over
the perceived usefulness of the behaviour.
HYPOTHESIS 10: PERCEIVED BEHAVIORAL CONTROL INTENTION
The perceived control over the use of digital technologies to monitor lifestyle has a positive influence over
the intention to do it.
HYPOTHESIS 11: PERCEIVED BEHAVIORAL CONTROL BEHAVIOUR
The perceived control over the use of digital technologies to monitor lifestyle has a positive influence over
the implementation of this behaviour.
HYPOTHESIS 12: INTENTION BEHAVIOUR
The intention to use of digital technologies to monitor lifestyle has a positive influence over the
implementation of this behaviour.
The hypotheses and the constructs are reported in Figure 20.
Figure 20 Hypotheses representation
Now the hypotheses are validated providing explanations and proofs of results of previous researches.
H1: ONLINE HEALTH LITERACY PERCEIVED BEHAVIORAL CONTROL
Starting from hypothesis 1, the new assumption is that the propensity and the confidence an individual has
on searching for health information on Internet has an influence on the perceived control over using digital
technologies to monitor the lifestyle.
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The reasoning behind this hypothesis is that if a person is not very confident and is not able to manage
health online information, it should be more difficult for that person to consider relevant the monitoring of
health parameters, since it would be less easy for him or her to make sense of these data. At the same
time, he or she would consider more difficult to manage the use of these technologies, due for example to
a lack of resources like knowledge and confidence with digital technologies. In order to develop good
health literacy, the individual should have a good confidence with the technologies that give the access to
the online information. In a way, this behavior can be considered as a measurement of past experience. As
we know from the literature analysis past experience can have an influence of future behaviors. So
individuals that have good online health literacy, so a positive past experience with digital technologies, are
more likely to have a higher perceived control of them.
The article “Health literacy and health information technology adoption: the potential of a new digital
divide” (Mackert M. , MabryFlynn, Champlin, Donovan, & Kathrynn Pounders, 2016) confirmed with a study
that the level of individual health literacy has an impact toward the use of health digital technologies,
“Patients with low health literacy were less likely to use Health Information Technologies tools”. In
particular, greater health literacy was significantly associated with greater perceived ease of use and
perceived usefulness. In the case of this article the focus is on health literacy in general, defined as how
people obtain, understand, use, and communicate about health information to make informed decisions.
Differently in our analysis the focus is just on online health literacy, defined as how people understand, use
and communicate about health information obtained online. Since we are investigating the adoption of
digital technologies, online health literacy should be even more related than the general health literacy.
H2: PERCEIVED DOCTOR OPINION PERCEIVED BEHAVIORAL CONTROL
H3: PERCEIVED DOCTOR OPINION PERCEIVED USEFULNESS
H4: PERCEIVED DOCTOR OPINION ATTITUDE
H5: PERCEIVED DOCTOR OPINION INTENTION
H6: PERCEIVED DOCTOR OPINION BEHAVIOUR
Others explorative hypotheses are 2,3,4,5 and 6 that start from the perceived doctor opinion construct.
This construct has been added consequently to the emerging role played by the doctor in the promotion of
the studied behavior. In particular, these relationships are inspired by the principle of authority from the
persuasion theories by Cialdini, where the doctor can exercise an authority power, due to his recognized
role and knowledge. Different articles highlight how the figure of a health counsellor can be determinant to
promote the use of the investigated technologies, but still there are not quantitative studies that test this
element. In the article that tested the acceptance of wearable among adults aged over 50 (Mercer, et al.,
2016), it seems that participants were interested in using wearable trackers if their doctors or other
healthcare professionals would be interested in the data provided from the devices. In the hypothesis, we
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wanted to test at which level of the behaviour formation the perceived doctor opinion has an impact, so if
it has an impact on the perceived usefulness, on perceived behavioural control, on the attitude, on the
intention or directly on the behaviour. The direct relationship highlights how the doctor suggestion can
have a strong influencing power, it could stimulate a sort of automatic behaviour without passing through
attitudes and intention.
H7: PERCEIVED USEFULNESS ATTITUDE
The construct perceived usefulness derives from the TAM (Davis, 1993); defined as the prospective user’s
subjective probability that using a specific application system will increase his or her job performance.
Perceived usefulness in hypothesis 7 is assumed to have a positive influence on attitude, since positively
valued outcomes often increase one’s affect toward the means of achieving those outcomes. As we
analyzed in the literature review there are different studies that tried to test the role of perceived
usefulness in the adoption of digital technologies for the monitoring of lifestyle. Many studies confirmed
that the perceived usefulness is positively related to attitude towards using smartwatches. In the article
“Wearable technologies: the role of usefulness and visibility in smartwatch adoption” (Chuah, Rauschnabel,
Krey, & Nguyen, 2016), the authors explain that perceived usefulness was a relevant driver in particular for
respondents who perceive smartwatches as a technological attribute rather than a fashion accessory, in
which case visibility was more important. In this article, it was highlighted a difference between users and
not-users, in particular the last ones have shown to perceive smartwatches as more useful. In fact users
might have experienced issues when operating the new technology leading to lower positive attitude
levels.
Another evidence was found in the article “Wearable fitness technology: a structural investigation into
acceptance and perceived fitness outcomes” (Lunney, Cunningham, & Eastin, 2016). In this study, applied
to wearable fitness devices, researchers tested some variables of the TAM, such as perceived usefulness
and perceived ease of use, combined with some variables of the TPB, like subjective norms, attitude and
use of wearable fitness technologies. Perceived usefulness significantly influenced individual’s acceptance
of technology and was found to be a key factor that influences attitude.
H8: ATTITUDE INTENTION
H10: PERCEIVED BEHAVIORAL CONTROL INTENTION
H11: PERCEIVED BEHAVIORAL CONTROL BEHAVIOUR
H12: INTENTION BEHAVIOUR
The TPB (Ajzen, 1991) provides the conceptual framework for the present model. Hypotheses 8, 10, 11, 12
are derived from this theory. As in the original TRA, a central factor in the TPB is the individual’s intention
to perform a given behaviour. Intentions are assumed to capture the motivational factors that influence a
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behavior; they are indications of how hard people are willing to try, of how much of an effort they are
planning to exert, in order to perform the behavior. Hypothesis 12 assumes that the stronger the intention
to engage in a behavior, the more likely should be its implementation.
Perceived behavioural control plays a relevant role in the TPB. As in the model proposed by Ajzen,
hypotheses 10 and 11 assume that perceived behavioural control influences behaviour directly and
indirectly through behavioural intention. At least two rationales can be offered for this hypothesis. First,
holding intention constant, the effort expended to bring a course of behavior to a successful conclusion is
likely to increase with perceived behavioral control. For instance, even if two individuals have equally
strong intentions to learn to ski, and both try to do so, the person who is confident that he can master this
activity is more likely to persevere than the person who doubts his ability. The second reason for expecting
a direct link between perceived behavioral control and behavioral achievement is that perceived behavioral
control could often be used as a substitute for a measure of actual control. Whether a measure of
perceived behavioral control can substitute for a measure of actual control depends, of course, on the
accuracy of the perceptions. Perceived behavioural control may not be particularly realistic when a person
has relatively little information about the behavior, when requirements or available resources have
changed, or when new and unfamiliar elements have entered into the situation. Under those conditions, a
measure of perceived behavioral control may lead to little accuracy of behavioral prediction. However, to
the extent that perceived control is realistic, it can be used to predict the probability of a successful
behavioral attempt (Ajzen, 1985). Hypothesis 8, following the TRA, assumes that attitude toward the
behaviour permits to predict behavioural intentions with a high degree of accuracy.
H9: PERCEIVED BEHAVIORAL CONTROL PERCEIVED USEFULNESS
In addition to the traditional TPB and TAM hypothesis, we wanted to test if there is a relationship also
between the perception of the control over the behavior, and the perception of the utility of the behavior.
The reason why we wanted to verify this relationship is to see how much the perception of control over the
behaviour can influence not only intention and behaviour but also indirectly attitude. What we assume, is
that very likely when a person knows that he or she can perform a behaviour in a good way, the individual
is more prone to judge this behavior as useful. At the base of the assumption, there is the fact that the
individual needs in a way to justify his competences and abilities as meaningful.
From the definition of the constructs and from the research hypotheses is clear that our goal is testing if
the combination of variables, coming from the TAM and the TPB, is verified in the context of digital
technologies for monitoring lifestyle. In addition to those variables we aim at making some consideration
on the role played by the doctor and on the importance of the online health literacy.
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3.3.3 Questionnaire analysis
Due to the increasing interest for the use of digital technologies in the healthcare sector, the School of
Management of Politecnico di Milano in 2007 founded a dedicated Observatory with the aim of analyzing
and promoting the role of digital technologies for the improvement of Italian healthcare services. This
study is based on a collaboration with this Observatory. The latter provided the delivery of a survey, while
we contributed to the analysis of the data and the elaboration of a statistical framework able to give
support for decision makers on the diffusion of such technologies.
The delivery of the survey was commissioned to DoxaPharma39. The questionnaire had a more general
purpose of exploring the beliefs, intentions and behavior of individuals towards the use of technologies in
the healthcare. Many different topics were addressed in the survey. In particular, the main investigations
were electronic health record, telemedicine, electronic prescription, use of online services, search and
creation of healthcare related information on Internet and use of digital technologies to monitor lifestyle.
For our study, we mainly focused our attention on this last group of questions, on how this behaviour can
be stimulated among individuals.
The survey was delivered in the form of telephonic interviews lasting 20 minutes to the Italian population.
The interviews were distributed in a representative way by macro-geographic area (North-East, North-
West, South and Islands), sex, age, schooling and employment compared to the reference universe. One
thousand observations were collected during the period from 10 to 13 March 2016.
Data coming from the survey were analyzed using the software Microsoft Excel. In particular we organized
the rows as the different responders and the columns as the different questions, a part from the first one,
which represents an identification code of each interviewer. Hence 223 columns and 1000 rows made the
overall database.
The following Table 17 shows the distribution of observations in percentage in terms of sex, age, education,
residence and residence size. In addition, Figure 21 shows the distribution of observations in the different
geographic areas of the country in terms of percentage. It is possible to see how the interviewees are quite
well distributed in terms of demographic varaibles. As a consequence, considering also the relevant
number of responders, we can conclude that the data collected could be a good proxy of the Italian
population.
39 DoxaPharma is specialized in market research in the pharmaceutical and health sector. It belongs to the Doxa
Group, whose goal is building partnerships with client companies, taking charge of their needs, with a problem-solving
approach in order to deliver original, innovative and ad-hoc responses. http://www.doxapharma.it/en/
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Figure 21 Distribution in different geographic areas
In addition to the demographic characteristics, the survey considers also if the interviewees are affected by
chronic diseases and if they are used to practice sport activities. This information is interesting and valuable
because are factors that affect a lot the use of digital technologies to monitor lifestyle. For example, a
chronic patient is more interested in measuring the critical parameters of his disease, while a sportive
people is more focused on monitoring his trainings.
EDUCATION
Elementary school 21
High school junior 32
High school 35
University 12
RESIDENCE
City 31
Town 69
RESIDENCE SIZE
Less than 30000 inhabitants 55
From 30.000 to 100.000
inhabitants
21
From 100.000 a 500.000
inhabitants
24
SEX
Male 48
Female 52
AGE
15-17 3
18-24 8
25-34 13
35-44 18
45-54 18
55-64 15
65-74 16
>75 9
Table 17 Distribution of education, residence and residence size
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Figure 22 and Figure 23 describe the survey population in terms of chronic diseases and sportive lifestyle.
Data are reported in percentage.
Figure 22 Population by chronic disease
Figure 23 Population by sport attitude
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3.3.4 Data analysis
The analysis of the data starts from the preparation of the Excel database, which contains the answers to
the survey. All the questions of the survey allow responders answering with the option “I don’t know”. This
kind of comment doesn’t provide any kind of information. Consequently, to interpreter the data in the
appropriate way and to make them as cleanlier as possible, we left empty all the cells of the database
containing the answer “I don’t know”. Looking at the entire database the 5,13% of the overall number of
cells was empty.
Following the preparation of the Excel database, the data analysis continued with the verification of the
presence of common method variance and the computation of the Cronbach’s Alpha to authenticate the
constructs robustness. Finally, we moved on with the analysis of the model using the Structural Equation
Modeling (SEM) technique. The aim of the SEM is to understand if the model is valid and hence if it is a
good approximation of the population.
The computation of all these analysis (common method variance, Cronbach’s Alpha and SEM) has been
conducted using the software STATA.
In the following pages the three methods will be better explained from a theoretical point of view.
3.3.4.1 Common method variance Most researches agree on considering common method variance as a potential problem in behavioural
research (Podsakoff, MacKenzie, & Lee, 2003). In particular, common method variance is defined as the
variance attributed to the measurement method rather than to the constructs the measures represent
(Chang, Witteloostuijn, & Eden, 2010). Method bias are considered problems since they are one of the
main sources of measurement error, which threatens the validity of the conclusions about the relationship
between measures and is recognized to have both a random and a systematic component. Despite both
types of measurement errors are critical, systematic measurement error is extremely problematic because
it provides an alternative explanation for the observed relationships between measures of different
constructs that is independent of the one hypothesized.
Different potential sources of common method bias were identified by researches (Podsakoff, MacKenzie,
& Lee, 2003):
- Method effects produced by a common source: the respondent providing the measure of the
predictor and the criterion variable is the same person.
- Method effects produced by item characteristics: items are presented to respondents to produce
artefactual covariance in the observed relationships.
- Method effects produced by item context: any influence or interpretation that a subject might
ascribe to an item solely because of its relation to the other items making up an instrument.
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- Method effects produced by measurement context: the broader research context in which the
measures are obtained. Chief among these contextual influences are the time, location, and media
used to measure the constructs.
Obviously, the impact of common method variance should be minimized. Researchers recommended four
approaches that are represented in Figure 24 in order to avoid or correct common method variance
(Chang, Witteloostuijn, & Eden, 2010). The first approach is to avoid any kind of potential common method
variance in the research design stage by using different sources of information for some of the key
measures. In particular, the dependent variable should be constructed using information from other
sources than the independent variables. Another possibility is to collect data at different points in time. The
second strategy is about designing and administering the questionnaire: from mixing the order of the
questions to using different scale types. Respondents should be assured of the anonymity and
confidentiality and researchers should avoid including ambiguous, vague and unfamiliar terms. The third
recommended remedy is complicated specifications of regression models to reduce the likelihood of
common method variance. In fact, respondents are unlikely to be guided by a cognitive map that includes
difficult-to-visualize interaction and non-linear effects and this is less probable the more complicated the
model. The fourth approach is made by several statistical remedies. In particular, the most popular one is
the Harman’s one-factor analysis, which includes all the items from all of the constructs in the study into a
factor analysis to determine whether the majority of the variance can be accounted for by one general
factor. In particular, it examines the unrotated factor solution to determine the number of factors that are
necessary to account for the variance in the variables. The basic assumption of this technique is that if a
substantial amount of common method variance is present, either if a single factor will emerge from the
factor analysis or if one general factor will account for the majority of the covariance among the measures.
The first two remedies are ex ante approaches implemented in the research design stage. On the contrary
remedies three and four are ex post strategies implemented after the research has been conducted.
We decided to conduct the Harman’s single-factor test in order to verify the presence of common method
variance.
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Figure 24 Approaches for handling common method variance
3.3.4.2 Cronbach’s Alpha The Cronbach’s Alpha has the objective to verify the construct robustness. It is computed among the items
of the same construct and provides information about their level of correlation. If the items of the same
construct have a high level of correlation, and so the Cronbach’s Alpha is close to 1, it means that each item
provides a valuable contribution and all the items refer to the same construct. If the Cronbach’s Alpha is
close to 0, there is a low level of correlation among the items and as a consequence is possible to conclude
that some of them don’t measure the construct in the appropriate way. In this case researchers need to
reformulate the construct, eliminating those items that reduce the value of the Cronbach’s Alpha.
3.3.4.3 Structural Equation Modeling Structural equation modeling (SEM) are statistical procedures for testing measurement, functional,
predictive, and causal hypotheses (Bagozzi, 2011). In other words, SEM is a collection of statistical
techniques that allow a set of relationships between one or more independent variables, either continuous
or discrete, and one or more dependent variables, either continuous or discrete, to be examined.
SEM is an a priori hypothesis about a pattern of linear relationships among a set of observed and
unobserved variables. The objective in using SEM is to determine whether the a priori model is valid, rather
than to ‘find’ a suitable model (Shah & Goldstein, 2005).
The name SEM includes two fundamental characteristics of this tool: the processes are represented by a
series of structural equations (e.g. regression) and these kinds of structural equations can be modelled in
order to better visualize the studied processes.
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SEM as a method for measuring relationships among latent variables has been around since early in the
20th century originating in Sewall Wright’s 1916 work. Despite a slow but steady increase in its use, it was
not until the monograph by Bagozzi in 1980 that the technique was brought to the attention of a much
wider audience of marketing and consumer behaviour researchers. Among the years, SEM has been widely
used in psychology, marketing, strategic management and organizational research. In addition, while
Operations Management (OM) researchers were slow to use this new statistical approach, SEM has more
recently become one of the preferred data analysis methods among empirical OM researchers, and articles
that employ SEM as the primary data analytic tool now routinely appear in major OM journals (Shah &
Goldstein, 2005)
Benefits of SEM:
SEMs are not new, they are part of the existing family of multivariate statistical techniques. Indeed one
benefit is that SEMs are generic tools and provide a broad, integrative function conveying the synergy and
complementarity among many different statistical methods. It is possible to distinguish between the so-
called first-generation statistical models (e.g., correlation analysis, exploratory factor analysis, multiple
regression, ANOVA, canonical correlation analysis) and second-generation methods (i.e., SEMs:
confirmatory factor analysis and structural equation models) (Bagozzi, 2011). The use of SEMs yields
benefits not possible with first-generation statistical methods. One important benefit is that it is possible to
take into account types of error confounding first-generation procedures. For example, random or
measurement error in indicators of latent variables can be modelled and estimated explicitly. Systematic or
method error can also be represented. The result is that focal parameters corresponding to hypotheses are
purged of particular kinds of bias, and certain errors in inference avoided.
By way of summary, researchers identified a list of benefits SEMs may offer (Bagozzi, 2011):
1. Provides integrative function (a single umbrella of methods under leading programs).
2. Helps researchers to be more precise in their specification of hypotheses and operationalization of
constructs.
3. Takes into account reliability of measures in tests of hypotheses in ways going beyond the
averaging of multi-measures of constructs.
4. Guides exploratory and confirmatory research in a manner combining self-insight and modeling
skills with theory.
5. Often suggests novel hypotheses originally not considered and opens up new avenues for research.
6. Is useful in experimental or survey research, cross-sectional or longitudinal studies, measurement
or hypothesis testing endeavors, within or across groups and institutional or cultural contexts.
7. Is easy to use.
8. Is fun.
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Path analysis and Confirmatory Factor Analysis:
SEM is a technique to specify, estimate, and evaluate models of linear relationships among a set of
observed variables in terms of a generally smaller number of unobserved variables. SEM models consist of
observed variables (also called manifest or measured, MV for short) and unobserved variables (also called
underlying or latent, LV for short). LVs are hypothetical constructs and hence theoretical concepts that
cannot be directly measured. They signify the notion that the variables in the theories and hypotheses are
typically framed as abstractions. In SEM unobserved variables are typically represented by multiple MVs
that serve as indicators of the underlying constructs. LV can be independent (exogenous) or dependent
(endogenous) in nature. Exogenous variables generate fluctuations in the values of others LVs of the model.
Endogenous variables are impacted, in a direct or in an indirect way, by the exogenous variables (Shah &
Goldstein, 2005)
SEM models are commonly described in visual form using path diagrams, which specify patterns of
directional and non-directional relationships among MVs. The only LVs in such models are error terms.
Thus, Path Analysis (PA) provides for the testing of structural relationships among MVs when the MVs are
of primary interest or when multiple indicators for LVs are not available (Shah & Goldstein, 2005). Path
diagrams consist of two parts: measurement model and structural model. On one hand the measurement
model aims at answering at the question “how are the constructs related to measurable variables?”; on the
other hand, the structural model answers the question “what are the relationship between the
constructs?”.
Specification of the measurement model is a crucial factor in SEM; Confirmatory Factory Analysis (CFA) is
used as a tool to validate the measurement model before specifying and estimating the structural model.
CFA considers the issue of how to measure a theoretical variable in a study.
The difference between Path Analysis and Confirmatory Factor Analysis is better explained in Figure 25.
In particular CFA aims, first of all, at defining the measurement model and hence if the constructs are
unidimensional; if the model is congeneric and if there is a sufficient number of indicators per construct. A
congeneric model is a model in which each measured variable is related to exactly one construct. Secondly
is important to set the scales for the different constructs and verify if the sample size is sufficiently large. As
we’ll see in the following pages, adequacy of sample size has a significant impact on the reliability of
parameter estimates, model fit, and statistical power. Suggested approaches for determining sample size
include establishing a minimum (e.g., 200); having a certain number of observations per MV; having a
certain number of observations per parameters estimated and through conducting power analysis (Shah &
Goldstein, 2005). Statistical power (i.e. the ability to detect and reject a poor model) is critical to SEM
analysis because, in contrast to traditional hypothesis testing, the goal in SEM analysis is to produce a non-
significant result between sample data and the implied covariance matrix derived from model parameter
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estimates (Shah & Goldstein, 2005). Therefore, according to MacCallum et al. (1996), minimum sample size
is defined as a function of degrees of freedom that is needed for adequate power (0.80) to detect close
model fit, to assess the power of models in our sample (Shah & Goldstein, 2005).
Figure 25 Path Analysis and Confirmatory Factor Analysis
At this point researchers can estimate the validity of the model. SEM can be used to evaluate the validity of
the constructs: to what extent the measured items reflect the theoretical latent constructs. The most
important aspects of the constructs validity are the convergent validity and the discriminant validity.
Convergent validity is made by a set of indicators that show how the items of the same construct share a
proportion of variance. The first indicator of this category is the Average Variance Extracted (AVE). It is
given by the sum of the squared standardized factor loadings over the number of items in the specific
construct taken in consideration.
In particular the squared standardized factor loadings indicate the amount of variation in the indicator that
can be explained by the factor. Adequate convergence is obtained in case of AVE > 0,50. The second
indicator of the convergent validity is the Construct Reliability (CR). High construct reliability (CR > 0,70)
indicates high internal reliability (Bagozzi, 2011).
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Where is the error variance in the variable i and is given by:
The discriminant validity aims at evaluating if each construct is unique and differs from the others of the
studied model. To sum up the construct validity checks the robustness of the constructs evaluating if each
of them is sufficiently internally homogenous and externally inhomogeneous.
After the construct validity researchers should assess the model validity. In order to understand if it is
consistent with data, some parameters, called fit indices, must be analyzed. Fit indices are commonly
distinguished as either absolute or incremental (Shah & Goldstein, 2005). In general, absolute fit indices
indicate the degree to which the hypothesized model reproduces the sample data. While, incremental fit
indices measure the proportional improvement in fit when the hypothesized model is compared with two
reference models: a worst case or null model, and an ideal model that perfectly represents the modelled
phenomena in the studied population.
The most basic measure of absolute fit is the Chi-Square statistics, which can be used to test the null
hypothesis that the estimated or implied variance-covariance matrix of indicators reproduces the observed
or sample variance-covariance matrix. For SEMs, a good fit is obtained when the Chi-square statistic is non
significant, which by convention is taken to happen for p-values ≥ 0,05. Because the Chi-square statistics is
sensitive to sample size, it becomes difficult to achieve satisfactory models fits as the sample size increases
(Shah & Goldstein, 2005). As a result, researchers have proposed a number of indexes of practical fit in
addition to the Chi-Square statistics. Other commonly used measures include root mean square error of
approximation (RMSEA) and the root mean square residual (RMR or SRMR). The last indicator is the square
root of the average squared residuals. Values lower than 0,05 are considered excellent even if values lower
than 0,1 are satisfactory. RMSEA gives the average amount of misfit for a model per degree of freedom. If
the misfit is small the model can be considered a satisfactory proxy of the reality; the opposite happens in
case the misfit is relevant. A RMSEA lower than 0,05 suggests a good performance; values till 0,1 can be
considered a reasonable approximation of the population; values higher than 0,1 show a low proxy of the
reality.
There are many incremental fit indices; some of the most popular are normed fit index (NFI), non-normed
fit index (NNFI or TLI), comparative fit index (CFI) and incremental fit index (IFI or BL89). In particular, in our
study we considered just the comparative fit index, which is an indicator of relative non-centrality between
a hypothesized model and the null model of modified independence (i.e., a model where only error
variances are estimated). Its value change from 0 to 1 and the closer it is to 1, the better is the studied
model because it means the Chi-Square statistics is lower than the degree of freedom of the model.
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Model respecification:
Although no model fits the real world exactly, a desirable outcome in SEM analysis is to show that a
hypothesized model provides a good approximation of real world phenomena, as represented by an
observed set of data. When an initial model of interest does not satisfy this objective, and hence when the
parameters don’t reach satisfactory values, researchers often alter the model to improve its fit to the data.
Modification of a hypothesized model to improve its parsimony and/or fit to the data is termed a
‘‘specification search’’. A specification search is designed to identify and eliminate errors from the original
specification of the hypothesized model (Shah & Goldstein, 2005).
Jöreskog and Sörbom describe three strategies in model specification (and evaluation): (1) strictly
confirmatory, where a single a priori model is studied; (2) model generation, where an initial model is fit to
data and then modified (frequently with the use of modification indices) until it fits adequately; and (3)
alternative models, where multiple a priori models are studied. In particular, for our work we adopted the
second strategy: an initial model was defined on the base of the literature analysis and the gaps and then,
according to the data, we modified it until it fits the threshold parameters.
In Figure 26 each latent variable (ellipse) is connected to one or more rectangles, which designate
measurements of the latent variables. The connections of latent variables to manifest variables are
represented by arrows. Latent exogenous variables are labeled with Greek ξ; latent endogenous variables
with η; indicators of ξ with x, and indicators of η with y. Error terms for indicators are δ for x and ε for y.
Error terms for latent endogenous variables are drawn as ζ. Depending on custom, some researchers call
error terms disturbances or residuals (Bagozzi, 2011).
There are three equations, which are fundamental to SEM. Equation (1) represents the directional
influences of the exogenous LVs (ξ) on their indicators (x). Equation (2) represents the directional influences
of the endogenous LVs (η) on their indicators (y). Thus, Equations (1) and (2) link the observed (manifest)
variables to unobserved (latent) variables through a factor analytic model and constitute the
‘‘measurement’’ portion of the model. Equation (3) represents the endogenous LVs (η) as linear functions
of other exogenous LVs (ξ) and endogenous LVs plus residual terms (ζ). Thus, Equation (3) specifies
relationships between LVs through a structural equation model and constitutes the ‘‘structural’’ portion of
the model (Bagozzi, 2011).
(1)
(2)
(3)
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Figure 26 Mathematical model
Where x is the measures of exogenous manifest variables, Λx the effect of exogenous LVs on their MVs
(matrix), δ the error of measurement in exogenous manifest variables, y the measures of endogenous
manifest variables, Λy the effect of endogenous LVs on their MVs (matrix), ε the error of measurement in
endogenous manifest variables, ξ the latent exogenous constructs, η the latent endogenous constructs, Γ
the effect of exogenous constructs on endogenous constructs (matrix), B the effect of endogenous
constructs on each of the other endogenous constructs (matrix) and ζ is the errors in equations or residuals.
3.3.5 Result analysis
The analysis of the results coming from the model was conducted considering the main parameters of the
SEM and the literature analysis. In fact, in this phase we discovered if the hypotheses previously set were
verified for the research context. In addition, we tried to comprehend the strength of the relationship
expressed by the verified hypothesis, by analyzing the path coefficient indicated in the SEM analysis. We set
some numerical ranges to define the strength of the relationship, described as follows:
- Path coefficient <0.2: weak impact;
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- 0,2< path coefficient <0.6: medium impact;
- Path coefficient >0.6: strong impact.
3.3.6 Wrap up on the process
The process of the definition of the final model has been long and full of iterations. After the general
definition of the constructs and their possible relations according to the literature analysis, it was necessary
to verify also the results of the SEM. These two parts, the literature and the data analysis, should be
coherent.
The first model that came up to our mind was bigger than the one we selected at the end of the process. In
particular, compared to the final one, in the first model there were additional constructs such as subjective
norms, patient-doctor collaboration and willingness to pay. On the contrary perceived doctor opinion was
not considered as a unique construct, but it was a portion of subjective norms.
We aimed at investigating the impact of the willingness to pay because on one side it has not yet been
analyzed in deep by researchers and on the other side because could be a relevant driver for transforming
intention into behavior. Despite the interesting premises from a literature point of view, the data coming
from the survey regarding the willingness to pay were very poor. At the question “how much are you
willing to pay for a digital technology to monitoring your lifestyle?” most of interviewees answered with the
option “I don’t know”. Consequently, since the construct willingness to pay didn’t satisfy the criteria
defined for the measurement model, we were forced to eliminate it from the model.
On the contrary subjective norms was considered in the initial model because it is one of the constructs
that compose the TPB and, as we previously said, our object is integrating the variables of the TPB with the
variables of the TAM. Subjective norms refer to the perceived social pressure to engaging or not in a
behavior. Considering the particular context in which our research is set, the perceived social pressure
could be measured looking at different actors: family and friends, doctors, media and advertising.
The construct patient-doctor collaboration measured the willingness of people to share their personal
information regarding their lifestyle with their doctors and the perceived interest of the doctor in receiving
these information.
These two constructs, subjective norms and patient-doctor collaboration, were problematic. In fact, their
items and the associated values were considered too similar. As a consequence, the discriminant validity
was not satisfying. After some failed variations, we concluded that the best solution was to consider only
one of the two constructs: subjective norms. We preferred to test the perceived social pressure, rather
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than the sharing of the information between the doctor and the patient because according to our opinion
the results of this last construct were more trivial.
Finally, another variation regarding the construct subject norms was performed. In fact, since researchers
didn’t already investigate the role of the doctor we preferred to completely focus our attention on this
actor to better understand the impact that could have on the adoption of digital technologies for tracking
lifestyle. As a consequence, from the initial construct we removed the questions associated to the
perceived social pressure generated by family, friends, media and advertising. Furthermore, we changed
the name of the construct from subjective norms to perceived doctor opinion.
In addition to these main variations, others of lower impact were implemented in order to improve the
model. For example, some items were removed because, despite a satisfactory level of the Cronbach’s
Alpha, they were considered too different from the others that compose the construct. Hence in the
computation of the SEM, the value of the Average Variance Extracted (AVE) was below the predefined
threshold.
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4. EMPIRICAL RESEARCH
4.1 THE QUESTIONAIRRE The empirical research at the base of this work started from a questionnaire committed by the Observatory
of Digital Innovation in Healthcare of Politecnico di Milano. The university collaborated with DoxaPharma
with the general purpose of exploring the beliefs, the intentions and the behaviours of individuals towards
the use of digital technologies in healthcare. Many different topics were addressed in the survey. In
particular, the main investigations were Electronic Medical Record, Telemedicine, Electronic prescription,
use of online services, search and creation on Internet of healthcare related information and use of digital
technologies to monitor lifestyle. As a consequence, the key questions of the survey were the followings:
- How much do citizens know and use Electronic Medical Record, Telemedicine and Electronic
prescription?
- Which are the digital services related to healthcare that citizens use the most?
- How much do citizens use Internet to answer questions related to their health conditions?
How much are they satisfied of the results?
- Which is the utilization level of the digital technologies to monitor lifestyle?
For our study, we used mainly the questions related to this last issue: the use of digital technologies to
monitor lifestyle.
4.1.1 Results from the survey
Before defining the model and analysing the relationship among the different constructs, it is possible to
perform some preliminary observations and statistical analysis.
First of all, we can reach some conclusions looking at the way in which interviewers answered to the survey
questions and in particular if there are evident differences considering the demographic characteristics.
This analysis could be also useful for selecting the control variables we want to include in the model. In fact,
considering the different sub-areas that compose a demographic variable, if responders replied in the same
way, it means that the variable taken into account is not meaningful. On the other hand, considering a
demographic variable, if results are different, it could be reasonable to introduce it in the model.
The previous described analysis can be done per each item. Table 18 reports per each item the number of
valuable observations, the mean, the standard deviation and the minimum and maximum value.
CONSTRUCT ITEM N° OBS MEAN ST.
DEVIATION
MIN MAX
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Perceived usefulness PU_1 935 5.08 2.99 1 10
PU_2 942 5.13 2.98 1 10
PU_3 951 5.47 2.99 1 10
Online health literacy OHL_1 928 5.11 3.05 1 10
OHL_2 921 4.98 2.87 1 10
Attitude ATT_1 953 7.41 2.61 1 10
ATT_2 943 6.02 2.97 1 10
ATT_3 934 6.28 2.92 1 10
Perceived doctor opinion PDO_1 879 3.89 2.82 1 10
PDO_2 910 3.75 2.78 1 10
Perceived behavioural
control
PBC_1 949 4.89 2.97 1 10
PBC_2 927 4.79 2.94 1 10
PBC_3 907 5.49 2.86 1 10
Intention INT_1 949 3.91 2.99 1 10
INT_2 945 4.01 2.99 1 10
INT_3 950 4.03 3.07 1 10
Behaviour BHV_1 1000 1.42 0.69 1 3
BHV_2 1000 1.33 0.64 1 3
BHV_3 1000 1.32 0.62 1 3
BHV_4 1000 1.30 0.58 1 3
Table 18 Constructs mean and standard deviation
Perceived usefulness
From Table 18 we can notice that the population is still not convinced on the usefulness of digital
technologies to monitor lifestyle. A possible explanation could be related to the fact that the benefits of
these devices are not completely communicated to the population. Moreover, since these technologies are
quite new, they are not yet integrated with the healthcare systems and hence if people don’t have the
appropriate competences in managing the data, they could perceive them as something useless for staying
healthy. Probably these digital technologies are not recognised as reliable.
Looking at the demographics variables there are no relevant differences to report.
Online health literacy
Is interesting to observe from Table 18 that the average value of online health literacy testify a medium
confidence on the use on internet to search for health information. Obviously, the ability of moving inside
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the jungle of Internet is higher among young people since they are more familiar with this new technology.
Another relevant distinction could be considered looking at the education level: graduated people have a
higher security in using Internet. Since they are more educated they probably have more experience with
the use of Internet, and this has increased their predisposition and confidence on using the technology.
Attitude
Data in Table 18 shows a relevant interest in monitoring crucial health parameters. The major focus is on
heart monitoring not just because it is one of the most important parameter, but also because is very hard
to have a real perception of the heart frequency during the day without controlling it with a device.
Concerning the demographic variables there are not big differences. The only comment we can do regards
the age: people in the age between 35 and 44 years old tend to be more incline to monitor their health
parameters. Is the first range of age in which people start to think about prevention and at the same time is
confident with digital technologies such as wearables and mobile applications.
Looking at the chronic and the sportive responders, results are quite trivial: sportive people tend to
monitor steps and trainings in order to value their improvements while chronic patients are more
interested in observing the heart and the sleep quality since these parameters could have a higher impact
on their health conditions.
The positive attitude measured trough this construct could be a consequence of the increasing number of
commercial campaigns organized by the health institutions with the purpose of promoting prevention.
Despite this we have seen that the majority of responders don’t perceive useful monitoring the same
parameters with digital technologies. It is an interesting observation that again could be the proof of the
poor communication of the benefits that digital devices could generate in monitoring lifestyle. It is evident
that should be better investigated the way through which the benefits are communicated to the
population.
Perceived doctor opinion:
Few responders believe their doctor would suggest them to monitor their lifestyle, and even a lower
percentage supposes the doctor expects a similar behaviour. As said before, responders do not perceive
digital technologies to monitor lifestyle as something useful and they have the same sensation for the
perceived doctor opinion.
Despite this, it is interesting to report an observation concerning the level of education: less educated
people have higher perceived doctor opinion. If we consider only those who have the primary school
diploma, the mean of the two items grows to 4,5 and 4,2 respectively. This behaviour could be reasonable
because generally more educated people tend to solve by themselves some health problems, as we have
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seen looking at the results about online health literacy. As a consequence, they have lower relation and
confidence with their doctors and so they probably neither know what doctors think and expect from them.
Perceived behavioural control
In Table 18 interviewees recognize as something quite easy monitoring the lifestyle through digital
technologies. Regarding the demographic variables there are not particular distinctions, a part from the
question about the economic availability. In fact, young people (from 15 to 24) seem to have higher
economic resources, despite a great percentage of this segment does not have a job or, even if they have
one, the salary is probably not so high. As a consequence, the reason behind this result could be that since
they are more confident in the use of digital technologies in general, they are also more in favour of
investing their money in them. In addition, it is interesting to notice that people with higher educational
level have also higher economical availability for lifestyle monitoring. This could be explained considering
that generally to graduated people are associated jobs with higher responsibilities and hence higher
salaries that could be used for the studied behaviour. Another distinction is between chronic and healthy
people: the first ones have fewer resources to spend in digital technologies for tracking lifestyle. Although
they recognise as really important monitoring the most crucial parameters as the heart frequency, they
generally have lot of expenses for their medical treatments and so they would avoid another investment.
Intention:
The low value of this construct indicate that the responders are not really interested in monitoring their
lifestyle with digital devices in the nearly future as presented in Table 18.
Regarding personal characteristics, the only relevant observation is about sportive people who are more
inclined to adopt these technologies in the future. To control and monitor their trainings, they need devices
for tracking the most important parameters.
Behaviour:
The most used applications for tracking lifestyle are those that monitor the heart frequency and the steps,
but in all the cases very few people already adopt any kind of application as we can observe from Table 18.
Probably as we have seen before, the benefits of these solutions are not completely communicated to the
population and hence just few use them.
Sportive people tend to use more the applications, while chronic patients are less inclined even because
generally they belong to the oldest segment of the population. The adoption of applications for controlling
lifestyle is higher for young people because they are more confident with similar technologies. The only
exception regards the app for tracking the heart frequency: it is a crucial parameter for both sportive and
chronic interviewees at any age, as we have seen also in other constructs.
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This first analysis helped us in choosing the control variables to insert in the model. The personal
characteristics that mostly impact the different constructs are the age, the level of education, the sportive
lifestyle and the presence of chronic diseases. We decided to introduce all of them at the end of the model,
directly on behaviour, in order to understand how they could have an impact on the use of quantified self
devices considering also all the drivers behind the adoption.
Table 19 reports the questions of the survey associated to the control variables selected.
CONTROL VARIABLE QUESTION
Age Write your age
Sportiness How often are you used to practise sports
activities?
1. At least three times a week
2. At least once a week
3. At least once in a month
4. At least once in a year
5. Less than once in a year
6. Never
Instruction Write your title of study
Chronic diseases Are you affected by chronic diseases or long-term
health problems?
1. Yes
2. No
3. I don’t know
Table 19 Control variables
4.1.2 Constructs analysis
Once the constructs have been defined it is necessary to verify the presence of common method variance,
which refer to the variance attributed to the measurement method rather than to the constructs the
measures represent. The approach we adopted in order to check the presence of these errors is the
Harman’s single-factor test. It includes all the items from all of the constructs in the study into a factor
analysis to determine whether the most of the variance can be accounted for by one general factor. If a
substantial amount of common method variance is present, either a single factor will emerge from the
factor analysis or one general factor will account for the majority of the covariance among the measures.
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The results of the Harman’s single-factor test, obtain through Stata software, reveal the presence of 24
different factors and the first factor account for the 38,47% of the variance, which is below the threshold of
50%. Hence, we can conclude common method variance doesn’t affect our analysis.
At this point we can proceed evaluating the internal consistency of the constructs.
Constructs robustness was analysed through the Cronbach’s Alpha, which was computed using the
software Stata. In particular, high values of the Cronbach’s Alpha mean that the items that built a construct
refer to the same meaning and hence each of them provides a valuable contribution for the construct. In
our work, we considered as threshold value alpha equal to 0,7.
Table 20 represents per each construct the items that compose it and the value of the Cronbach’s Alpha.
CONSTRUCT ITEMS CRONBACH’S ALPHA
Perceived Usefulness PU_1; PU_2; PU_3 0,9050
Online Health Literacy OHL_1; OHL_2 0,7637
Attitude ATT_1; ATT_2;ATT_3 0,7926
Perceived Behavioural Control PBC_1; PBC_2; PBC_3 0,7751
Perceived Doctor Opinion PDO_1; PDO_2 0,8606
Intention INT_1; INT_2; INT_3 0,9421
Behaviour BHV_1; BHV_2; BHV_3; BHV_4 0,8114
Table 20 Cronbach’s Alpha
It is possible to see that all the constructs have a Cronbach’s Alpha higher than 0,7. We can conclude that
the definition of the constructs and the items is appropriate.
4.2 DATA ANALYSIS
4.2.1 Model analysis with SEM
To understand the validity of the model and the impact of the hypotheses previously explained, the SEM
technique was applied. The analysis was performed through the software Stata and the results are shown
in Figure 27.
According to the SEM, is essential to validate both the measurement model and the structural model. The
first one aims at understanding the relations among the items and the associated construct, while the
second one values the relations among the different constructs and so if the hypotheses are reasonable.
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4.2.1.1 Measurement model The quality of the measurement model is determined by the convergent validity and the discriminant
validity. The convergent validity defines how well the items of a construct converge on the construct itself.
The discriminant validity defines how well the constructs of the model are different among them.
The convergent validity was assessed through two indicators: composite reliability (CR) and average
variance extracted (AVE). As previously defined in the chapter about the SEM methodology, these two
indicators can be computed as following:
Where:
j is the construct taken under analysis
is the factor loading of the item i and represents the relationship from the construct j to the item i
is the error variance in the variable i and is given by:
Figure 27 SEM model results
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Researchers proposed the following three measure criteria:
- The factor loadings should exceed 0,5;
- The AVE of each construct should exceed 0,5;
- The CR of each construct should exceed 0,7.
Table 21 reports the values of the above-mentioned indicators in the case of our model.
CONSTRUCT ITEM FACTOR LOADING AVE CR
Perceived
usefulness
PU_1 0,8856 0,7642 0,9070
PU_2 0,8897
PU_3 0,8465
Online health
literacy
OHL_1 0,7603 0,5932 0,7447
OHL_2 0,7800
Attitude ATT_1 0,7384 0,7458 0,7900
ATT_2 0,7335
ATT_3 0,7654
Perceived doctor
opinion
PDO_1 0,8615 0,7595 0,8633
PDO_2 0,8814
Perceived
behavioural
control
PBC_1 0,7682 0,6959 0,7407
PBC_2 0,5774
PBC_3 0,7422
Intention INT_1 0,9180 0,8452 0,9424
INT_2 0,9163
INT_3 0,9237
Behaviour BHV_1 0,6753 0,5008 0,8000
BHV_2 0,7262
BHV_3 0,7025
BHV_4 0,7256
Table 21 Measurement model indicators
All the constructs fall in the acceptable ranges.
The discriminant validity is computed comparing the squared roof of the average variance extracted and
the correlation coefficient. Results are reported in Table 22.
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PU OHL ATT PBC PDO INT BHV
PU 0,8742
OHL 0,4318 0,7702
ATT 0,6617 0,4729 0,8636
PBC 0,8212 0,5942 0,5656 0,8342
PDO 0,8003 0,3531 0,4486 0,6824 0,8715
INT 0,8371 0,4575 0,5548 0,8116 0,7331 0,9193
BHV 0,5317 0,3930 0,4333 0,5656 0,3242 0,6330 0,7077
Table 22 Discriminant validity
According to the results, the variance extracted is higher than the correlation coefficient for all the
constructs of the model: the fact reveals that constructs are empirically distinct.
As good results have been achieved both in terms of convergent validity and in terms of discriminant
validity, the result of the measurement model can be considered good.
4.2.1.2 Structural model After the validation of the measurement model, is time to analyse the relations among the different
constructs identifying the significant ones. We decided that a hypothesis is supported if the p-value is lower
than 0,05.
Main results are reported in Table 23.
HYPOTHESIS PATH COEFFICIENT P-VALUE STATISTICAL VALIDITY
H1: OHL PBC 0,4382 0,000 Supported
H2: PDO PBC 0,6187 0,000 Supported
H3: PDO PU 0,4367 0,000 Supported
H4: PDO ATT - 0,3185 0,000 Supported
H5: PDO INT 0,3203 0,000 Supported
H6: PDO BVH - 0,3028 0,000 Supported
H7: PU ATT 0,8999 0,000 Supported
H8: ATT INT 0,0889 0,019 Supported
H9: PBC PU 0,5627 0,000 Supported
H10: PBC INT 0,5766 0,000 Supported
H11: PBC BHV 0,2433 0,008 Supported
H12: INT BHV 0,6099 0,000 Supported
Table 23 Supported hypothesis
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All the relationships expressed in the hypotheses of the study have a statistically significant validity. In
addition, all the hypotheses are verified, except for H4 and H6. It is possible to notice from Table 23 that
the path coefficients of these hypotheses testify the negative relationship among the constructs, contrarily
to the initial hypotheses of a positive relation.
At this point of the work it is important to analyse the model validity also considering the fit indices. In our
study we analysed both absolute and incremental fit indices. The first ones indicate the degree to which the
hypothesized model reproduces the sample data, while the second measure the proportional improvement
in fit when the hypothesized model is compared with two reference models: a worst case or null model,
and an ideal model that perfectly represents the modeled phenomena in the studied population. Adopted
absolute fit indices are the root mean square error of approximation (RMSEA) and the root mean square
residual (RMR or SRMR). For what concern incremental fit indices, we just selected the comparative fit
index (CFI).
FIT INDEX THRESHOLD VALUE OUR RESULT
RMSEA < 0,1 0,051
SRMR < 0,1 0,078
CFI > 0,8 0,955
Table 24 Structural model, fit indices
As we can notice in Table 24 all the indicators present good results and so we can conclude that the model
validity is satisfied.
Finally is important to consider and comment also the R-squared. The R-squared is the variance explained
in a dependent variable by a set of independent variables. From the results obtained running the SEM
model on Stata represented in Table 25, we obtain the R-squared for each dependent variable, and the
overall R-squared that indicates the variance explained by the overall model. Considering the values
obtained we can see that the overall variance explanation is very good: 97,8% of the variance is explained
by the model. Regarding the single variables, there is a very good variance explication for perceived
usefulness and intention. While the explication is weaker for attitude, perceived behavioural control and
behaviour but always higher than 43%.
VARIABLE R-SQUARED
PERCEIVED BEHAVIOURAL CONTROL 0.5748
ATTITUDE 0.4615
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BEHAVIOUR 0.4389
PERCEIVED USEFULNESS 0.8115
INTENTION 0.7503
OVERALL 0.9780
Table 25 R-squared
4.2.1.3 Control variables In our model, we have introduced four control variables directly on behaviour. In particular the considered
control variables are age, sportiness, level of instruction and presence of chronic diseases.
Considering the statistical results coming from the adoption of the SEM, we can conclude if the
hypothesises on the control variables are supported by the data. Results are summarized in Table 26.
HYPOTHESIS PATH COEFFICIENT P-VALUE STATISTICAL VALIDITY
AGE BHV - 0,1141 0,002 Supported
SPORTINESS BHV - 0,0869 0,021 Supported
INSTRUCTION BHV -0,0162 0,645 Not Supported
CHRONIC BHV -0,0139 0,699 Not supported
Table 26 Control variables, supported hypothesis
Looking at the results on the p-value, only two control variables are significant: sportiness and age.
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5. RESULTS DISCUSSION
SEM can be used to verify the research model (Hershberger, 2003). From the analysis of the p-value, we
can see which hypotheses are supported. All hypotheses presented a significant relationship, which means
that all the assumed relationships were statistically verified. At this point is important to analyze the results
of each single hypothesis and see what is the coefficient, or the part of variance explained.
H1: ONLINE HEALTH LITERACY PERCEIVED BEHAVIOURAL CONTROL
Path coefficient: 0,4382
P-value: 0,000
Statistical validity: Supported
Online health literacy has a positive significant influence on perceived behavioural control. This means that
a good knowledge and confidence, also due to past experience with the search for health online
information, can be a good predictor of the perceived ability to use digital technologies to monitor the
lifestyle. If an individual is able to manage the jungle of information available on Internet, it means he is
familiar with the health topic and with digital technologies interfaces. Moreover, since an individual is
interested in searching for health information on Internet and has the time of doing that activity, very likely
he will also have the time for monitoring his lifestyle. The activity of searching for health-related
information online has been investigated in previous studies, which show that individuals search for health
information to get prepared before a doctor visit or when going to the doctor is not possible or difficult due
to, for instance, a lack of time (Fiksdal, et al., 2014). The result testifies the fact that an individual
concerned with the search for health online information can be more inclined to the use of digital
technologies for monitoring the lifestyle. The path coefficient that connects the two constructs is 0.4382
that is considered a medium effect as we can see in Figure 28, highlighting that previous knowledge on a
related technology and issue can impact the adoption of the latter through perceived behavioural control.
The coefficient is not very high because while time and resources used to search for online information can
be similar and comparable to the ones for monitoring, the economic resources necessary for the behaviour
implementation are different. The search of online information is related to a less specific investment of
money: once the individual has a device and an Internet connection, he or she can access to all kind of
information. While the purchase of a wearable, which is a more specific tool, requires an investment of
money for an identified purpose. The same individual that has the time and the knowledge to search for
online information, very likely can dedicate some of this resources for the monitoring of the lifestyle with a
technology, while maybe he has not always the economic availability to do such a specific investment. In
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fact, as we have previously seen with a first analysis of how responders answered at the survey, the
economic availability is associated to level of instruction and so the type of profession. Higher levels of
education are generally associated to higher salaries and, hence, more money available to spend for the
studied behaviour.
The questions forming the construct “online health literacy” are mainly referred to the ability of using
Internet and distinguishing valuable resources in this context. This ability derives from a previous
experience. Recent studies investigated how past experience can have an effect on behaviour intention.
What emerged is that the role of past experience is different depending on its features. In particular, a
relevant past experience can have an important effect in behaviour intention, and can prevent the
individual in engaging in cognitive information processes, determining a low influence of attitude on
intention (Kidwell & Jewell, 2007). In that case, past experience acts as a strong protector of habitual
behaviour. In our case, the past experience with the use of online technologies for searching health
information is on average on a good level (5 points over 10) and it has a considerable impact on TPB model,
confirming the hypothesis of the study.
H2: PERCEIVED DOCTOR OPINION PERCEIVED BEHAVIORAL CONTROL
Path coefficient: 0,6187
P-value: 0,000
Statistical validity: Supported
H3: PERCEIVED DOCTOR OPINION PERCEIVED USEFULNESS
Path coefficient: 0,4367
P-value: 0,000
Statistical validity: Supported
H4: PERCEIVED DOCTOR OPINION ATTITUDE
Path coefficient: -0,3185
P-value: 0,000
Statistical validity: Supported
Figure 28 Online health literacy influence
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H5: PERCEIVED DOCTOR OPINION INTENTION
Path coefficient: 0,3203
P-value: 0,000
Statistical validity: Supported
H6: PERCEIVED DOCTOR OPINION BEHAVIOUR
Path coefficient: -0,3038
P-value: 0,000
Statistical validity: Supported
From hypothesis 2 to hypothesis 6, we can see the influence of perceived doctor opinion on the different
determinants of the model. It is interesting to compare the different coefficients. The relationship among
perceived doctor opinion and perceived behavioural control is the strongest, with a coefficient of 0.6187,
followed by perceived usefulness with 0.4367 and intention with 0.3203. On the contrary, there is a direct
negative effect of perceived doctor opinion not only on attitude, but also on behaviour. There are different
explanations for the results observed.
Perceived behavioural control is strongly influenced by perceived doctor opinion; this is an important result
that shows how the individual can feel more confident with digital technologies. In particular, the doctor
can be seen as a guarantee that reduces the risks perceived by the individual in the use of these new
devices, increasing their reliability. Perceived doctor opinion affects positively the perceived usefulness,
which means that individuals can be influenced in their judgement of the technologies by their doctors. In
order to understand the utility of monitoring lifestyle, doctors can play a relevant role. At the same time,
the negative influence of perceived doctor opinion on attitude suggests that, if the doctor has a negative or
positive opinion on the use of digital technologies to monitor lifestyle, the patients will form opposite
attitudes. This result is unexpected, because if interpreted as “the more the doctor is interested in digital
health monitoring technologies, the less individual will adopt them” it generates a contradiction between
the attitudes formed by perceived doctor opinion through perceived usefulness and the ones formed
directly through perceived doctor opinion. This ambivalence has an impact on the strengths of the
attitudes. We can imagine that an individual holding contradicting attitudes can assume different intentions
and behaviours in different contexts. Analysing more in detail the data, we can hypothesize that the
negative path coefficient can be determined by the fact that, although the average value assumed by
perceived doctor opinion in the questionnaire has been quite low, the attitude has recorded a quite high
value on average. This means that the questionnaire tested the fact that, even if the doctor is not perceived
as really interested in the use of digital technologies for healthcare, patients are interested in such
technologies and believe they are relevant. We cannot be sure that the same happens for behaviour, the
doctor is perceived as not really interested in promoting digital health monitoring technologies, and in fact
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behaviours values on average are on 1,3/3 that means that not a lot of respondents are interested in the
use of the studied technologies. Considering this last result, we can conclude that when the individual
perceives the doctor suggestions as orders, he will probably reject the suggestions and act in the opposite
way as the negative path coefficient shows. While on intention, perceived doctor opinion has a positive
medium effect, and the two variables assume more or less the same average value. Also this result can
testify that, when not perceived as an order that want to act directly on behaviour, doctor’s suggestion is
taken under consideration.
In conclusion as we can see from Figure 29, respondents cannot be directly influenced by doctor opinion:
the latter construct can influence on behaviour indirectly. In a way, these results testify the change in the
relationship between doctors and patient. While in the past it was more likely that people trusted their
doctor and believed his opinion was valuable because based on a scientific knowledge, now they listen to
the doctor and are influenced by his opinion, but at the same time give lot of importance to many other
variables.
Figure 29 Doctor opinion influence
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H7: PERCEIVED USEFULNESS ATTITUDE
Path coefficient: 0,8999
P-value: 0,000
Statistical validity: Supported
H8: ATTITUDE INTENTION
Path coefficient: 0,0889
P-value: 0,019
Statistical validity: Supported
H10: PERCEIVED BEHAVIORAL CONTROL INTENTION
Path coefficient: 0,5766
P-value: 0,000
Statistical validity: Supported
H11: PERCEIVED BEHAVIORAL CONTROL BEHAVIOUR
Path coefficient: 0,2433
P-value: 0,008
Statistical validity: Supported
H12: INTENTION BEHAVIOUR
Path coefficient: 0,6099
P-value: 0,000
Statistical validity: Supported
The hypotheses, derived from the theories of behaviour and presented in Figure 30, are all verified. In
detail:
- From the TAM, perceived usefulness has a high positive effect on attitude. People form positive
attitudes toward behaviours that are perceived as useful. If a behaviour is considered able of improving
an individual performance, it will be evaluated as a positive behaviour. This relationship is supported by
a quite high path coefficient, which demonstrates that, in the case of digital health monitoring
technologies, the communication of the benefits, and so of the usefulness of the technology, is
fundamental to create positive attitudes toward it.
- Attitude has a weak influence on intentions. This result expresses how in this case the attitude path
for the behaviour intention formation is not a relevant one. As observed in the study (Kidwell & Jewell,
2007) the impact of attitude on intention can be moderated by past experience. This result means that
past experience can limit the influence on attitudes and information cognitive processing in the
RESULTS DISCUSSION
128
behaviour formation. When an individual has a strong past experience, he will be influenced by this
experience more than other information on future behaviours. This finding opens the way for
additional researches. On the one hand, it could be that past experience with health related
technologies could have an impact on attitude toward the use of digital health monitoring technologies
So, it could be interesting to study if online health literacy has a moderating effect as a control variable
on the relationship between attitudes and intention. From the analysis of the survey, attitude recorded
a high value, while intention recorded on average a lower one. In this case we can hypothesize that a
strong negative past experience with related technology can disable the relationship among attitudes
and intentions. The assumption of this interpretation is that a positive or negative past experience not
only have a strong influence on perceived behavioural control but it can be able also to decrease the
impact that attitudes have on intention. What could be interesting to research is if the path coefficient
between attitude and intention increases, in case of a higher value of perceived doctor opinion. On the
other hand, we can hypothesize that explicit attitude do not influence intention because there is an
influence from implicit attitudes. As we said in the literature analysis, the strength of attitude depends
on its the level of ambivalence. So in this case the low affection on intention proves that attitude is
weak because it can assume different values over different contexts of measure.
- Perceived behavioural control has a positive influence on intention, and at the same time it has a
lower positive influence on behaviour. Also this result testify how in the research context, the
behaviour formation has to pass through intention. In this case due to the medium effect of the direct
relationship, we cannot assume perceived behavioural control to be a measure of actual control.
- Intention has a quite strong positive influence on behaviour, meaning that it is a good predictor of
behaviour. The result shows how intentions are the most effective precedents of behaviour. In the
healthcare context, it seems that intentions play a significant part, probably because the decisions in
this context are considered quite sensible, and for this reason there is a deep behaviour formation.
RESULTS DISCUSSION
129
Figure 30 Theories' hypothesis
H9: PERCEIVED BEHAVIORAL CONTROL PERCEIVED USEFULNESS
Path coefficient: 0,5627
P-value: 0,000
Statistical validity: Supported
Perceived behavioural control has a solid positive influence on perceived usefulness. Similar to the
reasoning behind the hypothesis between perceived ease of use and perceived usefulness present in TAM
model, the easier is to perform the behaviour, the more the individual performance can be improved, and
so increased perceived usefulness. In fact, as modelled in Figure 31, the more the individual is confident
with the behaviour, the better he can perform. So, perceived behavioural control is a powerful variable to
leverage on, since it has impact on different variables.
RESULTS DISCUSSION
130
Figure 31 Impact of pbc on pu
CONTROL VARIABLES
For the age and the sportiness, the level of the p-value is lower than the threshold for this indicator (0,05),
hence we can conclude that the related hypotheses are supported. In particular, both the path coefficients
result negative. As said before, the more we increase the age, the less responders are inclined to adopt
digital technologies to monitor lifestyle. Furthermore, sportiness is computed in a way in which a low value
means a sportive people, while a high result means a sedentary people. As a consequence, the negative
path coefficient is interpreted saying that the less an individual lives following a sportive lifestyle, the less
he is in favour of the studied behaviour.
On the contrary, the level of the p-value associated to the control variables instruction and chronic disease
are higher than 0,05 so the hypotheses are not supported. Considering the overall model and all the
constructs, the education of a person and the presence of chronic diseases are not relevant driver in the
adoption of digital technologies in monitoring the lifestyle.
OVERALL MODEL
In order to give a meaning to all these results, we tried to individuate the strongest path that can lead to
behaviour. Firstly, since attitude has a shy influence on intention, we excluded this path. Looking at the
construct that has one of the strongest influences on intention, perceived behavioural control seems the
determinant that better predicts it. The predecessor of perceived behavioural control are online health
literacy and perceived doctor opinion. The identification of the key variables to efficiently promote the
diffusion of the use of digital technologies is important to comply with the previously mentioned goal of
making the investments in healthcare digitalization profitable in the future. As we can see from Figure 32
stimulating perceived behavioural control by leveraging on the promotion of online health literacy and on
the new doctors’ role can be a way to increase the promotion of the use of digital technologies for the
lifestyle monitoring.
RESULTS DISCUSSION
131
Figure 32 Overall model
CONCLUSIONS
132
6. CONCLUSIONS In this chapter, we would like to answer to the main research questions, identifying the implications that
the model tested presents both from a theoretical and practical point of view. Consequently, we would try
to evidence the limits of the work and the related possible future researches.
6.1 CONTRIBUTIONS From the theoretical point of view the model has tested, with a reliable and large empirical database, some
of the main constructs of the theories of behaviour prediction. The main contribution is given by the testing
of the construct perceived behavioural control, by the TPB theory. From the present literature review,
there aren’t studies that investigated this factor on the adoption of digital technologies for lifestyle
monitoring. What emerged is that this variable plays an important role in the behaviour prediction. This
result answered to the first research question of this thesis, explicated in Table 27. In detail, this factor
influences behaviour formation mainly through intention. In fact, what emerged from the study is that
apart from intention, there is not another element with a strong direct influence on behaviour. This means
that the passage from intention formation is constrained. The next important contribution has been
understanding how this intention formation occurs. In particular, the model suggests that a good positive
attitude is not enough to have good positive intention. It is at this point that perceived behavioural control
show its influencing power on intention also due to its precedents. Focusing on this issue, another
important contribution is given by the verified importance of online health literacy as an antecedent of
perceived behavioural control, which answered to the second research question explicated in Table 27. The
ability to use a technology to search for online health information and being able to understand it, can
open the way to the adoption of other health related technologies such as monitoring ones. Due to the
changing relationship between patient and doctor, we included a doctor related factor. The model
contributed in testing the fact that this relationship is evolving, and the role of the two actors is different
from the past. The model answered to the third research question explicated in Table 27, testing that the
opinion of the doctor can positively influence the behaviour in an indirect way, by affecting intention and
perceived usefulness. While the direct relationship between doctor opinion and behaviour resulted to be
negative. This can witness the fact that when the doctor’s opinion is perceived as an “order” to behave in a
way, the patient rejects it; while when the doctor opinion is perceived as an open discussion, to explain and
teach the individual on an issue, the patient accepts it.
Before considering the practical actions that could be implemented to encourage the new doctor-patient
relationship, we should analyze how to make doctors in favor of a similar behavior so that they can suggest
it to their patients. A first way could be including the doctors in the development process of the devices for
tracking lifestyle. This solution is a double win situation: doctors can better understand the advantages of
these technologies and companies can save time and money. In fact, keeping in mind since the beginning
CONCLUSIONS
133
the opinion of the doctors and their needs in terms of data analysis, will make companies anticipate the
changes in the product development process reducing the costs and increasing the customer satisfaction.
In addition to the involvement of doctors at early stages of the development process another solution
could be educate doctors on the benefits of similar devices. Health care providers and governments could
promote trainings in order to reduce the adoption barriers and facilitate also the oldest generation of
doctors that maybe is less confident with the use of Internet and digital technologies.
On the practical side, the study was able to demonstrate that in order to promote the diffusion of digital
health monitoring technologies, there are some sensible factors over which it is convenient to focus. It
could be useful leveraging on perceived behavioural control, by empowering the individual with the
necessary resources to adopt the studied behaviour. Some practical examples of the actions aimed at
increasing perceived behavioural control, so the behaviour adoption, could be: give the device for free to
people that can’t afford it, or give a discount for a particular segment of the population. As well as acting on
the device design, decreasing the time needed by the user to manage the technology, that can consist in
easier data management and configuration. An important resource that should be stimulated is the
knowledge on the use of technology, and in particular on the management of the health data. This is
testified by the result on online health literacy. At the same time, the model suggests to leverage on the
doctor influencing power over intention, paying attention on the new relationship between doctor and
patient. The suggestion considering the overall results is that, in order to promote the adoption of digital
technologies for the monitoring of lifestyle, individuals should be educated more than pushed toward a
purchase. In this perspective, the doctor role should be the one of an expert advisor. This new figure should
be coherent with the change in the doctor-patient relationship (Eysenbach, 2001). A character that in a way
loses some authority traits, to level the past distance present in the relationship with the patient.
It is important that by entering this leadership role, the doctor does not feel deprived of his power, but
collaborates with the patient, supporting him in his choices. To make that possible, the doctor has to
understand the benefit of using health monitoring technologies. Only in this case, the perceived doctor
opinion can influence behavioural intention. As the vaccines issue, doctor opinion is important but still
some people do not agree on their usefulness due to the economic interest pharmaceutical companies
have on the market. The same situation can occur for digital monitoring technologies, that’s why we
suggest that doctor opinion should not be related to one brand, but with the technology category in
general.
Thanks to this work, we understood that individuals hold positive attitudes toward digital health monitoring
technologies, they are quite aware of the usefulness of this technology, but despite that the behaviour is
still shy if compared to the average attitude value. To increase the behaviour adoption, intention should be
stimulated. A practical solution to do that can be a promotional campaign supported by government and
CONCLUSIONS
134
institutions, since the first main aim is to educate citizens. The inclusion of a doctor as testimonial can be a
way to communicate the utility of digital health monitoring technologies. The project of the advertising
should keep in mind that the characters should represent the new doctor role: no more an autocratic figure
who imposes to the patient what doing, but a health consultant who gives suggestions. The message of the
campaign should explain in the clearest possible way how easy is to use the technology. In particular, it
should highlight how effortless is its adoption. An idea could be providing a quick and easy explanation of
how to set and then read the data gathered by the device. Concerning this issue, it could be useful to
mention the doctor as a figure that can help the person in the setting and in the data interpretation. In this
way, the doctor is depicted as the health counselor, the patient perceives a better control over the
behaviour, and together with the doctor can improve his health literacy.
With the general aim of investigating the factors that affect the adoption of health technology monitoring,
the study shows that attitudes are not a good predictor of intention. Even if individuals seem to have
positive attitudes toward the studied technologies, which is demonstrated by the high average value of
attitude recorded, this is not enough to develop such positive intention and behaviour. The main
theoretical contributions is that in order to perform the studied behaviour, individuals need not only
positive attitudes that represents a more abstract element, but they need also a more concrete one that is
the control on the behaviour. In a way before adopting the behaviour they need to be sure to be able to
control it and not waste resources.
RESEARCH
QUESTION
EMPIRICAL EVIDENCE THEORETICAL
CONTRIBUTION
PRACTICAL
CONTRIBUTION
R.Q.1: How the
perception of the
individual
concerning the
easiness of control
over the behaviour
is related to the
adoption of the
behaviour itself?
Perceived behavioural
control has a strong
relationship with
intention but a lower
one directly on
behaviour.
Intention plays a relevant
role in the behaviour
formation. At the same
time perceived
behavioural control is a
strong antecedent of
intention.
In order to promote the
use of digital health
monitoring technologies,
it should be useful to act
on the resources needed
to implement the
behaviour by proving
them to individuals. And
increasing the technology
easiness of use.
R.Q.2: Are there
some specific
resources needed
for the adoption of
Online health literacy
has a medium effect
on perceived
behavioural control.
The ability to use Internet
to find information on
health issues is related to
an higher control over the
Knowledge on both web
surfing activity and health
issues, is a key factor to
stimulate the perception
CONCLUSIONS
135
the studies
behaviour? In
addition to time and
money, is some
peculiar knowledge
needed to develop
interest in the
behaviour?
studied behaviour. of easiness to control
over using digital health
monitoring technologies.
R.Q.3: How the
doctor opinion and
the doctor-patient
relationship can
affect the use of
digital health
monitoring
technologies?
Perceived doctor
opinion has different
effects on the model.
It has good positive
impact on PU and PBC.
It has a medium
positive effect on
intention and a
negative one on
attitude and
behaviour.
Doctor opinion plays a
role in the decision of
using digital health
monitoring technologies.
When the doctor gives an
order to change the
behaviour, the patient
reject it. While, if he gives
a suggestions, the patient
accepts it.
The role of the doctor is
changing in the patient
decision system. It can
still have an influence, but
its intervention should be
coherent with the “new”
empowered patient.
Doctors should be
educated and involved in
the technology
development.
Table 27 Contributions
6.2 LIMITS AND FUTURE RESEARCH The analysis that we have conducted lead to some important take-aways but it also raised some limitations
that could be improved in future researches. A model is a representation or simplified version of a concept,
phenomenon, relationship, structure, system, or an aspect of the real world. By definition a model has to
make some simplifications. Thus, is clear that every model has some limitations — also the one that we
have developed. In the rest of the paragraph we outline the limits of our work as well as potential future
researches that would be interesting to conduct in order to fulfill these limits. Table 28 recaps the limits
and how future researches could address them.
LIMITS FUTURE RESEARCHES
1. Useless results on WTP Introduce a WTP question with different price ranges to
avoid the “I don’t know” answer
2. Only citizen and patient
participated in the questionnaire
Involve healthcare professional in the data collection
CONCLUSIONS
136
3. Habitual, persistence and
repetitive behaviour were not
considered
Focus on habitual behaviour study
4. Lack of investigation on the
antecedents of perceived doctor
opinion
Deepen the research on perceived doctor opinion
antecedent
5. Motivation not considered Use of goal setting and motivation theories
6. Two-item constructs Increase the number of items per construct
7. Use of a non-brand specific
description
Use of a specific brand product as a reference
8. National research Enlarge the research to international landscape
Table 28 Limits and future research
1. One of the first element we were interested was the willingness to pay for digital technologies in
order to monitor lifestyle. Unlucky, even if the questionnaire investigated this issue, the answers
were not satisfactory. In fact, a large majority of the interviewees answered “I do not know”. This
result is probably due to the fact that the question was open and without a comparing parameter
the respondents weren’t able to set a price. For this reason, we would suggest for future
researches to ask the question in a close way, with different ranges of prices. An example could be
the following: how much are you willing to pay for a wearable device for collecting and monitoring
data on your lifestyle?
- 0, not willing to pay
- 1, from 1€ to 25€
- 2, from 26€ to 50€
- 3, from 51€ to 100€
- 4, more than 100€
- 99, I do not know
2. In our research, and from the literature analysis, one of the gap and not investigated point is the
role played by doctors in the promotion of digital technologies in the healthcare sector. In the
questionnaire used to analyze data, the participants were patients, as it is in the majority of the
other studies. The involvement of just one part of the stakeholder system is a limitation. It could be
interesting to collect not only the patient opinion, but also the one of single doctors and healthcare
providers.
CONCLUSIONS
137
3. Another issue that emerged from the literature analysis has been the fact that after the first six
months of use, consumers abandon their wearables. In our research the main aim was to
investigate the factors that influence adoption, while the sustained use of health related digital
technologies has not been investigated. This aspect should be addressed, with model and theories
that refer to habitual and repetitive behaviours. What we suggest is to better analyze how it is
possible to lead to a habit formation for the use of such technologies.
4. Thanks to the questions available on the questionnaire we were able to form the construct
perceived doctor opinion. The research on this factor can be deepened by introducing and
analyzing some antecedents of it, in order to understand its formation. This analysis can be useful
due to the complex interpretation of the results about the relationship of the construct with other
variables obtained in this study.
5. The proposed model has as a focus the behavioural theories but it does not consider motivation,
which is a limitation. Thus, future research should focus on motivation, through goal-setting. In
particular, this link is relevant for users interested in the monitoring of lifestyle also for fitness
purposes. Previous research on fitness related motivations demonstrates a close link between
goalsetting and motivations. Goal orientation theory of achievement motivation postulates that
motivations are impacted by goals on a relative scale, based on the individual. This means that
different goals yield different levels of motivation in different individuals (Cumming & Hall, 2006).
Furthermore, individuals set goals based on a “personal theory of what achievement means to
them for that situation or task” (Harwood, Hardy & Swain, 2000, p. 236), which diversifies goals as
well as the motivation to achieve them.
6. Moreover, the use of two-item measures is also considered a limitation in this study. This may
impact reliability of the items measured. While a multi-item measure would be more appropriate
to examine complex constructs.
7. Further, the use of a non-brand specific description of smartwatches allows respondents to freely
express influences of attitude formation without being potentially biased by a specific product.
However, this advantage corresponds with the limitation that brand related factors, such as brand
attitude or loyalty, were not included or controlled for. For example, one could argue that a person
with high brand attachment (Belaid & Behi, 2011) or brand love (Batra, Ahuvia, & Bagozzi, 2012)
towards Apple, would buy any product of Apple regardless of the specific item. Another aspect that
could be considered related to the brand is its reputation in the healthcare sector. Since the
CONCLUSIONS
138
investigated digital technologies have the purpose of monitoring healthcare parameters, a higher
brand reputation in the healthcare could lead to a higher adoption.
8. The questionnaire was administered to a sample of Italian citizen. While the use of a sample of one
country allows us to control for various exogenous factors and thus increase internal validity,
generalizability might be limited.
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ATTACHED: SURVEY
146
ATTACHED: SURVEY The present attached aims at showing all the questions of the survey that were used in order to draw the
model. The questions will be presented by construct.
PERCEIVED USEFULNESS
PU_1: Monitorare il mio stile di vita tramite strumenti digitali migliorerebbe la mia salute
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
ATTACHED: SURVEY
147
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
PU_2: Monitorare il mio stile di vita tramite strumenti digitali mi renderebbe più capace nel
mantenermi in salute
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
PU_3: Monitorare il mio stile vita tramite strumenti digitali è utile
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
ATTITUDE
ATT_1: Quanto ritiene sia rilevante, su una scala da 1 a 10, Monitorare la frequenza cardiaca?
10:10 Molto rilevante
ATTACHED: SURVEY
148
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla rilevante
99:(Non sa\Non ricorda)
ATT_2: Quanto ritiene sia rilevante, su una scala da 1 a 10, Monitorare il numero di passi
giornalieri?
10:10 Molto rilevante
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla rilevante
99:(Non sa\Non ricorda)
ATT_3: Quanto ritiene sia rilevante, su una scala da 1 a 10, Monitorare gli allenamenti (es. corsa,
bici)?
10:10 Molto rilevante
9:9
8:8
7:7
6:6
5:5
4:4
3:3
ATTACHED: SURVEY
149
2:2
1:1 Per nulla rilevante
99:(Non sa\Non ricorda)
PERCEIVED DOCTOR OPINION
PDO_1: Il mio medico di famiglia pensa che dovrei monitorare lo stile di vita con strumenti digitali
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
PDO_2: Il mio medico di famiglia si aspetta che io monitori lo stile di vita con strumenti digitali
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
ONLINE HEALTH LITERACY
OHL_1: Quanto è d'accordo con la seguente affermazione, su una scala da 1 a 10...So come usare
internet per rispondere alle mie domande sul mio stato di salute
ATTACHED: SURVEY
150
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
OHL_2: Quanto è d'accordo con la seguente affermazione, su una scala da 1 a 10...So distinguere
risorse di valore da risorse di scarsa qualità in ambito sanitario trovate su internet
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
PERCEIVED BEHAVIOURAL CONTROL
PBC_1: Ho il tempo per monitorare il mio stile di vita attraverso gli strumenti digitali
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
ATTACHED: SURVEY
151
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
PBC_2: Ho le disponibilità economiche per acquistare strumenti digitali per il monitoraggio dello
stile di vita
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
PBC_3: Monitorare il mio stile tramite strumenti digitali è facile
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
INTENTION
ATTACHED: SURVEY
152
INT_1: Prevedo di monitorare il mio stile di vita tramite strumenti digitali nei prossimi mesi
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
INT_2: E' probabile che monitorerò il mio stile di vita tramite strumenti digitali nei prossimi mesi
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
INT_3: Sono intenzionato a monitorare il mio stile di vita tramite strumenti digitali nei prossimi
mesi
10:10 Molto d'accordo
9:9
8:8
7:7
6:6
5:5
4:4
ATTACHED: SURVEY
153
3:3
2:2
1:1 Per nulla d'accordo
99:(Non sa\Non ricorda)
BEHAVIOUR
BHV_1: Per monitorare il suo stile di vita Lei utilizza una... App per monitorare battiti
1:Non utilizzo e non sono interessato
2:Non utilizzo ma sono interessato
3:Utilizzo
BHV_2: Per monitorare il suo stile di vita Lei utilizza una... App per monitorare passi
1:Non utilizzo e non sono interessato
2:Non utilizzo ma sono interessato
3:Utilizzo
BHV_3: Per monitorare il suo stile di vita Lei utilizza una... App per monitorare allenamenti (es.
corsa, bici)
1:Non utilizzo e non sono interessato
2:Non utilizzo ma sono interessato
3:Utilizzo
BHV_4: Per monitorare il suo stile di vita Lei utilizza una... App per monitorare le calorie
1:Non utilizzo e non sono interessato
2:Non utilizzo ma sono interessato
3:Utilizzo
CONTROL VARIABLES
CHRONIC DISEASES: E' affetto da una o più malattie croniche (es. diabete, bronchite cronica,
ipertensione, ecc.) o problemi di salute di lunga durata?
1:Si
2:No
3:(Non so\Non ricordo)
ATTACHED: SURVEY
154
AGE: Anni
INSTRUCTION: Titolo di studio
1:Laurea
2:Università senza laurea
3:Scuola media superiore con diploma
4:Scuola media superiore senza diploma
5:Scuola media inferiore con licenza
6:Scuola media inferiore senza licenza
7:Scuola elementare con licenza
8:Scuola elementare senza licenza
9:Nessuna scuola
10:(Non indica)
SPORTINESS: Con quale frequenza svolge attività sportiva?
1:Almeno tre volte a settimana
2:Almeno una volta a settimana
3:Almeno una volta al mese
4:Almeno una volta all'anno
5:Meno di una volta all'anno
6:Mai