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Developing and evaluating a smartphone application for tuberculosis amongst private sector
academic clinicians in India
Mémoire
Tripti Pande
Maîtrise en Santé Communautaire – Santé mondiale Maître ès sciences (M. Sc.)
Québec, Canada
© Tripti Pande, 2017
Developing and evaluating a smartphone
application for tuberculosis amongst private sector
academic clinicians in India
Mémoire
Tripti Pande
Sous la direction de :
Marie-Pierre GAGNON, directrice de recherche
Madhukar PAI, codirecteur de recherche
iii
Résumé Contexte : La tuberculose est la première cause de mortalité au monde et parmi les 10,4
millions de cas de tuberculose par année, 2,8 millions proviennent de l’Inde. De ce fait, il est
considéré comme le pays ayant le plus haut taux d’incidence de la tuberculose au monde.
Une manque de qualité des soins est une cause majeure pour l’épidémie de la tuberculose en
Inde. Le secteur privé, qui n’est pas réglementé, prend soin de 50% des patients ayant la
tuberculose. Des études précédentes indiquent le mauvais diagnostic ou le mauvais traitement
comme des facteurs qui sont présents dans le secteur privé. Ce secteur comprend plusieurs
types de médecins, dont ceux qui ont un diplôme en médecine et ceux qui n’ont pas de
diplôme en médecine mais pratiquent la médicine. Une amélioration dans l’éducation des
cliniciens utilisant les technologies mobiles, dont les applications mobiles, pourrait être une
solution pour améliorer et assurer la qualité des soins des patients ayant la tuberculose.
Toutefois, il existe peu d’études sur les technologies mobiles pour la tuberculose en Inde.
Objectifs : Ce mémoire vise à évaluer l’expérience de l’usager et l’acceptabilité d’une
application mobile (LearnTB) parmi les cliniciens académiques du secteur privé en Inde.
Méthodes: L’étude a utilisé une approche à deux étapes. Cinq cliniciens (étape 1) et 101
cliniciens (étape 2) ont été contactés à Kasturba Hospital Manipal, Manipal, Inde entre février
et mars 2017. L’expérience des participants était évaluée par le System Usability Scale.
L’acceptabilité était évaluée par un questionnaire adopté du Technology Assessment Model.
Les résultats étaient analysés à l’aide des statistiques descriptives, la régression linéaire
multiple ainsi que la régression logistique.
Résultats : Des taux de réponse de 100% et 99% ont été obtenus pour la première et
deuxième partie respectivement. L’expérience de l’usager était vraiment positive. En ce qui
concerne l’acceptabilité, une analyse de cheminement a confirmé la relation directe entre
l’utilité perçue et l’intention d’utilisation, et la relation indirecte entre la facilité d’utilisation
perçue et l’intention d’utilisation. La régression logistique a permis de cibler les items qui
influencent fortement l’intention d’utilisation.
Conclusion : L’expérience de l’usager pour LearnTB était vraiment positive, et l’utilité
perçue a le plus grand impact sur l’intention d’utilisation (acceptabilité). Cette étude permet
d’avoir une analyse préliminaire de l’acceptabilité des cliniciens concernant les technologies
iv
mobiles pour la tuberculose en Inde. D’autres recherches dans ce domaine sont requises afin
d’assurer l’implantation optimale de ces technologies.
v
Abstract Background: Tuberculosis (TB) is the leading infectious killer, and India accounts for 2.8
of the 10.4 million TB cases that occur each year, making it the highest TB burden country
worldwide. Poor quality of TB care is a major driver of the epidemic in India. India’s large
private, unregulated health sector manages over 50% of the TB patients, with studies showing
suboptimal diagnosis and treatment in the private sector. This sector comprises of health
professionals who are medically trained, and those who are not medically trained but are
practicing medicine. Better education of doctors using mobile health (mHealth) applications
is a possible solution. However, little is known about mHealth around TB in India.
Objective: This masters thesis aimed to evaluate the user experience and acceptability of a
smartphone application for TB (LearnTB) amongst private sector academic clinicians in
India.
Methods: This study adopted a two part approach. Five clinicians (part 1) and 101 clinicians
(part 2) were contacted at Kasturba Hospital Manipal, Manipal, India between February and
March 2017. The user experience of participants (part 1) was evaluated based on the System
Usability Scale (SUS). Acceptability (part 2) was evaluated based on the Technology
Acceptance Model (TAM). Data were analyzed using descriptive statistics, multiple linear
regression as well as logistic regression analysis.
Results: Response rates of 100% and 99% were achieved for part 1 and part 2, respectively.
User experience was very positive. Regarding acceptability, a path analysis confirmed the
direct relationship between perceived usefulness and intention to use, and the indirect
relationship between perceived ease of use and intention to use. Logistic regression analysis
helped target items strongly influencing intention to use.
Conclusion: The user experience with LearnTB was very positive, and perceived usefulness
has the highest impact on intention to use (acceptability). This study provides a preliminary
analysis of mHealth interventions for TB in India, and emphasizes the need for future
research in this domain.
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TABLE OF CONTENTS
Résumé ................................................................................................................................. iii Abstract .................................................................................................................................. v
List of Tables ..................................................................................................................... viii List of Figures ....................................................................................................................... ix
Abbreviations ........................................................................................................................ x Acknowledgement ................................................................................................................ xi
Avant – propos ................................................................................................................... xii CHAPTER 1 Introduction ................................................................................................. 1
CHAPTER 2 Context ......................................................................................................... 5 2.1 General information: Tuberculosis ...................................................................... 5 2.2 General epidemiology of TB .................................................................................. 6 2.3 Revised National TB Control Programme (RNTCP) ......................................... 7 2.4 Health portrait of India ......................................................................................... 7
2.4.1 Geographic, demographic and economic context ............................................. 7 2.4.2 Health systems: the increasing gap ................................................................... 9
2.5 General information: mHealth ........................................................................... 10
CHAPTER 3 Theoretical Framework ............................................................................ 12 CHAPTER 4 Literature review ....................................................................................... 17
4.1 User experience: the importance of usability studies ........................................ 17 4.2 Development of mHealth strategies .................................................................... 18 4.3 Acceptability of mHealth interventions .............................................................. 19 4.4 Factors influencing adoption of mHealth interventions ................................... 21 4.5 Knowledge gap & justification ............................................................................ 22
CHAPTER 5 Research Question and Objectives .......................................................... 25 5.1 Question ................................................................................................................ 25 5.2 Objectives .............................................................................................................. 25 5.3 Hypothesis ............................................................................................................. 25
CHAPTER 6 Mobile application ..................................................................................... 26
CHAPTER 7 Methodology .............................................................................................. 29 7.1 Study design .......................................................................................................... 29 7.2 Study setting ......................................................................................................... 29 7.3 Study population & recruitment ......................................................................... 30 7.4 Data collection ...................................................................................................... 32 7.5 Data analysis ......................................................................................................... 34 7.6 Ethics consideration and approvals .................................................................... 35
CHAPTER 8 Manuscript ................................................................................................. 37
CHAPTER 9 Discussion ................................................................................................... 74 9.1 Main results .......................................................................................................... 74 9.2 Strengths and limits ............................................................................................. 74
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9.3 Contribution to public health .............................................................................. 76
CHAPTER 10 Conclusion .................................................................................................. 78 CHAPTER 11 References .................................................................................................. 79
Appendix A : Proof of submission ..................................................................................... 84 Appendix B : Search strategy ............................................................................................ 85
Appendix C : SUS questionnaire ....................................................................................... 86 Appendix D : Tasks for user experience (part 1) ............................................................. 88
Appendix E : TAM questionnaire ..................................................................................... 89 Appendix F : Consent forms .............................................................................................. 92
viii
List of Tables Table 1 : Part 1 – Sociodemographic characteristics (n=5) ................................................. 66Table 2 : Variables and items present in TAM questionnaire (n=100) ................................ 66Table 3 : Part 2 – sociodemographic characteristics (n=100) .............................................. 68Table 4 : Pearson’s correlation coefficients between variables (n=99) ............................... 69Table 5 : Multiple linear regression model for Intention to Use .......................................... 70
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List of Figures Figure 1 : Political map of India (source : www.worldmaps.org) ......................................... 8Figure 2: Original 10 statement SUS questionnaire developed by J. Brooke in 1996 ........ 13Figure 3: Original Technology Assessment Model (TAM) by Davis 1989 ........................ 15Figure 4: Adapted version of TAM including user experience ........................................... 16Figure 5: LearnTB application – home page ....................................................................... 27Figure 6: LearnTB application – subsections present in Section 6 “Childhood tuberculosis”
....................................................................................................................................... 27Figure 7: LearnTB application – figure of chest x-ray in Section 4 “Role of chest x-rays in
the management of tuberculosis” .................................................................................. 28Figure 8: Map of Manipal, Karnataka, India (source : http://szusicon2017.com/travel/) ... 30
x
Abbreviations BCE: Before common era
CE: Common era
CÉRUL: Comité d’éthiques de la recherché avec des êtres humains de l’Université Laval
DOI: Diffusion of innovation theory
DOTS: Directly observed treatment short course
eHealth: Electronic health
GDP: Gross domestic product
HBC: High TB burden country
IU: Intention to use
LMIC: Low and middle income country
MBBS: Bachelor of medicine and bachelor of surgery
MDR-TB: Multi-drug resistant tuberculosis
mHealth: Mobile health
MUEC : Manipal University ethics committee
NAATS: Nucleic acid amplification tests
NTP: National tuberculosis program
PDA: Personal digital assistants
PEU: Perceived ease of use
PTB: Pulmonary tuberculosis
PU: Perceived usefulness
RNTCP: Revised national tuberculosis control program
SMS: Short messaging service
SUS: System usability scale
TAM: Technology assessment model
TB : Tuberculosis
TB-HIV: Tuberculosis and HIV coinfection
TPB: Theory of planned behavior
TRA: Theory of reasoned action
UTAUT: Unified theory of acceptance and use of technology
WHO : World Health Organization
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Acknowledgement This thesis project has been brought to its stage today through the TMA Pai Endowment
Fund, and the help and support of many key people. Firstly, I would like to thank my
supervisor Dr. Marie Pierre Gagnon for her constant support and reassurance throughout the
whole entire thesis process. From the day that we had met to discuss my thesis project, you
did not cease to help me in any way possible. Your confidence and continuous drive to make
me reach my goals has brought me to the stage I am at today; submitting my masters thesis,
thank you! I would also like to thank my co-supervisor Dr. Madhukar Pai for his constant
push to allow me to reach the heights I did with this thesis project. Your constant
encouragement has helped me create new opportunities and achieve the goals I had planned
for this thesis project, thank you!
I am grateful to Dr. Zelalem Temesgen and Mr. Al Seyoum for their countless hours allowing
the prototype version of the LearnTB application be ready for demonstration in India. I would
also like to thank Dr. Kavitha Saravu for being an amazing field supervisor in Manipal. The
way you flawlessly integrated me into the hospital environment since the first day I reached
India definitely eased the data collection process and helped me reach my sample size goal.
Also, a special thank you to Dr. Deekthish Mahadev for your dedication to helping me
achieve my target sample size, as well as Dr. Shipra Rai and Dr. Raghavendra Rao for their
efforts in participant recruitment.
Thank you to all my friends and colleagues in Manipal for your immense hospitality and
easing my stay in India. Many thanks to all the interns, post graduate residents, junior faculty,
senior faculty and staff of Kasturba Hospital Manipal for participating in this research study.
As I submit this thesis, I would like to thank my family for their immense encouragement
during my masters degree. We have gone through it together, and thank you for always being
there. Thank you to all my friends who have supported me and shown me the positive side
of the situation throughout these two years- special thank you to Johanne and Priscille!
Finally, thank you to all my professors for your support and mentorship throughout my
academic career, it has helped me reach where I am today. Thank you everyone!
xii
Avant – propos This masters thesis comprises of one original article titled “Evaluating clinicians’ user
experience and acceptability of LearnTB, a smartphone application for tuberculosis in
India”. The primary author of this article is Tripti Pande, and the co-authors include her
thesis supervisor Dr. Marie-Pierre Gagnon, co-supervisor Dr. Madhukar Pai, followed by Dr.
Kavitha Saravu, Dr. Zelalem Temesgen, Mr. Al Seyoum, Dr. Shipra Rai, Dr. Raghavendra
Rao, Dr. Deekshith Mahadev. Tripti Pande, author of the article and this thesis, designed and
conducted the study as well as wrote the manuscript. Drs. Marie-Pierre Gagnon and
Madhukar Pai helped design the study and edit the manuscript. Dr. Kavitha Saravu provided
field supervision at Kasturba Hospital Manipal, Drs Shipra Rai, Raghavendra Rao and
Deekshith Mahadev helped in participant recruitment and data collected at Kasturba Hospital
Manipal. Dr. Zelalem Temesgen and Mr. Al Seyoum helped in the development of the
LearnTB application. All co-authors helped finalize the manuscript for submission.
The article has been inserted into Chapter 8 – Manuscript, of this thesis project. A detailed
introduction, context and methods section has been presented prior to the article, to help
readers understand the need for such a research study. The article presents similar sections,
however does not contain as much detail. By presenting a pre-lude to the manuscript, the
author hopes that all readers will understand the scientific and social justification for such a
research study.
The article was submitted to the mHealth journal on May 9th, 2017 and accepted without
revisions on June 27th, 2017 (DOI: 0.21037/mhealth.2017.07.01). The proof of submission
is presented in Appendix A.
1
CHAPTER 1 Introduction
Tuberculosis (TB), caused by Mycobacterium tuberculosis, is an airborne infectious disease
known to be the leading infectious disease killer in the world (1). In 2015, the World Health
Organization (WHO) reported 10.4 million new cases of TB causing 1.8 million deaths
worldwide (2). Furthermore, there is a gap of 4.3 million between incident and notified cases
of TB. India, Indonesia and Nigeria account of 50% of this gap (2). There are 30 high TB
burden countries in the world (HBCs), composed largely of low- and middle- income
countries (LMICs) (3), where India is the highest TB burden country, accounting for 27%
(2.8 million) of the world’s TB cases, and 29% of the 1.8 million TB deaths (2).
The End TB strategy, previously proposed by the WHO, had three milestones for 2020 to
end TB burden worldwide: 35% reduction in TB deaths, 20% reduction of TB incidence rate
and 0% of TB affected families should face catastrophic costs due to treatment (3). These
numbers have since been revised to 95%, 90% and 0% respectively for 2035. The current
rate of decline of TB is 1.5% (3). To achieve the End TB strategy goals, this rate must
increase to 4-5% per year (2).
Scientific studies had previously underestimated the TB incidence rate, due to insufficient
data provided by countries (4). India has increased its case notification rate by 34% between
2013 and 2015, allowing an improved estimation of the world wide TB burden (2). Research
continues to highlight the constant neglect and mismanagement of TB patients within India’s
two prominent health care sectors, the public and private sector, causing the high burden of
disease (5, 6).
The public sector is regulated by the Indian government, whereas the private sector is not (7).
Over the past 20 years, the private sector has grown from managing 5-10% of the general
patient population to 82% (5). Moreover, this sector manages 50% of India’s TB cases and
studies have reported patients prefer private clinics as opposed to public clinics. The private
sector lacks regulation regarding diagnostic and prescribing practices (8) due to its “for-
2
profit” nature and the high heterogeneity amongst health care professionals. There are
qualified formal doctors, as well as unqualified/informal providers and practitioners of
alternative medicine. Nevertheless, patients prefer private sector clinics as they are of closer
proximity to vulnerable populations residing in remote rural areas, and there is minimal wait
time (8). Studies have shown the increased need to implicate the private sector, especially
health care professionals, in TB control interventions throughout India (9). The implication
of NIKSHAY, an electronic TB case notification system, has significantly increased the case
notification rates in India, due to its presence in the private sector. Between 2013 and 2015
NIKSHAY increased India’s case notification by 34%, which is the largest TB case
notification increase worldwide (4). The latter emphasizes the potential of the new era digital
technologies in health, especially in the Indian context.
Mobile health (mHealth) is defined as the delivery of health care services and information
through the internet and telecommunication technologies (10). It has the potential to play a
vital role in delivering health care to remote populations lacking human resources, through
the use of mobile phones (10). India is the second highest mobile phone consumer due to its
low cost handsets, thus making it an excellent target for such interventions (11). Although
there are numerous different types of mHealth interventions, such as personal digital
assistants (PDAs), and short messaging service (SMS), mobile applications (smartphone
applications) are continuing to increase their market. mHealth is largely consumer driven and
previous studies have focused largely on developing mHealth interventions, and evaluating
the acceptability of the intervention after diffusion. A systematic review by Iribarren et al.
identified a total of 1332 mHealth applications of which 24 focused on TB (patient support,
health care provider management, and awareness) (12). Very few applications had been
formally studied in usability or acceptability (12). There are few studies on mHealth
strategies for TB amongst health care professionals in India and to our knowledge there is no
research study evaluating the user experience and acceptability of a smartphone application
amongst clinicians in the private sector in India. Better understanding of mHealth strategies
amongst private health care professionals in India will help increase uptake and eventually
help the future of TB patients in India.
3
With this perspective, this study aims to understand the user experience and acceptability of
a smartphone application for tuberculosis amongst private sector clinicians in India. The
private sector is targeted due to its unregulated nature. Health care professionals practicing
in the private sector often do not have the resources (laboratory equipment, lab personnel
financial stability) to perform proper diagnosis and treatment of TB. Studies show
complicated care seeking pathways, extensive diagnostic delays (13), widespread empirical
management (14) and poor adherence to established standard of care in the private sector
(15). All factors are largely attributable to the high TB burden present in India today.
Clinicians have been chosen as they are an educated population who are in contact with TB
patients everyday. They are the target population due to their ability to assure proper
diagnostic and treatment methods for TB. TB is chosen as the disease of interest, as it is the
highest infectious disease killer in the world, and of highest prevalence in India.
This thesis has been separated into 10 detailed chapters to facilitate understanding. In the first
two chapters, the author presents the introduction of the topic and detailed context of
tuberculosis in general, mHealth in general, TB burden in India, and mHealth interventions
currently in place in India. Subsequently Chapter 3 presents the theoretical frameworks
adopted for this thesis project and chapter 4 provides an overview of the existing scientific
literature on user experience, developing mHealth interventions, acceptability of mHealth
interventions, factors influencing adoption by health professionals and highlights the existing
knowledge gap. This chapter is concluded with a scientific and social justification for this
thesis project. Chapter 5 presents a structured view of the research question, objectives and
hypotheses. The mobile application of interest, LearnTB is presented in detail, with figures,
in Chapter 6. Chapter 7 provides an indepth overview of the methodology used to conduct
this thesis project, including explanation on the study design, setting, population, methods
for data collection, data analysis and ethics approvals obtained. Chapter 8 presents the
inserted manuscript. Chapter 9 presents a general overview of the results, highlights study
strengths and limits followed by an explanation regarding the thesis’ contribution to public
4
health. Finally, chapter 10 presents a brief conclusion regarding future recommendations for
scientific studies.
5
CHAPTER 2 Context
This chapter focuses on presenting the general context for both TB and mHealth. It will first explain the etiology and general epidemiology of TB, followed by a section regarding the Revised National Tuberculosis Control Program in India, an initiative assembled in 1997 to facilitate TB control in the public health sector of India. We will then elaborate on the TB burden in India and provide an overview of the two main health sectors in India, the public and private sector. The final section will elaborate on mHealth technologies and their presence around the world. It will mainly focus on providing an overview of the functionalities of mHealth and the different types of mHealth interventions existing worldwide and in India.
2.1 General information: Tuberculosis
Tuberculosis (TB) is an airborne bacterial infection caused by Mycobacterium tuberculosis.
Although there are numerous forms of TB such as extra pulmonary TB, military TB, and
laryngeal TB; pulmonary TB (PTB) is the most prominent form. Being an airborne disease,
TB can be transmitted via cough (16), rendering it to be a highly infectious agent in confined
spaces such as overpopulated health care centers and/or hospitals. There are two phases of
TB, the latent phase and active phase (16). Approximately 5-10% of individuals infected with
TB will progress to active TB cases during their lifetime (16). The remaining cases are known
to be latent TB cases. Although the chances of a TB infection progressing from the latent TB
phase to the active TB phase are quite minimal, the aggressive nature of the TB infection
demands timely diagnosis and adequate treatment.
There are numerous diagnostic tests for TB such as sputum smear microscopy, rapid
diagnostic tests, nucleic acid amplification tests (NAATS) as well as liquid and solid cultures
(2). Most TB programs use direct sputum smears to confirm TB diagnosis, however the
preferred gold standard is microbiological confirmation; liquid or solid culture (17, 18).
Regarding the treatment of TB, a 6-month regimen with four first line drugs; rifampicin,
isoniazid, ethambutol and pyrazinamide is prescribed (2). Due to lack of treatment adherence
amongst patients, the rates of multi-drug resistant TB (MDR-TB) have progressively
increased (2), thus leading to the availability of second and third line drug regimens as well.
6
2.2 General epidemiology of TB
Based on the annual WHO Global TB Report, there were 10.4 million new TB cases
worldwide. 60% of these cases emerged from India, Indonesia, China, Nigeria, Pakistan and
South Africa (2). Notified TB cases increased by 34% between 2013 and 2015 due to India’s
increased case notification system, however a global 4.3 million gap remains between
incident and notified cases (2). The largest proportion, 50%, of this gap is due to India,
Indonesia and Nigeria. Worldwide, 49 million patients with TB were treated between 2000
and 2015, however significant gaps between diagnostic practices and treatment regimens
continue to exist (2). In 2015, approximately 6.1 million TB patients had access to quality
care, however 4.3 million patients did not receive proper care. This gap can be closed through
better reporting, diagnosis and access to care (4).
India accounts for 27%, 2.8 million, of TB cases worldwide, thus making it the highest TB
burden country in the world (4). An estimated 40% of India’s population is infected with TB,
however the large majority are in the latent phase (19). TB prevalence is known as the number
of active TB cases. In 2015, 1.28 million TB cases were undergoing treatment under the
Revised National Tuberculosis Control Programme (RNTCP), however a rate of 111 per
100 000 cases were notified to the RNCTP (public sector) and 184 802 TB cases were notified
to the private sector (19). Drug resistant TB rates have drastically increased over the years,
leading India to be the country with the second highest multi drug resistant TB (MDR-TB)
burden. It accounts for 16% of the estimated 480 000 new cases of MDR-TB (20). TB human
immunodeficiency virus (TB-HIV) coinfection is prominent in India as TB is the most
common HIV related coinfection (21). Currently, 5% of TB cases in India are co-infected
with HIV (21). The TB HIV coinfection is often of higher risk to patients with latent TB
disease. In India, every two people out of five (2/5) have latent TB disease, thus increasing
their risk of developing TB-HIV coinfection (21). The national budget for TB is USD (United
States Dollar) $ 280 million, where 38% is domestic funding and 62% is international funding
(22). As reported by National Tuberculosis Programs (NTP), the total expenditure by the
NTP per TB case notified is USD $28. This is significantly lower than other HBCs, for
example Brazil where it is USD $118 (23). Globally, India’s TB burden is highly visible and
7
attributed to numerous factors, including lack of governmental support, lack of management
and the disparity between the health care sectors, the public and private sectors.
2.3 Revised National TB Control Programme (RNTCP)
The Revised National Tuberculosis Programme (RNCTP), launched in 1997 by the
Government of India under the recommendation of the WHO, aimed to reduce numerous
managerial issues such as lack of treatment adherence, non-standard treatment regimens and
lack of systematic information on treatment (24). This programme also implemented the
Directly Observed Treatment Short-course (DOTS) strategy encouraging patients to take the
proper medication and adhere to treatment regimens. The DOTS strategy also provides basic
TB diagnosis and treatment to all patients in India (24). Despite the promising nature of the
RNCTP strategy, India remains the highest TB burden country in the world due to the
disparities amongst the two health care sectors in India, the public and private sector. The
RNTCP DOTS strategy is not highly implemented in the Indian private sector.
2.4 Health portrait of India
2.4.1 Geographic, demographic and economic context
India, officially known as the Republic of India, is the second most populated country in the
world, with a population of 1.3 billion (22). It is bordered by six different countries; Pakistan,
China, Nepal, Bhutan, Bangladesh and Myanmar (23). Its northern mountain range, the
Himalayas, define the South Asian sub-continent from the rest of Asia. India is surrounded
by bodies of water as well, the Bay of Bengal to the east and the Arabian sea to the west. It
is known for its diverse religious and traditional culture. It has a vast history of colonization,
starting with the Indus civilization from 2600 – 2000 Before Common Era (BCE), followed
by the Muslim rule (Mughal era) in the 8th century Common Era (CE), then the Portuguese
lead by Vasco de Gama in 1498 ending with the 200-year British colonization. India gained
its independence on August 15th 1947, with 29 states and 6 union territories, and since has
been an independent democracy (23). Since independence, India’s economy has been
growing substantially, and it now hosts three of the world’s fastest growing high technology
8
cities; Bangalore, Chennai and Hyderabad (23). The total health expenditure is
approximately 4.7% of the Gross Domestic Product (GDP)1 and the gross national income
per capita is 5 international dollars (Int’l $)2 (22). Life expectancy at birth is 70 years for
females and 67 years for males, which is a healthy life expectancy according to WHO
standards (25). Literacy rates has increased to 72.2% (26) since 1951 (18.3%), however the
gap remains large between female literacy rates, 54.2% and male literacy rates, 75.9% (27).
It is visible that India has improved drastically in numerous aspects since independence and
continues to do so, however the TB burden and health care system continues to remain a
prominent issue.
Figure 1 : Political map of India (source : www.worldmaps.org)
1 The Gross Domestic Product (GDP) is an elaborate calculation of the total domestic uses of goods and services including exports but excluding imports (https://www.insee.fr/en/metadonnees/definition/c1365). 2 The international dollar, also referred to as the Geary-Khamis dollar, is a unit of currency permitting comparison with the United States Dollar through the same purchasing power parity (www.worldbank.org).
9
2.4.2 Health systems: the increasing gap
There are two main health sectors in India, the government controlled public sector and the
growing private sector. The public sector, as mentioned previously, is government controlled
and standardized (DOTS) treatment is offered at all public hospitals and clinics (28). The
RNTCP increasingly promotes universal access to quality TB diagnosis and treatment for all
patients in the community (29). 90% of TB cases are confirmed via positive sputum smears
in the public sector, unfortunately this is not so for the private sector (29). As mentioned
previously, microbiological confirmation is the preferred international gold standard
diagnostic test, however sputum smear is the most commonly used confirmatory diagnostic
test (17). The unmanaged and unregulated private sector has grown over the past 20 years. It
manages 82% of general patients and 50% of India’s TB population (5). The National Health
Survey-3 reported 70% of households in urban areas and 63% of households in rural areas
visit the private sector as their primary source of healthcare (30). It is often preferred by TB
patients due to social determinants such as distance, accessibility, responsiveness, and
opening hours (30). Numerous studies have shown a TB diagnosis-delay of two months, and
visits to three health care providers prior to receiving a treatment regimen (31). Additionally,
diagnostic techniques are not regulated in the private sector which leads to high cost of
reliable diagnostic equipment, and use of unreliable tests, i.e. blood tests (29). Despite one
third of the medically trained clinicians practicing in the private sector, there is a discrepancy
between what is reported by practitioners and what patients adhere to (32). Furthermore, a
large proportion of cases treated in the private sector are often left unreported, thus increasing
concern regarding this vastly unregulated sector (28). Private practitioners often do not
adhere to treatment regimens that are commissioned by the WHO, and often do not assure
treatment completion (33). There is a highly heterogeous population of private practitioners,
ranging from formally trained clinicians to informally trained practitioners and alternative
medicine practitioners. The inconsistency of care is often due to poor knowledge of TB
amongst informally trained practitioners, inaccessibility to proper training as well as
inadequate supervision and re-training. In summary, the large discrepancy in management
and regulation of the two health care sectors in India is apparent. The numerous lacunas in
the private health sector further emphasizes the need for extensive research in this sector, and
10
increases the need for public private partnerships, which is advocated by numerous studies
(31, 34).
2.5 General information: mHealth
Mobile health (mHealth) is defined as the delivery of health care services and information
through internet and telecommunication technologies (10). It has the potential to play a vital
role in delivering health care to remote populations lacking human resources, through the use
of mobile phones (10). mHealth technologies are not limited to mobile phones, they also
include; personal digital assistants (PDAs), PDA phones (ie. Blackberry®), smartphones (ie.
iPhone), portable media players (ie. MP3s), video-game consoles (ie. Nintendo), and ultra-
portable computers (ie. tablets) (35). Such technologies have been gaining popularity over
the past decade, due to their low cost interventions (35). mHealth technologies allow users
to overcome geographic barriers, as well as personal barriers, such as stigma and loss of
privacy (11). Previous studies have shown the rapid growth of mobile communications in
low income countries, thus permitting large geographical coverage (35-38). mHealth
strategies aim to support health care providers through education, patient management, and
support in diagnosis, as they are largely consumer centered and consumer driven (39, 40).
Although there is limited evidence on the effectiveness of mHealth, a systematic review
conducted by Gagnon et al. identified 33 studies evaluating adoption of mHealth
interventions amongst health care professionals. Less than half the studies were conducted
on physicians or medical residents and only one study was conducted in India (39).
India is the world’s second largest mobile phone consumer base, due to low cost handsets
and affordable calling plans (11). There are approximately 877 million (96%) wireless
subscribers in India (41). mHealth and eHealth technologies have been increasing steadily in
India for TB, namely SMS interventions. Studies have shown the use of SMS for TB
treatment adherence have increased compliance from 40% to 90% (11). Moreover, mHealth
interventions also include awareness, counselling services and data collection (42). An e-
Health web based case notification strategy largely implemented in 2012, NIKSHAY, has
significantly increased TB case notification in India, as reported in the WHO Global TB
11
Report 2015 (4). Through the introduction of this eHealth platform in both the private and
public sectors, the RNTCP has been able to actively engage the private sector in their TB
strategies. Between 2013 and 2015, this platform has increased TB case notifications in India
by 34%, the largest case notification improvement worldwide (4). The success of this eHealth
strategy demonstrates the potential of other eHealth, mHealth, telemedicine or mobile
technology interventions to be accepted in the Indian health care environment.
12
CHAPTER 3 Theoretical Framework
This chapter aims to present the two theoretical frameworks used for this study. To facilitate understanding this research study was separated into two parts; part 1 being a user experience study and part 2 being an acceptability study. To ensure understanding of terms used in our study, the following definitions have been listed:
- Acceptability: intention to use - Clinician: resident doctors and academic clinicians practicing at Kasturba Hospital
Manipal (Manipal University) - Mobile application: smartphone applications only - Usability: used as a proxy for user experience
PART 1:
Usability has been increasingly studied by researchers to optimize usage of mobile
technologies or information technologies (43). Although usability studies follow different
theoretical frameworks, the most common, free, and easy to use framework is the System
Usability Scale (SUS) developed by J. Brooke in 1996 (44). SUS, a Likert scale largely used
in usability studies, measures user experience, the utility including the efficacy and
satisfaction with which users accomplish specific tasks (45). It was created using statements
insisting extreme agreement or disagreement to specific statements. A pool of 50 questions
were initially used to create SUS. These questions were presented to respondents and
statements soliciting extreme agreement or extreme disagreement were used in the final SUS
questionnaire (46). According to Brooke et al. ambiguous questions are not good to determine
a participant’s attitude towards a specific technology. Therefore, SUS is often the scale
preferred by authors of usability studies (46). They assess participants’ immediate reaction
to the use of a specific technology, prior to any discussion with the researcher (46). Such
studies are often used to understand preliminary needs of users to improve prototype mobile
technologies, or to evaluate the usability of an existing technology. A SUS usually comprises
of 10 statements evaluating the user experience (Figure 2), however our study used the
modified version of SUS with 9 statements preceding acceptability assessment.
13
Figure 2: Original 10 statement SUS questionnaire developed by J. Brooke in 1996
PART 2:
There are numerous theoretical frameworks which exist to examine mHealth strategies
and/or interventions and their acceptability amongst users, namely the Theory of Planned
Behaviour (TPB) (47), the Diffusion of Innovation Theory (DOI) (48), the Unified Theory
of Acceptance and Use of Technology (UTAUT) (49). Each respective framework aims to
evaluate a different aspect of user acceptability, also classified as intention to use. The TPB
aims to demonstrate the attitudes and personality traits existent and influential to human
behaviour. The TPB originates from the Theory of Reasoned Action (TRA), but includes the
notion of behavioural control for behaviours over which humans have incomplete volitional
control (47). A separate theory, the DOI, aims to take a different approach to understanding
14
the acceptance of a technology. The theory diverges from the norm of persuading individuals
to change to a more evolutionary technique involving “reinvention” of product and
behaviours to better fit the needs of the individuals (50). The diffusion of new innovations,
as defined by Rogers, involves “an innovation that is communicated through certain channels
over time among the members of a social system” (48). This theory emphasizes the impact
time can have on the rate of acceptance of a specific innovation. It further examines a five
stage innovation decision process involving knowledge, persuasion, decision,
implementation, and confirmation. Adopters of the technology are also further classified into
4 categories, early adopters representing 13.5% of the study population, early majority
(34%), late majority (34%) and laggards (16%) (50). Each of the categories mentioned
previously differ from one another due to personality variables, as explained in the DOI.
Finally, the UTAUT has four key constructs; performance expectancy, effort expectancy,
social influence and facilitating conditions. This theory is largely limited to organizational
contexts and bases acceptance to use a technology on the latter. Based on predictions
suggested by this theory, performance expectancy, effort expectancy and social influence
should encourage behavioural intention and facilitate conditions for acceptance of a
technology (49).
Although all models mentioned above present numerous different factors influencing
intention to use, there are nuances which render them unsuitable for our research study.
Firstly, the TPB is not specific to information systems, or information technologies.
Secondly, the DOI is focused on preliminary steps of the development of an innovation, in
our case a mHealth intervention, which is not the main focus of our study. Finally, although
the UTAUT aims to evaluate individual factors in an organizational context, it is largely
focused on technical factors. The proposed theoretical framework for our study is the widely
used Technology Acceptance Model (TAM).
15
Figure 3: Original Technology Assessment Model (TAM) by Davis 1989
The Technology Assessment Model (TAM) proposed by Davis in 1989 is considered to be
influenced by the Theory of Reasoned Action (51). This framework is used to evaluate the
acceptability of an information technology by users in an organizational setting. The TAM
suggests that the acceptance (behavioral intention to use) of a new information technology is
affected directly and indirectly by a user’s attitude towards use (A), and two internal
individual beliefs; perceived usefulness (U) and perceived ease of use (E). Perceived
usefulness is defined to be “the user’s subjective probability that using a specific application
system will increase his or her job performance within an organizational context” (51),
whereas perceived ease of use is defined as “the degree to which the prospective user expects
the target system to be free of effort” (51). Behavioural intention to use measures the
likelihood of a person employing the intervention, whereas attitude is related to the user’s
evaluation of the desirability of employing the intervention (52). One of the advantages of
the TAM is that it can be generalized amongst all populations, however one of its
disadvantages is that it does not consider environmental factors such as institutional, or social
factors which can influence technology acceptance. Based on the original TAM presented in
Figure 3, perceived usefulness has a direct influence on behavioural intention to use, whereas
perceived ease of use has an indirect influence, through attitude. Our study plans to use a
modified version of TAM, eliminating the Attitude variable (Figure 4). Based on a previous
study conducted by Asua et al., attitude and perceived usefulness have shown
16
multicollinearity, and as attitude is more generic than perceived usefulness, it was eliminated
from the TAM model. For this reason, our study has also eliminated attitude from our
modified TAM (53).
Figure 4: Adapted version of TAM including user experience
User
experience
Perceived
Usefulness (PU)
Perceived Ease of
Use (PEU)
Behavioural intention to
use
Part 1 Part 2
17
CHAPTER 4 Literature review
This chapter provides an extensive overview of existing literature in mHealth strategies worldwide as well as ones targeting TB. A review of literature was performed using a broad search strategy on three main databases; PUBMED, EMBASE and Web of Science. Additional articles were also found through the bibliographies of included articles. The search strategy is presented in Appendix B. This chapter has been divided into five sections to facilitate understanding. We have regrouped the existing studies into the first four sections; user experience, development of mHealth strategies, acceptability of mHealth interventions, and factors influencing adoption of mHealth interventions. The final section presents the knowledge gap in the existing literature as well as the scientific and social justification of this thesis topic.
4.1 User experience: the importance of usability studies
User experience studies are important influencers in mHealth intervention development, as
they can potentially help increase eventual uptake and acceptability of the intervention (46,
54). Despite their high importance, few scientific studies have reported evaluation of user
experience. Previous studies have indicated the usefulness of SUS to quantify user experience
(54, 55). Both studies used mixed-methods by incorporating an interview following the SUS
questionnaire to enable qualitative results regarding the mHealth interventions. The study
conducted by Uddin et al. in Canada aimed to develop a mobile application to capture the
user’s electrocardiogram and transmit it to a mobile phone, in real time (54). The authors
assessed usability and task completion through both questionnaires and debriefing
interviews. The authors identified the main barrier as insufficient knowledge of the
application. Users took more time to complete given tasks, as they were unfamiliar with the
application (54). The study conducted by Gunter et al. in the United States of America (USA)
assessed the usability of the WoundCheck application, which allows patients to take a picture
of their wound and send it to their health care provider from their home (55). Participants are
given the application alongside a training program post – operation to assure proper
understanding of the application. The authors noticed a high degree of usability of the
application, as the average usability (SUS) score was 83.3, however similarly to Uddin et al.
the authors noticed delays in task completion as users were not familiar with the application
(55). Based on previous studies, the need for user experience is evident. A study conducted
by Mourouzis et al. stated the importance of usability studies namely to assure that the
18
application of choice responds to a specific problem (56). The authors also suggest
developing a beta version (prototype version) of the application, performing the usability
study on a set of participants and then improving the application prior to dissemination to the
public. Although some studies, presented below, have adopted this strategy, the two studies
Uddin et al. and Gunter et al. evaluated the user experience of the final versions of their
respective applications.
4.2 Development of mHealth strategies
In the context of evaluating the acceptability of mHealth strategies, certain studies aimed to
understand the context to further improve their mHealth intervention prior to evaluating the
acceptability of the application. A study conducted by Narasimhan et al. in India further
demonstrates this notion through their iterative pilot project study design. The authors aimed
to use their study to facilitate the eventual acceptance of their mHealth intervention by
improving the design of the software and service over time. The authors adopted an eight-
step approach to assure proper usability of their mHealth intervention. As there are numerous
theoretical frameworks which can be adopted to evaluate acceptance of technologies, the
authors adopted a House of Quality matrix as their framework to evaluate the “arrival to users
needs” (57). Regarding the health problem studied, the authors aimed to assist in treatment
adherence of TB medication amongst TB patients. Treatment adherence remains to be a large
issue in LMICs such as India, as patients often refrain from taking their medication after the
first two months of the six-month regime (58). Despite the positive results disseminated by
the authors, a limitation of the study was noted to be an absence of ownership of mobile
phones and knowledge of use amongst illiterate and elderly populations living in remote rural
areas (57).
This type of iterative study design is not limited to treatment adherence studies. For instance,
a study conducted by Ginsburg et al. in Ghana aimed to address childhood pneumonia
mortality and improve health care providers’ ability to diagnose and manage childhood
pneumonia via a mobile health application, mPneumonia (59). Similar to the study conducted
by Narasimhan et al., the researchers adopted an iterative development study design to create
19
their mPneumonia application. The researchers transformed a paper-based algorithm into a
step-by-step electronic questionnaire with instructions, and visual aids (i.e., pictures, videos)
on an Android mobile phone based system or a tablet technology. Although this study aimed
to understand the usability of the mobile based application, it did not report sufficient findings
regarding the acceptability of the application, therefore rendering it unable to reduce the
knowledge gap (59).
4.3 Acceptability of mHealth interventions
As mentioned in Chapter 2, section 2.5, there are numerous types of mHealth interventions
such as telecommunications (SMS), and eHealth, which aim to support health care
professionals or patients in various aspects such as treatment adherence, protocol compliance
and education. Based on a systematic review conducted by Kallander et al. summarizing
mHealth interventions focussing on community health workers in LMICs, the most common
mHealth intervention used was telecommunication (SMS) (60).
Three studies conducted in India aimed to understand the acceptability of a mHealth
applications amongst rural health care workers (61), community health workers (62), and the
general population respectively (41). Two studies aimed to enforce protocol compliance
using a mHealth intervention, through different methods (61, 62). The study conducted by
Gautham et al. focused on three conditions: fever, diarrhoea and respiratory problems (61),
whereas the second study conducted by Modi et al. focused on promotional maternal,
newborn and child health services amongst pregnant women or new mothers (62).
Additionally, both studies did not refer directly to the framework used to develop or evaluate
the acceptance of the mHealth intervention. However, Modi et al. mentioned the use of a
framework recommended by the Medical Research Council (United Kingdom) (62). The
study conducted by DeSouza et al. aimed to understand the acceptability of health care
interventions via mobile phones, specifically SMS (41). The authors performed a household
study, and 99% of participants expressed interest in receiving health promotion on their
mobile phones. However, a significant proportion of the participants preferred to receive
information through voice calls rather than SMS (41). The authors concluded that a major
20
barrier was literacy levels in rural villages. SMS reminders are often in English, and patients
in rural settings prefer communicating in their local languages (41). Similarly, the main
limitation stated by Gautham et al. as well as Modi et al. was the lack of previous exposure
to a mobile phone by the health care providers.
Contrary to the three studies mentioned above, a study conducted in Colombia aimed to
investigate the potential benefits in performance of community health care workers regarding
point of care clinical guidelines implemented as an interactive job aid on a mobile phone
based application (63). For the purpose of this study, an interactive job aid was defined as a
mobile-based application with audio-visual effects. Although the researchers provided
human simulated patients to the community health workers, they found that interactive job
aids on a mobile phone help reduce error rates and increase protocol compliance amongst
community health workers. The researchers highlighted the main limitation of the study to
be the use of human simulated patients rather than real patients as results may vary in real
patients due to disparities in medical conditions (63).
Further emphasizing this point, a systematic review conducted by Iribarren et al. aimed to
determine the number of TB apps, evaluate their functionality and determine if there was any
testing available on the apps (12). The authors identified 1332 health applications, of which
only 24 were TB related. Most applications targeting clinicians were not tested formally.
There were numerous functionality problems with the applications such as incapacity to
open, unavailability of files, and lack of up to date information (12). This systematic review
elucidates the need for acceptability studies for mobile applications targeting TB. A
systematic review conducted by Aranda-Jan et al. identified 44 studies evaluating mHealth
interventions in Africa, of which four focused on staff evaluation, monitoring and guidelines’
compliance (64). The largest number of studies (n=19) assessed patient follow up and
medication adherence. Although the feasibility of mHealth interventions for patient follow
up and treatment adherence was agreed upon unanimously by all studies included in the
systematic review, very few studies presented information regarding health workers’
guideline compliance (64, 65). Only one study evaluated health education, but amongst the
21
general population (66). This emphasizes the need for studies evaluating health
workers’/health professionals’ guideline compliance and health education in LMICs such as
India.
4.4 Factors influencing adoption of mHealth interventions
A systematic review conducted by Gagnon et al. aimed to identify studies evaluating factors
influencing the adoption of mHealth strategies amongst health care professionals (39). The
review was performed on all published studies between the years 2001 and 2014. The
researchers included 33 studies regarding mHealth strategies and factors influencing
adoption amongst health care professionals. They identified health care professionals as
nurses, physicians, residents, health workers, pharmacist and other providers (39). Overall
results of the systematic review indicated more than half the studies were performed in
developed countries, such as Canada, the United States of America and the United Kingdom.
A few studies were performed in Asia and Africa, of which only one study was performed in
India (39). All studies were largely conducted amongst health care professionals such as
nurses, midwives and community health care workers. This demonstrates the lack of
knowledge and studies present worldwide regarding the perspective of health care clinicians
and mHealth interventions.
Upon further review of the literature, four studies were retrieved evaluating factors
influencing mHealth adoption using an adapted version of the Technology Assessment
Model (TAM) (67-70). This theoretical model, further elaborated in chapter 5, is most
commonly used in mHealth acceptability studies. A study conducted in Germany adopted
Unified Theory of Acceptance and Use of Technology (UTAUT), an advanced extension to
the TAM model, as their favored theoretical framework for acceptability analysis (71). All
studies evaluated different groups such as; university faculty members (68), university
students (70), nursing home residents (69), and inpatient diagnostic groups (71). Although
all studies used the original TAM theoretical framework to assess the acceptability of the
mHealth interventions, some additional factors were added such as: job relevance, lack of
learning management system availability, lack of usage experience (68), and technology
22
anxiety and perceived enjoyment (69). The study conducted by Alharbi et al. (Saudi Arabia)
aimed specifically to evaluate the organizational context (68) and the study by Park (Korea)
aimed to evaluate solely the individual context (70). Contrary to the aforementioned studies,
Huang et al. (Taiwan) and Henneman et al. (Germany) aimed to test their technologies on
patients, namely inpatient diagnostic groups (71) and nursing home residents (69) rather than
health care professionals. Overal,l all three TAM studies concluded similar findings to those
suggested by the TAM theoretical framework, where an increase in perceived usefulness
increases the degree of positivity to usage, which therefore increases the behavioural
intention to use (67, 68, 70).
4.5 Knowledge gap & justification
Despite the limited scientific evidence studies have demonstrated a positive uptake of
mHealth technologies, namely telecommunications, applications for protocol compliance,
and applications for self-evaluation, amongst their respective populations (55, 57-59, 61-63,
71). Further emphasizing this point, Florez-Arango et al. (Colombia) reported a decrease in
error rates and enhanced protocol compliance amongst health care workers due to the mobile
application (63). However, two main questions soliciting increased research are: the
acceptance of such a mHealth technology amongst health care clinicians in India, and the
implementation of an mHealth technology to increase TB knowledge.
Firstly, there is lack of knowledge in the acceptance of mHealth interventions or strategies
amongst health care clinicians in India. Most studies included in the analysis focused their
mHealth strategies towards health care providers/workers (59, 61-63, 67, 68) or towards
patients (57, 58, 69, 71). The systematic review conducted by Gagnon et al., identified no
studies evaluating an mHealth intervention amongst physicians or clinicians in a LMIC (39).
Our study’s pertinence is further justified by the systematic reviews conducted by Iribarren
et al. elaborating the lack of testing of mobile applications for TB, targeting clinicians (12)
and Kallander et al. emphasizing the lack of studies evaluating smartphone mHealth
interventions in LMICs (60). With the exception of one study conducted amongst nurses and
doctors in Spain (67), a developed country, the knowledge gap regarding the perceptions of
23
health care clinicians towards the acceptance of a mHealth intervention for TB in LMICs is
evident. As indicated by Akter et al. mHealth is largely consumer driven and consumer
centered (40), therefore the need for studies in this specific population (health care clinicians)
in an LMIC, such as India, is highly visible. Additionally, Mouzoukis et al. have emphasized
the need for usability studies assuring the mHealth intervention responds to a specific
problem (56). The study has elucidated numerous steps which may lead to positive uptake of
a mHealth intervention, namely: study the user, involve clinicians and health professionals,
study health care landscape, and focus on core use cases (56).
Secondly, there are limited studies evaluating mHealth strategies increasing TB knowledge
amongst health care clinicians. Of the studies included in this literature review, four studies
observed mHealth platforms on diseases or services which were un-related to tuberculosis:
maternal and child health services (62), pneumonia (59), geriatric self assessment (69) and
fever, diarrhoea and respiratory problems (61). Although the Nihamsan et al. (India) study
evaluated treatment adherence to TB, our mobile application aims to educate Indian private
sector clinicians on TB diagnosis and treatment practices as this has been noted to be a
significant problem in India requiring public health attention (13).
Thirdly, most studies did not include their reference theoretical framework. Most studies
stating their reference theoretical framework used the TAM (67, 68) or a derivation, the
UTAUT (71). However, these studies were conducted in developed countries (67, 68, 71) or
amongst populations other than health care clinicians (68, 71). Additionally, few published
studies have evaluated the user experience regarding mHealth technologies. The two studies
included in this literature review evaluated user experience after having created the final
version of their respective applications. As indicated in previous studies, user experience or
usability studies are most fruitful when done on a beta/prototype-version of the application,
prior to dissemination (46, 56). Our study aims to assess the user experience, prior to
evaluating the acceptability of the mHealth intervention. As mentioned previously, mHealth
strategies are often consumer driven, thus to maximize the uptake of our smartphone
application, our study aims to further understand consumer needs.
24
Our study aims to surpass limitations presented in the previous studies, namely limited
accessibility (59) and knowledge of mobile technology by study populations (54, 55, 61, 62,
69). A survey conducted in 2013 by the Heart Care Foundation of India on numerous working
class professionals, reported that 31% of family physicians used smartphones, and 70% of
them keep their cellphone on them constantly (72). This survey validates the knowledge of
mobile phone technologies amongst health care clinicians in India.
In summary, the knowledge gap regarding mHealth strategies in India is apparent
emphasizing the need for more research in this field. Due to India’s growing mobile phone
user-base and TB burden, perhaps mHealth technologies may be a part of the solution to help
reduce TB incidence. To our knowledge, a scientific study evaluating user experience and
the factors influencing acceptance of a mHealth strategy for TB amongst health care
clinicians in India has not yet been documented, therefore bringing upon the interest of our
study in the public health platform.
25
CHAPTER 5 Research Question and Objectives
5.1 Question
Based on the context presented in the previous chapters, our study aims to answer the
following two questions:
1. What is the user experience of the private academic clinicians in India while using a
smartphone application for tuberculosis?
2. What factors influence the acceptability of a smartphone application for tuberculosis
amongst private sector academic clinicians in India?
5.2 Objectives
The main objective of the study is evaluating the user experience and acceptability of a
smartphone application – LearnTB – amongst private sector academic clinicians in India.
This will be further evaluated through the following sub objectives:
1. Identify the user experience with the aim of improving the mobile application
2. Identify, with the help of TAM, the factors influencing adoption of the smartphone
application.
5.3 Hypothesis
Based on the theoretical background, and context presented above, our study aims to
evaluate the following hypothesis, to support our research questions:
1. The usability of the mobile application can influence the user experience of the
private sector academic clinicians.
2. The TAM constructs shall explain a significant proportion of clinicians’ intention to
use the mobile application.
a. Perceived usefulness is positively correlated to intention to use
b. Perceived ease of use is positively correlated to intention to use
c. Perceived ease of use is positively correlated to perceived usefulness
26
CHAPTER 6 Mobile application
This chapter gives an indepth explanation of the smartphone application used in this study, LearnTB. It permits the readers to visualize the application and understand its origin.
The smartphone application of interest for this study, LearnTB, was inspired by the contents
of Let’s Talk TB, a free, online handbook aimed for general practitioners in India (available
http://www.letstalktb.org). The handbook has been specifically created for the Indian
medical context, and many chapters have been co-authored by Indian doctors. The LearnTB
application, a collaboration between Mayo Clinic Center for Tuberculosis (Rochester, USA)
and McGill International Tuberculosis Centre (Montreal, Canada), aims to educate Indian
clinicians regarding the definition, diagnosis, treatment, management and counselling
practices available for TB. The primary version of the application was created by Global
Innovative Services Inc. (Maryland, USA) This study tested the pilot website (prototype)
version of the application.
LearnTB presents numerous different sections regarding TB management, such as childhood
TB, latent TB infection, extra pulmonary TB, and common pitfalls of TB management
(Figure 5). Each section is based on International Standards of TB Care, as well as WHO
guidelines and Standards for TB Care in India (73, 74). When clicking on a specific section
(ie. Childhood tuberculosis), participants are presented with subsections involving
suspecting, diagnosing and treating TB for each respective case (Figure 6). Dosage tables,
and specific names of drugs are also included to facilitate understanding and ensure
comprehension. A subsection regarding chest x-rays for TB presents figures of different types
of TB lesions to help visualize the extend and manifestations of TB disease (Figure 7). The
section focusing on the TB HIV co-infection also includes frequently asked questions to help
clinicians asses specific/special cases. In addition to reference material, most sections present
short quizzes to assure proper comprehension and emphasize educational learning from the
application. A multiple choice response format was chosen for quizzes and upon selection of
the incorrect answer, clinicians were prompted to read a short explanation of the correct
answer. This emphasized the educational aspect of the LearnTB application.
27
Figure 5: LearnTB application – home page
Figure 6: LearnTB application – subsections present in Section 6 “Childhood tuberculosis”
28
Figure 7: LearnTB application – figure of chest x-ray in Section 4 “Role of chest x-rays in the
management of tuberculosis”
29
CHAPTER 7 Methodology
This chapter illustrates all methods used for this research study. To facilitate comprehension, it has been separated into 6 detailed sections: study design, study setting, study population, data collection (part 1 and part 2), data analysis (part 1 and part 2), as well as ethics considerations and approvals. Hereinafter, the first part of the study will be named “usability (part 1)” and the second part, “acceptability (part 2)”.
7.1 Study design
As the main purpose of the study was to describe the user experience and acceptability of the
LearnTB smartphone application amongst private sector clinicians in India, a cross-sectional
study design with no comparator group was used. Cross sectional studies are used to describe
a subpopulation within a reference population based on a specific outcome, at a given point
of time (75). They do not evaluate associations over a period, thus causality cannot be
inferred. Previous studies have used a cross sectional design for their user experience and/or
acceptability evaluations as well, thus this study design was preferred for our study as well
(53, 67, 68, 70).
7.2 Study setting
Both parts of the study took place at Kasturba Hospital Manipal, Manipal, India located in
the south Indian state of Karnataka. Karnataka, located on the west coast of India, is one of
the four large south Indian states with a population of 61.1 million (76). It has an overall TB
case notification rate of 98 per 100 000 (77) and approximately 40 000 TB patients are treated
in private clinics annually (78). It experiences one of the highest TB HIV coinfection
epidemics in India. Karnataka is separated into 30 districts, one of which is the Udipi district
which hosts Kasturba Hospital Manipal (Figure 8). The town of Manipal has a population of
280 000 which is largely comprised of Manipal University students, faculty and staff (79).
Kasturba Hospital Manipal, a tertiary teaching hospital for Kasturba Medical College,
Manipal University, has 2023 beds, 300 consultant doctors, 200 duty doctors, and 2200
support staff (80). It is ranked as the second best private medical college in India and is the
first medical hospital in Karnataka to achieve a National Board Accreditation of Hospitals
30
(80). Overall, Kasturba Hospital Manipal comprises of 104 professors, 103 associate
professors, 102 assistant professors, 66 senior residents, 685 post graduate students, 183
interns, and 1561 MBBS (medical undergraduate degree) students (81).
Figure 8: Map of Manipal, Karnataka, India (source : http://szusicon2017.com/travel/)
7.3 Study population & recruitment
Academic clinicians practicing at Kasturba Hospital Manipal were the target population for
our study. All clinicians having completed their MBBS and working at Kasturba Hospital
Manipal were eligible to participate in the study. The numbers presented in the section above
(section 7.2) illustrate the large clinician population present at Kasturba Hospital Manipal.
In India, one third of clinicians practice in the private sector and studies have shown that
some private practitioners do not have access to information or training programs regarding
TB diagnosis and treatment (82). Based on a study conducted by Verma et al. medical
colleges should be involved in training activities for health care professionals and operational
31
research studies for TB (82). The latter, in addition to mismanagement of TB in the private
sector, justifies the choice of the target population.
For the usability study (part 1), five participants were recruited in February 2017 through non
probabilitistic convenience sampling. A sample size of 5 was chosen as previous studies have
indicated that most errors (mean 85.5%) in technologies are identified by five participants
(83, 84). Usability studies aiming to assess user experience problems in prototype
technologies, such as LearnTB do not require large sample sizes (84).
For the acceptability study (part 2), 100 participants were recruited via non probabilistic
convenience sampling as well as snow ball sampling between February 2017 and March
2017. A minimum sample size of 100 was sufficient as the sample size calculation was based
on a theoretical outcome of a standard deviation of 1.07, evaluated by previous studies (67,
70). Type I error (a) of 5% and a margin of error of 3% were used. Participants were
contacted informally through their unit heads, and asked to meet with the lead researcher TP
to participate in the study. Non-probabilistic convenience sampling is often preferred when
there are the following three criteria: (a) the target population is difficult to identify, (b) the
target population is specific and have limited time, (c) the study is a pilot study (85). Our
study responds to two out of the three previously mentioned criteria, thus non-probabilistic
sampling was preferred. Furthermore, snow ball sampling was used for the second part of the
study to increase the sample size. It is defined as a sampling strategy that researchers adopt
when the target population is difficult to reach (85). Study participants were asked to relay
the information about the research project to their peers to increase visibility of the research
project. Based on observations made while in the field, academic clinicians are often a
difficult target population as they are highly burdened with work in private hospital settings.
For this reason, snow ball sampling was also used to help attain the desired sample size of
100 participants. TP was paired with an intern doctor at Kasturba Hospital Manipal who
helped recruit participants for the study. Participants were contacted by their superiors
through word of mouth and also through the use of mobile chatting applications such as
32
WhatsApp. These methods helped increase visibility of the project, enable snowball
sampling and reach the desired sample size.
7.4 Data collection
Prior to each individual part of the study, all participants were given detailed consent forms
indicating the goals and objectives of this study. Participants were asked to read and sign
each consent form. All participants were assured of confidentiality and anonymity of
responses. Theoretically, part 1 and part 2 of the study are supposed to be conducted
consequtively to ensure that comments provided by the participants during the usability study
can be incorporated and tested in the acceptability study. However, due to time constraints,
our study conducted both parts simultaneously.
Part 1: Usability
The usability testing was done through a 9-statement, paper-based English questionnaire
using SUS (Appendix C). The original SUS comprises of 10 statements, however one
statement “the application was easy to use” was omitted as it would be repeated in the second
part (acceptability) of the study. The content validity of the questionnaire was justified, as
the statements were from the original version of a SUS questionnaire which has a high
Cronbach alpha (a = 0.91) (86). A total of 5 participants were contacted to participate in this
part of the study and all 5 (100%) responded to the questionnaires. Participants were given
the application in its website format, on an iPad and asked to complete two tasks (Appendix
D). The tasks forced the participants to use the LearnTB application, and understand the
different functions within the application. Participants were given approximately 5 minutes
to complete the tasks. Once completed, they were asked to rate 9 statements on a 5-point SUS
scale ranging from “strongly disagree” (1) to “strongly agree” (5). Sociodemographic
characteristics such as age, sex, title, years of clinical experience3, previous use of mobile
applications, and comfort using mobile applications, were also collected. Participants were
3 Years of clinical experience were defined as after graduating from the MBBS degree (an undergraduate medical degree in India).
33
asked to address their questions and concerns after completing the questionnaire, to best
capture the user experience. Any suggestions or informal discussions with the participants
were written in a journal to facilitate ad hoc analysis of results. Though a qualitative
assessment was not the goal of this study, participants were better able to express their
comments verbally than through a questionnaire format. For this reason, comments were
written in a joural and used as supplementary information to support the quantitative results
of the study.
Part 2: Acceptability
The acceptability testing of the LearnTB application was evaluated via an adapted version of
the TAM questionnaire (67, 68). The internal validity of the questionnaire was assured as
previous studies having used TAM presented a Cronbach alpha greater than 0.7 (53, 67, 68,
70). A total of 101 medical employees of Kasturba Hospital, Manipal were contacted and
100 (99%) responded. Participants were given English paper-based questionnaires during
individual sessions and group sessions with the lead researcher, TP. Each individual session
lasted approximately 10 minutes, and each group session lasted 20 minutes. The self-
administered questionnaires, comprised of 15 items (Appendix E), were answered following
a brief presentation of the LearnTB application. Participants were asked to rate each item
through a 7-point Likert scale ranging from “totally disagree” (1) to “totally agree” (7). The
mean scores of each individual item were computed and the mean of means of each variable
(perceived usefulness, perceived ease of use, and intention to use) were used to perform
statistical analysis. Sociodemographic variables such as age, sex, position, title, years of
clinical experience4, previous use of mobile application, and willingness to use mobile
applications, were also collected.
4 Years of clinical experience were defined as after graduating from the MBBS degree (an undergraduate medical degree in India).
34
7.5 Data analysis
Both parts of the study were analyzed simultaneously via Excel and statistical analysis
software (SAS 9.4).
Part 1: Usability
Descriptive statistics were computed via Excel for the usability testing (part 1) of our study.
Data was inputted onto an excel sheet, by coding results “strongly disagree” as 1 and
“strongly agree” as 5. Individual SUS scores were calculated following the theoretical
formula. One was subtracted from each value of an odd numbered question, and each value
of an even numbered question was subtracted by 5 (46). Finally, the total value was
multiplied by 2.78 to achieve a SUS score ranging from 0-100, as opposed to 0-36.
Originally, the total SUS scores are multiplied by 2.5, however our SUS questionnaire
comprised of 9 statements rather than 10 statements, thus our values were multiplied by 2.78.
Descriptive statistics were computed for the sociodemographic variables. The mean SUS
score and standard deviation were also calculated. A theoretical value of 68 was assumed as
the threshold for above average results (87). All qualitative comments addressed by
participants were inputted into a logbook and saved to report to the app developers.
Part 2: Acceptability
For the acceptability testing (part 2), descriptive and inferential statistics were computed
through SAS 9.4. Once again, data was inputted onto an Excel sheet by coding results “totally
disagree” as 1 and “totally agree” as 7. Item 5 of perceived ease of use “The use of the
LearnTB application could interfere with the usual follow up of my patients” was rated in
reverse order, thus “totally disagree” as 7 and “totally agree” as 1, as it was a negative
statement. The internal reliability and validity of the TAM questionnaire was confirmed as
the Cronbach alpha was calculated for all three theoretical values, perceived ease of use,
perceived usefulness and intention to use. All three values for the Cronbach alpha were
greater than 0.7 (88).
35
Descriptive statistics such as the mean and standard deviations of all theoretical values were
calculated. The Pearson’s correlation coefficient (r) was calculated between the following
variables: perceived ease of use, perceived usefulness, intention to use. Multiple linear
regression analysis was used to determine a predictive model for intention to use, as all
theoretical values (perceived ease of use, perceived usefulness and intention to use) followed
a normal Gaussian distribution (53). A predictive model was evaluated for perceived ease of
use, perceived usefulness, intention to use, as well as all the sociodemographic variables;
age, sex and title. The latter were considered potentially modifying variables in this predictive
model. The 95% confidence intervals (CI) were calculated and a statistical significance of
0.05 was used. Path analysis and logistic regression analysis were also performed. Path
analysis helped determine the direct and indirect relationships between the three theoretical
constructs, namely perceived ease of use, perceived usefulness and intention to use (89).
Logistic regression analysis was used to target specific items influencing intention to use.
The dependent variable – intention to use – was dichotomized according to the mean.
Participants with a intention score less than or equal to 6 (£ 6) were qualified as “low to
moderate” intention to use and those with a score greater than 6 (> 6) were considered as
“high” intention to use. Logistic regression analysis was conducted to help guide future
research and public health recommendations concerning mHealth strategies/interventions
(53). When performing statistical analysis on specific items, if there were missing data points
they were removed from the total number of participants.
7.6 Ethics consideration and approvals
This study obtained ethics approvals from the Comité d’éthique de la recherche avec des
êtres humans de l’Université Laval (CÉRUL) (ref: 2016-165) in Québec City, Canada as well
as the Manipal University Ethics Commitee (MUEC) (ref: MUEC/020/2016-17) in Manipal,
India.
All participants were asked to sign an obligatory consent form prior to participate in the study
(Appendix F). The consent form outlined the goal and objectives of the research study, and
the usage of the results. The lead researcher also explained the consent form verbally to
36
assure complete comprehension. The questionnaires and the consent form were in English to
facilitate understanding. All participants were assured of confidentiality and anonymity of
their responses. The answered questionnaires and signed consent forms are kept in a secure
location in Québec City, Canada only accessible to the lead researcher. Five (5) years after
the publication of the results, the questionnaires will be destroyed.
The study did not place any coercion on participants. All participants were educated on their
right to leave the study at any point in time, and their choice to not respond to certain
questions. The researcher took all measure to assure the respect of the participant’s autonomy
and assure that their decision to voluntarily participate in the study was based on informed
consent (90). The researcher assured that all participants knew the steps of the research
project, including publication of the results. Finally, the main priority of our study was to
assure beneficence of the target population, the academic clinicians at Kasturba Hospital
Manipal.
37
CHAPTER 8 Manuscript
Evaluating clinicians’ user experience and acceptability of LearnTB, a smartphone application for tuberculosis in India
Tripti Pande1, Kavitha Saravu2,3, Zelalem Temesgen4, Al Seyoum4, Shipra Rai2, Raghavendra Rao2, Deekshith Mahadev2, Madhukar Pai5, Marie-Pierre Gagnon1
Affiliations : 1 Département de médecine préventive et sociale, Université Laval, Québec Canada 2 Department of Medicine, Kasturba Medical College, Manipal University, Manipal India 3 Manipal McGill Center for Infectious Diseases, Manipal University, Manipal India 4 Mayo Clinic Center for Tuberculosis, Mayo Clinic, Rochester, Minnesota, USA 5 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal Canada
Corresponding author: Marie-Pierre Gagnon, PhD
Canada Research Chair in Technologies and Practices in Health 1050 Ave De la Médecine
Université Laval Québec, Canada
G1V 0A6 Phone: 418-525-4444 ext. 52169
Email: [email protected]
Short running title: User experience and acceptability of LearnTB application in India
38
Résumé
Contexte: La tuberculose est la première cause de mortalité au monde et parmi les 10,4
millions de cas de tuberculose par année, 2,8 millions proviennent de l’Inde. De ce fait, il est
considéré comme le pays ayant le plus haut taux d’incidence de la tuberculose au monde.
Une manque de qualité des soins est une cause majeure pour l’épidémie de la tuberculose en
Inde. Le secteur privé, qui n’est pas réglementé, prend soin de 50% des patients ayant la
tuberculose. Des études précédentes indiquent la mauvaise diagnostique ou le mauvais
traitement comme des facteurs qui sont présents dans le secteur privé. Une amélioration dans
l’éducation des cliniciens utilisant les technologies mobiles, dont les applications mobiles,
pourrait être une solution. Toutefois, il existe peu d’études sur les technologies mobiles pour
la tuberculose en Inde.
Méthodes: Cette étude vise à évaluer l’expérience de l’usager et l’acceptabilité d’une
application mobile (LearnTB) parmi les cliniciens académiques du secteur privé en Inde.
L’étude à utiliser une approche à deux étapes. Cinq cliniciens (étape 1) et 101 cliniciens
(étape 2) ont été contactés à Kasturba Hospital Manipal, Manipal, Inde entre février et mars
2017. L’expérience de l’usager des participants était évaluée par le System Usability Scale.
L’acceptabilité était évaluée par un questionnaire adopté du Technology Assessment Model.
Les résultats étaient analysés à l’aide des statistiques descriptives, la régression linéaire
multiple ainsi que la régression logistique.
Résultats: Des taux de réponse de 100% et 99% ont été obtenus pour la première et deuxième
partie respectivement. L’expérience de l’usager était vraiment positive. En ce qui concerne
l’acceptabilité, une analyse de cheminement a confirmé la relation directe entre l’utilité
perçue et l’intention d’utilisation, et la relation indirecte entre la facilité d’utilisation perçue
et l’intention d’utilisation. La régression logistique a permis de cibler les items qui
influencent fortement l’intention d’utilisation.
Conclusion: L’expérience de l’usager pour LearnTB était vraiment positive, et l’utilisé
perçue a le plus grand impact sur l’intention d’utilisation (acceptabilité). Cette étude permet
d’avoir une analyse préliminaire de l’acceptabilité des cliniciens concernant les technologies
mobiles pour la tuberculose en Inde. D’autres recherches dans ce domaine sont requises afin
d’assurer l’implantation optimale de ces technologies.
39
Abstract
Background: Tuberculosis (TB) is the leading infectious killer, and India accounts for 2.8
of the 10.4 million TB cases that occur each year, making it the highest TB burden country
worldwide. Poor quality of TB care is a major driver of the epidemic in India. India’s large
private, unregulated sector manages over 50% of the TB patients, with studies showing
suboptimal diagnosis and treatment in the private sector. Better education of doctors using
mobile applications (apps) is a possible solution. While India has seen an explosion of mobile
phone services, and while the use of mobile health interventions has been gaining interest,
little is known about mHealth around tuberculosis in India.
Methods: Our study aimed to understand the user experience and acceptability of a
smartphone application, LearnTB, amongst private sector academic clinicians in India. This
study was conducted amongst 101 clinicians at Kasturba Hospital, Manipal, India. The user
experience of participants (part 1) and acceptability (part 2) were evaluated with the use of
two valid, English, paper-based questionnaires. The first questionnaire was based on the
System Usability Scale (SUS); the second questionnaire was based on the Technology
Acceptance Model (TAM). Data were collected during February and March 2017 and were
analyzed using descriptive statistics, multiple linear regression as well as logistic regression
analysis.
Results: A response rate of 99% was achieved; 100 participants responded to the second
questionnaire and 100% of the participants responded to the first questionnaire. User
experience was very high (mean SUS score = 94.4 [92.07 – 96.76]). Perceived usefulness
was significantly correlated to intention to use (r = 0.707, p<0.0001), and perceived ease of
use was significantly correlated to perceived usefulness (r =0.466, p<0.0001). Path analysis
confirmed the direct relationship between perceived usefulness and intention to use (0.936,
p<0.0001), and the indirect relationship between perceived ease of use and intention to use
(0.5102, p<0.0001) . Logistic regression analysis helped target items strongly influencing
intention to use, such as “The use of the LearnTB application is compatible with my work
habits” (OR = 3.20 [1.04 – 9.84], p. 0.004) and “The use of the LearnTB application could
promote good clinical practice” (OR = 5.23 [1.35 – 20.29], p.0.016) .
40
Conclusion: The first part of the study indicated high user experience of the LearnTB
application. The TAM questionnaire (second part) explained a significant portion of the
variance in clinicians’ intention to use the LearnTB application. The perceived usefulness of
the application has the highest impact on the clinicians’ intention to use the Learn TB
application. This study provides a preliminary analysis of mobile health interventions for
tuberculosis in India, and emphasizes the need for future research in this domain.
Key words: mHealth, Technology Assessment Model (TAM), User Experience,
Tuberculosis, Mobile Application
41
INTRODUCTION
CONTEXT
Tuberculosis (TB) is the worlds highest infectious disease killer (1). It is an airborne
infectious disease, caused by Mycobacterium tuberculosis, leading to 10.4 million cases
worldwide and causing 1.8 million deaths (1, 2). The End TB strategy by the World Health
Organisation (WHO) aims to reduce TB deaths, as well as incidence rate by 95% and 90%
respectively. Additionally, the number of families facing high costs due to treatment are
aimed to be reduced to 0% (3). The current rate of decline of TB incidence is 1.5% annually;
however this rate must increase to 4-5% per year to achieve the End TB strategy goals.
India, the highest TB-burden country, accounts for 27% (2.8 million) of the world’s 10.4
million TB cases (4). India accounts for 29% of the 1.8 million TB deaths globally, and also
for 16% of the estimated 480,000 new cases of multidrug-resistant TB (MDR-TB).
Approximately 40% of the Indian population is estimated to be latently infected with TB (5).
Research continues to highlight the constant neglect and mismanagement of TB patients in
India, contributing to the high burden of disease (6, 7). Additional challenges include low
financial investments, HIV-coinfection, poverty, malnutrition, and an unregulated private
health sector that is not engaged in TB control efforts (8).
The health care system in India is divided into two different sectors, public and private. The
public sector (ie. government services), encourages the Directly Observed Treatment Short-
course (DOTS) strategy, and is implemented by the Revised National Tuberculosis
Programme (RNTCP). This strategy provides basic TB diagnosis and treatment to all patients
in India, and encourages patients to take proper medication and adhere to treatment regimens.
Furthermore, it offers free TB care, including diagnosis and treatment (6). Despite the
availability of free TB care in the public sector, a majority of patients in India prefer to seek
care in the private sector.
42
The private sector, largely unregulated by the Indian government, has grown immensely over
the past 20 years and now manages 82% of the general patient population as well as 50% of
India’s TB patients (9). The National Health Survey-3 reported 70% of households in urban
areas and 63% of households in rural areas visit the private sector as their primary source of
healthcare (10). Patients prefer private clinics as they are of closer proximity to vulnerable
populations residing in remote areas, and offer a reduced wait time (7). Approximately 6
million (75%) of clinicians practice in the private sector, and 0.31% implement RNTCP
regulations (11). The Indian private sector is highly heterogenous, with qualified formal
doctors, as well as unqualified/informal providers, and practitioners of alternative systems of
healthcare.
Studies show complicated, long care seeking pathways and extensive diagnostic delays (12),
widespread empirical management (13), and poor adherence to established standards of care
in the private sector (14). Often this is due to poor knowledge about TB, especially among
informal care providers, inaccessibility to proper training, and inadequate supervision and re-
training. The new era of digital technologies in health may have the potential to provide a
solution, especially in India, a country where mobile telephony has exploded in the past
decade.
Mobile health (mHealth), is defined as the delivery of health care services and information
through mobile phones and other wireless telecommunication technologies (15). It has the
potential to play a vital role in delivering health care to remote populations lacking human
resources (15). mHealth technologies are not limited to mobile phones, they also include:
personal digital assistants (PDA), portable media players (MP3), video-game consoles
(Nintendo), smartphones, and ultra-portable computers (tablets) (16). They are often
consumer centered and consumer driven, and aim to provide support to health care providers
through education, patient management and support in diagnosis (17, 18). India is the second
highest mobile phone consumer base in the world, due to its low-cost handsets and affordable
calling plans (19). mHealth and electronic health (eHealth) strategies have been increasing
steadily in India with the biggest example being NIKSHAY (http://nikshay.gov.in), the TB
43
case-based notification system. Implemented in 2012, it has increased India’s TB case
notification by 34%, affecting total TB estimates worldwide (4). The success of this ehealth
strategy demonstrates the potential of other ehealth and mHealth interventions to be accepted
in the Indian healthcare environment.
PREVIOUS mHEALTH INTERVENTIONS
User experience studies are important influencers to mHealth interventions as they can
potentially increase uptake and acceptability of the intervention (20, 21). Despite their high
importance, few scientific studies have reported evaluation of user experience regarding
mHealth interventions. In the literature, user experience is often quantified through the
System Usability Scale (SUS). This scale has been used in over 600 studies and has proved
to be highly reliable (20). Previous studies have used the SUS to evaluate user experience of
an mHealth smartphone application once the final version of the application was ready for
diffusion (21, 22). However, user experience studies are best suitable for applications which
are presented in their beta version (prototype version) to further improve the application prior
to dissemination to the public (20-22).
Previous research has evaluated the acceptability of mHealth interventions amongst a
specific population. Studies have concentrated their efforts in improving their interventions
prior to evaluating the acceptability (23, 24) and others investigated the acceptability of
mHealth interventions demonstrating positive results (25-28). Each of these studies evaluated
interventions other than smartphone applications, such as short messaging service (SMS),
telecommunication interfaces as well as ehealth interventions emphasizing protocol
compliance and treatment adherence respectively. Authors reported lack of knowledge to be
the biggest limitation regarding the mHealth intervention. This impeded proper assessment
of the application, as participants did not feel comfortable using it (25-28). Based on a
systematic review conducted by Gagnon et al., most mHealth studies evaluating acceptability
of smartphone applications have been conducted amongst health care professionals such as
nurses, pharmacists and health workers (18). Furthermore, studies have been largely
concentrated in the western hemisphere: Canada, United States of America and Europe (18).
44
Amongst studies evaluating factors influencing mHealth adoption, the Technology
Acceptance Model (TAM) has been used with different groups of health care professionals,
including doctors and nurses, university faculty members, university students, nursing home
residents and inpatient diagnostic groups (29-33).
A systematic review by Iribarren et al. identified 1332 health applications amongst three
major application stores, of which 24 were TB related (34). Most applications targeting
clinicians were not formally tested. There were numerous problems with the applications
such as inability to open the app, and lack of up to date information (34). This systematic
review underscored the need for acceptability studies for mobile applications targeting TB.
In summary, the knowledge gap regarding mHealth interventions in India is evident through
the limitations present in the aforementioned studies. Primarily, most studies were conducted
in the western hemisphere or in countries other than low and middle income countries
(LMIC), where delivery of healthcare is needed the most. Secondly, very few studies
evaluated mHealth interventions targeting tuberculosis. Thirdly, there were few studies
conducted on doctors and clinicians, the population with the greatest ability to influence
healthcare. Finally, most studies reported their biggest limitation to be inability to understand
the mHealth intervention. To our knowledge, a scientific study evaluating the user experience
and acceptability of an mHealth intervention targeting TB amongst health care clinicians in
India has not yet been documented. Our study aims to overcome the barriers listed by
previous studies by focusing on: (a) India, the highest TB burden country; (b) tuberculosis, a
disease known to be a public health threat in India; (c) clinicians, who are influential players
in TB diagnosis and management; and (d) conducting a two-part study evaluating user
experience and then acceptability of a prototype version of an mHealth smartphone
application.
45
OBJECTIVES
Our study aimed to understand the user experience and acceptability of a newly-developed
smartphone application, LearnTB, amongst private sector academic clinicians in India.
Specifically, we aimed to (1) assess user experience with the aim of improving the LearnTB
mobile application and (2) identify, based on the TAM, the factors influencing adoption of
LearnTB by the private sector academic clinicians.
For our study purposes, the term “acceptability” is defined as the intention to use, “clinician”
refers to resident and academic clinicians practicing at Kasturba Hospital Manipal and
“mobile application” refers to smartphone applications only, specifically the prototype
version of the LearnTB application
THEORETICAL FRAMEWORK
Part 1:
Based on previous studies, the most common, free and easy to use framework for usability
studies is the System Usability Scale (SUS), a Likert scale, developed by J. Brooke in 1996
(20) (Figure 1). The SUS was developed by presenting a series of 50 questions to
respondents; only statements soliciting extreme agreement or extreme disagreement were
selected to be included in the final SUS scale (20). Brooke et al. emphasize the inaccuracy
of ambiguous questions in determining a participants’ attitude towards a specific technology.
For this reason, the SUS, is largely used in usability studies to measure user experience, the
utility which includes the efficacy and satisfaction with which users accomplish specific tasks
(18). Usability studies assess participants’ immediate reaction to the use of a specific
technology, prior to any discussion with the researcher (20). The SUS comprises of 10
statements evaluating the user experience. However our study used a modified version of the
SUS comprising of 9 statements preceding the acceptability assessment.
Part 2:
46
The proposed theoretical framework for the second part of our study is the widely used
Technology Acceptance Model (TAM). The TAM, proposed by Davis in 1989, is considered
to be influenced by the Theory of Reasoned Action (18). This framework is used to evaluate
the acceptability of an information technology by users in an organisational setting. The
TAM suggests that the acceptance (behavioral intention to use) of a new information
technology is affected directly and indirectly by a user’s attitude towards use, and two
internal individual beliefs: perceived usefulness and perceived ease of use. Perceived
usefulness (PU) is defined to be “the user’s subjective probability that using a specific
application system will increase his or her job performance within an organizational context”
(18), whereas perceived ease of use (PEU) is defined as “the degree to which the prospective
user expects the target system to be free of effort” (18). Behavioural intention to use (IU)
measures the likelihood of a person employing the intervention (18). One of the advantages
of the TAM is that it can be generalized amongst all populations, however one of its
disadvantages is that it does not consider environmental factors such as institutional or social
factors which can influence technology acceptance. Based on TAM studies, perceived
usefulness has a direct effect on intention to use whereas perceived ease of use has an indirect
effect on intention to use but a direct effect on perceived usefulness. For our study purposes,
we used an adapted version of TAM, comprising of perceived ease of use and perceived
usefulness as the independent variables and intention to use as the sole dependant variable
(Figure 2).
Study hypothesis:
Based on the theoretical framework presented above, the study hypothesis are listed below.
They have been validated through previous TAM studies as well (35).
1. The usability of the LearnTB application can influence the user experience of the
private sector academic clinicians
2. The TAM constructs will explain a significant proportion of clinician intention to use
the LearnTB application:
a. Perceived usefulness is positively correlated with intention to use
b. Perceived ease of use is positively correlated with intention to use
47
c. Perceived ease of use is positively correlated with perceived usefulness
METHODS
LEARNTB MOBILE APPLICATION
The smartphone application of interest for this study, LearnTB (Figure 3), was inspired by
the contents of the Let’s Talk TB, a free, online handbook aimed for general practitioners
(GP) in India (available http://www.letstalktb.org ). The handbook was specifically created
for the Indian medical context, and many chapters have been co-authored by Indian
physicians. This application, a collaboration between Mayo Clinic Center for Tuberculosis
(Rochester, USA) and McGill International Tuberculosis Centre (Montreal, Canada), aims to
educate Indian clinicians regarding the definition, diagnosis, treatment, management and
counselling practices available for TB. The primary version of the application was created
by Global Innovative Services Inc. (Maryland, USA). This study tested the pilot website
version of the application.
LearnTB presents numerous different sections regarding TB management, such as diagnosis
of TB, treatment of TB, management of childhood TB, latent TB infection, extra pulmonary
TB, common pitfalls of TB management and counselling practices in India (Figure 4). Each
section was based on International Standards of TB Care, as well as WHO guidelines and
Standards for TB Care in India (36, 37). When clicking on a specific section (ie. Childhood
tuberculosis), participants were presented with subsections involving suspecting, diagnosing
and treating TB for each respective case (Figure 5). Dosage tables, and specific names of
drugs were also included to facilitate understanding. A subsection regarding chest x-rays for
TB presented figures of different types of TB lesions to help visualize the extent and
manifestations of TB disease (Figure 6). The section focusing on the TB HIV co-infection
also included frequently-asked questions to help clinicians asses specific/special cases. In
addition to reference material, most sections presented short quizzes to assure proper
comprehension and to emphasize educational learning from the application.
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STUDY SETTING AND DESIGN:
The study was conducted at Kasturba Hospital Manipal, a tertiary teaching hospital affiliated
with Manipal University, located in the south Indian state of Karnataka. It has approximately
1500 practicing doctors including senior faculty members, junior faculty members, lecturers,
postgraduate students and interns, and over 1500 medical students (38, 39). Both parts of the
study were conducted successively, following a cross sectional study design respectively.
PARTICIPANT RECRUITMENT
For the first part of our study, five participants were recruited in February 2017 and for the
second part of our study, 101 participants were contacted between February 2017 and March
2017. A sample size of 5 was chosen for the first part of the study, as previous studies have
indicated that most errors (mean 85.5%) in technologies are identified by five participants
(40, 41). Furthermore, user experience studies aiming to assess prominent problems in
prototype technologies, such as LearnTB, often do not require large sample sizes (41). A
minimum sample size of 100 participants was sufficient for the second part of the study, as
the calculation was based on the theoretical outcome of a standard deviation of 1.07,
evaluated by previous studies (29, 32). Theoretical values for type 1 error (𝛼) of 5% and a
margin of error of 3% were used. The lowest non-response rate of 40%, was reported in a
previous study using the TAM amongst clinicians, thus this was taken into consideration
during the sample size calculation (29). A minimum sample size of 100 participants was
adequate to achieve all theoretical values of standard deviation, type 1 error and the margin
of error used in the sample size calculation.
Both parts of the study recruited participants through a convenience non-probabilistic
sampling approach. Additionally, for the second part of the study, snowball sampling was
also used. Participants were asked to respond to the questionnaire and share the objectives
of the study with their peers. Furthermore, participants were contacted by faculty members
of Kasturba Hospital Manipal and organized a meeting time with the lead researcher, TP. All
participants were required to have completed their Bachelor of Medicine, Bachelor of
49
Surgery (MBBS) degree, and be working at Kasturba Hospital Manipal. All participants were
asked to sign a consent form indicating their understanding of the research study and its
objectives. All questionnaires were in English as well as confidential and anonymous.
DATA COLLECTION
Part 1:
Data was collected through a 9-statement paper-based self-administered questionnaire
involving SUS for the first part of our study. One statement, “the application was easy to
use”, was omitted as it would be repeated in the second part of our study. The content validity
of the questionnaire was justified, as the statements were from the original version of a SUS
questionnaire which is highly reliable (Cronbach alpha (α) = 0.91) (42). Participants were
given the application in its website format, on an iPad and asked to complete two tasks. Upon
completion of the tasks, they were asked to immediately rate 9 statements on a 5-point SUS
scale ranging from “strongly disagree” to “strongly agree”. Participants were asked to address
their questions and concerns after completing the questionnaire, to best capture the user
experience.
Part 2:
Data was collected via an adapted version of the TAM questionnaire (29, 30). Participants
having participated in the first part were not asked to participate in the second part of the
study. A total of 101 medical employees of Kasturba Hospital, Manipal were contacted and
100 (99%) responded. Participants were given English paper-based questionnaires during
individual sessions and group sessions with the lead researcher, TP. The self-administered
questionnaires, comprised of 15 items, were answered following a brief presentation of the
LearnTB application (Table 1). Each individual session lasted approximately 10 minutes,
and each group session lasted 20 minutes. Participants were asked to rate each item through
a 7-point Likert scale ranging from “totally disagree” to “totally agree”. The mean scores of
each individual item were computed and the mean of means of each variable (perceived
usefulness, perceived ease of use, and intention to use) were used to perform statistical
50
analysis. Sociodemographic variables such as age, sex, position, title, years of clinical
experience, previous use of mobile application, and willingness to use mobile applications,
were also collected in both parts of the study. Years of clinical experience were defined as
years after graduating from the MBBS degree (an undergraduate medical degree in India).
STATISTICAL ANALYSIS
Part 1:
The statistical analysis was completed through Excel 2016 for the first part of the study.
Theoretically, the 10 statement SUS responses lead to values ranging from 0-40, which are
then multiplied by a factor of 2.5 resulting to values ranging from 0-100, thus facilitating
comprehension (20). Our study included 9 statements, thus decreasing our range of response
values to 0-36. To permit values ranging from 0-100, our response values were multiplied by
a factor of 2.78. One was subtracted from responses corresponding to odd numbered
questions, and those corresponding to even number questions were subtracted by 5, as per
the theoretical formula of SUS (20). A theoretical value of 68 was assumed as the threshold
for above average results (43).
Part 2:
The statistical analysis software (SAS 9.4) was used to compute descriptive and inferential
statistics of the data. The Cronbach alpha (a) values of the three theoretical values, perceived
ease of use, perceived usefulness and intention to use were calculated to assure reliability and
validity of the TAM questionnaire (44, 45). We computed descriptive statistics such as mean,
and standard deviations (SD) of the theoretical variables; perceived usefulness, perceived
ease of use, and intention to use. Correlations between the three theoretical values, age, sex,
and title were evaluated using the Pearson’s coefficient of correlation (r). All variables
followed a normal distribution (Gaussian distribution), thus multiple linear regression
analysis was used to determine a predictive model for intention to use (46). A predictive
model was evaluated for all theoretical variables (perceived usefulness, perceived ease of
use, intention to use), as well as age, sex and title. The 95% confidence intervals (CIs) were
51
calculated, and a statistical significance of 0.05 was used. Ad hoc analyses such as path
analysis and logistic regression were also performed. Path analysis helped confirm the direct
and indirect relationships between theoretical variables, perceived usefulness, perceived ease
of use and intention to use (46). Logistic regression analysis was used to target specific items
which influence intention to use, to help guide future studies and public health
recommendations regarding mHealth strategies. The dependent variable, intention to use was
dichotomized by the mean; participants with an score less than or equal to 6 were qualified
as “low to moderate” intention to use, and those greater than 6 were considered as “high”
intention to use.
ETHICS APPROVAL
This study was approved by the ethics review board of Université Laval (ref: 2016-165), in
Québec City, Canada as well as the MUEC, Manipal University Ethics Committee (ref:
MUEC/020/2016-17), in Manipal, India.
RESULTS
Part 1
The sociodemographic characteristics of the participants in the user experience study are
shown in Table 1. Amongst the 5 participants of this part of the study, 3 were male and 2
were females. Three participants were aged below 30 years. Two participants were medical
residents/interns, two were senior faculty members and one was a junior faculty member.
Three participants had previously used mobile applications for clinical use, and four felt
comfortable using mobile applications. The mean years of clinical experience was 4.8 with
a SD of 7.4. The average SUS score was 94.4 with a standard deviation of 2.7. The individual
SUS scores and standard deviations for each characteristic are shown in Figure 7.
Part 2
Characteristics of the population
52
The sociodemographic characteristics of the population for the second part of the study are
presented in Table 2. Of the 101 people who were asked to participate in the study, 100
responded to the paper-based questionnaires (99%). There were 60 males (60%) and 40
females (40%) who responded to the questionnaires. A great majority of participants (94%)
were under the age of 30 years, four (4%) were between 30-39, one (1%) between 40-49 and
one (1%) over the age of 50. The average years of clinical experience was 2.88 with a SD of
4.20, and 82 participants (82.8%) were medical residents/interns. Fifteen participants were
junior faculty members and 3 were senior faculty members. Overall, 86% of participants had
previously used mobile applications for clinical use, and 92 (95.8%) felt comfortable using
mobile applications.
Internal reliability
The content validity of the adapted TAM questionnaire was justified as the Cronbach alpha
(𝛼) values were higher than 0.70 justifying the reliability of TAM (Table 3) (45). Originally
the Cronbach alpha (𝛼) was low for perceived ease of use. An acceptable value of the
Cronbach alpha (𝛼= 0.75) was obtained by eliminating the item PEU_5 “The use of the
LearnTB application could interfere with the usual follow up of my patients”.
Descriptive statistics and multiple linear regression analysis
The descriptive statistics of the TAM constructs are presented in Table 3, including the mean
and SD of each of the theoretical variables. The Pearson’s correlation (r) between perceived
ease of use, perceived usefulness, intention to use, age, sex, and title are presented in Table
4. There was a statistically significant correlation between perceived usefulness and intention
to use, as well as perceived ease of use and perceived usefulness. Other variables, such as
sex, age, title did not present statistically significant correlations with intention to use.
The multiple linear regression analysis presented in Table 5 reports a significant variance
(R2) of 50.5%. Perceived ease of use is not a significant predictor of intention to use, however
perceived usefulness has a large significant effect on intention to use. Upon adding the other
53
variables – sex, age, and title – to the equation, the model did not change. However, the
variance (R2) increased to 52.6%. Path analysis, aiming to understand direct and indirect
relationships between the theoretical variables confirmed that perceived usefulness has a
significant direct relationship to intention to use (0.94; p <0.0001), whereas perceived ease
of use has a significant indirect relationship to intention to use (0.51; p<0.0001).
In order to identify potential targets for future interventions, we conducted a logistic
regression analysis of each of the items within perceived usefulness and perceived ease of
use, on intention to use. The items most strongly predictive of intention to use are: item PEU
4 “Using the LearnTB application could help me get the most out of my time assessing my
patients” with a significant odds ratio (OR) =2.393 [1.346 – 4.253]; p<0.003); item PU 4
“The use of the LearnTB application is compatible with my work habits” (OR = 3.201 [1.041
– 9.840]; p 0.0042); and item PU 5 “The use of the LearnTB application could promote good
clinical practice” (OR = 5.243 [1.354 – 20.297]; p 0.0164).
DISCUSSION
Due to the vast knowledge gap discussed previously, our study aimed to overcome many
barriers addressed in previous studies. This study was conducted to understand the user
experience and acceptability of a smartphone application amongst private sector academic
clinicians in India. Based on the results presented in part 1, the overall user experience was
high (94.4). This is known to be a very good user experience score, as the threshold is 68
(22). Academic clinicians were excited to see an application for TB, one of the clinicians
mentioned “A similar app with various other medical conditions would be very helpful”, thus
emphasizing the high user experience score. Contrary to other studies (21, 22), there were
very few barriers noted during data collection regarding incapability of using the application,
or difficulty completing the tasks. Furthermore, the participants responded unanimously
(strongly disagree) to the two questions addressing unfamiliarity; “I think I would need
support of a technical person to be able to use the LearnTB application” and “I need to learn
a lot of things before I could get going with the LearnTB application”. This is a positive note
54
for the LearnTB application as studies have shown that the rate at which a new technology is
accepted often increases when a user can learn how to use the technology on their own (44).
Nevertheless, upon informal discussions with the participants, some provided
recommendations such as including “education videos” and “adding a checklist for
diagnostic and treatment techniques”. As this study was done on the website version
(prototype version) of the application, each of these recommendations can be taken into
consideration for improving the application.
The second part of the study aimed to understand the acceptability of the LearnTB application
amongst the private sector academic clinicians. In concordance with previous studies, our
study reported a significant correlation between perceived ease of use and perceived
usefulness, confirming our third hypothesis (47, 48). This is supported by the original TAM,
as perceived ease of use has a direct influence on perceived usefulness, and an indirect
influence on intention to use (49). Our results also showed that perceived usefulness of the
LearnTB application is strongly correlated to intention to use, whereas perceived ease of use
is not. The strong correlation between perceived usefulness and intention to use supports our
first hypothesis and has been avidly shown in previous studies having used TAM or a
derivation of TAM as their theoretical framework (48, 50, 51). This is in concordance with
the original TAM model, where perceived usefulness has a direct influence on intention to
use (49). This was also presented through the path analysis performed in our study, where
perceived usefulness had a significant direct relationship to intention to use. Furthermore, the
high regression coefficient for the linear relationship between perceived usefulness and
intention to use is also explained by the strong correlation between the two variables.
Although participants’ perceived usefulness of the application was quite high, their main
concerns were regarding data requirements and internet connectivity while using the
application. Many clinicians addressed the connectivity issues present in large parts of India,
and the lack of wireless internet connection thus limiting use of certain applications. Such
factors must be taken into consideration when creating future versions of the LearnTB
application as well as other mHealth studies.
55
Unlike certain previous studies, such as Cajita et al. and Zhang et al. (48, 51, 52), our results
did not report a correlation between perceived ease of use and intention to use, which rejects
our second hypothesis. Nevertheless, this is consistent with previous findings as it is possible
that increased experience with mobile phones reduces the impact of perceived ease of use on
intention to use (50, 53). Chen et al. hypothesize that the perceived usefulness of an
application may be of greater importance to a medical practitioner than the perceived ease of
use (50), however Omar et al. hypothesize the opposite (52). The lower impact of perceived
ease of use on intention to use in our results can be supported through the increased use of
mobile technologies in India. As mentioned previously, there are 877 million mobile
subscriptions (28), thus ease of use of the mobile technologies may not be as important to
clinicians, as they are accustomed to them. The fact that the item “The use of the LearnTB
application is compatible with my work habits” was significantly associated with intention
to use further emphasizes this point as clinicians who find that using the LearnTB application
is compatible with their work habits are 3.2 times more likely to have a high intention to use
it. Factors such as sex, age and title did not change the linear regression model significantly,
contrary to previous hypotheses (51). This solicits further investigation as a “non-uniform”
population could help further analyze their effect on the relationship between perceived ease
of use, perceived usefulness and intention to use.
Our study presents numerous strengths. Primarily, to our knowledge, it is the first to evaluate
the user experience and acceptability of a smartphone mHealth intervention for TB amongst
clinicians in India. It is also amongst the few studies which test a pilot version of an mHealth
application prior to creating the final version. This is one of the biggest stregnths of our study.
Secondly, the questionnaires used in both parts of the study were valid and reliable. Both
questionnaires had previously been tested and had Cronbach alpha’s greater than 0.7 (29, 32,
54). Thirdly our study achieved a response rate of 100% and 99% for part 1 and part 2
respectively, increasing our study power as well as study credibility. Finally our study
assessed TB which is a prominent issue in India, once again increasing the credibility of our
study.
56
Limitations
Despite the numerous strengths of our study, the results must be interpreted while taking
certain limitations into consideration. Primarily, our study has a degree of selection bias as
participants were sampled using a non-probabilistic sampling method, specifically
convenience and snow-ball sampling. Due to time constraints, this was the most feasible for
our study, however future studies should adapt a probabilistic approach which could further
increase generalizability of the results. Additionally, future studies should be done on a
heterogeneous population of numerous hospitals in India.
Our study was conducted on a homogeneous population consisting only of academic
clinicians at Kasturba Hospital Manipal, a top ranked private medical school, and therefore
does not reflect the heterogeneity of private sector providers in India. Secondly, participants
could have been subject to social desirability bias, as the researcher administering the
questionnaires, TP, had been a part of the application development process. Thirdly, the study
design was cross sectional, thus permitting analysis of only one time point. The study was
designed in two parts to allow for improvement of the application prior to the acceptability
analysis, however due to time constraints both parts were done simultaneously at one time
point. Future studies should use longitudinal designs to; 1) allow time between the user
experience study and the acceptability study and 2) observe acceptance rates of users, perhaps
using the diffusion of innovation theory as a theoretical framework (44). Finally, the SUS
scale used in our study comprised of only 9 statements as compared to the theoretical model
of 10 statements. The mathematical procedure using a factor of 2.78 to analyse SUS scores
has not been previously validated, to our knowledge, thus a validated model would have been
favorable.
CONCLUSION
To our understanding, our study was the first to formally evaluate user experience and
acceptability of a mHealth intervention for tuberculosis amongst private sector clinicians in
India. Our results indicated that the intention to use an application is largely dependent on
57
the perceived usefulness. The overall user experience of the LearnTB application was high
as well. These results show positive intention to use of the LearnTB application and
encourage further development for public use. As the application used in this study was
presented as a preliminary website version, the final application will need further testing
amongst a larger, heterogeneous, population of Indian private and public sector clinicians.
Although, the website version was well received by the academic clinicians, many concerns
regarding accessibility, network problems and data usage were raised. It is important to
address these issues prior to dissemination of the final LearnTB application. Develpers have
indicated that the fully developed version of the LearnTB application will not require internet
connection. Despite being the second highest mobile phone consumer base in the world, India
presents numerous network problems and data usage concerns. Future mHealth applications
should take such factors into consideration to allow for better uptake and usage. Our
exploratory study has provided sufficient information to allow eventual dissemination of an
improved version of the LearnTB application with the aim of helping proper diagnosis and
treatment of TB, eventually decreasing the high burden of TB in India.
ACKNOWLEDGEMENT
This study is part of a masters of science thesis project of TP. We would like to thank all
participants of the study and all staff members of Kasturba Hospital who helped in participant
recruitement.
AUTHOR CONTRIBUTIONS
TP is the primary author of this study, she designed and conducted the study as well as wrote
the manuscript. MPG (supervisor) and MP (co-supervisor) helped design the study, and edit
the manuscript. KS provided supervision in the field. SR, RR, DM helped during patient
recruitment and data collection. ZT provided and reviewed content present in the LearnTB
application. AS supported the development of the TB app. Finally, all co-authors helped
finalize the manuscript for submission.
58
CONFLICT OF INTEREST
The authors do not have any conflicts of interest to declare.
FUNDING
This study was funded by the TMA Pai Endowment fund.
59
Figure 1: The Original System Usability Scale (SUS) developed by J. Brooke 2996
60
User
experience
Perceived
Usefulness (PU)
Perceived Ease
of Use (PEU)
Behavioural
intention to use
Part 1 Part 2
Legend :
Direct relation
Indirect relation
Figure 2: Adapted theoretical framework
61
Figure 3: LearnTB application- welcome page
62
Figure 4: LearnTB application – different sections
63
Figure 5: LearnTB application – subsections present in section 6 “Childhood tuberculosis”
64
Figure 6: Learn TB application - image of chest x-ray within section 4 “The role of chest
x-rays in the management of tuberculosis”
65
Figure 7: Part 1 - average SUS scores across different sociodemographic groups. The error
bars represent the 95% confidence intervals (n=5)
86
88
90
92
94
96
98
100
102
AVER
AG
E SU
S SC
OR
E
CHARACTERISTIC
66
Table 1 : Part 1 – Sociodemographic characteristics (n=5)
Characteristic N (%) Sex Male 3 (60) Female 2 (40) Age <30 years 3 (60) 30-39 years 1 (20) >40 years 1 (20) Title Medical resident/student 2 (40) Junior faculty member 1 (20) Senior faculty member 2 (40) Number of years of clinical experience 4.8 ±7.4
Previous use of mobile applications✝
Yes 3 (60)
No 1 (20) Comfortable with using mobile applications
Yes 4 (80)
No 1 (20)
✝Variable contains missing information
67
Table 2 : Variables and items present in TAM questionnaire (n=100)
Variables Items Mean ±SD Cronbach alpha (𝜶)
Perceived Usefulness (PU) 5.918 ±0.675 0.898 PU 1 The use of the LearnTB application could
help me assess my patients more adequately 5.904 ±0.893
PU 2 I think that it would be easy to perform the tasks necessary to assess my patients using the Learn TB application
5.967 ±0.857
PU 3 The use of the LearntB application could improve my assessment of my patients
5.925 ± 0.722
PU 4 The use of the LearnTB application is compatible with my work habits
5.606 ± 1.109
PU 5 The use of the LearnTB application could promote good clinical practice
6.00 ± 0.879
PU 6 Using the LearnTB application could improve my performance in patients care
6.01 ± 0.725
PU 7 Using the LearnTB application could facilitate the care of my patients
5.989 ± 0.809
Perceived Ease of Use (PEU) 5.596 ±0.579 0.757 PEU 1 I think that the LearnTB application would be
easy to use 6.03± 0.842
PEU 2 I think that the LearnTB application is a flexible technology to interact with
6.15 ± 0.737
PEU 3 I think that I could easily learn how to use the LearnTB application
6.39 ± 0.637
PEU 4 Using the LearnTB application could help me get the most out of my time assessing my patients
5.58 ± 1.129
Intention to use (IU) 6.00 ± 0.854 0.907 IU 1
I have the intention to use the LearnTB application for patient care
5.88 ± 0.966
IU 2 I have the intention to use the LearnTB application when it becomes available in my health center
6.02 ± 0.920
IU 3 I have the intention to use the LearnTB application when necessary to provide health care to my patients
6.12 ±0.902
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Table 3 : Part 2 – sociodemographic characteristics (n=100)
Characteristics N Percentage (%)
Sex Male 60 60 Female 40 40 Age <30 94 94 30-39 4 4 40-49 1 1 50-59 1 1 >60 0 0 Title✝ Medical resident/ student 82 82.83 Junior faculty member 15 15.15 Senior faculty member 3 2.02 Technical staff 0 0 Years of experience 2.88 ±4.20☨ Previous use of mobile applications Yes 86 86 No 14 14 Comfortable with using mobile application* Yes 92 95.83 No 4 4.17 ✝This characteristic contains missing variables
☨These values represent the mean and standard deviation (SD)
69
Table 4 : Pearson’s correlation coefficients between variables (n=99)
IU PU PEU Age Sex Title
IU 1.0000
PU 0.70812 1.0000 <0.0001
PEU 0.27248 0.46072 1.0000 <0.0001 <0.0001
Age 0.01773 0.01577 -0.19606 1.0000 0.8617 0.8769 0.0518
Sex -0.11124 -0.25962 -0.00374 -0.07902 1.0000 0.2730 0.0095 0.9707 0.4369
Title 0.04116 -0.05946 -0.28707 0.69350 -0.16293 1.0000 0.6859 0.5588 0.0040 <0.0001 0.1071
PEU: Perceived Ease of Use; PU: Perceived Usefulness; IU: Intention to Use
70
Table 5 : Multiple linear regression model for Intention to Use
Variables Beta Standard error
95% confidence interval
P value
Unadjusted values
PU 0.936 0.101 0.734 – 1.138 <0.0001
PEU -0.109 0.119 -0.345 – 0.127 0.3625
Adjusted values
PU 0.982 0.107 0.769 – 1.195 <0.0001 PEU -0.092 0.125 -0.341 – 0.156 0.4616 Age -0.238 0.210 -0.656 – 0.179 0.2605 Sex 0.189 0.132 -0.073 – 0.452 0.1558 Title 0.319 0.198 -0.074 – 0.713 0.1111
PU: Perceived Usefulness; PEU: Perceived ease of use
71
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CHAPTER 9 Discussion
This chapter presents a brief overview of the discussion, along with the strengths and limits of our study. The section is concluded with this study’s contribution to public health. As this section has been elaborated in detail within the manuscript presented above, it provides an overview of what has previously been stated to avoid repetition.
9.1 Main results
Our study aimed to understand the user experience and acceptability of a smartphone
application for TB, LearnTB, amongst private sector academic clinicians in India. The study
was divided into two parts, as explain in Chapter 7 – Methodology. The participants were
first presented the LearnTB application, asked to perform two tasks, and answer a user
experience questionnaire. The second part of the study involved a presentation of the
LearnTB application as well, followed by an acceptability questionnaire inspired by the TAM
framework. Our results indicate that perceived usefulness strongly influences intention to
use, and the overall user experience of the LearnTB application was high. Two out of the
three hypothesis determined for this study were confirmed and detailed ad hoc analysis
insinuated new avenues for future research in this domain. Through path analysis, the direct
relationship between perceived usefulness and intention to use, as well as the indirect
relationship between perceived ease of use and intention to use, were confirmed. Informal
discussions with the participants highlighted certain queries, specifically related to
accessibility of the application (internet access, network problems). Such problems should
be addressed in the future version of the LearnTB application, and taken into consideration
in upcoming mHealth studies as well.
9.2 Strengths and limits
Strengths
Our study is the first to evaluate the user experience and acceptability of a smartphone
mHealth intervention for tuberculosis amongst clinicians in India. It is also among the few
studies which test a pilot version of an mHealth application prior to creating the final version.
75
This is one of the biggest strengths of our study. As mentioned previously, mHealth is a
consumer driven domain, thus testing a pilot version of an application prior to dissemination
to the public, is ideal (56). The questionnaires used in the study, for both user experience
(part 1) and acceptability (part 2), were valid and reliable. Both questionnaires had been
tested in previous studies, and they had a Cronbach alpha greater than 0.91 (86) and 0.80-
0.94 respectively (67, 70). Furthermore the questionnaires were created in English as it is
second language in India (91). Our study achieved a response rate of 100% and 99% for user
experience (part 1) and acceptability (part 2), respectively. This is another major strength of
our study, increasing the study power and credibility. Finally, our study assessed TB, an issue
which is prominent and of importance in India thus increasing the relevance of our study to
public health.
Limits
Despite the numerous strengths of our study, the results must be interpreted while taking
certain limitations into consideration. Primarily, our study has a degree of selection bias as
participants were sampled using a non-probabilistic sampling method, specifically
convenience and snow-ball sampling. Due to time constraints, this was the most feasible for
our study, however future studies should adapt a probabilistic approach which could further
increase generalizability of the results. The results of this study cannot be generalized to all
clinicians in India, as our sample population and study setting were very specific, thus
decreasing our external validity. Our study was conducted on a homogeneous population
consisting only of academic clinicians at Kasturba Hospital Manipal, one of the top ranked
private medical school, and therefore does not reflect the heterogeneity of private sector
providers in India. Future studies should ideally be done on a larger and heterogeneous
population within numerous hospitals (both private and public) in India. Secondly,
participants could have been subject to social desirability bias, as the researcher
administering the questionnaires, TP, had been a part of the application development process.
Moreover, participants were contacted by their unit heads, thus participants could have
shown interest in the study to respect seniority. Response rates of 100% and 99% can also be
considered indicative of the latter. As participants were asked to participate by their unit
76
heads, our study may be subject to social desirability bias as well. Our sample size was not
reflective of the population of employees at Kasturba Hospital Manipal. The results indicate
82.8% of participants were medical residents/interns and 94% were under the age of 30. The
employee population of Kasturba Hospital Manipal is 73.6% medical residents/interns (81),
thus our values could have surestimated the results. A future study presenting an equal
distribution of participant demography would allow for a better understanding and
comparaison of participant intention to use of the LearnTB application. Thirdly, the study
design was cross sectional, thus permitting analysis of only one time point. The study was
initially designed in two parts to allow for improvement of the application prior to the
acceptability analysis. However, due to time constraints, both parts were done simultaneously
at one time point. Ideally, the first five participants should have done the user experience
study and suggestions should have been implemented in the LearnTB application, and the
same five participants should have been re-contacted to evaluate the difference in the user
experience of the application. As mentioned previously, due to time constraints this was not
possible. Additionally, due to the latter, participants were not able to use the LearnTB
application during their routine medical practice. Future studies should use longitudinal
designs to; 1) allow time between the user experience study and the acceptability study and
2) observe uptake and acceptance rates of users, perhaps using the diffusion of innovation
theory as a theoretical framework (48). Finally, the SUS scale used in our study comprised
of only 9 statements as compared to the theoretical model of 10 statements. The mathematical
procedure using a factor of 2.78 to analyse SUS scores has not been previously validated, to
our knowledge, thus a validated model would have been favorable.
9.3 Contribution to public health
Despite the limitations listed previously, the results of this study can have a significant impact
on mobile technology research in India. As India is the second largest consumer of mobile
phones in the world, mobile technologies have great potential in this market. Through
observation, it was noted that doctors communicate largely through their mobile phones
regarding patient cases, or during hospital rotations. Thus, mobile technologies can impact
patient care and management in a meaningful manner. This smartphone application,
77
LearnTB, addressed an issue which is largely present in India. It is the country with the
highest TB burden in the world, and vast differences in medical education. Using this
application, all health care professionals in India will be able to access reliable and valid
information regarding diagnosis, treatment and management of TB. The results of this study
are largely positive, indicating interest in mobile applications and the potential for positive
uptake as well as acceptability, empowering proper diagnosis and treatment of TB, a disease
which kills millions in India every year.
78
CHAPTER 10 Conclusion
This thesis project aimed to understand the user experience and acceptability of a smartphone
application for tuberculosis, LearnTB, amongst private sector academic clinicians in India.
This was evaluated through a two-part approach involving assessment of user experience and
acceptability of the smartphone application. Primarily the smartphone application was
created using the Let’s talk TB handbook as a reference followed by an evaluation amongst
academic clinicians at Kasturba Hospital Manipal, Manipal India. The overall results of the
study have presented positive user experience and high intention to use of the LearnTB
application. Academic clinicians were excited and intrigued by the usefulness of the
application, which lead to a high intention to use. As this study was the first of its kind,
certain lessons should be taken from this experience. Future studies involving smartphone
technologies for tuberculosis in India should not only take the vast mobile consumer presence
into consideration, but also the lack of network accessibility and internet accessibility.
Numerous clinicians expressed concerns over lack of internet availability in certain regions
of India. Clinicians were immensely interested in an application which would not require
data usage, or Wi-Fi usage. Furthermore, clinicians showed interest to a free of charge
application versus a paid application, to increase accessibility. mHealth strategies addressing
the issues mentioned above, may increase their rates of acceptance amongst clinicians in
India, however acceptability testing is encouraged in all cases, as it enables proper
understanding of consumer needs. This exploratory, descriptive thesis project has provided
sufficient information allow future studies to build upon, explore new avenues and lead to
eventual dissemination of the LearnTB application with the aim of helping proper diagnosis
and treatment of TB, eventually decreasing the high burden of TB in India.
79
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73. Hopewell PC, Pai M, Maher D, Uplekar M, Raviglione MC. International standards for tuberculosis care. The Lancet infectious diseases. 2006;6(11):710-25. 74. Mohan A. International standards of tuberculosis care. The National medical journal of India. 2006;19(6):301. 75. Levin KA. Study design III: Cross-sectional studies. Evidence-based dentistry. 2006;7(1):24-5. 76. Shastri S, Naik B, Shet A, Rewari B, De Costa A. TB treatment outcomes among TB-HIV co-infections in Karnataka, India: how do these compare with non-HIV tuberculosis outcomes in the province? BMC public health. 2013;13(1):838. 77. Central TB Division. Revised National TB Control Programme 2015 [Available from: http://www.tbcinidia.nic.in/. 78. Government of Karnataka. Revised National Tuberculosis Control Programme (RNCTP) 2016 [Available from: http://www.karnataka.gov.in/hfw/nhm/pages/ndcp_cd_rntcp.aspx. 79. Manipal University. About Us 2017 [Available from: https://manipal.edu/mu/about-us.html. 80. KMC Hospital Mangalore. Kasturba Hospital, Manipal 2017 [Available from: http://www.kmchospitalsmangalore.com/index.php?option=com_content&task=view&id=59&Itemid=118. 81. Manipal KMC. About KMC 2017 [Available from: https://manipal.edu/kmc-manipal/hospital/services.html. 82. Verma R, Khanna P, Mehta B. Revised national tuberculosis control program in India: the need to strengthen. Int J Prev Med. 2013;4(1):1-5. 83. Nielsen J, Landauer TK, editors. A mathematical model of the finding of usability problems. Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems; 1993: ACM. 84. Six JM, Macefield R. How to Determine the Right Number of Participants for Usability Studies 2016 [Available from: http://www.uxmatters.com/mt/archives/2016/01/how-to-determine-the-right-number-of-participants-for-usability-studies.php. 85. Kitchenham B, Pfleeger SL. Principles of survey research: part 5: populations and samples. ACM SIGSOFT Software Engineering Notes. 2002;27(5):17-20. 86. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Intl Journal of Human–Computer Interaction. 2008;24(6):574-94. 87. Sauro J. Measuring usability with the system usability scale 2011 [Available from: https://measuringu.com/sus/. 88. Tavakol M, Dennick R. Making sense of Cronbach's alpha. International journal of medical education. 2011;2:53. 89. Land KC. Principles of path analysis. Sociological methodology. 1969;1:3-37. 90. Massé R, Saint-Arnaud J. Ethique et santé publique: enjeux, valeurs et normativité: Presses Université Laval; 2003. 91. India To. Indiaspeak: English is our 2nd langauge 2010 [Available from: http://timesofindia.indiatimes.com/india/Indiaspeak-English-is-our-2nd-language/articleshow/5680962.cms.
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Appendix A : Proof of submission
85
Appendix B : Search strategy The search strategy involved the use of three medical databases namely PubMed,
EMBASE and Web of Science. Due to the limited literature available regarding this topic, broad search terms, such as “mHealth” and “tuberculosis” were used. There were no restrictions regarding languages or dates in the search strategy. Furthermore, literature was also retrieved via retrospective searches (i.e. references of studies used and/or found) and with the help of a systematic review conducted by Gagnon et al. (39). Studies conducted on mHealth strategies involving other diseases, both non-communicable and infectious were also included. Additionally, mHealth strategies involve numerous platforms, such as mobile applications, short message service (SMS), personal digital assistants and other wireless devices (10), therefore all studies involving the latter were also included. Studies involving all health care professionals were included, as there were few studies specific to health care clinicians. As there are a limited number of studies investigating this topic, the following search terms were used, in each respective database: Pubmed: ((("Tuberculosis"[Mesh]) AND ( "Tuberculosis/analysis"[Mesh] OR "Tuberculosis/anatomy and histology"[Mesh] OR "Tuberculosis/classification"[Mesh] OR "Tuberculosis/diagnosis"[Mesh] OR "Tuberculosis/education"[Mesh] OR "Tuberculosis/epidemiology"[Mesh] OR "Tuberculosis/mortality"[Mesh] OR "Tuberculosis/organization and administration"[Mesh] OR "Tuberculosis/prevention and control"[Mesh] OR "Tuberculosis/statistics and numerical data"[Mesh] OR "Tuberculosis/transmission"[Mesh] )) AND "Diagnosis/diagnosis"[Mesh]) AND "Telemedicine"[Mesh] EMBASE: 'mhealth' AND ('tuberculosis'/exp OR 'tuberculosis’) Web of Science: (mhealth) AND TOPIC: (tuberculosis) Timespan: All years. Search language=Auto
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Appendix C : SUS questionnaire Questionnaire #: Date:
PURPOSE OF THE QUESTIONNAIRE To identify user experience amongst private sector academic clinicians in India, with the
goal of improving the development of the smartphone application LearnTB What are you asked to do: You will be asked to perform a series of tasks using the LearnTB application and then respond to a two-part questionnaire. This questionnaire should not take more than 3-5 minutes to complete. You will be asked for general demographic information and to respond to 10 statements regarding your experience while using the smartphone application. All participants should have had previous access to the LearnTB application and must be members of Manipal University. The anonymity of the questionnaires is assured. STATEMENT OF CONSENT: I have read the above information, and have received answers to any questions I asked. I consent to take part in the study. I agree to participate in this study:
Please select A SINGLE OPTION for each statement.
SECTION 1 : DEMOGRAPHIC INFORMATION 1. Sex � Male
� Female 2. Age � <30 years
� 30-39 years � 40-49 years � 50-59 years � >60 years
3. Title � Medical resident/student � Junior faculty member � Senior faculty member � Technical staff
4. Number of years of clinical experience
5. I have previously used mobile applications in my clinical work
� Yes � No
6. I feel comfortable using mobile applications in my clinical work
� Yes � No
87
Here are 9 statements related to various factors that may influence the user experience related to the use of the LearnTB application. Please indicate your level of agreement with each of the following statements using the scale presented below: Please select A SINGLE OPTION for each statement
-2 Strongly disagree
-1 Disagree
0 Neither agree nor disagree
1 Agree
2 Strongly agree
SECTION 2: USER EXPERIENCE 1. I think that I would like to use the
LearnTB application frequently -2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
2. I found the LearnTB application unnecessarily complex
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3. I think that I would need support of a
technical person to be able to use the LearnTB application
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
4. I found the various functions in the LearnTB application were well integrated
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
5. I thought there was too much inconsistency in the LearnTB application
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
6. I imagine that most people would
learn to use the LearnTB application very quickly
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
7. I found the LearnTB application very awkward to use
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
8. I felt very confident using the LearnTB application
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
9. I needed to learn a lot of things before I could get going with the LearnTB application
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
Additional comments:
Thank you for your participation!
88
Appendix D : Tasks for user experience (part 1)
Developing and evaluating a smartphone application for tuberculosis amongst private
sector clinicians in India
Researcher: Tripti Pande
Supervisor(s): Dr. Kavitha Saravu (Manipal University)
Dr. Marie-Pierre Gagnon (Université Laval)
Dr. Madhukar Pai (McGill University)
Length: 5 minutes
OVERALL TASK: Please Indicate where you found each of the responses
TASK 1: General tasks
- How many of the sputum tests are WHO endorsed?
- Name three scenarios should one always remember when working with a TB patient?
TASK 2: Case Scenario
1. You have performed numerous tests and established that your patient has latent TB
infection (LTBI). You are asked to provide a full diagnostic overview of the patient
and provide treatment information. How will you go about?
o Please respond to the question at the end of the LTBI section and provide your
final score
89
Appendix E : TAM questionnaire Questionnaire #: Date:
PURPOSE OF THE QUESTIONNAIRE To determine the factors influencing intention to use the mHealth application LearnTB
amongst private academic clinicians in India What are you asked to do: You will be asked to perform a series of tasks using the LearnTB application and then respond to a two-part questionnaire. The questionnaire should not take more than 5-7 minutes to complete. You will be asked for general demographic information and to respond to 15 statements regarding specific factors which could influence the acceptability of the smartphone application. Having previous experience using a mobile application is not necessary to respond to this questionnaire. All participants must be members of Manipal University (Manipal, India). The anonymity of all responses is assured. STATEMENT OF CONSENT: I have read the above information, and have received answers to any questions I asked. I consent to take part in the study:
SECTION 1 : DEMOGRAPHIC INFORMATION 7. Sex � Male
� Female 8. Age � <30 years
� 30-39 years � 40-49 years � 50-59 years � >60 years
9. Title � Medical resident/student � Junior faculty member � Senior faculty member � Technical staff
10. Number of years of clinical experience
11. I have previously used mobile applications � Yes � No
12. I feel comfortable using mobile application
� Yes � No
90
Here are 15 statements related to various factors that may influence the acceptance of an mHealth intervention for TB. Please indicate the level of agreement with each of the following statements using the scale presented below: Please select A SINGLE OPTION for each statement:
-3 Totally disagree
-2 Disagree
-1 Slightly disagree
0 Neither
agree nor disagree
1 Slightly agree
2 Agree
3 Totally agree
SECTION 2: PERCEIVED USEFULNESS 1. The use of the LearnTB application could
help me assess my patients more adequately
-3 ⊡
�
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
2. I think that it would be easy to perform the tasks necessary to assess my patients using the LearnTB application
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
3. The use of the LearnTB application could
improve my assessment of my patients -3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
4. The use of the LearnTB application is
compatible with my work habits -3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
5. The use of the LearnTB application could
promote good clinical practice -3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
6. Using the LearnTB application could improve my performance in patients care
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
7. Using the LearnTB application could facilitate the care of my patients
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
SECTION 3: PERCEIVED EASE OF USE
8. I think that the LearnTB application would be easy to use
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
9. I think that the LearnTB application is a
flexible technology to interact with -3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
10. I think that I could easily learn how to use the LearnTB application
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
91
-3 Totally disagree
-2 Disagree
-1 Slightly disagree
0 Neither
agree nor disagree
1 Slightly agree
2 Agree
3 Totally agree
11. Using the LearnTB application could help me get the most of out of my time assessing my patients
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
12. The use of the LearnTB application could
interfere with the usual follow up of my patients
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
SECTION 4: INTENTION TO USE
13. I have the intention to use the LearnTB application for patient care
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
14. I have the intention to use the LearnTB application when it becomes available in my health centre
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
15. I have the intention to use the LearnTB
application when necessary to provide health care to my patients
-3 ⊡
-2 ⊡
-1 ⊡
0 ⊡
1 ⊡
2 ⊡
3 ⊡
Additional comments:
Thank you for your participation!
92
Appendix F : Consent forms Consent form – PART 1: Development of Application and User Experience
You are being asked to take part in a research study regarding the development of a smartphone application for tuberculosis amongst private academic clinicians in India. Please read this form carefully prior to taking part in the study. Should you have any additional questions, please do not hesitate to ask the researcher in charge, Tripti Pande. Objective of the study: Identify the user experience of private academic clinicians in India while using the LearnTB application for tuberculosis. What are you asked to do: You will be asked to perform a series of tasks using the LearnTB application and then respond to a two-part questionnaire. This questionnaire should not take more than 5 minutes to complete. What are the risks and benefits: The main benefit of this study is to provide an application containing diagnostic, treatment, counselling and general information about tuberculosis in India. There are no potential risks in this research study. Confidentiality and Anonymity: All participants can be assured of the confidentiality of their responses. All questionnaires are anonymous and will remain in a secure location which will be only accessible to Tripti Pande, the principal researcher of this study, and her supervisor, Dr. Marie-Pierre Gagnon from Université Laval in Quebec City, Canada. Person to contact if you have further questions: If you have any further questions, please contact Tripti Pande, the principal researcher of this study. Tripti Pande (MSc – Global Health student) Phone: +91 92054 36732 Email: [email protected] (preferred method of communication) STATEMENT OF CONSENT: I have read the above information, and have received answers to any questions I asked. I consent to take part in the study. Please click here if you consent to participate in this study Date:
93
Consent form – PART 2: Acceptability of application You are being asked to take part in a research study regarding the factors influencing the acceptability of a smartphone application for tuberculosis amongst private academic clinicians in India. Please read this form carefully prior to taking part in the study. Should you have any additional questions, please do not hesitate to ask the researcher in charge, Tripti Pande. Objective of the study: To identify, with the help of the Technology Assessment Model, the factors which influence acceptance of a smartphone application by academic clinicians in the private sector of India. What are you asked to do: You will be asked to download the LearnTB application and respond to a two-part questionnaire. The researchers will guide all participants regarding the usage of the application and participants will be given a couple of minutes to observe the application on their own. The questionnaire should not take more than 10 minutes to complete. What are the risks and benefits: The main benefit of this study is to provide an application containing diagnostic, treatment, counselling and general information about tuberculosis in India. There are no potential risks in this research study. Confidentiality and Anonymity: All participants can be assured of the confidentiality of their responses. All questionnaires are anonymous and will remain in a secure location which will be only accessible to Tripti Pande, the principal researcher of this study, and her supervisor, Dr. Marie-Pierre Gagnon from Université Laval in Quebec City, Canada. Person to contact if you have further questions: If you have any further questions, please contact Tripti Pande, the principal researcher of this study. Tripti Pande (MSc – Global Health student) Phone: +91 92054 36732 Email: [email protected] (preferred method of communication) STATEMENT OF CONSENT: I have read the above information, and have received answers to any questions I asked. I consent to take part in the study. Please click here if you consent to participate in this study: Date: