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Home-based cardiac rehabilitation: Development and evaluation of a novel intervention withtelemonitoring guidance and wearable sensors

Kraal, J.J.

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Citation for published version (APA):Kraal, J. J. (2016). Home-based cardiac rehabilitation: Development and evaluation of a novel intervention withtelemonitoring guidance and wearable sensors.

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HOMEBASED

CARDIACREHABILITATION

Jos Kraal

Development and evaluation of a novel intervention

with telemonitoring guidance and wearable sensors

Uitnodigingvoor het bijwonen van de openbare

verdediging van mijn proefschrift

Op vrijdag 18 november om 12:00 uur in de AgnietenkapelOudezijds Voorburgwal 229 - 231

1012 EZ te Amsterdam

U bent van harte uitgenodigdvoor de receptie ter plaatse

na a�oop van de verdediging

Jos KraalJan Mankesstraat 3-31061 SR, [email protected]

Paranimfen:

Leonie ThijssingWouter Gude

[email protected]

HOME-BASEDCARDIAC

REHABILITATIONDevelopment and evaluation of a

novel intervention with telemonitoringguidance and wearable sensors

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Jos Kraal

HOMEBASED


Jos Kraal










Paranimfen:


[email protected]

HOME-BASEDCARDIAC


novel intervention with telemonitoringguidance and wearable sensorsHOME

BASEDCARDIAC

REHABILITATION

Jos Kraal










Paranimfen:


[email protected]

HOME-BASEDCARDIAC



JosKraal_offertenr407539_Cover_DUBBELzijdig-FULLcolor_Krasvastlaminaat.indd 2-6 5-10-2016 14:35:34

HOME-BASEDCARDIAC

REHABILITATIONDevelopment and evaluation of a novel intervention with telemonitoring guidance and wearable sensors

Jos Kraal

Colofon

Home-based cardiac rehabilitation: Development and evaluation of a novel intervention with telemonitoring guidance and wearable sensors

Author: Jos KraalCover & book design: isontwerp.nl ~ Ilse Schrauwers, Den BoschPrinting: ipskampprinting.nl ~ EnschedeISBN: 978-94-92303-09-7

© Jos Kraal 2016All rights reserved. No part of this thesis may be reproduced, stored or transmitted in any form or by any means, without prior permission of the referenced journal or the author.

Financial support by the Dutch Heart Foundation for the publication of this thesis is gratefully acknowledged.

http://isontwerp.nl

https://www.proefschriften.net/

HOME-BASEDCARDIAC

REHABILITATIONDevelopment and evaluation of a novel intervention with telemonitoring guidance and wearable sensors

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctoraan de Universiteit van Amsterdamop gezag van de Rector Magnificus

prof. dr. ir. K.I.J. Maexten overstaan van een door het College voor Promoties ingestelde commissie,

in het openbaar te verdedigen in de Agnietenkapelop vrijdag 18 november 2016, te 12:00 uur

door Jozua Johannes Kraalgeboren te Venhuizen

Promotiecommissie

Promotor: Universiteit van Amsterdam

Copromotores: University of ManchesterMáxima Medisch Centrum

Overige leden: Universiteit van AmsterdamUniversiteit van AmsterdamUniversiteit van AmsterdamRadboud Universiteit NijmegenUniversiteit Hasselt

Prof. dr. A. Abu-Hanna

Dr. N.B. Peek Dr. H.M.C. Kemps

Prof. dr. R.J.G. Peters Prof. dr. L. Witkamp Prof. dr. F. NolletProf. dr. M.T.E. Hopman Dr. P. Dendale Dr. V.A. Cornelissen Katholieke Universiteit Leuven

Faculteit der Geneeskunde

Table of contents

Chapter 1 Introduction and outline of this thesis 7

Chapter 2 The influence of training characteristics on the effect of aerobic 17 exercise training in patients with coronary artery disease: A meta-regression analysis

Chapter 3 The influence of training characteristics on the effect of aerobic 49 exercise training in patients with chronic heart failure: A meta-regression analysis

Chapter 4 Energy expenditure estimation in beta-blocker medicated cardiac 83 patients by combining heart rate and body movement data

Chapter 5 Effects and costs of home-based training with telemonitoring 99 guidance in low-to-moderate cardiac risk patients entering cardiac rehabilitation: The FIT@Home study (Study Protocol)

Chapter 6 Effects of home-based training with telemonitoring guidance in low- 117 to-moderate cardiac risk patients entering cardiac rehabilitation: Short-term results of the FIT@Home study

Chapter 7 Clinical and cost-effectiveness of home-based cardiac rehabilitation 129 compared to conventional, centre-based cardiac rehabilitation: Results of the FIT@Home study

Chapter 8 General discussion and summary 153

Appendices Nederlandse samenvatting 165 PhD Portfolio & list of publications 171 Curriculum Vitae 174 Dankwoord 175

7Chapter 1Introduction and

outline of this thesis

8

In this thesis we explore the opportunities to improve secondary prevention interventions for patients after a cardiac incident. With the current developments in (health-) technology, wearable sensors, eHealth and The Internet of Things, conducting a regular “one-size-fits-all” intervention for patients entering cardiac rehabilitation is out-dated and limits effectiveness. In addition, to control the vast increase in healthcare costs, novel interven-tions should be tested on cost-effectiveness and subsequently implemented to evolve and sustain healthcare. This thesis describes opportunities to make cardiac rehabilitation more appealing for cardiac patients who are unwilling or unable to participate in centre-based cardiac rehabilitation, without losing its clinical effectiveness.

Introduction

BackgroundCardiovascular disease is one of the leading causes of death. Across Europe, cardiovascular disease results in 4.1 million deaths per year and accounted for almost 47% of all deaths in 2014 [1]. Health-care costs associated with cardiovascular disease in the European Union are estimated to amount for over €100 billion a year, almost 10% of the total healthcare expenses [2]. In the Netherlands, over 600,000 people have currently been diagnosed with coronary artery disease (CAD). Although the prevalence of CAD is decreasing, CAD still represents almost 30% of all deaths [3].

Similar to other chronic diseases, CAD is mainly caused by individual behavioural and lifestyle factors, such as smoking, physical inactivity and an unhealthy diet [4], causing increased blood pressure, raised blood glucose and lipid levels, overweight and obesity. Prevention of CAD and its recurrence (secondary prevention) can be achieved by adopting a healthy lifestyle, consisting of regular physical exercise, a healthy diet, reducing stress levels and smoking cessation [5,6]. Physical exercise has shown to reduce cardiovascular risk factors (e.g. reduced blood pressure, decreased triglyceride levels and increased HDL cholesterol) [7] and has a direct influence on the heart and cardiovascular system. With physical exercise, the myocardial oxygen demand decreases, endothelial function improves and the development of coronary collateral vessels is stimulated [8,9]. Therefore, physical exercise is considered a crucial part of cardiac rehabilita-tion [6,10]. Cardiac rehabilitation is a multidimensional intervention provided to patients after a cardiac event (myocardial infarction, angina pectoris) or intervention (percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG) and/or pharmacological treatment) [6]. Previous studies have shown that structured exercise-based cardiac rehabilitation programmes reduce mortality, prevent hospital readmission and improve quality of life in patients with CAD in a cost-effective manner [10–12]. Therefore, cardiac rehabilitation is highly recommended in both US and EU guidelines [13,14] and fully reimbursed by the health insurance in the Netherlands.

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Barriers in cardiac rehabilitationAlthough the beneficial effects of exercise-based cardiac rehabilitation were shown repeat-edly, two persistent barriers limit the effectiveness of cardiac rehabilitation. First, participation rates are low, causing eligible patients to miss out on the beneficial effects of cardiac rehabilita-tion. Second, traditional cardiac rehabilitation programmes tend to focus on short-term physical recovery rather than long-term improvements in physical fitness and activity levels.

A cohort study performed in the Netherlands showed that only 28% of the eligible patients participated in cardiac rehabilitation [15], confirming the results of a European survey that concluded that cardiac rehabilitation services are vastly under-utilised [16]. Although system-atic barriers (e.g. lack of referral for cardiac rehabilitation) can partly explain low uptake numbers, practical and personal barriers are also evident [17]. For example, distance and travel time to the outpatient clinic has been shown to negatively affect participation rates. Similarly, a lack of time due to work resumption is a major reason for not participating in cardiac rehabilitation [18]. In addition, personal barriers can be a reason to refrain from cardiac rehabilitation participation, such as reluctance to participate in group-based training, or individual training preferences that deviate from the training provided at the outpatient clinic [18].

Previous studies showed that patients that do participate in cardiac rehabilitation improve their physical fitness, quality of life and initiate an active lifestyle on the short-term but often relapse into old lifestyle habits over time [19,20]. Therefore, the focus during cardiac rehabilitation should be on long-term lifestyle changes. A critical event is the transition from supervised training in the outpatient clinic to individual training in the home environment after cardiac rehabilitation. In traditional cardiac rehabilitation programmes, guidance on integration of exercise training in routine daily life is often not provided and no assistance from healthcare professionals is avail-able when the patient is unable to maintain the lifestyle changes.

Home-based training with telemonitoring guidanceTo address the aforementioned barriers, cardiac rehabilitation programmes should be better tailored to patients’ individual needs, constraints and preferences, without losing its clinical effectiveness. A proposed solution is exercise-based cardiac rehabilitation in the home envi-ronment. Home-based cardiac rehabilitation does not require journeys to the outpatient clinic, training sessions can be scheduled individually and independently, and cardiac rehabilitation can be combined with work resumption [18,21]. In addition, home-based cardiac rehabilita-tion provides the opportunity to combine evidence-based behavioural change-strategies with modern wearable sensor techniques in the telemonitoring guidance, during the integration of exercise training in daily routine.

Previous studies have shown that structured home-based cardiac rehabilitation is safe and short-term results of home-based cardiac rehabilitation are similar to the results of centre-based

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cardiac rehabilitation [22–25]. However, it is important to note that the interventions described in these studies vary considerably with respect to both the prescribed training protocols and the telemonitoring guidance provided during home-based training. There is no general guide-line for home-based training yet, hence training protocols from centre-based cardiac rehabilitation are often translated to the home environment [26]. However, those recommendations cannot be translated directly to home-based training. For instance, fitness equipment used in the outpatient clinic is seldom available at home and high-intensity interval training is difficult to perform during outdoor walking or cycling. Therefore, we should define the characteristics of exercise training that determine the improvement in physical fitness, so we can provide recommendations for designing a feasible and effective home-based training programme for cardiac rehabilitation patients.

Recent advances in sensor technology and ubiquitous connectivity have created opportuni-ties to monitor and guide patients that are exercising in the home environment in real time. Wearable sensors and Internet applications (e.g. heart rate monitors and online dashboards) can provide an accurate insight in training data, which can be used by both the patient and the physical therapist to improve future exercise sessions [27]. Wearable sensors can also be used to monitor physical activity behaviour in the home environment [28]. Although physical inactivity is a major risk factor for cardiovascular disease [29], physical activity levels are rarely monitored during cardiac rehabilitation. Currently, patients are asked about their physical activity behaviour at the start and end of the rehabilitation programme using questionnaires, which often leads to socially desirable responses [30]. With wearable sensors (e.g. accelerometers and/or heart rate monitors) reliable physical activity measurements are obtained and can be used to monitor physical activity levels during cardiac rehabilitation [30,31].

If exercise data and physical activity data are combined with evidence-based behavioural-change coaching techniques during telemonitoring guidance, the intervention has the potential to induce more sustainable results. As such, motivational interviewing, a technique to approach people that are engaged in behavioural change, has shown to improve the success rate of interventions [32]. Feedback on objective training data and insight in training progress enhances a patients’ self-ef-ficacy, the confidence in one’s own ability to execute and complete a task or goal [27]. In addi-tion, independent training in the home environment promotes the development of self-man-agement skills (e.g. action planning, problem solving, decision making), which are required to maintain an active lifestyle after completion of cardiac rehabilitation [33,34]. If those techniques are included in the telemonitoring guidance of home-based cardiac rehabilitation, we expect it can improve long-term benefits of cardiac rehabilitation, and that home-based cardiac rehabili-tation is an eligible alternative to centre-based cardiac rehabilitation. However, cost-effectiveness of a novel intervention is essential for wide scale implementation. Unfortunately, cost-effective-ness analyses of home-based cardiac rehabilitation interventions are scarce and the sparse liter-ature is inconclusive due to the high variation in training protocols and telemonitoring guidance in home-based interventions [35–37]. Therefore, a novel intervention like home-based cardiac

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rehabilitation requires a comprehensive cost-effectiveness analysis to determine whether the intervention is a valuable alternative to conventional centre-based cardiac rehabilitation.

The aim of this thesis was to develop and evaluate the effectiveness of a home-based training intervention that suits the preferences of a patient entering cardiac rehabilitation, without losing its clinical effectiveness. Therefore, we addressed the following three objectives: · To identify which characteristics of a training protocol (i.e. training intensity, session

frequency, session duration, programme length) determine physical fitness improvement after exercise training in cardiac rehabilitation patients, exploring the best training approach for home-based training.

· To develop a physical activity prediction-model to accurately assess and monitor daily activity behaviour in the home-environment.

· To compare the clinical effectiveness and cost-effectiveness of home-based exercise training including telemonitoring guidance with conventional centre-based exercise training in low-to-moderate cardiac risk patients entering cardiac rehabilitation.

Outline of this thesis

The first part of this thesis focuses on the development of a home-based cardiac rehabilitation intervention. In two systematic literature reviews we study the effect of individual training char-acteristics on the improvement of physical fitness in coronary artery disease patients (Chapter 02) and chronic heart failure patient (Chapter 03). Although previous literature showed that exercise training in both groups resulted in an improvement in physical fitness, it is unclear to what extent the different training characteristics determine this improvement. We analyse each training programme in terms of session frequency, session duration, training intensity and programme length. In addition, we analyse the effect of the product of those four char-acteristics, energy expenditure, and study the effect of those characteristics on the improve-ment of physical fitness. In Chapter 04 we focus on the assessment of physical activity levels of cardiac rehabilitation patients, and develop an energy expenditure prediction model to assess and monitor physical activity levels of patients at home during cardiac rehabilitation. Based on previous methods validated on healthy adults, we combine heart rate and body movement data with patient characteristics to accurately estimate physical activity levels of patients in the home environment. In Chapter 05 we combine the results of the abovementioned chapters and describe the rationale and methodology of the FIT@Home trial, a home-based cardiac rehabili-tation intervention with telemonitoring guidance.

In Chapter 06 we describe the interim short-term results of the randomised controlled trial. Data on physical fitness, health-related quality of life and training adherence of the first 25 patients included in the trial are reported. The final results from the FIT@Home trial, both short- and

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long-term are described in Chapter 07. In addition to physical fitness and health-related quality of life, also physical activity levels, patient satisfaction and cost-effectiveness of home-based exer-cise training is compared with centre-based exercise training. The results and the implications of this trial, combined the first chapters of this thesis, are summarised and discussed in Chapter 08.

13References

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Rehabilitation, J. Cardiopulm. Rehabil. Prev. 36 (2016) 217–229.

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analysis., Br. J. Gen. Pract. 55 (2005) 305–12.

33) J.J. Carlson, G.J. Norman, D.L. Feltz, B. a Franklin, J. a Johnson, S.K. Locke, Self-efficacy, psychosocial factors,

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363–373.

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Prev. Cardiol. (2015).

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Jos J Kraal*, Tom Vromen*, Joël Kuiper,Ruud Spee, Hareld MC Kemps, Niels Peek

*Both authors contributed equally to the manuscript

Submitted for publication

Chapter 2The influence of training

characteristics on the effect ofexercise training in patients with

coronary artery disease: Systematic review and meta-regression analysis

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Abstract

Although exercise-based cardiac rehabilitation improves exercise capacity of patients with coronary artery disease, it is unclear which characteristics of the exercise programme determine the improvement in exercise capacity. Both total energy expenditure and its constituent training characteristics (training intensity, session frequency, session duration and programme length) vary considerably in clinical studies, making it hard to compare studies directly. Therefore, we performed a systematic review and meta-regression anal-ysis to assess the effect of total energy expenditure and individual training characteristics of aerobic exercise programmes on exercise capacity. We identified randomised controlled trials comparing continuous aerobic exercise training with usual care for patients with coro-nary artery disease. Studies were included when 1) the exercise programme was described in terms of training intensity, session frequency, session duration and programme length, and 2) improvement in exercise capacity was reported as peak oxygen uptake. Total energy expenditure of each exercise programme was calculated from the four training character-istics. The effect of the training characteristics and total energy expenditure on exercise capacity was determined using mixed effects linear regression analyses. The analyses were performed with and without total energy expenditure as covariate. Twelve studies were included in the analyses. Whereas total energy expenditure was significantly related to the improvement of exercise capacity, no effect was found for its constituent training character-istics after adjustment for total energy expenditure. This suggests that the design of an exer-cise programme should primarily be aimed at total energy expenditure rather than on one specific training characteristic. Therefore, exercise programmes can be tailored to patients’ preferences without losing its effectiveness.

Introduction

Exercise-based cardiac rehabilitation (ECR) improves exercise capacity and quality of life, and decreases cardiovascular mortality and morbidity in patients with coronary artery disease (CAD) [1–3]. Exercise training is therefore considered a crucial component of cardiac rehabilitation and is highly recommended in both European and American clinical guidelines [4,5]. Furthermore, ECR is widely accepted and implemented in daily practice for CAD patients. Because exercise capacity is strongly associated with morbidity and mortality in CAD patients, it is important to understand which factors determine the beneficial effects of exercise. Moreover, if we understand which training characteristics are the strongest determinants of improvement in exercise capacity after exercise training, the most effective exercise programme can be prescribed [6].

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Studies with healthy adults showed that the effects of exercise training improve when total energy expenditure of an exercise programme increases [7]. Recently, two systematic reviews confirmed that total energy expenditure was the strongest predictor of improvement in exercise capacity in chronic heart failure patients [8,9]. Total energy expenditure of an exercise programme is determined by session frequency, session duration, training intensity and programme length. Although practice guidelines describe the content of an exercise programme, the individual char-acteristics of an exercise programme vary considerably in practice and between training studies [1,10,11]. Therefore, the individual effect of the training characteristics remains under debate.

Vanhees et al. showed in a retrospective cohort study that session frequency and training inten-sity were strong predictors for the improvement in exercise capacity [12]. Other studies indi-cated that although a minimum energy expenditure was required for the improvement in exer-cise capacity, adjustments to individual characteristics above a certain cut-off value (e.g. session duration of 30 minutes, session frequency of twice a week) did not influence the improvement in exercise capacity [13,14]. However, those studies did not adjust for total energy expenditure of the exercise programmes. Since energy expenditure is comprised by the four training charac-teristics, a correction for energy expenditure is necessary to identify the individual effect of the training characteristics. Therefore, studies comparing training protocols to determine the effect of a training characteristic should therefore perform an isocaloric comparison (i.e. a comparison in which energy expenditure of both exercise programmes are matched).

Previous studies with isocaloric comparisons but dissimilar session frequency and/or programme length in the exercise programmes showed that the improvement in exercise capacity was similar [15,16]. In addition, the effect of training intensity appears inconclusive. Whereas two systematic reviews showed a superior effect on exercise capacity after high intensity training compared to moderate intensity training [17,18], other studies comparing isocaloric programmes showed no differences between the exercise programmes [19–21]. However interpretation of the results of these studies is hampered, as exercise programmes in those studies did not only differed with respect to training intensity but also to training modality. High intensity training is performed using an interval protocol, whereas moderate intensity training is performed using a continuous training protocol. Therefore, the individual effect of the training characteristics remains unknown.

With large sets of exercise data, regression analyses can be used to explore the individual effect of the training characteristics on the improvement of exercise capacity. In addition, a correc-tion for energy expenditure can be performed in the analyses. Therefore, the objective of this systematic review and meta-regression analysis was to investigate which training characteristics determine the improvement of exercise capacity after ECR in CAD patients, correcting for total energy expenditure of the exercise programme.

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Methods

Literature search strategyWe conducted a systematic literature search in the database of EMBASE and MEDLINE to find papers published between 1st of April 2007 and 1st of April 2015, addressing aerobic exercise training after cardiac rehabilitation for CAD patients. In the search strategy, which involved a mix of MeSH-terms and free text terms, we combined synonyms on three topics: population and diagnosis (i.e. coronary artery disease, cardiac patients, myocardial infarction), therapy (i.e. cardiac rehabilitation, secondary prevention, exercise training, physical training) and outcome (i.e. physical function, exercise capacity, exercise tolerance). The search was limited to randomised controlled trials published between 01-04-2007 and 01-04-2015 and written in English. The complete search strategy is described in appendix A. The protocol of this systematic review and meta-regression analysis is published in the Prospero database (http://www.crd.york.ac.uk/pros-pero) with registration number CRD42014014846. The search strategy and methodology of this meta-analysis is based on a similar meta-analysis with chronic heart failure patients published elsewhere [9].

Study SelectionWe included randomised controlled trials comparing continuous aerobic exercise programmes with usual care in CAD patients. Only studies reporting change in peakVO2 to evaluate training effects were included. Studies evaluating interval training, resistance training or cardiac reha-bilitation modalities not affecting exercise capacity (e.g. cognitive therapy, stress-manage-ment) were excluded. Studies that reported the results of a combination of aerobic exercise with strength training were excluded as well. All included studies were required to describe the aerobic exercise programme in detail, with at least information concerning session frequency, session duration, programme length and training intensity (% of peak heart rate, peakVO2 or maximum workload). When important data concerning the exercise programme or outcome parameter were missing, authors were contacted to retrieve the missing data.

Data collection processFour couples of researchers screened the titles and abstracts using the abovementioned in- and exclusion criteria. Both researchers in each couple performed the screening independently. Afterwards, they compared the results and reached consensus. The full papers of the selected articles were screened by three couples of researchers to make a final decision on inclusion in a similar procedure. When no consensus was reached between the two researchers, a third researcher decided whether the article was included. Data concerning patient characteristics, exercise programme, outcome measures, and risk of bias of the included studies were extracted from the full texts and stored in a Microsoft Access database.

21

Energy expenditureWe calculated energy expenditure (J.kg-1) for all interventions by multiplying total training time (i.e. session frequency*session duration*programme length) with training intensity. First, training intensity was converted to a percentage of peakVO2 using a conversion table from the American College of Sports Medicine [22]. Second, the oxygen consumption (VO2 ml.min-1.kg-1) per inter-vention was calculated using pre-training exercise capacity (peakVO2) multiplied with training intensity (% of peakVO2), and total programme length in minutes. Finally, total oxygen uptake (in ml.min-1.kg-1) was converted to Joules per kg under the assumption that consumption of one liter oxygen equals 20.93 Joule [22].

Risk of bias quality assessmentThe methodological quality of the included articles was assessed using the Cochrane Collab-oration’s tool for assessing risk of bias [23]. The tool identifies seven potential sources of bias in randomised trials (i.e. random sequence generation, allocation concealment, blinding of partic-ipants and personnel, blinding of outcome assessments, incomplete outcome data, selective reporting and other forms of bias). Two independent researchers classified each potential risk of bias as ‘low’, ‘high’ or ‘unclear’, with the last category indicating either lack of information or uncertainty concerning the potential bias.

Synthesis of resultsThe relationship between training characteristics and exercise-related changes in exercise capacity (peakVO2) was determined using mixed-effects linear regression analyses. First, a linear regression analysis was used to assess differences in exercise capacity between exercise training and usual care. Second, the effect of energy expenditure and the four training characteristics was assessed separately by five univariate analyses. Subsequently, the effect of the four training characteristics (i.e. session frequency, session duration, training intensity and programme length) was assessed by four multivariate regression analyses with total energy expenditure as a covariate. Model fit was assessed using residual deviance and I2, which describes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error. Large values of I2 indicate inconsistency in the result of the underlying studies. The effect of training characteristics was assessed by their Z-score. Because of the large number of statistical compari-sons we considered p-values below 0.01 as significant. When heterogeneity (i.e. I2) was high and sufficient data were available, the analysis was repeated without outliers. Baseline differences between groups were tested using an independent student t-test with a significance level of 0.05.

22

Figure 1: Inclusion flowchart of the selected studies

Results

Study selectionWe identified 812 unique studies from the EMBASE and MEDLINE databases. Figure 1 provides an overview of the search and election studies records. Through screening of titles and abstract we excluded 539 studies, and 188 studies were excluded after full-paper review. From the remaining 29 studies, 17 were included in an analysis for chronic heart failure patients published elsewhere [9] and 12 were included in this review. One study randomised their participants in two interven-tion groups (i.e. high intensity training and moderate intensity training) and one control group [24]. Therefore, we included this study as two separate comparisons with the same control group.

23

Study characteristicsAn overview of the baseline characteristics is provided in Table 1. Median sample size of the included studies was 46 patients, ranging from 20 to 118 included patients. A total number of 367 patients were randomised to aerobic exercise training, and 326 patients received usual care. In the control group, median age was 57.0 (range 52.0 to 69.1) and 84% were male. In the training group, median age was 54.3 (range 52.0 to 71.1) and 87% were male. There were no significant differences between groups at baseline.

Table 1: Baseline characteristics of included studies

Study N, # N, # Male, #

(%)

Male, #

(%)

Age, # Age, # Peak VO2

(ml.min-1.kg-1)

Peak VO2

(ml.min-1.kg-1)

INT CON INT CON INT CON INT CON

Aamot 2010 [25] 20 19 15 (75) 14 (74) 61 58 30.6 ± 6.7 29.8 ± 6.1

Balen 2008 [26] 30 30 21 (70) 23 (77) 59 61 20 ± 4.4 17.9 ± 4.6

Benetti 2010a [24] 29 29 29 (100) 29 (100) - - 29.2 ± 2.2 31.6 ± 3.9

Benetti 2010b [24] 29 29 29 (100) 29 (100) - - 32 ± 5.3 31.6 ± 3.9

Bilinska 2010 [27] 59 59 29 (49) 29 (49) 54 54 24.3 ± 4.5 24.7 ± 4.3

Giallauria 2011 [28] 37 38 28 (76) 32 (84) 61 60 16.4 ± 1.5 16.7 ± 2.2

Giallauria 2012 [29] 24 26 23 (96) 23 (88) 54 52 13 ± 3 14 ± 4

Giallauria 2013 [30] 25 21 22 (88) 18 (86) 54 54 14 ± 3 14 ± 5

Lee 2009 [31] 20 19 20 (100) 19 (100) 52 52 22.2 ± 3.9 22.7 ± 3.1

Mameletzi 2011 [32] 10 10 10 (100) 10 (100) 69 71 22 ± 3.9 21 ± 4.2

Oliveira 2014 [33] 47 45 40 (85) 37 (45) 55 59 27.6 ± 7.3 26.9 ± 5.6

Ribeiro 2012 [34] 20 18 18 (90) 13 (72) 54 57 30.8 ± 7.8 32.6 ± 5.8

Su 2011 [35] 17 12 17 (100) 12 (100) 52 52 22.5 ± 3.7 21.2 ± 2.1

Data provided as mean ± standard deviation, unless stated otherwise,

INT=intervention; CON=control; peakVO2=maximal oxygen consumption

24

Programme characteristicsTraining intensity was described as %HRmax in six studies and as %peakVO2 in six studies. Median programme length was 12 weeks (range 3 to 28) with a median of 3 sessions per week (range 2 to 5). Session duration varied from 20 to 60 minutes, with a median of 30 minutes. Median training intensity was 65% of peakVO2 (range 45% to 79% of peakVO2). Total training time varied from 260 to 2700 minutes, with a median of 1080 minutes, and total energy expenditure varied from 74 to 1300 J.kg-1 with a median of 364 J.kg-1. The programme characteristics of the included studies are described in Table 2.

Table 2: Exercise programme characteristics of included studies

Study Prog

ram

me

leng

th

(wee

ks)

Sess

ion

freq

uenc

y

(n/w

eek)

Sess

ion

dura

tion

(min

)

Inte

nsit

y

(% p

eakV

O2)

Tota

l tra

inin

g vo

lum

e

(min

)

EE to

tal

(Jou

le.k

g-1 )

EE w

eek

(Jou

le.k

g-1)

Δ P

eak

VO2

(ml.m

in-1

.kg-1

)

Δ P

eak

VO2

(ml.m

in-1

.kg-1

)

INT INT INT INT INT INT INT INT CON

Aamot 2010 [25] 4 2 32.5 44.5 260 74.0 18.5 0.1 0.9

Balen 2008 [26] 3 5 45 55 675 155.2 51.7 3.2 0.6

Benetti 2010a [24] 12 5 45 78.9 2700 1300.1 108.3 12.4 -2.4

Benetti 2010b [24] 12 5 45 66.3 2700 1197.2 99.8 5.9 0

Bilinska 2010 [27] 6 3 60 66.3 1080 363.7 60.6 1.8 1.1

Giallauria 2011 [28] 26 3 30 68.4 2340 548.6 21.1 4.6 -0.4

Giallauria 2012 [29] 26 3 30 65 2340 413.3 15.9 4 1

Giallauria 2013 [30] 26 3 30 65 2340 445.0 17.1 4 1

Lee 2009 [31] 12 3 20 62.5 720 208.8 17.4 2.8 -0.3

Mameletzi 2011 [32] 28 3 30 60 2520 663.6 23.7 4.6 -0.9

Oliveira 2014 [33] 8 3 30 69 720 288.5 36.1 2.1 -0.1

Ribeiro 2012 [34] 8 3 35 60 840 324.4 40.6 3.1 0.3

Su 2011 [35] 12 3 20 62.5 720 211.6 17.6 2.6 0.6

Data provided as mean ± standard deviation, unless stated otherwise.

INT=intervention; CON=control; peakVO2=maximal oxygen consumption; EE=energy expenditure

25

Effect of training characteristicsThe mean difference in improvement peakVO2 between the intervention group and control group was 4.19 ml.min-1.kg-1 (p<0.01, 95% CI 1.90 to 6.48, figure 2). Table 3 presents the results from univariate regression and multivariate regression analyses. Total energy expendi-ture was significantly associated with improvement of exercise capacity, showing that an increase of energy expenditure of 100 J.kg-1 was associated with a peakVO2 improvement of 0.91 ml.min-1.kg-1 (p<0.01, 95% CI 0.77 to 1.05). All other training characteristics were significantly associated with improvement of peakVO2 in the univariate analyses. Although heterogeneity for energy expenditure was low (I2=17%), it was high for all individual training characteristics (I2 ranging from 79% to 89%). When adjusting for total energy expenditure, none of the individual training characteristics was significantly associated with improvement of peakVO2. After the adjustment, heterogeneity was low for all characteristics (I2 ranging from 0% to 22%). Detailed results of the regression analyses are provided in Appendix B.

Table 3: Results of regression analyses, with and without correction for energy expenditure

Training

characteristics

Effect scale Effect Size

(ml.min-1.kg-1)

95% CI

(ml.min-1.kg-1)

p-value I2 AIC

Univariate regression analyses

Total EE 100 Joule.kg-1 0.91 0.77-1.05 <0.001* 16.9 53.3

Session frequency 1 session/week 1.36 0.85-1.88 <0.001* 78.5 67.1

Training intensity 10% peakVO2 0.70 0.37-1.03 <0.001* 83.5 69.7

Session duration 10 minutes 1.15 0.51-1.79 <0.001* 87.3 71.5

Programme length 2 weeks 0.47 0.15-0.78 0.003* 88.6 73.3

Multivariate regression analyses, correcting for energy expenditure

Session duration 10 minutes -0.40 -0.81-0.01 0.058 0.0 49.8

Training intensity 10% peakVO2 -0.17 -0.40-0.17 0.422 18.3 51.6

Session frequency 1 session/week -0.15 -0.77-0.47 0.633 21.3 51.8

Programme length 2 weeks -0.05 -0.21-0.11 0.579 22.0 52.0

Effect size is given as change in peakVO2

I2=residual heterogeneity; AIC=Akaike’s information coefficient (model fit); peakVO2=maximal oxygen consumption;

EE = energy expenditurem, * = significant at p<0.01

26

Stud

y

Fixe

d eff

ect m

odel

Rand

om e

ffect

s m

odel

Het

erog

enei

ty: I

−sq

uare

d=86

.1%

, tau

−sq

uare

d=14

.19,

p<

0.00

01

Aam

ot IL

et a

l.

Bale

n S

et a

l.

Bene

tti M

et a

l.

Bene

tti M

et a

l.

Bilin

ska

M e

t al.

Gia

llaur

ia F

et a

l.

Gia

llaur

ia F

et a

l.

Gia

llaur

ia F

et a

l.

Lee

BC e

t al.

Mam

elet

zi e

t al.

Oliv

eira

et a

l

Ribe

iro F

et a

l.

Su M

Y et

al.

Tota

l

367

20

30

29

29

59

24

37

25

20

10

47

20

17

Mea

n

0.1

3.2

12.4

5.9

1.8

4.0

4.6

4.0

2.8

4.6

2.1

3.1

2.6

SD

8.9

5265

3

7.4

2967

0

4.5

6837

0

6.3

0713

9

6.3

0158

7

4.9

7996

0

2.8

8270

7

4.9

7996

0

6.3

0476

0

5.7

1576

8

11.3

7145

5

11.2

7430

7

6.6

5281

9

Expe

rimen

tal

Tota

l

355

19

30

29

29

59

26

38

21

19

10

45

18

12

Mea

n

0.9

0.6

−2.

4

−2.

4

1.1

1.0

−0.

4

1.0

−0.

3

−0.

9

−0.

1

0.3

0.6

SD

8.56

2126

6.93

6137

5.53

7147

5.53

7147

6.60

1515

5.51

3620

2.60

7681

6.92

8203

4.15

5719

5.29

0558

8.17

4962

9.36

3760

3.29

0897

Cont

rol −

15−

10−

50

510

15

Mea

n di

ffer

ence

MD

4.7

8

4.1

9

−0.

80

2.6

0

14.8

0

8.3

0

0.7

0

3.0

0

5.0

0

3.0

0

3.1

0

5.5

0

2.2

0

2.8

0

2.0

0

95%

−CI

[ 4.0

1; 5

.55]

[ 1.9

0; 6

.48]

[−6.

30;

4.70

]

[−1.

04;

6.24

]

[12.

19; 1

7.41

]

[ 5.2

5; 1

1.35

]

[−1.

63;

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]

[ 0.0

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.91]

[ 3.7

5; 6

.25]

[−0.

55;

6.55

]

[−0.

24;

6.44

]

[ 0.6

7; 1

0.33

]

[−1.

83;

6.23

]

[−3.

77;

9.37

]

[−1.

67;

5.67

]

W(fi

xed)

100% −−

2.0

%

4.5

%

8.7

%

6.4

%

11.0

%

7.0

%

38.4

%

4.7

%

5.3

%

2.6

%

3.7

%

1.4

%

4.4

%

W(r

ando

m)

−−

100%

6.2%

7.7%

8.5%

8.2%

8.7%

8.3%

9.3%

7.8%

8.0%

6.7%

7.4%

5.4%

7.7%

Figure 2: Forest plot describing the effect of exercise training on peakVO2

27

As appears from Figure 2, the study by Benetti et al. found a much larger effect on peakVO2 than the other included studies, and was probably causing high heterogeneity in the meta-re-gression. We therefore performed a sensitivity analysis without this study. The sensitivity anal-ysis showed that heterogeneity improved for all univariate and multivariate analyses (Table 4). Energy expenditure and all four characteristics remained significantly associated with improve-ment of exercise capacity in the univariate analyses, and none of the characteristics were signifi-cantly associated with improvement of exercise capacity after adjustment for energy expendi-ture. Detailed results of the regression analyses are provided in Appendix C.

Others (risk of bias, publication bias)Results of the risk of bias analysis (Appendix A) showed that overall methodological quality was good. Differences in quality between studies were small. The funnel plots (Appendix A) showed little evidence for publication bias.

Table 4: Results of the sensitivity analysis, with and without correction for energy expenditure

Training

characteristics

Effect scale Effect Size

(ml.min-1.kg-1)

95% CI

(ml.min-1.kg-1)

p-value I2 AIC

Univariate regression analyses

Total EE 100 Joule.kg-1 0.83 0.64-1.01 <0.001* 0.0 40.7

Programme length 2 weeks 0.36 0.28-0.43 <0.001* 0.0 39.4

Training intensity 10% peakVO2 0.47 0.30-0.65 <0.001* 26.7 44.7

Session frequency 1 session/week 0.90 0.50-1.31 <0.001* 41.9 45.6

Session duration 10 minutes 0.67 0.18-1.17 0.008* 69.7 50.2

Multivariate regression analyses, correcting for energy expenditure

Session duration 10 minutes -0.36 -0.85-0.14 0.160 0.0 38.0

Programme length 2 weeks 0.23 -0.10-0.57 0.176 0.0 38.2

Session frequency 1 session/week 0.04 -0.64-0.72 0.907 0.0 39.7

Training intensity 10% peakVO2 -0.07 -0.22-0.35 0.653 0.0 42.8

Effect size is given as change in peakVO2

I2=residual heterogeneity; AIC=Akaike’s information coefficient (model fit); peakVO2=maximal oxygen consumption;

EE = energy expenditure. * = significant at p<0.01

28

Discussion

The present study showed that total energy expenditure of exercise programmes is a strong determinant of the effect of ECR on exercise capacity in CAD patients. Whereas the meta-re-gression analysis showed that the four constituent parameters of total energy expenditure (i.e. session duration, session frequency, training intensity and programme length) were all related to the improvement in exercise capacity, no independent effect of any of the four training charac-teristics was observed after correction for total energy expenditure.

Although the beneficial effects of exercise training on exercise capacity have already been estab-lished in prior clinical trials and meta-analyses [3,36], the effect of individual training characteris-tics on improvement in exercise capacity was not well established. Results from our meta-anal-ysis indicate that total energy expenditure is an important determinant of improvement in exercise capacity, and that the effects of the individual training characteristics disappear when we adjust for energy expenditure. This is in line with studies comparing individual training char-acteristics using isocaloric exercise programmes [15,16,21]. Therefore, we recommend that ECR programmes should be aimed at a high total energy expenditure without specific preference for a high training intensity or other training characteristics.

The results from our regression analyses provide additional information compared to the systematic reviews and randomised controlled trials previously performed. The regression anal-ysis calculates an effect size for each individual training characteristic. The effect sizes illustrate how we can enhance the effect of the exercise programme if energy expenditure is not taken into account. If we assume that the range of the included studies determines practice variation, a maximum improvement of 1.60 ml.min-1.kg-1 peakVO2 can be achieved by increasing inten-sity from 45 to 79% of peakVO2. Similarly, according to the results of the sensitivity analysis, an improvement of 2.71 ml.min-1.kg-1 is achieved by an increase in session frequency from 2 to 5 sessions per week, while an improvement of 2.69 ml.min-1.kg-1 peakVO2 is achieved by increasing session duration from 20 to 60 minutes per session and 4.45 ml.min-1.kg-1 peakVO2 by increasing programme length from 3 to 28 weeks. However, these results are based on the assumption that there is a linear dose-response relationship for exercise training. Previous studies indicated that the beneficial effects of exercise deteriorate with vigorous exercise [7]. Therefore, the results are primarily applicable within the range described in this review.

In our first analysis, all training characteristics were significantly related to improvement in exer-cise capacity. However, these effects were absent after correction for total energy expendi-ture. This implies that prescription of an exercise programme should primarily be focused on total energy expenditure rather than on one specific training characteristic. Therefore, factors such as training adherence, patients’ preference and determinants of sustainability of training effects should be taken into account when designing an exercise programme. First, a high

29

training adherence improves the effectiveness of ECR [37]. Previous studies indicated that a high training intensity can reduce training adherence, whereas an increase in session frequency or session duration are not associated with a reduction in adherence [38–40]. Second, an exercise programme aligned with the preferences of a patient (e.g. training type, training characteristics, location) improves motivation and training adherence, indirectly influencing the improvement in exercise capacity [38,41]. In view of the abovementioned facts, an exercise programme can be translated to the home-environment for patients that prefer exercise training at home. For instance, patients with little time available for exercise can perform training sessions with a short duration and high session frequency. Similarly, as a high training intensity is difficult to reach and control in a home environment without fitness equipment, endured walking or biking sessions at a moderate training intensity can be performed. Furthermore, the development of telemon-itoring opportunities to sustain guidance in the home environment (i.e. wearable sensors and increased connectivity) provides an opportunity to design a feasible and effective home-based training programme that can induce an optimal short-term and long-term training effect [42,43].

LimitationsFirst, due to strict criteria in the inclusion and exclusion procedure, sample size and variation in training characteristics among the included studies was low. Studies that lacked information concerning the training characteristics were excluded from the analyses. Consequently, the unaccounted variability in the univariate regression analyses was high, indicating a low model fit. Therefore, the results must be interpreted with cause. Second, we assumed a linear dose-re-sponse relationship in exercise training. However, previous literature showed that excessive exercise may hamper the improvement in exercise capacity [44]. Therefore, our results can only be interpreted with respect to the variance of training characteristics in the included studies. Because the variance of the included studies is within the borders of a regular ECR programme, we expect that our results and recommendations are applicable to ECR programmes. Third, our analyses were based on the exercise programme reported in the included studies. However, the actual exercise performed during the programme often deviates from the prescribed exer-cise programme, which is reported in the study. Conraads et al. showed that the actual training intensity in a high intensity training group was lower than prescribed in the exercise programme, while the actual intensity of the moderate intensity training group was higher than prescribed [21]. They discussed that this could be an important factor influencing the improvement in exer-cise capacity in both groups, and suggested that the adherence to the prescribed programme should be measured during the exercise programme and reported in the study. Because our analyses are based on the prescribed exercise programmes, the results could be different when both the performed exercise programmes and the prescribed exercise programmes were reported and included in the analyses. Finally, several studies reported no standard deviation of the change from baseline in peakVO2. Therefore, a correlation between baseline and follow-up assessment of peakVO2 was assumed at p=0.7. This could have affected the results.

30

ConclusionThis study showed that total energy expenditure of an exercise programme is the main determi-nant of improvement in exercise capacity in CAD patients. To increase total energy expenditure, all four training characteristics appear suitable to adjust. To optimise the effectiveness of ECR, we recommend to take into account other factors determining the sustainability of exercise training, like patient preference and adherence.

AcknowledgementsWe want to thank Rutger Brouwers, Anne-Marieke Mulder-Wiggers and Mariette van Engen- Verheul for their help during the screening of records. Dr. Gerben ter Riet and Dr. Gert Valkenhoef are acknowledged for their help in constructing the methodological framework and analyses.

31

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acute myocardial infarction., J. Card. Fail. 17 (2011) 108–114.

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induced hypoperfusion and improves left ventricular function., Eur. J. Nucl. Med. Mol. Imaging. 40 (2013)

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(2011) 217–227.

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in residual viable myocardium within infarct zone., J. Magn. Reson. Imaging. 34 (2011) 60–8.

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Capacity in patients with myocardial infarcts and patients with coronary bypass surgery, Am J Cardiol. 76

(1995) 1014–1019.

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patients with chronic heart failure: a randomized controlled trial., Eur. J. Cardiovasc. Prev. Rehabil. 17 (2010)

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34

Appendix A

Medline search strategy#1 (("Heart Diseases/diet therapy"[Mesh] OR "Heart Diseases/psychology"[Mesh] OR "Heart Diseases/rehabili-

tation"[Mesh] OR "Heart Failure"[Mesh] OR "Heart Diseases/prevention and control"[Mesh] OR heart failure[-

tiab] OR heart*[tiab] OR cardiac*[tiab] OR coronary[tiab] OR myocardial[title]) AND ("Myocardial Revasculariza-

tion"[Mesh] OR "heart failure"[Mesh] OR "Myocardial Ischemia"[Mesh] OR "Acute Coronary Syndrome"[Mesh]

OR “Myocardial Infarction”[Mesh] OR “Angina Pectoris”[Mesh] OR "percutaneous coronary intervention"[tiab]

OR "heart failure"[tiab] OR “congestive heart failure”[tiab] OR “chronic heart failure”[tiab] OR “left ventricular

disfunction”[tiab] OR “cardiomyopathy”[tiab] OR "acute coronary syndrome*"[tiab] OR "stable angina pecto-

ris"[tiab] OR "unstable angina pectoris"[tiab] OR "myocardial ischemia"[tiab] OR "cardiac rehabilitation"[tiab]

OR "cardiac patients"[tiab] OR "myocardial revascularization"[tiab] OR “myocardial infarction”[tiab] OR “Non-ST

Segment Elevation Myocardial Infarction”[tiab] OR “coronary artery bypass graft"[tiab] OR “CABG”[tiab] OR

"stent"[tiab] OR "angioplasty"[tiab] OR "PTCA"[tiab] OR “PCI”[tiab] OR “OPCAB”[tiab] OR "Cardiac Resynchroniza-

tion Therapy"[Mesh] OR cardiac resynchronization therap*[tiab] OR cardiac resynchronisation therap*[tiab]))

#2 ("rehabilitation" [Subheading] OR "Secondary Prevention"[Mesh] OR secondary prevention[tiab] OR cardiac

rehabilitation[tiab] OR "exercise program*"[tiab] OR exercise training[tiab] OR exercise[title] OR rehabilitation

program*[tiab] OR Physical training[tiab] OR training program*[tiab] OR “interval training”[tiab] OR "Heart

Failure/rehabilitation"[MAJR] OR "Coronary Disease/diet therapy"[MAJR] OR "Recovery of Function"[MeSH

Terms])

#3 ("Exercise Tolerance"[MeSH Terms] OR "Motor Activity"[MeSH Terms] OR "Exercise Test"[MeSH] OR "Exer-

cise"[Mesh] OR "Exercise Therapy"[MeSH Terms] OR “exercise capacity”[tiab] OR exercise tolerance[tiab]

OR exercise[tiab] OR physical activity[tiab] OR “interval training” OR "Cardiovascular Diseases/rehabilita-

tion"[MAJR] OR "Coronary Artery Disease/rehabilitation"[MAJR] OR "Coronary Disease/diet therapy"[MAJR]

OR physical function*[tiab])

#4 ("drug therapy"[Subheading] OR "Adolescent"[Mesh] OR "Child"[Mesh] OR "Infant"[Mesh] OR "surgery

"[Subheading] OR "Diabetes Mellitus"[Majr] OR "Platelet Aggregation Inhibitors"[Mesh] OR "Drug Therapy,

Combination"[Mesh] OR "Obesity"[Mesh] OR "Heart Transplantation"[Mesh] OR "Dementia"[Mesh] OR "Sleep

Apnea Syndromes"[Mesh] OR "Parkinson Disease"[Mesh] OR "Kidney Failure, Chronic"[Mesh] OR “Syndrome

X”[tiab])

#5 (“2007/31/03”[PDat] : “2015/01/04”[PDat]) AND (English[lang]) AND (Randomised Controlled Trial[ptyp])

#6 #1 AND #2 AND #3 AND #5 NOT #4

35

Appendix A, figure 2: Funnel-plot of sensitivity analyses

Appendix A, figure 1: Funnel-plot

0 5 10 15

3.0

2.0

1.0

0.0

Mean difference

Stan

dard

err

or

Funnel plot CAD

Mean difference

Stan

dard

err

or

Funnel plot CAD

−2 0 2 4 6 8 10

3.0

2.0

1.0

0.0

36

Appendix A, figure 3: Risk of bias

0% 20% 40% 60% 80% 100%

Su MY et al.

Ribeiro F et al.

Oliveira et al

Mameletzi et al.

Lee BC et al.

Giallauria F et al.

Giallauria F et al.

Giallauria F et al.

Bilinska M et al.

Benetti M et al.

Benetti M et al.

Balen S et al.

Aamot IL et al.

Low risk Unclear High risk

37

Appendix B

Results from the multivariate regressions analyses

Session frequencyMixed-Effects Model (k = 13; tau^2 estimator: DL)

tau 2̂ (estimated amount of residual heterogeneity): 0.5925 (SE = 1.2155)

tau (square root of estimated tau 2̂ value): 0.7697

I 2̂ (residual heterogeneity / unaccounted variability): 21.29%

H 2̂ (unaccounted variability / sampling variability): 1.27

Test for Residual Heterogeneity: QE(df = 11) = 13.9758, p-val = 0.2343

Test of Moderators (coefficient(s) 1,2): QM(df = 2) = 147.1148, p-val < .0001

Model Results: se zval pval ci.lb ci.ub

freq -0.1514 0.3175 -0.4769 0.6334 -0.7737 0.4708

expend 0.0098 0.0017 5.6583 <.0001 0.0064 0.0132 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Training intensityMixed-Effects Model (k = 13; tau^2 estimator: DL)







odel Results: se zval pval ci.lb ci.ub

intensity -1.1677 1.4530 -0.8036 0.4216 -4.0156 1.6802

expend 0.0101 0.0015 6.8293 <.0001 0.0072 0.0130 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

38

Session durationMixed-Effects Model (k = 13; tau^2 estimator: DL)

tau 2̂ (estimated amount of residual heterogeneity): 0 (SE = 0.9659)

tau (square root of estimated tau 2̂ value): 0






duration -0.0396 0.0209 -1.8920 0.0585 -0.0806 0.0014 .

expend 0.0111 0.0012 9.1473 <.0001 0.0087 0.0134 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Programme lengthMixed-Effects Model (k = 13; tau^2 estimator: DL)








length -0.0234 0.0422 -0.5544 0.5793 -0.1060 0.0593

expend 0.0095 0.0012 8.0662 <.0001 0.0072 0.0119 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

39

Results from the univariate regression analyses

Total energy expenditureMixed-Effects Model (k = 13; tau^2 estimator: DL)







expend 0.0091 0.0007 12.5754 <.0001 0.0077 0.0105 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1






Test for Residual Heterogeneity: QE(df = 12) = 55.7314, p-val < .0001


freq 1.3603 0.2627 5.1781 <.0001 0.8454 1.8752 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

40








intensity 6.9781 1.6753 4.1652 <.0001 3.6945 10.2617 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1








duration 0.1149 0.0325 3.5311 0.0004 0.0511 0.1786 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

41








length 0.2331 0.0798 2.9225 0.0035 0.0768 0.3895 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

42

Appendix C

Results of the sensitivity analysis, multivariate regression analysis









freq 0.1445 0.3132 0.4613 0.6446 -0.4694 0.7584

expend 0.0070 0.0019 3.6611 0.0003 0.0033 0.0108 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1









intensity 0.6592 1.4674 0.4492 0.6533 -2.2169 3.5354

expend 0.0071 0.0018 3.9603 <.0001 0.0036 0.0106 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

43









duration -0.0200 0.0227 -0.8800 0.3789 -0.0645 0.0245

expend 0.0090 0.0015 5.8718 <.0001 0.0060 0.0120 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1









length 0.0549 0.0400 1.3721 0.1700 -0.0235 0.1333

expend 0.0060 0.0015 3.8940 <.0001 0.0030 0.0090 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

44

Results of the sensitivity analysis, univariate regression analysis








expend 0.0078 0.0008 10.2059 <.0001 0.0063 0.0093 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1








freq 0.9033 0.2059 4.3869 <.0001 0.4997 1.3069 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

45








intensity 4.7475 0.8956 5.3008 <.0001 2.9921 6.5029 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1








duration 0.0673 0.0252 2.6710 0.0076 0.0179 0.1168 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

46








length 0.1779 0.0202 8.8204 <.0001 0.1383 0.2174 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

49

Tom Vromen*, Jos J Kraal*, Joël Kuiper, Ruud Spee, Niels Peek, Hareld MC Kemps*Both authors contributed equally to the manuscript

International Journal of Cardiology. 2016; 208:120-127

Chapter 3The influence of training

characteristics on the effect of aerobic exercise training in

patients with chronic heart failure: A meta-regression analysis

50

Abstract

Although aerobic exercise training has shown to be an effective treatment for chronic heart failure patients, there has been debate about the design of training programmes and which training characteristics are the strongest determinants of improvement in exercise capacity. Therefore, we performed a meta-regression analysis to determine a ranking of the individual effect of the training characteristics on the improvement in exercise capacity of an aerobic exercise training programme in chronic heart failure patients. We focused on four training characteristics; session frequency, session duration, training intensity and programme length, and their product; total energy expenditure. A systematic literature search was performed for randomised controlled trials comparing continuous aerobic exer-cise training with usual care. Seventeen unique articles were included in our analysis. Total energy expenditure appeared the only training characteristic with a significant effect on improvement in exercise capacity. However, the results were strongly dominated by one trial (HF-action trial), accounting for 90% of the total patient population and showing controversial results compared to other studies. A repeated analysis excluding the HF-ac-tion trial confirmed that the increase in exercise capacity is primarily determined by total energy expenditure, followed by session frequency, session duration and session intensity. These results suggest that the design of a training programme requires high total energy expenditure as a main goal. Increases in training frequency and session duration appear to yield the largest improvement in exercise capacity.

Introduction

The beneficial effects of aerobic exercise training (AET) programmes for chronic heart failure (CHF) patients have been demonstrated throughout the last decades [1,2]. However, training charac-teristics of exercise programmes (i.e. training intensity, session duration, session frequency and programme length) vary considerably between trials [3–6]. Although it is generally conceived that all of these characteristics influence training results in CHF patients, it remains unclear to what extent they determine the effect of AET separately. In recent years, training intensity has been a point of debate. In the first decade of the 21st century, most AET-trials studied training programmes with low to moderate training intensity, showing mainly beneficial effects on exer-cise capacity [6,7]. Several smaller trials showed a greater improvement in exercise capacity, using a high training intensity [8,9]. However, trials directly comparing moderate and high inten-sity training programmes showed conflicting results [3,10,11]. Moreover, in most of the studies comparing different intensities, high intensity exercise was performed as interval training, while the low to moderate intensity exercise group underwent continuous training. The pure effect of

51

training intensity therefore remains clouded by the use of different training modalities. Besides training intensity, data on the influence of other training characteristics on exercise capacity in CHF patients (i.e. session duration, session frequency and programme length) are scarce and therefore remains largely unclear. Available data consist of a substudy of the HF-action trial-which showed that high training volume (product of session duration, session frequency and programme length) was positively correlated with improvement in peak VO2, and Vanhees et al. showing that session frequency can be an important determinant of training effects in CHF patients [12]. A recent meta-analysis on this topic suggested that high training intensity and high training volume elicit the greatest improvement in exercise capacity in CHF patients [13]. However, analyses were performed without adjustment for total energy expenditure of the training programme. A correction for total energy expenditure, the product of session duration, session frequency, training intensity and programme length, is required to identify the effect of the individual training characteristics.

The objective of this meta-regression analysis was to explore which programme characteristics determine improvement in exercise capacity after exercise training in CHF patients, taking two constraints into consideration: 1) To identify the effect of the individual training characteristics, an adjustment for total energy expenditure was made in the analyses of session duration, session frequency, programme length and training intensity. 2) To isolate the effect of training intensity, the analyses was focused on aerobic continuous training as training modality.

Methods

Literature search strategyA search in both Medline and Embase was performed, for original articles written in English published between 1st of April 2007 to 1st of April 2015 and evaluating the effect of AET-pro-grammes on exercise capacity in CHF patients. The search strategy involved a mix of MeSH-terms and free text terms with synonyms on three different topics: population (i.e. heart failure, coronary artery disease, cardiac patients), therapy (i.e. cardiac rehabilitation, secondary preven-tion, physical training, exercise program) and outcome (i.e. exercise capacity, physical function, exercise tolerance). This search was combined with a search strategy to identify English written randomised controlled trials published between 01-04-2007 and 01-04-2015 and a search to limit for the correct diagnosis (i.e. NOT kidney failure, diabetes mellitus or obesity). The full elec-tronic search strategy of the Medline database is described in Appendix A. The protocol of this meta-analysis was published in the Prospero database (http://www.crd.york.ac.uk/prospero) with registration number: CRD42014014846.

52

Study selectionRandomised controlled trials comparing continuous AET programmes to usual care, using peakVO2 to evaluate exercise capacity, were included. Studies evaluating resistance training, interval training or CR modalities not affecting exercise capacity (e.g. relaxation therapy, educa-tion, cognitive therapy) were excluded. To be able to properly compare different exercise proto-cols, only studies describing the AET protocol in terms of session duration, session frequency, programme length and training intensity (% of peak heart rate, heart rate reserve, peakVO2 or maximum workload) were included. To rule out the confounding influence of training modality on peakVO2, studies that reported the results of a combination of aerobic exercise with strength training were excluded. When crucial data concerning the outcome parameter or training protocol was missing, authors were contacted to retrieve the missing data.

Data collection processAfter the electronic searches, the abovementioned inclusion and exclusion criteria were used to screen all titles and abstracts in four groups of two researchers. Both researchers performed the screening independently, after which they compared the results and reached consensus. Of the selected articles, the full text was screened by three groups of two independent researchers to make the final decision on inclusion in a similar procedure. When no consensus was reached between two researchers, a third researcher decided whether the article was included or not. Data of included papers were extracted from full texts and stored in a Microsoft Access data-base through a form with predefined items describing study and patient characteristics, exercise protocol, outcome measurements and risk of bias assessment.

Energy expenditureEnergy expenditure in joules/kilogram (J.kg-1) was calculated for every exercise intervention by multiplying training duration with training intensity. All training intensities were converted to a percentage of peakVO2 using a conversion table from the American College of Sports Medicine [14]. Following this, training VO2 in ml.min-1.kg-1 was calculated using the pre-training exercise capacity and training intensity and was multiplied with total programme length of the exer-cise programme in minutes. This resulted in total oxygen consumption in ml.kg-1, which then was converted to J.kg-1 using the equation of oxygen consumption and energy expenditure, according to the American College of Sport Medicine standards [14]. One liter of consumed oxygen is assumed to equal 5 kilocalories and one kilocalorie equals 4.186 Joule. Therefore, we assumed that 1 liter consumed oxygen equals 20.93 Joule.

Risk of bias quality assessmentThe methodological quality of each included article was assessed using the Cochrane Collabo-ration's tool for assessing risk of bias [15]. This tool identifies seven potential sources of bias in the design and conduct of randomised trials (random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome

53

data, selective reporting and other forms of bias). An assessment requires that for each poten-tial source of bias the risk is classified as ‘low’, ‘high’, or ‘unclear’, with the last category indicating either lack of information or uncertainty over the potential bias.

Synthesis of resultsThe relationship between the training characteristics and training-related changes in exercise capacity, expressed as peakVO2, was determined using a meta-regression analysis. The effect of total energy expenditure was assessed using a univariate meta-regression. The effect of the four constituents of total energy expenditure (training intensity, session duration, session frequency and programme length) was assessed by four multivariate meta-regressions, with total energy expenditure as a covariate. Model fit was assessed using residual deviance and the consistency parameter I2, which describes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (chance). This is variability that would not be observed if all effect estimates were drawn from a single (unimodal) Gaussian distribution – large values of I2 indicate inconsistency in the results of the underlying studies. The effect of training character-istics was assessed by their Z-score. Because of the large number of statistical comparisons, we considered p-values below 0.01 as significant. If there was evidence of inconsistency (i.e., I2 was large) and there was sufficient data to estimate the heterogeneity parameter, the analysis was repeated without outliers. Baseline differences between groups were tested with an indepen-dent t-test using a significance level of 0.05.

Results

Study selectionThe literature search identified 812 unique records from the MEDLINE and EMBASE databases. An overview of the search and selection of records is presented in Figure 1. We excluded 593 records after screening of titles and abstracts and 187 records were excluded after full-paper review. From the remaining 32 records, 14 were included in an analysis for coronary artery disease patients and will be published elsewhere and 17 were included in this review. One of the included studies from Sandri et al. [16] stratified their participants in two intervention groups and two control groups based on age (i.e. ≤55 or ≥ 65). Therefore, we included this study as two separate comparisons.

Study characteristicsBaseline characteristics of the included studies are provided in Table 1. Median sample size of the included studies was 32 participants, with 17 studies ranging from 21 to 154 participants and one study with 2183 participants[6]. A total number of 1488 participants were randomised to aerobic exercise training (of which 1074 in the HF-action trial [6]) with a median age of 60.0 (range 50-72). In the 15 studies that reported gender, 82.9% of the participants were male. The usual care group

54

consisted of 1447 participants (of which 1109 in the HF-action trial) with a median age of 62.0 (range 49-72) and 83.0% of the participants were male. Eleven studies reported NYHA classifica-tion and 16 studies reported LVEF of their participants. The distribution of both NYHA classifica-tion and LVEF were similar between the usual care and exercise group (weighted mean %NYHA I/II/III/IV: 0.5/62.6/36.1/0.7 and 1.0/61.7/36.4/1.0 respectively; weighted mean LVEF: 26.8% (SD 7.1%) and 27.0% (SD 6.8%) respectively). No significant differences between groups were observed.

Programme characteristicsAn overview of the programme characteristics of the included studies is presented in Table 2. Training intensity was reported as %HRR in one study, as %HRmax in one study, as %VAT in two studies and as %peakVO2 in 14 studies. Median programme length was 12 weeks (range 4-39), with a median of 4 sessions per week (range 3-20). Median session duration was 30 minutes (range 18-57) with an intensity of 65.0% (SD 8.5%) of peakVO2. Total training volume varied from 1080 to 8160 minutes with a median of 1640 minutes and total energy expenditure varied from 217 to 2548 J.kg-1 with a median of 323 J.kg-1.

Effect of training characteristicsAs shown in Figure 2, mean difference in peakVO2 between the intervention group and control group was 2.10 ml.min-1.kg-1 (p<0.001, 95% CI 1.34-2.86). Only total energy expenditure was signifi-cantly associated with improvement in peakVO2, leading to an improvement in peakVO2 of 0.29 ml.min-1.kg-1 with each increase in energy expenditure of 100 J.kg-1 (p<0.001, 95% CI 0.22-0.36, Table 3). The individual training characteristics were not additionally associated with improve-ment in peakVO2 after adjusting for the effect of total energy expenditure (Table 3). However, the HF-action trial [6] accounted for almost 90% of the variation in outcomes in this analysis and there was strong evidence of inconsistency (I2=62%). When the analysis was repeated, excluding the HF-action trial, mean difference in peakVO2 was 2.23 ml.min-1.kg-1 between groups (p<0.001, 95% CI 1.52-2.94, Figure 3), with low methodological heterogeneity (I2=25%). Session frequency, session duration and training intensity were significantly associated with peakVO2 improvement after adjustment for energy expenditure (Table 4). Every additional training session per week was associated with an improvement in peakVO2 of 0.29 ml.min-1kg-1. (p<0.01, 95% CI 0.11-0.47); an increase of training session duration of 10 minutes was associated with an improvement in peakVO2 of 0.31 ml.min-1.kg-1, (p<0.01, 95% CI 0.10-0.52) and an increase in training intensity of 10% was associated with an improvement in peakVO2 of 0.15 ml.min-1.kg-1 (p<0.01, 95% CI 0.04-0.25). Programme length in weeks was not significantly associated with peakVO2 improvement. Total energy expenditure was significantly associated with improvement in peakVO2 as well; an increase in energy expenditure of 100 J.kg-1 was associated with an improvement in peakVO2 of 0.29 ml.min-1.kg-1 (p<0.001, 95% CI 0.21-0.37).

55

Figure 1: inclusion flowchart of selected articles

56


Study N, # Male, # LVEF, % NYHA, % I/II/III/IV

NYHA, %I/II/III/IV

Age, # Age, # PeakVO2

(ml.min-1.kg-1)PeakVO

2

(ml.min-1.kg-1)

INT vs CON INT vs CON INT vs CON INT CON INT CON INT CON

Antunes-Correa 2014 [27] 17 vs 17 13 vs 15 28±2 vs 29±1 -/70/30/- -/70/30/- 56±2 54±2 18±1.0* 17±1.0*

Beer 2008 [28] 11 vs 11 - 29±10 vs 25±10 - - 53±12 58±6 21.7±3.8 19.5±2.7

Brubaker 2009 [29] 30 vs 29 19 vs 20 32±9 vs 30±9 -/50/50/- -/55/42/3 70±5 70±6 14.1±0.6* 13.5±0.6*

Eleuteri 2013 [30] 11 vs 10 11 vs 10 28±2 vs 30±2 -/100/-/- -/100/-/- 66±2 63±2 14.8±0.7* 16.7±0.4*

Erbs 2010 [31] 18 vs 19 18 vs 19 24±5 vs 25±4 -/-/100/- -/-/100/- 60±11 62±10 15.3±3.3 15.4±3.8

Kulcu 2007 [32] 23 vs 21 17 vs 15 - -/53/47/- -/59/41/- 58±11 60±11 20.1±5.6 22.6±6.3

Maiorana 2011 [33] 12 vs 12 11 vs 11 29±3 vs 37±3 25/67/8/- 33/50/17/- 61±3 64±2 14.5±1.3* 15.1±1.3*

Malfatto 2009 [34] 27 vs 27 19 vs 20 31±6 vs 33±6 - - 65±11 67±9 14.1±3.2 14.4±3.6

Mandic 2009 [35] 14 vs 13 11 vs 10 30±11 vs 28±11 - - 63±11 62±13 16.0±5.1 16.6±6.0

Mezzani 2013 [36] 15 vs 15 15 vs 15 28±7 vs 30±5 - - 65±7 63±7 15.7±2.4 17.0±1.6

Myers 2007 [4] 12 vs 12 12 vs 12 32±7 vs 35±4 - - 56±5 55±7 19.7±3.2 18.8±4.3

O’Connor 2009 [6] 1074 vs 1109 752 vs 812 25±7 vs 25±8 -/62/37/1 -/64/35/1 59±12 60±13 14.4 14.5

Passino 2008 [37] 71 vs 19 62 vs 14 35±1 vs 36±3 14/72/14/- 16/63/21/- 61±2 63±2 14.8±0.6* 14.7±1.1*

Patwala 2009 [38] 25 vs 25 - - - - - - 18.7±3.4 18.1±3.8

Sandri, 2012 [16] 15 vs 15 12 vs 12 29±6 vs 28±6 -/47/53/- -/53/47/- 72±4 72±3 12.9±1.4* 13.1±1.5*

Sandri 2012 [16] 15 vs 15 12 vs 13 27±6 vs 28±5 -/53/47/- -/60/40/- 50±5 49±5 13.3±1.6* 13.6±1.3*

Terziyski 2009 [39] 15 vs 7 15 vs 7 33±6 vs 34±6 - - 51±10 54±9 13.9±3.1 15.3±2.3

Vasiliauskas 2007 [8] 83 vs 71 - 42±4 vs 44±5 -/65/35/- -/61/39/- 58±43 60±5 22.1±5.2 20.4±4.2


* Data provided as mean ± standard error of the mean.

LVEF=left ventricular ejection fraction; NYHA=New York heart association functional classification;

INT=intervention; CON=control; peakVO2=maximal oxygen consumption.

57


Study N, # Male, # LVEF, % NYHA, % I/II/III/IV

NYHA, %I/II/III/IV

Age, # Age, # PeakVO2

(ml.min-1.kg-1)PeakVO

2

(ml.min-1.kg-1)

INT vs CON INT vs CON INT vs CON INT CON INT CON INT CON

Antunes-Correa 2014 [27] 17 vs 17 13 vs 15 28±2 vs 29±1 -/70/30/- -/70/30/- 56±2 54±2 18±1.0* 17±1.0*

Beer 2008 [28] 11 vs 11 - 29±10 vs 25±10 - - 53±12 58±6 21.7±3.8 19.5±2.7

Brubaker 2009 [29] 30 vs 29 19 vs 20 32±9 vs 30±9 -/50/50/- -/55/42/3 70±5 70±6 14.1±0.6* 13.5±0.6*

Eleuteri 2013 [30] 11 vs 10 11 vs 10 28±2 vs 30±2 -/100/-/- -/100/-/- 66±2 63±2 14.8±0.7* 16.7±0.4*

Erbs 2010 [31] 18 vs 19 18 vs 19 24±5 vs 25±4 -/-/100/- -/-/100/- 60±11 62±10 15.3±3.3 15.4±3.8

Kulcu 2007 [32] 23 vs 21 17 vs 15 - -/53/47/- -/59/41/- 58±11 60±11 20.1±5.6 22.6±6.3

Maiorana 2011 [33] 12 vs 12 11 vs 11 29±3 vs 37±3 25/67/8/- 33/50/17/- 61±3 64±2 14.5±1.3* 15.1±1.3*

Malfatto 2009 [34] 27 vs 27 19 vs 20 31±6 vs 33±6 - - 65±11 67±9 14.1±3.2 14.4±3.6

Mandic 2009 [35] 14 vs 13 11 vs 10 30±11 vs 28±11 - - 63±11 62±13 16.0±5.1 16.6±6.0

Mezzani 2013 [36] 15 vs 15 15 vs 15 28±7 vs 30±5 - - 65±7 63±7 15.7±2.4 17.0±1.6

Myers 2007 [4] 12 vs 12 12 vs 12 32±7 vs 35±4 - - 56±5 55±7 19.7±3.2 18.8±4.3

O’Connor 2009 [6] 1074 vs 1109 752 vs 812 25±7 vs 25±8 -/62/37/1 -/64/35/1 59±12 60±13 14.4 14.5

Passino 2008 [37] 71 vs 19 62 vs 14 35±1 vs 36±3 14/72/14/- 16/63/21/- 61±2 63±2 14.8±0.6* 14.7±1.1*

Patwala 2009 [38] 25 vs 25 - - - - - - 18.7±3.4 18.1±3.8

Sandri, 2012 [16] 15 vs 15 12 vs 12 29±6 vs 28±6 -/47/53/- -/53/47/- 72±4 72±3 12.9±1.4* 13.1±1.5*

Sandri 2012 [16] 15 vs 15 12 vs 13 27±6 vs 28±5 -/53/47/- -/60/40/- 50±5 49±5 13.3±1.6* 13.6±1.3*

Terziyski 2009 [39] 15 vs 7 15 vs 7 33±6 vs 34±6 - - 51±10 54±9 13.9±3.1 15.3±2.3

Vasiliauskas 2007 [8] 83 vs 71 - 42±4 vs 44±5 -/65/35/- -/61/39/- 58±43 60±5 22.1±5.2 20.4±4.2



LVEF=left ventricular ejection fraction; NYHA=New York heart association functional classification;

INT=intervention; CON=control; peakVO2=maximal oxygen consumption.

58

Table 2: Training programme characteristics of included studies

Study Programme

duration (weeks)

Session

per week

Duration per

session (min)

Intensity

(%peakVO2)

Total training

volume (min)

EE total-1

(Joule.kg-1)

EE week-1

(Joule.kg-1)

Δ Peak VO2

(ml.min-1.kg-1)

Δ Peak VO2

(ml.min-1.kg-1)


Antunes-Correa 2014 [27] 17 3 38.63 66 1970.13 489.2 28.8 3.0 0.0

Beer 2008 [28] 8.67 5 45 70 1950.75 619.3 71.4 3.6 1.3

Brubaker 2009 [29] 16 3 35 62.5 1680 309.4 19.3 -0.2 0.1

Eleuteri 2013 [30] 13 5 30 59.4 1950 358.6 27.6 1.7 -0.7

Erbs 2010 [31] 12 8 29.38 60 2820 541.1 45.1 2.5 -0.7

Kuclu 2007 [32] 8 3 50 55 1200 277.3 34.7 3.5 2

Maiorana 2011 [33] 12 3 40 60 1440 261.8 21.8 2.7 -1.0

Malfatto 2009 [34] 13 3 40 60 1560 275.8 21.2 3.0 -0.2

Mandic 2009 [35] 12 3 30 60 1080 216.7 18.1 1.3 0.1

Mezzani 2013 [36] 13 5 30 57.3 1950 366.8 28.2 1.4 -0.9

Myers 2007 [4] 8 18 56.67 70 8160.48 2351.9 294.0 5.1 0.0

O’Connor 2009 [6] 12 3 30.8 68.3 1108.8 227.9 19.0 0.6* 0.2*

Passino 2008 [37] 39 3 30 65 3510 705.7 18.1 2 -0.6

Patwala 2009 [38] 13 3 30 78.9 1170 361.6 27.8 1.37 -0.01

Sandri, 2012 [16] 4 20 20 70 1600 302.0 75.5 4.2 0.2

Sandri 2012 [16] 4 20 20 70 1600 311.3 77.8 4.8 -0.2

Terziyski 2009 [39] 8 5 40 50 1600 232.4 29.1 2.3 0.2

Vasiliauskas 2007 [8] 26 14 17.83 85 6490.12 2548.1 98.0 3.3 -2.5



PeakVO2=maximal oxygen consumption; EE=energy expenditure; INT=intervention; CON=control

59

Table 2: Training programme characteristics of included studies

Study Programme

duration (weeks)

Session

per week

Duration per

session (min)

Intensity

(%peakVO2)

Total training

volume (min)

EE total-1

(Joule.kg-1)

EE week-1

(Joule.kg-1)

Δ Peak VO2

(ml.min-1.kg-1)

Δ Peak VO2

(ml.min-1.kg-1)


Antunes-Correa 2014 [27] 17 3 38.63 66 1970.13 489.2 28.8 3.0 0.0

Beer 2008 [28] 8.67 5 45 70 1950.75 619.3 71.4 3.6 1.3

Brubaker 2009 [29] 16 3 35 62.5 1680 309.4 19.3 -0.2 0.1

Eleuteri 2013 [30] 13 5 30 59.4 1950 358.6 27.6 1.7 -0.7

Erbs 2010 [31] 12 8 29.38 60 2820 541.1 45.1 2.5 -0.7

Kuclu 2007 [32] 8 3 50 55 1200 277.3 34.7 3.5 2

Maiorana 2011 [33] 12 3 40 60 1440 261.8 21.8 2.7 -1.0

Malfatto 2009 [34] 13 3 40 60 1560 275.8 21.2 3.0 -0.2

Mandic 2009 [35] 12 3 30 60 1080 216.7 18.1 1.3 0.1

Mezzani 2013 [36] 13 5 30 57.3 1950 366.8 28.2 1.4 -0.9

Myers 2007 [4] 8 18 56.67 70 8160.48 2351.9 294.0 5.1 0.0

O’Connor 2009 [6] 12 3 30.8 68.3 1108.8 227.9 19.0 0.6* 0.2*

Passino 2008 [37] 39 3 30 65 3510 705.7 18.1 2 -0.6

Patwala 2009 [38] 13 3 30 78.9 1170 361.6 27.8 1.37 -0.01

Sandri, 2012 [16] 4 20 20 70 1600 302.0 75.5 4.2 0.2

Sandri 2012 [16] 4 20 20 70 1600 311.3 77.8 4.8 -0.2

Terziyski 2009 [39] 8 5 40 50 1600 232.4 29.1 2.3 0.2

Vasiliauskas 2007 [8] 26 14 17.83 85 6490.12 2548.1 98.0 3.3 -2.5



PeakVO2=maximal oxygen consumption; EE=energy expenditure; INT=intervention; CON=control

60

Figure 2: Forest plot with HF-action

Stud

y

Fixe

d eff

ect m

odel

Rand

om e

ffect

s mod

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Het

erog

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ty: I

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nes−

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oran

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et a

l. 20

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n

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et a

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ay

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2009

Mar

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zani

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s J e

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2007

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al.

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Apr

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2009

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8.3

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4.88

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5.14

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8.38

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5.68

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3.0

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7

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0

95%

−CI

[ 0.5

7; 0

.90]

[ 1.3

4; 2

.86]

[−0.

82;

6.82

]

[−0.

77;

5.37

]

[−2.

82;

2.22

]

[ 0.3

8; 4

.42]

[−0.

12;

6.52

]

[−3.

46;

6.46

]

[−0.

85;

8.25

]

[ 0.2

9; 6

.11]

[ 0.0

7; 2

.33]

[ 0.4

3; 4

.17]

[ 0.1

5; 1

0.05

]

[ 0.4

3; 0

.77]

[−1.

23;

6.43

]

[ 0.5

5; 2

.21]

[ 0.2

2; 9

.78]

[−3.

15; 1

1.15

]

[−1.

10;

5.30

]

[ 3.2

5; 8

.35]

W(fi

xed)

100% −−

0.2

%

0.3

%

0.4

%

0.7

%

0.2

%

0.1

%

0.1

%

0.3

%

2.1

%

0.8

%

0.1

%

89.6

%

0.2

%

4.0

%

0.1

%

0.1

%

0.3

%

0.4

%

W(r

ando

m)

−−

100%

3.2

%

4.4

%

5.7

%

7.4

%

3.9

%

2.0

%

2.4

%

4.7

%

11.5

%

8.0

%

2.1

%

15.4

%

3.1

%

13.1

%

2.2

%

1.1

%

4.1

%

5.7

%

1.70

5.6

8777

3 1

7

11

30 11 18

2 12

27

14

15 12

1074

71

25

15

15

15 13

61

Figure 3: Forest plot without HF-action

Stud

yTo

tal

Mea

nSD

Expe

rimen

tal

Tota

lM

ean

SD

Cont

rol

Mea

n di

ffer

ence

MD

95%

−CI

W(fi

xed)

W(r

ando

m)

Fixe

d eff

ect m

odel

Rand

om e

ffect

s m

odel

Het

erog

enei

ty: I

−sq

uare

d=25

.2%

, tau

−sq

uare

d=0.

4603

, p=

0.16

42

Ant

unes

−Co

rrea

201

4

Beer

200

8

Brub

aker

200

9

Eleu

teri

2013

Erbs

201

0

Kulc

u 20

07

Mai

oran

a 20

11

Mal

fatt

o 20

09

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dic

2009

Mez

zani

201

3

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rs 2

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onno

r 200

9

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ino

2008

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ala

2009

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ri 20

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r

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i 201

0

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liaus

kas

2007

1488 1

7

11

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11

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12

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12

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25

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15

15

83

3.0

0

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20

1.7

0

2.5

0

3.5

0

2.7

0

3.0

0

1.3

0

1.4

0

5.1

0

N

A

2.0

0

1.3

7

4.8

0

4.2

0

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3.3

0

5.6

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3

3.6

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5

4.9

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7

2.4

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8

5.14

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8.3

8510

6

5.6

8382

3

5.4

5527

3

1.5

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0

2.6

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7

6.18

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NA

11.7

4850

1

1.4

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0

6.6

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5

9.9

9499

9

3.5

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9

8.0

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5

1447 17 11 29 10 19 21 12 27 13 15 12 1109 19 25 15 15 7 71

0.0

0

1.3

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70

−0.

70

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0

−0.

20

0.10

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0.0

0

N

A

−0.

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−0.

01

−0.

20

0.2

0

0.2

0

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0

5.68

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3.67

4235

4.88

8361

2.30

7919

5.14

8786

8.38

5106

5.68

3823

5.45

5273

1.50

0000

2.61

3427

6.18

3041

NA

5.96

5761

1.49

0000

6.67

4485

9.99

4999

3.56

5109

8.04

9845 −10

−50

5

10

1.9

0

2.2

3

3.0

0

2.3

0

−0.

30

2.4

0

3.2

0

1.5

0

3.7

0

3.2

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1.2

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2.3

0

5.1

0

2.6

0

1.3

8

5.0

0

4.0

0

2.1

0

5.8

0

[ 1.3

9; 2

.42]

[ 1.5

2; 2

.94]

[−0.

82;

6.82

]

[−0.

77;

5.37

]

[−2.

82;

2.22

]

[ 0.3

8; 4

.42]

[−0.

12;

6.52

]

[−3.

46;

6.46

]

[−0.

85;

8.25

]

[ 0.2

9; 6

.11]

[ 0.0

7; 2

.33]

[ 0.4

3; 4

.17]

[ 0.15

; 10.

05]

[−1.

23;

6.43

]

[ 0.5

5; 2

.21]

[ 0.2

2; 9

.78]

[−3.

15; 1

1.15]

[−1.1

0; 5

.30]

[ 3.2

5; 8

.35]

100% −−

1.8

%

2.8

%

4.1

%

6.4

%

2.4

%

1.1

%

1.3

%

3.1

%

20.4

%

7.5

%

1.1

%

0.0

%

1.8

%

38.3

%

1.1

%

0.5

%

2.6

%

4.0

%

−−

100%

3.1

%

4.5

%

6.2

%

8.6

%

3.9

%

1.9

%

2.2

%

4.9

%

16.5

%

9.5

%

1.9

%

0.0

%

3.1

%

20.5

%

2.0

%

0.9

%

4.2

%

6.1

%

62

Table 3: Ranking of training characteristics on effect size and model fit, corrected for energy expenditure

Training characteristics Effect scale Effect Size*

(ml.min-1.kg-1)

95% CI

(ml.min-1.kg-1)

p-value I2 AIC

1. Session frequency 1 session/week 0.12 -0.01-0.25 0.065 0.00 58.86

2. Session duration 10 minutes 0.16 -0.00-0.33 0.051 10.83 61.10

3. Programme length 2 weeks 0.08 -0.01-0.17 0.100 11.81 61.91

4. Training intensity 10% peakVO2 0.08 -0.01-0.16 0.073 11.96 62.10

5. Total EE 100 Joule.kg-1 0.29 0.22-0.36 <0.001* 8.34 63.79

Analyses performed with HF-action included

* Effect size is given as change in peakVO2

I2=residual heterogeneity; AIC=Akaike’s information coefficient (model fit); peakVO2=maximal oxygen uptake.

EE=energy expenditure. *=significant at p<0.01

Table 4: Ranking of training characteristics on effect size and model fit, corrected for energy expenditure, without HF-action

Training characteristics Effect scale Effect Size

(ml.min-1.kg-1)

95% CI

(ml.min-1.kg-1)

p-value I2 AIC

1. Session frequency 1 session/week 0.29 0.11-0.47 0.002* 0.00 53.46

2. Session duration 10 minutes 0.31 0.10-0.52 0.003* 0.00 56.39

3. Training intensity 10% peakVO2 0.15 0.04-0.25 0.006* 0.00 57.78

4. Programme length 2 weeks 0.14 0.03-0.25 0.015 0.00 58.80

5. Total EE 100 Joule.kg-1 0.29 0.21-0.37 <0.001* 12.10 65.25

Analyses performed without HF-action

* Effect size is given as change in peakVO2

I2=residual heterogeneity; AIC=Akaike’s information coefficient (model fit); peakVO2=maximal oxygen consumption.

EE=energy expenditure. **=significant at p<0.01

As shown in Table 4, heterogeneity was low for all training characteristics (I2=0%), indicating no unmeasured confounding. The ranking of training characteristics based on residual devi-ance and I2 showed that energy expenditure was the strongest determinant, followed by session frequency, session duration and training intensity. Detailed results of the meta-regression analysis are provided in Appendix B and C.

63

Other (RoB, publication bias)Results of the risk of bias analyses (available in Appendix A) indicate that overall study quality was good and that it is unlikely that bias seriously alters the results of this meta-analysis. Differences in methodological quality were small between studies. The funnel plot of the second analysis showed little evidence for significant publication bias for peakVO2 (Appendix A)

Discussion

This study analysed the relation between training characteristics and changes in exercise capacity after continuous AET in CHF patients. The total energy expenditure of a training programme (the product of training intensity, session duration, session frequency and programme length) was the strongest determinant of improvement in exercise capacity. A second analysis, which excluded the HF-action trial, suggested that three distinct training characteristics (i.e. sessions frequency, session duration and training intensity) are individually associated with improvement in exercise capacity when corrected for total energy expenditure. Ranking of the training charac-teristics demonstrated the largest effect for session frequency and session duration, followed by training intensity and programme length.

The data of this analysis showed that although training characteristics vary among the indi-vidual studies, all but one measured an improvement in exercise capacity after aerobic exer-cise. This is in line with a previous meta-analysis that demonstrated beneficial effects of exercise training in CHF patients [2], regardless the variation in training characteristics. Yet, to prescribe the most effective training programme, more insight is needed in the contribution of distinct training characteristics on improvement in exercise capacity [17]. Recently, Ismail et al. published a systematic review to address this topic [13].

They concluded that the largest training effect could be achieved with a high-volume, high-in-tensity training programme. Although the effect of high-volume is demonstrated in our study as well, our data show that the effect of training intensity on training-induced improvements in exercise capacity is not greater than the effect of session duration and session frequency. A possible explanation for this discrepancy may be that we excluded interval-based training programmes from our analysis and adjusted for total energy expenditure. To our knowledge, however, no studies directly compared isocaloric interval and continuous training programmes of equal intensity.

When focusing on continuous AET, our data showed little difference in the effect of session frequency, session duration and training intensity on exercise capacity. However the effect sizes that can be achieved differ between the distinct characteristics and are dependent of prac-tical constraints. For instance, training intensity above 80% of peakVO2 is not possible when

64

performing continuous aerobic exercise. Consequently, intensity ranged from 50% to 80% of peakVO2 between the included studies, resulting in a maximal difference in training effect of 0.45 ml.min-1.kg-1 between the highest and lowest intensity programme. Alternatively, alterations in session duration and session frequency are less restricted by practical applications. Session duration varies between 18 and 57 minutes per session and session frequency between 3 and 20 sessions per week in the included studies. With an increase in session duration of 10 minutes, an additional improvement in peakVO2 of 0.31 ml.min-1.kg-1 can be achieved and one additional weekly exercise session induced an improvement in peakVO2 of 0.29 ml.min-1.kg-1. Given these results, it could be stated that increasing session frequency and duration is more effective in improving aerobic exercise capacity than increasing training intensity. Nonetheless, all three characteristics are significantly associated with improvement in exercise capacity in the second analysis. This implies that these training characteristics can be altered to comply with other factors that influence training effect, such as adherence and patient preference.

Previous studies have suggested that adherence drops when high training intensities are prescribed [18,19], whereas higher session frequencies and longer session durations do not reduce training adherence [19,20]. In addition, a training programme developed according to a patients prefer-ences, is more likely to improve training adherence. Consequently, training effect will improve and this effect is more likely to be maintained after the initial cardiac rehabilitation phase [21].

Currently, a decline in exercise capacity is often perceived when cardiac rehabilitation is finished, because patients are unable to maintain their activity levels [22,23]. However, as it is common knowledge that inactivity is a very important risk factor for developing cardiovascular morbidity and higher mortality rates, it is of crucial importance that patients maintain their physically active lifestyle [24,25]. In the light of the present study, physical activity recommendations for CHF patients should preferably be based on energy expenditure as opposed to exercise dura-tion alone in current recommendations for the general population [24]. When properly validated, mobile apps could help CHF patients to monitor energy expenditure and they can search for an activity which best suits them in terms of duration and intensity as long as the caloric expendi-ture is met. Moreover, a training programme that is not only tailored to a patients’ preference, but also to a patients’ home environment (i.e. availability of fitness equipment at home, sport clubs in the neighborhood) with activity recommendations based on energy expenditure afterwards could likely induce the largest short-term and long-term training effect [27,28].

Limitations A first limitation to this meta-analysis is the lack of heterogeneity in training characteristics between the included studies. Due to the strict criteria of this review, variation of training char-acteristics between the included studies was low. This resulted in a low model fit and high unac-counted variability in the univariate meta-regression analysis. The strict criteria resulted in an exclusion of studies that evaluated other training modalities besides continuous aerobic exercise

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(i.e. interval or strength training), studies without a non-exercise control group, and studies that lacked details on all training characteristics. This strengthened the comparison of individual training characteristics during continuous exercise, but reduced the sample size.As our analyses only covers the variance in training characteristics used in the included trials, it remains unclear what can be achieved with an intensity, duration or frequency that exceeds this variance. As such, earlier research in general population showed a U-curved relationship of response to exercise. This relationship indicates that the beneficial effect of exercise increases with higher intensity or duration, but that increasing to very high intensities or frequencies might not solicit further benefits [25].However, it is unclear if this relationship might also exist in CHF patients.

Finally, the standard deviation of the change from baseline in VO2 was not reported in several studies. For this analysis, we assumed that the correlation between the baseline and follow-up measurements was p=0.7. This could be a source of error.

Unanswered questions and future researchBecause the HF-action trial accounted for 90% of the total patient population, it dominated the results of the first analysis and the analysis was repeated without the HF-action study. Interest-ingly, the results of the HF-action trial are not consistent with the body of evidence provided by the smaller studies. It is unclear what causes this discrepancy. A possible explanation could be any form of bias in particular in the smaller trials. However, the risk of bias analysis that we performed showed no differences between the HF-action trial and the smaller trials (Appendix A). Generally, results provided by large multi-centre randomised controlled trials are considered of higher quality than small single-centre trials, due to selection-bias in small studies. Nonethe-less, the results from almost all small studies are different than the HF-action trial. Therefore, large randomised controlled trials are required to validate either the results of the HF-action trial, or the small studies. In addition, future research should focus on the effect of training modus on exer-cise capacity. Although several studies compare interval with continuous training, other training characteristics (i.e. training intensity, session duration) often differ between groups, troubling a clear comparison between continuous and interval training modus. Future research requires large trials in which a standard exercise programme is compared with an isocaloric exercise programme where only one training characteristic is altered.

ConclusionsThis systematic review and meta-analysis showed that improvement in exercise capacity of CHF patients undergoing continuous aerobic exercise training is primarily determined by the total energy expenditure (the product of training intensity, session duration, session frequency and programme duration) of the training programme. For common training programmes described in the literature, increases in training frequency and session duration appear to yield the largest immediate gain in exercise capacity. However, the results of the largest study are inconsistent with the smaller trials, indicating more large randomised controlled trials are required in future research.

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Conflicts of interest None.

AcknowledgementsWe want to thank Rutger Brouwers, Anne-Marieke Mulder-Wiggers and Mariette van Engen-Ver-heul for their help during the screening of records. Dr. Gerben ter Riet and Dr. Gert Valkenhoef are acknowledged for their help in constructing the methodological framework and analyses.

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Appendix A

Medline search strategy#1 (("Heart Diseases/diet therapy"[Mesh] OR "Heart Diseases/psychology"[Mesh] OR "Heart Diseases/rehabili-

tation"[Mesh] OR "Heart Failure"[Mesh] OR "Heart Diseases/prevention and control"[Mesh] OR heart failure[-

tiab] OR heart*[tiab] OR cardiac*[tiab] OR coronary[tiab] OR myocardial[title]) AND ("Myocardial Revasculariza-

tion"[Mesh] OR "heart failure"[Mesh] OR "Myocardial Ischemia"[Mesh] OR "Acute Coronary Syndrome"[Mesh]

OR “Myocardial Infarction”[Mesh] OR “Angina Pectoris”[Mesh] OR "percutaneous coronary intervention"[tiab]

OR "heart failure"[tiab] OR “congestive heart failure”[tiab] OR “chronic heart failure”[tiab] OR “left ventricular

disfunction”[tiab] OR “cardiomyopathy”[tiab] OR "acute coronary syndrome*"[tiab] OR "stable angina pecto-

ris"[tiab] OR "unstable angina pectoris"[tiab] OR "myocardial ischemia"[tiab] OR "cardiac rehabilitation"[tiab]

OR "cardiac patients"[tiab] OR "myocardial revascularization"[tiab] OR “myocardial infarction”[tiab] OR “Non-ST

Segment Elevation Myocardial Infarction”[tiab] OR “coronary artery bypass graft"[tiab] OR “CABG”[tiab] OR

"stent"[tiab] OR "angioplasty"[tiab] OR "PTCA"[tiab] OR “PCI”[tiab] OR “OPCAB”[tiab] OR "Cardiac Resynchroniza-

tion Therapy"[Mesh] OR cardiac resynchronization therap*[tiab] OR cardiac resynchronisation therap*[tiab]))

#2 ("rehabilitation" [Subheading] OR "Secondary Prevention"[Mesh] OR secondary prevention[tiab] OR cardiac

rehabilitation[tiab] OR "exercise program*"[tiab] OR exercise training[tiab] OR exercise[title] OR rehabilitation

program*[tiab] OR Physical training[tiab] OR training program*[tiab] OR “interval training”[tiab] OR "Heart

Failure/rehabilitation"[MAJR] OR "Coronary Disease/diet therapy"[MAJR] OR "Recovery of Function"[MeSH

Terms])

#3 ("Exercise Tolerance"[MeSH Terms] OR "Motor Activity"[MeSH Terms] OR "Exercise Test"[MeSH] OR "Exer-

cise"[Mesh] OR "Exercise Therapy"[MeSH Terms] OR “exercise capacity”[tiab] OR exercise tolerance[tiab]

OR exercise[tiab] OR physical activity[tiab] OR “interval training” OR "Cardiovascular Diseases/rehabilita-

tion"[MAJR] OR "Coronary Artery Disease/rehabilitation"[MAJR] OR "Coronary Disease/diet therapy"[MAJR]

OR physical function*[tiab])

#4 ("drug therapy"[Subheading] OR "Adolescent"[Mesh] OR "Child"[Mesh] OR "Infant"[Mesh] OR "surgery

"[Subheading] OR "Diabetes Mellitus"[Majr] OR "Platelet Aggregation Inhibitors"[Mesh] OR "Drug Therapy,

Combination"[Mesh] OR "Obesity"[Mesh] OR "Heart Transplantation"[Mesh] OR "Dementia"[Mesh] OR "Sleep

Apnea Syndromes"[Mesh] OR "Parkinson Disease"[Mesh] OR "Kidney Failure, Chronic"[Mesh] OR “Syndrome

X”[tiab])

#5 (“2007/31/03”[PDat] : “2015/01/04”[PDat]) AND (English[lang]) AND (Randomised Controlled Trial[ptyp])

#6 #1 AND #2 AND #3 AND #5 NOT #4

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Appendix A, figure 1: Funnel-plot with HF-action

Appendix A, figure 2: Funnel-plot without HF-action

−6 −4 −2 0 2 4 6 8

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Appendix A, figure 3: Risk of bias

Low risk Unclear High risk

Vasiliauskas et al.

Terziyski et al.

Sandri et al.

Sandri et al.

Patwala et al.

Passino et al.

O'Connor et al.

Myers et al.

Mezzani et al.

Mandic et al.

MalfaGo et al.

Maiorana et al.

Kulcu et al.

Erbs et al.

Eleuteri et al.

Brubaker et al.

Beer et al.

Antunes-Correa et al.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

73

Appendix B








expend 0.0029 0.0003 8.2190 <.0001 0.0022 0.0036 ***

---Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1









freq 0.1197 0.0650 1.8422 0.0654 -0.0077 0.2471 .

expend 0.0016 0.0007 2.1894 0.0286 0.0002 0.0030 *

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

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Session intensityMixed-Effects Model (k = 18; tau^2 estimator: DL)







Model Results: se val pval ci.lb ci.ub

intensity 0.7689 0.4286 1.7937 0.0729 -0.0713 1.6090 .

expend 0.0022 0.0005 4.0819 <.0001 0.0012 0.0033 ***

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1









duration 0.0164 0.0084 1.9482 0.0514 -0.0001 0.0328 .

expend 0.0023 0.0005 4.4492 <.0001 0.0013 0.0033 ***

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

75









length 0.0383 0.0233 1.6467 0.0996 -0.0073 0.0839 .

expend 0.0022 0.0006 3.6604 0.0003 0.0010 0.0033 ***

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Appendix B, figure 1: Bubble plot of energy expenditure

Covariate expend

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Appendix B, figure 2: Bubble plot of training characteristics

Covariate intensity (meta-regression: intensity + expend - 1)Tr

eatm

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0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85

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Covariate duration (meta -regression: duration + expend - 1)

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Covariate freq (meta-regression: freq + expend -1)

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Covariate length (meta -regression: length + expend - 1)

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5 10 15 20 25 30 35 40

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77

Appendix C

Total Energy expenditureMixed-Effects Model (k = 17; tau^2 estimator: DL)







expend 0.0029 0.0004 6.8679 <.0001 0.0021 0.0037 ***

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1









freq 0.2903 0.0915 3.1735 0.0015 0.1110 0.4695 **

expend 0.0009 0.0008 1.1459 0.2518 -0.0006 0.0024

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

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Session intensityMixed-Effects Model (k = 17; tau^2 estimator: DL)








intensity 1.4691 0.5353 2.7445 0.0061 0.4200 2.5182 **

expend 0.0019 0.0006 3.3355 0.0009 0.0008 0.0030 ***

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1









duration 0.0312 0.0107 2.9274 0.0034 0.0103 0.0521 **

expend 0.0020 0.0005 3.9023 <.0001 0.0010 0.0030 ***

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

79









length 0.0692 0.0284 2.4332 0.0150 0.0135 0.1249 *

expend 0.0018 0.0006 2.9754 0.0029 0.0006 0.0030 **

---

Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Appendix C, figure 1: Bubble plot of energy expenditure

Covariate expend

Trea

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Appendix C, figure 2: Bubble plots of training characteristics

Covariate intensity (meta-regression: intensity + expend - 1)Tr

eatm

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0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85

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Covariate duration (meta -regression: duration + expend - 1) Covariate length (meta -regression: length + expend - 1)

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Jos J Kraal, Francesco Sartor, Gabriele Papini, Wim Stut, Niels Peek, Hareld MC Kemps, Alberto G Bonomi


Chapter 4Energy expenditure estimation in

beta-blocker medicated cardiac patients by combining heart rate

and body movement data

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Abstract

Background: Accurate assessment of energy expenditure provides the opportunity to monitor physical activity during cardiac rehabilitation. However, available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker medicated cardiac rehabilitation patients.

Methods: Sixteen male cardiac rehabilitation patients (age: 55.8±7.3 years, weight: 93.1±11.8 kg) underwent a physical activity protocol with 11 low to moderate intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom limited exercise test and resting meta-bolic rate assessment. Energy expenditure estimation models were developed using multi-variate regression analyses based on HR, body movement data and/or patient characteris-tics. In addition, a HR-flex model was developed.

Results: The model combining HR, body movement and patient characteristics showed the highest correlation and lowest error (R2=0.84, RMSE=0.834 kcal/min) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accu-racy (R2=0.83, RMSE=0.992 kcal/min).

Conclusions: Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods developed for healthy subjects. The proposed methodology does not require individual calibration and is based on data available in clinical practice.

Introduction

Physical inactivity is an expanding problem and is related to several chronic diseases including cardiovascular diseases, diabetes and obesity [1]. Lack of physical activity may lead to a chronic imbalance between energy intake and energy expenditure, which is associated with all-cause and cardiovascular mortality [2,3]. After a cardiac incident, patients are recommended to participate in cardiac rehabilitation to improve their physical fitness level and engage in a healthy lifestyle [4,5]. Cardiac rehabilitation is a multidisciplinary intervention to improve physical, psychological and social well being of patients after a cardiac incident [6]. However, the majority of the cardiac rehabilitation programmes focus on the improvement and enhancement exercise capacity and

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quality of life, but do not target sedentary time and overall physical activity behaviour in daily life. This may be related to the fact that to date, no standardized methods exist to assess phys-ical activity accurately and unobtrusively in cardiac patients in daily life [7]. Physical activity is a multi-dimensional human behaviour characterised by factors such as type, pattern, duration and intensity of physical tasks and can be quantified by determining energy expenditure [8]. Gold standard methods for the assessment of energy expenditure are indirect calorimetry and doubly labelled water. Although these methods were shown to be accurate, they are costly and obtru-sive, making them unsuitable for population-based studies or integration in clinical practice [9].Over the past decade, the use of wearable movement sensors for measuring physical activity in free-living conditions has become widespread. Heart rate (HR) monitors provide insight into the intensity of the aerobic work during physical activity. During activities with moderate to vigorous intensity, HR and oxygen consumption (VO2) are linearly related [10,11]. Therefore energy expen-diture, which is dependent on VO2[12], can be predicted using HR recordings. However, in free-living conditions energy expenditure assessment using HR has some important limitations[13]. First, the HR vs. energy expenditure relation is not linear during rest and low-intensity activi-ties and is substantially influenced by internal and external factors (i.e. temperature, emotional stress, caffeine) [14]. In addition, inter-individual variation in HR is large, suggesting the need for individual calibration processes to build a valid HR-based energy expenditure model [15]. Yet, combining HR and accelerometer data may reduce these limitations [16]. Tri-axial accelerome-ters have been used to quantify body movement and predict energy expenditure using linear models in healthy subjects and patients with a chronic disease [17–19]. Accelerometers are small and minimally obtrusive, with battery life and storage capacity enabling long-term monitoring from several days to weeks. Brage et al. described a branched equation model in which a combi-nation of HR and accelerometer recordings was used to estimate physical-activity related energy expenditure in healthy subjects [20]. Other studies showed that models combining HR and accel-erometer data significantly improved energy expenditure estimation accuracy as compared to single-input only models in children and healthy adults [21,22].

Although combining HR and accelerometer data for energy expenditure assessment seems to lead to higher estimation accuracy in healthy adults, these prediction models cannot be translated directly to cardiac patients. Beta-blocker therapy, which aims at reducing myocar-dial oxygen demand by lowering heart rate and blood pressure [23], plays an important role in the treatment of cardiovascular diseases and the prevention of future cardiac events. The heart rate-lowering effect of beta-blockers, which is highly variable between subjects, may impact the ability of HR to predict energy expenditure. Therefore, the main objective of this study was to develop an energy expenditure prediction model for beta-blocker medicated cardiac rehabilita-tion patients based on both HR and accelerometer data. For this aim, multivariate linear regres-sion models were developed, and different combinations of features were evaluated. In addition, a method based on a calibration protocol for model personalisation, called the flex-HR method [24], was evaluated as a benchmark solution.

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Methods

Study populationPatients were eligible for participation when they were admitted to cardiac rehabilitation two to four weeks after hospitalisation for myocardial infarction, unstable angina, or a revascularisa-tion procedure (percutaneous coronary intervention or coronary artery bypass grafting) at the Máxima Medical Center, Veldhoven (The Netherlands). High risk of further events was an exclu-sion criterion (i.e. patients with symptomatic heart failure, complex congenital heart disease, severe depression, arrhythmias or co-morbidity limiting exercise performance). Recruitment was performed between November 2013 and August 2014. Sixteen male patients that were partic-ipating in exercise-based cardiac rehabilitation were recruited. Fourteen patients were taking beta-blockers (metoprolol) at the time of the test. Each participant signed an informed consent form. The study was approved by the local Medical Ethical Committee of Máxima Medical Center, Veldhoven, The Netherlands.

Symptom limited exercise test A symptom limited exercise test was carried out on a cycle ergometer in an upright-seated posi-tion on an electromagnetically braked cycle ergometer (Lode Corrival, Lode BV, Groningen), using an individualized ramp protocol aiming at a test duration of 8-12 minutes. Ventilatory parameters were measured breath-by-breath (Masterscreen™ CPX, CareFusion, Hoechberg, Germany). The test was ended when the patient was not able to maintain the required pedalling frequency. PeakVO2 was recorded as the final 30-second averaged value of the test.

Physical activity protocolThe physical activity protocol, as described in Table 1, consisted of a randomly ordered set of 11 daily living activities of low to moderate intensity. Resting HR (RHR) was measured in the begin-ning of the test, and after each activity the patient had a recovery break, which lasted until the HR reached the resting value.

Energy expenditure measurementsOxygen uptake (VO2) and carbon dioxide production (VCO2) were measured for the entire duration of the activity protocol by the Cosmed K4 b2 (Cosmed, Rome, Italy) portable metabolic system, which is a breath-by-breath pulmonary gas exchange measurement system consisting of a face mask, an analyser unit, and a battery. Total energy expenditure (TEE) was then obtained using the Weir equation from the breath-by-breath measurements of O2 and CO2, averaged on a minute-by-minute basis and divided by resting metabolic rate (RMR) to determine the physical activity level (PAL). PAL is a popular parameter used to represent energy expenditure adjusted for RMR, which allows to compare measurements of TEE for subjects with different body size and composition. PAL can be used to determine activity intensity. Light, moderate, and vigorous intensity activities are characterised by a PAL < 3, < 6, and ≥ 6, respectively [25]. The average daily PAL for the healthy

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adult population is 1.7, a PAL of 1.2 represents the activity level of a bed-bound subjects [26], while an athlete participating in the Tour de France can reach a PAL of 5 [27].

RMR measurementPrior to the RMR assessment, patients were instructed to fast for 12 hours and to avoid sports activities for 24 hours. During the assessment, patients were asked to stay awake. RMR was measured using the Cosmed metabolic equipment with the Canopy option using the mean values of VO2 and VCO2 data collected over a five minutes stability interval according to Mata-rese’s recommendations [28].

Physical activity and heart rate measurementsPhysical activity was measured using an ActiGraph wGT3X+ activity monitor positioned at the waist (ActiGraph, Pensacola, Florida), which is a tri-axial accelerometer with dynamic range of ±6G and sampling frequency set to 40Hz. Acceleration data were used to determine activity counts per minute (ACmin) according to the following equation:

T

i=1ACmin = ∑ lνi -νl

where, for each i-sample in a minute period (T) the vector magnitude signal (v) is subtracted to the mean v over that minute to determine ACmin. The v signal is obtained from the Euclidean

Table 1: Routine of activity tasks randomly performed by the patients and their duration

Activity task Duration (minutes)

Sitting at rest 5

Standing at rest 2

Walking 4 km/h 3

Walking 4 km/h, slope 5% 3

Walking 5.5 km/h 3

Cycling 0W 70 rpm 3

Cycling 40W 70 rpm 3

Cycling 70W 70 rpm 3

Cleaning a table 3

Vacuuming 3

Putting dishes in a dish washer 3

W=Watt; rpm=rounds per minute

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norm of the x, y, z signal representing each sensing axis of the accelerometer. HR was recorded on a second-by-second frequency using a commercially available ECG chest belt (Garmin Ltd.) that wirelessly transmitted HR data to the ActiGraph accelerometer unit for synchronous data storage. Minute-by-minute average for HR and ACmin were computed to predict energy expen-diture. HR above rest (HRnet) was obtained by subtracting RHR from each value of the HR. Data were visually inspected and annotated to determine start and stop for each activity over the recorded acceleration signals. The recorded signals were processed and organized using MatLab (Matlab, Mathworks, Cambridge, MA).

Energy expenditure modellingTEE and PAL estimation models were developed using multivariate linear regression analysis. This method allowed for automatically and iteratively selecting which independent variables to include in the prediction models. Three different groups of independent variables were consid-ered: i) patient characteristics which included age, body weight, height, BMI, RMR, Beta-blocker dose (i.e. metoprolol 0, 50, 75 or 100mg/day), and peakVO2; ii) Body movement as described by ACmin, and the logarithm transform of ACmin; and iii) HR and HRnet information. Five different energy expenditure prediction models were derived using the following combination of inde-pendent variables: a) Body movement features; b) Body movement + patient characteristics; c) HR features; d) HR features + patient characteristics; e) Body movement + HR features + patient characteristics. Patient characteristics were included in the modelling process to account for between-individual variability in the relationship between HR or ACmin and energy expenditure (TEE or PAL) [29]. HR and body movement features were included to describe the within-indi-vidual variability in energy expenditure.

Additionally, we used the HR-flex method as alternative solution to account for between-pa-tient variability in the energy expenditure prediction models [24]. This method deploys HR and energy expenditure measurements during a set of cycling activities at increasing intensity (0W to 70W) to determine parameters of a linear prediction model applicable to any other activity for HR values above the HR-flex. HR-flex is defined as the lowest HR recorded during cycling at very low effort (0W). Below the HR-flex energy expenditure is set to resting values. The HR-flex method leads to development of an energy expenditure prediction equation by personalizing model parameters using data from a cycling calibration protocol.

StatisticsData are presented as mean ± standard deviation. Pearson correlation coefficient was calcu-lated to describe the association between ACmin, HR features and TEE or PAL for each individual patient data or for the entire dataset. Student t-test was used to assess significant differences in the estimation error of the energy expenditure prediction models between the development and validation samples. Development of the energy expenditure prediction model was carried out with data from 11 patients, while data from the remaining 5 patients were used for validation.

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For the HR-flex model, patient data from both groups (from the development and validation group) were used for developing the patient-specific prediction model. Significance level was set to p < 0.05. All statistical analyses were carried out using RStudio (version 0.98.507, R Develop-ment Core Team, Free Software Foundation Boston, MA, USA).

Table 2: Baseline characteristics

Development Group (n = 11)

Mean ± Sd Min - Max

Age, year 57.1 ± 6.6 45 - 64

Height, cm 182.8 ± 10.6 173 - 191

Weight, kg 95.2 ± 9.5 82 - 108

BMI, kg/m2 28.6 ± 3.5 23.8 - 33.7

RHR, bpm 54.4 ± 6.7 42 - 66

RMR, kcal/min 1.2 ± 0.1 1.0 - 1.4

Resting VO2, ml/min 324.7 ± 43.2 266.7 - 418.8

PeakVO2, ml/min 2326.8 ± 730.8 1429.3 - 3908.6

Max HR, bpm 138.5 ± 23.5 96.3 - 177.2

Beta-blocker dose, mg/day 48.3 ± 32.1 0 - 100

Validation Group (n = 5)

Mean ± Sd Min - Max

Age, y 53.124 ± 8.782 42 - 61

Height, m 175.5 ± 6.6 164 - 182

Weight, kg 94.8 ± 12.2 80 - 110

BMI, kg/m2 30.7 ± 3.1 25 - 33.2

RHR, bpm 59.1 ± 5.3 52 - 68

RMR, kcal/min 1.2 ± 0.2 1.0 - 1.4

Resting VO2, ml/min 351.8 ± 48.9 315.8 - 443.8

PeakVO2, ml/min 2369.3 ± 450.0 1839.4 - 3175.1

Max HR, bpm 151.7 ± 3.9 148.1 - 156.7

Beta-blocker dose, mg/day 80.3 ± 39.9 0 - 100

BMI= body mass index; RHR= resting heart rate; bpm= beats per minute; RMR= resting metabolic rate; VO2= oxygen

consumption; PeakVO2= maximum oxygen consumption; max HR= maximum heart rate.

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Results

Sixteen patients agreed to participate. The dataset included a set of static variables indicating the individual characteristics as well as a set of time-varying features like TEE, PAL, HR and ACmin for an average of 64 minutes/patient (total 1027 data points for each time-varying variables). Patients were divided in two groups for model development and validation. Baseline character-istics are presented in Table 2. On the entire study population, a significant association was found between peakVO2 and resting VO2 (R = 0.65, p < 0.01) and between peakVO2 and RMR (R = 0.52, p < 0.05). RHR was not significantly correlated with any of the patient characteristics. Max HR at the symptom limited exercise test was negatively associated with age (R = - 0.57, p < 0.05) and positively associated with peakVO2 (R = 0.63, p < 0.01).

A significant correlation was found between HR or body movement data (HR, HRnet, ACmin) and measures of energy expenditure (Table 3). On a group level HRnet showed the strongest association with energy expenditure as compared to ACmin or HR. On an individual level, the average correlation between HR and energy expenditure sharply increased, indicating a strong between-subject difference in the relationship between HR and TEE or PAL.

Results from the stepwise multivariate regression analysis are shown in Table 4 and Figure 1. Model e) including a combination of body movement, HR features, and patient characteristics showed the lowest estimation error and the largest correlation with both TEE and PAL (> 76% and > 83%). When HR features were omitted as input to the prediction model (Models a and b) energy expenditure estimates showed larger error (Table 4).

Table 3: Correlation between energy expenditure measures and body movement or HR features for data from the entire study population (Group) or from each individual patient (Individual)

Correlation TEE vs ACmin TEE vs HR TEE vs HRnet

p < 0.05 Mean Sd Mean Sd Mean Sd

Group 0.47 0.55 0.68

Individual 0.53 0.15 0.84 0.12 0.84 0.12

Correlation PAL vs ACmin PAL vs HR PAL vs HRnet

p < 0.05 Mean Sd Mean Sd Mean Sd

Group 0.49 0.49 0.56

Individual 0.53 0.15 0.84 0.12 0.84 0.12

TEE=total energy expenditure; ACmin= activity counts per minute; HR= heart rate; HRnet= (heart rate – resting heart

rate); PAL= physical activity level

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The HR-flex model showed the energy expenditure estimation accuracy was comparable with some of the models based on a combination of HR and body movement features (Table 4). However, accuracy was lower than the most accurate prediction model based on HR, body movement and patient characteristics (Figure 2). The HR-flex model accuracy was not possible to test using a hold-out group of patients (like the validation group) since the method required individual calibration to set the value of constituting parameters.

Table 4: Accuracy of the energy expenditure prediction models

TEE Development Validation

R2RMSE

(kcal/min)

Bias

(kcal/min)R2

RMSE

(kcal/min)

Bias

(kcal/min)

Model a) Movement features 0.64 0.984 0 0.65 1.031 -0.135

Model b) Movement + Patient features 0.63 0.99 0 0.57 1.156 -0.249

Model c) HR features 0.56 1.084 0 0.76 0.922 0.107

Model d) HR + Patient features 0.60 1.032 0 0.79 0.856 -0.079

Model e) Movement + HR + Patient

features

0.76 0.799 0 0.79 0.799 -0.126

HR-flex model 0.79 1.11 -0.22 - - -

PAL Development Validation

R2RMSE

(kcal/min)

Bias

(kcal/min)R2

RMSE

(kcal/min)

Bias

(kcal/min)

Model a) Movement features 0.61 1.262 0 0.62 1.278 -0.076

Model b) Movement + Patient features 0.67 1.161 0 0.68 1.178 0.008

Model c) HR features 0.74 1.026 0 0.72 1.123 0.22

Model d) HR + Patient features 0.75 1.006 0 0.79 0.986 0.145

Model e) Movement + HR + Patient

features

0.83 0.835 0 0.84 0.834 0.118

HR-flex model 0.80 0.846 -0.13 - - -

TEE= total energy expenditure; RMSE= root mean squared error; Bias= mean error; HR= heart rate; PAL= physical activity

level. The HR-flex model accuracy was not possible to test using a hold-out group of patients since the method required

individual calibration to set the value of constituting parameters.

Results from the development group were showed to demonstrate the large generalisation properties of the models not

affected by overfitting.

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Figure 1: Correlation between measured and predicted TEE for all the developed models based on a combination of HR, ACmin and patient characteristics

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Discussion

In this study we developed an energy expenditure prediction model for cardiac rehabilitation patients using beta-blockers, based on body movement and HR data. A multivariate regression model shows the lowest estimation error in the estimation of PAL and TEE when HR, body move-ment and patient characteristics are included. Personalisation of the prediction model using the HR-flex model with a cycling protocol for calibration resulted in somewhat lower accuracy than the most accurate multivariate model.

To our knowledge, this study is the first to validate energy expenditure estimation models based on acceleration and HR in cardiac patients taking beta-blockers. The observed correlations between body movement features and TEE as well as HR features and TEE were comparable to studies in healthy subject [30,31]. In addition, a prediction model using individual HR features or body movement features showed comparable coefficients with studies in healthy subjects [24,32]. As discussed in previous literature, accuracy of energy expenditure estimation can improve when HR and body movement data are combined [20,33]. A validation study in healthy subjects demon-strated a lower RMSE and higher correlation in a HR-body movement model estimating free-living PAEE (r2 = 0.78) as compared to a model using estimates from HR or body movement alone (r2 = 0.59 and r2 = 0.61 respectively) [20]. We observed similar results for the individual HR-body move-ment regression models in cardiac patients (TEE: r2 = 0.56 and r2 = 0.64 respectively), and a similar improvement in r2 and RMSE in the regression model when body movement, HR and patient

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PAL

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PAL

Figure 2: Measured and predicted PAL from a representative patient according to the HR-flex method and the multivariate model based on HR, body movement features and patient characteristics

Black line represents the measured PAL, light and dark blue lines the predicted PAL according to the prediction models.

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characteristics were combined (r2 = 0.76 for TEE, r2 = 0.83 for PAL). Studies validating HR-body movement models in free-living conditions showed comparable results [34,35].

Our results revealed that the accuracy of energy expenditure estimation improves when specific patient characteristics are included in the model. Although HR and oxygen consumption are linearly related, our results showed that variation in individual patient characteristics (e.g. fitness, age and gender) cause a high between-subject variation. Adding these patient characteristics and using individual calibration protocols (e.g. step-test, cycling protocol) can capture these variations and improve the accuracy of TEE/PAL estimation. In particular, the HR-Flex method, using HR data and a cycling protocol for calibration proved to be more accurate in TEE and PAL estimation than the models combining body movement and HR data only. These results are in line with the litera-ture, indicating the beneficial effects of individual calibration to capture between-individual vari-ances [34,36]. However, individual calibration also requires resource-demanding procedures, and is therefore unsuitable for large-scale studies and clinical practice. In addition, branched equation models, used in several studies to distinguish between low- and high intensity activities based on the transition between walking-running [20], are unsuitable for cardiac patients with limited phys-ical fitness levels. Therefore, we aimed at developing an energy expenditure model that could be applicable to patient data that are available at the start of a rehabilitation programme to success-fully monitor physical activity in these patients in daily life. Currently, general patient characteris-tics (i.e. age, body weight, BMI, medication use) are available in the electronic patient record and a symptom limited exercise test is performed at the start of each cardiac rehabilitation programme to determine physical fitness (peakVO2). With this data available, only RMR data are required before the most accurate multivariate regression model described in our results (Table 4, model e) can be implemented. However, RMR can be calculated using the Harris-Benedict equation [37], although RMR assessment as performed in this study is probably more accurate.

Clinical interpretation and future directionsWith the introduction of accurate energy expenditure models based on HR and body move-ment for cardiac patients using beta-blockers, physical activity behaviour can become a more eminent topic in cardiac rehabilitation. First, objective feedback on physical activity monitored during the intervention provides awareness concerning a patient’s current physical activity behaviour. With feedback and motivational coaching based on the objective physical activity data, patients are able to develop self-management skills and improve and/or maintain their physical activity behaviour [38]. Second, accurate assessment of physical activity is essential for evaluating the effectiveness of an intervention programme and for studying the dose-response relation between physical activity levels and health outcomes. Therefore, it is essential that accu-rate energy expenditure models are developed, using data that are routinely available at the start of cardiac rehabilitation without the need of additional procedures and resources. Although a slight improvement in the TEE and PAL estimation error was observed with individual calibration processes, generalizability is limited. With the developments in wearable sensors, HR and body

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movement data collection will become more accurate, comfortable and feasible in the home environment. Whereas this study showed that TEE and PAL can be accurately predicted using HR and body movement data in combination with patient characteristics and data derived from a symptom limited exercise test, the inclusion of activity recognition and improvement of tech-nology may lead to an even further reduce the energy expenditure estimation error [39]. Future studies can use our results as a roadmap for accurate physical activity monitoring with wearable sensors. If the methodology is confirmed in a larger cardiac rehabilitation population with mixed conditions, it will be more applicable in the cardiac rehabilitation setting.

Strengths and limitationsIn this study we developed energy expenditure estimation models using HR and body move-ment in cardiac patients using beta-blocker medication. Nevertheless, generalizing the results to the entire cardiac population (e.g. chronic heart failure) is not straightforward considering the composition of the study population. Furthermore, our results are based on laboratory investi-gation using an activity protocol including treadmill walking and ergometer cycling as modes of activity. It is not known how well the results apply to free-living conditions. The results of our study are based on a limited number of cardiac rehabilitation patients. Therefore, the results cannot be translated directly to a cardiac rehabilitation population with mixed cardiac conditions.

ConclusionIn conclusion, we developed an energy expenditure estimation model for beta-blocker medi-cated cardiac rehabilitation patients that showed the highest accuracy when HR, body move-ment data and patient characteristics were combined. Personalisation of the model using patient characteristics available at the start of the cardiac rehabilitation programme, results in an accurate and feasible model to estimate energy expenditure and physical activity levels of beta-blocker medicated patients. Additional personalisation using a cycling protocol did not result in a substantial improvement in the estimation of energy expenditure, indicating that this addi-tional personalisation protocol has little benefit in clinical practice.

Conflict of interestAuthors FS, GP, WS and AB are employed by Philips Research.

AcknowledgementsThis study received funding from ZonMW, The Netherlands Organisation for Health Research and Development (ZonMw project number 837001003) and was supported in kind by Philips Research.

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Jos J Kraal, Niels Peek, M Elske van den Akker-Van Marle,

Hareld MC Kemps

BMC Cardiovascular Disorders. 2013; 13(1):82

Chapter 5Effects and costs of home-based

training with telemonitoringguidance in low-to-moderate cardiac risk patients entering

cardiac rehabilitation: The FIT@Home study (Study Protocol)

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Abstract

Background: Physical training has beneficial effects on exercise capacity, quality of life and mortality in patients after a cardiac event or intervention and is therefore a core compo-nent of cardiac rehabilitation. However, cardiac rehabilitation uptake is low and effects tend to decrease after the initial rehabilitation period. Home-based training has the potential to increase cardiac rehabilitation uptake, and was shown to be safe and effective in improving short-term exercise capacity. Long-term effects on physical fitness and activity, however, are disappointing. Therefore, we propose a novel strategy using telemonitoring guidance based on objective training data acquired during exercise at home. In this way, we aim to improve self-management skills like self-efficacy and action planning for independent exer-cise and, consequently, improve long-term effectiveness with respect to physical fitness and physical activity. In addition, we aim to compare costs of this strategy with centre-based cardiac rehabilitation.

Methods/Design: This randomised controlled trial compares a 12-week telemonitoring guided home-based training programme with a regular, 12-week centre-based training programme of equal duration and training intensity in low to moderate risk patients entering cardiac rehabilitation after an acute coronary syndrome or cardiac intervention. The home-based group receives three supervised training sessions before they commence training with a heart rate monitor in their home environment. Participants are instructed to train at 70-85% of their maximal heart rate for 45–60 minutes, twice a week. Patients receive individual coaching by telephone once a week, based on measured heart rate data that are shared through the Internet. Primary endpoints are physical fitness and physical activity, assessed at baseline, after 12 weeks and after one year. Physical fitness is expressed as peak oxygen uptake, assessed by symptom limited exercise testing with gas exchange analysis; physical activity is expressed as physical activity energy expenditure, assessed by tri-axial accelerometry and heart rate measurements. Secondary endpoints are training adherence, quality of life, patient satisfaction and cost-effectiveness.

Discussion: This study will increase insight in long-term effectiveness and costs of home-based cardiac rehabilitation with telemonitoring guidance. This strategy is in line with the trend to shift non-complex healthcare services towards patients’ home environments.

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Introduction

Cardiovascular disease is a major cause of morbidity and mortality accounting for approximately 40,000 annual deaths in the Netherlands [1]. In 2007, associated healthcare costs were estimated at €6.9 billion [2], almost 10% of the total healthcare costs in the Netherlands. It is expected that, due to ageing, this economic burden will increase over the next decades. Cardiac rehabilitation (CR) is a multidisciplinary intervention aiming at physical and psychosocial recovery after an acute coronary syndrome (ACS) or cardiac intervention (coronary revascularisation procedure or valve surgery). In 1995 the Dutch CR committee released the first Dutch multidisciplinary CR guideline with updates in 2004 and 2011 [3]. Since this period, CR has become a fully reimbursed treatment in the Neth-erlands. Since 1990, the number of possible treatment modalities has gradually extended to four group-based interventions (exercise training, education therapy, lifestyle change therapy, and relaxation- and stress management). Exercise training is widely considered as a crucial part of CR [4] and therefore plays an important role in current CR programmes. Exercise-based CR has proven to have beneficial effects on morbidity and mortality [5]. Therefore, it is highly recommended in clinical guidelines [3,6,7]. Nowadays, exercise training is reimbursed in the Netherlands only when performed under direct medical supervision in a hospital or specialized CR clinic.

Barriers in cardiac rehabilitationDespite proven benefits, availability of comprehensive guidelines, adequate reimbursement and the widespread availability of CR in the Netherlands, CR uptake remains low. A recent study in a large Dutch cohort showed that only 28.5% of eligible patients actually receive CR [8]. In this study, a longer travelling distance to the nearest CR provider was strongly associated with lower CR uptake, suggesting that transport difficulties and/or lack of time form important barriers for patients to take part in centre-based CR programmes. Other patient-related factors impeding participation in centre-based CR programmes include problems in scheduling due to work resumption, care of dependents, and reluctance to take part in group-based therapy [9]. Besides low uptake of CR, its long-term effectiveness is limited because traditional centre-based exercise training programmes are primarily aimed at improving short-term exercise capacity rather than at inducing long-term lifestyle changes [10]. The graduation from a supervised to an unsupervised environment remains a pivotal event that is associated with a loss of effectiveness, often resulting in a decline in physical fitness and activity levels after the initial rehabilitation period [11,12].

Home-based exercise trainingTo address the abovementioned problems, there is a need for innovative rehabilitation methods that aim at an increase of CR uptake and more sustained effects on physical fitness and physical activity. As such, home-based exercise training has the potential to improve participation in CR programmes [9,13,14], especially in the younger working population and patients with transport difficulties. A meta-analysis showed that home-based exercise training was equally effective as centre-based exercise training in improving short term exercise capacity in CR patients [15].

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Studies published afterwards showed similar effects of home-based training in low to moderate risk CR patients [16], in patients with chronic heart failure [13,17], after coronary artery bypass grafting[18,19], and in elderly CR patients (i.e. ≥ 65 years) [20]. Two of these studies [18,19] did not use monitoring during home-based exercise training, while the others employed a variety of monitoring strategies such as telephone guidance [13,17,21] and home visits [20]. In addition, the training protocols show considerable variation with respect to duration, ranging from 8 to 52 weeks, and exercise intensity, ranging from low-level walking [22] to high intensity aerobic training [18]. Despite these variations in study populations, monitoring strategies and training protocols, all mentioned studies showed beneficial effects on short term exercise capacity after home-based training, and moreover, these training programmes were equally safe as centre-based exercise training.

Although home-based exercise training can be considered safe and short-term results are prom-ising, long term effects on physical fitness and activity levels remain questionable. Whereas Prescott et al. [23] showed an initial improvement in exercise capacity after eight weeks of home-based exercise training in CHF patients, exercise capacity levels decreased to baseline at one year follow-up. These results are in line with a study by Dracup et al. [22], also showing a decline in exercise capacity in the 6 months following a guided home-based walking programme. A possible explanation for these findings may be the sudden translation from a structured training programme to independent exercise [12]. To facilitate this transition, the patient’s responsi-bility for performing exercise training should be triggered at an early stage of the rehabilitation process by the development of self-management skills [24]. Important self-management skills for maintaining behavioural change are self-efficacy, action planning, problem solving and deci-sion making [25]. According to Lorig et al., self-efficacy, the confidence in one’s own abilities to execute and complete a task, is required to maintain behavioural change. When home-based training is initiated in the early stages of CR, patients will develop a familiarity with exercising at home. When coaching and support from a physical therapist is adequate, patients’ confi-dence in performing independent exercise can be expected to grow during the rehabilitation process. When CR and the supervision of a physical therapist ends, patients should have devel-oped sufficient confidence in their own ability to maintain independent exercise. Other relevant self-management skills are action planning, problem solving and decision making [25]. The early initiation of exercise at home forces the patients to plan their own training schedule. However, problem solving and decision making with respect to training duration and intensity can only be performed adequately when patients receive feedback from health care professionals based on actual training data. Also, patients should be able to monitor their training data themselves to be able to detect and solve problems at an early stage. Therefore, we hypothesize that remote monitoring of patients during home-based CR can be highly effective in improving self-man-agement skills. Supervision of a physical therapist will be important during the first stage of CR, but patients will develop action planning, problem solving and decision making skills to orga-nize, evaluate and adapt their training schedule during CR.

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As mentioned above, development of self-management skills can be expected to improve by application of remote patient monitoring that enable health care professionals and patients to review and discuss measured training data during the initial rehabilitation period. With the broad availability of wearable monitoring devices and internet access in most households, ample opportunities exist to create such innovative rehabilitation service. Preliminary studies using remote monitoring of physical activities and online coaching show promising results. Reid et al. [26] reported improved physical activity levels at one year follow up in ACS patients after partic-ipation in an internet-based activity programme with educational tutorials and remote moni-toring of self-reported physical activities. In their study, physical activity levels were assessed by a pedometer and a self-reported leisure-time questionnaire. Similar results were shown in randomised controlled trials in CR patients [27], patients after initial centre-based CR [28], and in patients with peripheral vascular disease [29], all demonstrating improved self-reported physical activity levels after pedo-/accelerometer based interventions with online counselling as compared to usual care and supervised exercise respectively. Follow-up duration of these studies, however, were relatively short (3–6 months).

Proposed solutionTo improve long-term effectiveness of home-based exercise training in CR patients, we propose to combine homebased training with telemonitoring of physical training parameters and online coaching. In this way, we aim to prepare patients more thoroughly for independent exercise without direct medical supervision. Although cost-effectiveness of previous home-based exer-cise training strategies appeared similar to regular centre-based exercise training [30], costs of this particular intervention using telemonitoring guidance has not been established. We hypothesize that costs of this home-based intervention do not exceed costs of regular CR, while improvements in physical activity and physical fitness can be maintained at the long-term.

Study objectivesThe objective of the present study is to investigate whether home-based exercise training with telemonitoring guidance results in better long-term physical fitness and activity levels than regular centre-based exercise training. Furthermore, both training strategies will be compared with respect to training adherence, patient satisfaction, health-related quality of life, and costs. We hypothesize that home-based training with telemonitoring guidance using objective training data during the rehabilitation period will increase motivation and self-efficacy for inde-pendent exercise in CR patients on the long term, resulting in superior increase in physical fitness and physical activity levels. In addition, we expect that the investments in monitoring devices and ICT services are compensated for by lower direct medical costs in the home-based training group due to fewer supervised exercise training sessions.

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Methods/Design

Study designThis study is designed as a monocentre randomised controlled trial at Máxima Medical Centre Veldhoven in the Netherlands. All subjects are requested to provide written informed consent before study entry. Data are collected at baseline (T0), after three months (T1) and after 12 months (T2). Patient recruitment has started in January 2013. The protocol for this study was approved by the Institutional Review Board of the Máxima Medical Centre Veldhoven in the Netherlands. The trial is registered at ClinicalTrials.gov with registration number NCT01732419.

Study population and randomisationAll patients entering outpatient CR after an ACS (myocardial infarction or unstable angina) or a revascularisation procedure (percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG)) with a low to moderate risk of further events are considered for participation. Patients are classified as low to moderate risk for further events by a cardiologist, based on the following criteria described in the Dutch cardiac rehabilitation practice guideline [31]: · Stable medical condition. · No severe psychological and/or cognitive disorders. · No angina pectoris during exercise. · Left ventricular ejection fraction > 40%. · No cardiac arrhythmias during exercise. · No significant valvular heart disease · No congenital heart disorders limiting exercise capacity. · No implantable cardioverter-defibrillator (ICD). · No comorbidities that affect rehabilitation (e.g. chronic obstructive pulmonary disease,

diabetes mellitus, locomotive disorders).

Additional inclusion criteria include access to internet facilities and PC at home. After baseline measurements, patients are randomly allocated to either home-based or centre-based training. Allocation is based on randomisation with variable block size (two or four), performed with dedi-cated computer software by a researcher (NP) who is not present at the time of allocation. To conceal allocation, numbered and sealed opaque envelopes are opened between the baseline cardiopulmonary exercise test and the start of exercise training. All treatment modalities other than physical training take place at the outpatient clinic as usual (i.e. education, lifestyle change therapy, and/or relaxation and stress management therapy).

Exercise training programExercise training is prescribed according to current recommendations of the European Associa-tion of Cardiovascular Prevention and Rehabilitation of the European Society of Cardiology [32]. In both groups patients participate in a 12-week training programme with at least two training

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sessions of 45 to 60 minutes per week. Patients are instructed to exercise with a training intensity of 70-85% of their maximal heart rate, which is assessed during maximal cardiopulmonary exer-cise testing at baseline.

Patients in the centre-based training (CT) group receive group-based training sessions at the outpa-tient clinic under direct supervision of two or three physical therapists specialized in CR. Group size varies between 6 to 8 participants and the patients receive an individually tailored training programme on a treadmill or an electromagnetically braked cycle ergometer. During the training period, physical therapists will record attendance. An evaluation of the 12-week training period will take place during the last centre-based training session. During this session the physical therapist encourages the patient to continue their physical activities in their own environment.

In the home-based training (HT) group, the first three training sessions are performed at the outpatient clinic under direct supervision of a physical therapist. During these sessions, patients are familiarized with training duration and intensity and they are instructed on how to use the wearable heart rate monitoring device (Garmin Forerunner 70). In addition, patients are asked about their preferred training modality in their home environment (e.g. cycling, walking/running, workout at health club), and given advice on how to implement this. Patients are instructed to wear the heart rate monitoring device during training sessions and to upload the recorded heart rate data to a web application (Garmin Connect) through the internet. Patients can use Garmin Connect to review their training data graphically and to relate these data to their personal goals. Training data are also accessed through Garmin Connect by a personal coach (exercise specialist or physical therapist specialized in CR). The coach provides feedback on training frequency, dura-tion and intensity once a week by telephone. Regarding safety issues when exercising at home, patients are asked to contact either the rehabilitation centre staff or their general practitioner if they experience any symptoms during or after exercising. After 12 weeks, the weekly coaching sessions are terminated. However, the patients are advised to continue their training with the heart rate monitoring device and web application.

Telemonitoring guidanceAfter three supervised training sessions in the outpatient clinic, patients in the HT group start training in their home environment. The coach remotely supervises the training sessions performed at home and offers appropriate support via telephone using a semi-structured interview. In the process of coaching the patient, two important factors are identified. First, in order to coach a patient properly, it is essential that goals are set at the start of the rehabilitation together with the patient. This not only improves the rate of success as it sets the locus of control internally [33], but also makes it possible to coach each patient on its own goals and tailor the coaching sessions to the patient in question. Secondly, self-efficacy should be enhanced by letting the patient take responsibility of achieving the goals that has been set. In order to achieve this, the coach will use principles of Motivational Interviewing during the telephone calls. Motivational Interviewing has

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been developed as method of approaching people that are engaged in behavioural changes and appears effective in various fields [34]. Its main features are client-centred and non-judgmental techniques, without confronting and arguing on themes that concern the content.

The purpose of the supervision by telephone is three-fold. First, it should be checked whether patients are correctly executing the training in their home environment and whether the training schedule or training modality does not lead to injuries or adverse events. Second, it should be checked whether the patients are adherent to the training schedule and making progress in their training goals or whether they experience motivational problems. Third, if non-adher-ence or motivational problems exist, motivational interviewing principles are used to detect and resolve barriers, activate patients’ motivation and enhance self-efficacy. The training schedule typically consists of a warm-up phase, a core phase and a cooling down phase. Each phase has a predefined length and intensity (in terms of a heart rate zone). The coach will check whether the patient is indeed correctly following these phases and whether the patient is able to keep the heart rate in the prescribed zone during the core phase. Based on this analysis, the coach will give training-specific advice or suggest the patient to adapt the training programme. If the patient is not able to complete exercise sessions according to the prescription, the coach and patient together explore possibilities for an alternative modality or schedule. For example, when the prescribed heart rate zone is not reached during walking, the patient is advised to try biking or running instead. We expect the length of the weekly telephone calls will vary between 10 and 20 minutes, depending on the available training data, encountered problems and training phase.

Outcome measuresMain endpoints are physical activity level and physical fitness, assessed at baseline, after 12 weeks and after one year. Secondary endpoints are health related quality of life, patient satisfaction, training adherence and cost-effectiveness. Health related quality of life and patient satisfaction are assessed at baseline, after 12 weeks, after 26 weeks and after one year. Training adherence is assessed in both groups during the 12-week exercise period and costs are assessed after 12 weeks, after 26 weeks and after one year. An overview of the study design is provided in Figure 1.

Measurements

Physical activity levelPhysical activity level is assessed by physical activity energy expenditure (PAEE), estimated from accelerometer and heart rate date that are measured during a period of five subsequent days. Participants will wear a compact tri-axial accelerometer (ActiGraph wGT3X + Monitor, ActiGraph) on the hip using an elastic band. The ActiGraph was designed to be worn continuously and previously shown to be a reliable monitor for assessment of daily activity levels [35]. Heart rate is measured with the same heart rate monitor as is used during training sessions at home (Garmin

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Figure 1: Flowchart of study design

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Forerunner 70). It consists of a chest strap and wristwatch receiver with a display providing a continuous reading of the heart rate data. During the assessment periods, patients are instructed to continuously wear both devices during the first night and during day-time of the next five days. Patients are blinded for the accelerometry data. To calculate PAEE, accelerometry data (counts per minute) are time-aligned with heart rate data (beats per minute) and resampled into 20-second epochs. A previously validated branched equation model [36] will be adapted and validated for CR patients and applied to the data to calculate PAEE (Mj per day).

Physical fitnessPhysical fitness is assessed in both groups by peak oxygen uptake, determined by maximal exer-cise testing with respiratory gas analysis at the outpatient clinic. This test is performed on a cycle ergometer (Lode Corrival, Groningen), using an individual ramp protocol aiming at a total test duration of 8–12 minutes. Patients are instructed to maintain a pedalling frequency of 70 rounds per minute. A twelve lead ECG is registered continuously. Peak oxygen uptake (peak VO2) is defined as the average value during the last 30 seconds of exercise. In addition, ventilatory thresholds are independently assessed by two physicians who are blinded to the allocation of the patients, using the V-slope method [37]. Assessment of physical fitness is performed at base-line, after three months and after 12 months.

Health related quality of life and patient satisfactionHealth related quality of life is assessed by the 36-Item Short Form (SF-36) at baseline, after three months, six months and after 12 months. This questionnaire consists of eight scales ranging from 0–100 and previously showed high reliability with good item-internal consistency, item-discrimi-nant validity and high reliability coefficients in diverse population groups [38]. Patient satisfaction is assessed after three months, using a modified version of the Dutch Consumer Quality-index (CQ-index) for rehabilitation medicine [39]. Both study groups receive identical questionnaires.

Training adherenceTraining adherence is assessed on a weekly basis during the 12-week rehabilitation period by the physical therapist or coach. Adherence in the CB group is assessed by the number of attended training sessions at the outpatient clinic. For patients in the HT group, the total number of training sessions is assessed via the Garmin Connect web application. This web application also provides insight in the time spent in the prescribed zone (i.e. 70-85% of maximal heart rate) during the training sessions, which is also used to describe adherence to the training programme.

Cost-effectivenessIn the economic evaluation, the effects of both interventions are compared and related to their difference in costs. Both a cost-effectiveness analysis using the primary outcome measure PAEE as effect measure and a cost-utility analysis using QALYs as outcome measure will be performed. No discounting is applied due to the limited time horizon of one year. The evaluation is performed

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from a societal perspective, making a distinction between direct (i.e. care-related) and indirect (i.e. other health-related) costs. Direct costs include the costs of rehabilitation such as profes-sional wages (physical therapist, exercise specialist), assessment (exercise testing) and equip-ment (heart rate monitor), and other healthcare use during the first year of follow up (hospital-isations for recurrent cardiac events, outpatient visits, paramedical visits, general practitioners visits, home care, and informal care). Indirect costs consist of lost productivity costs due to absenteeism from paid and unpaid work. Resource use of the interventions (both HT and CT) is measured prospectively alongside the clinical study as part of the case record form. Other healthcare resource use and absenteeism of paid and unpaid work is collected by means of a questionnaire based on the healthcare consumption, illness and work questionnaire (Tic-P [40]). This questionnaire is filled out by patients after 12 weeks, 6 months and 1 year. For the evaluation of healthcare use, standard prices published in the Dutch costing guideline are used [41]. Costs of absenteeism from paid work are calculated using the friction cost method [41]. In the cost-utility analysis QALYs are calculated from the health utility gain scores obtained with the SF-36 ques-tionnaire at baseline, 12 weeks, 6 months and 1 year [42]. Sensitivity analyses will be carried out on the perspective (healthcare only instead of societal perspective) and method to assess indi-rect costs (human capital approach instead of friction cost method).

Sample size analysisSample size calculation was performed for the primary endpoint PAEE after 1 year, using data from Bonomi et al. [43]. In this study PAEE in healthy subjects amounted to 4.0 ± 1.2 MJ/day. If the true difference in improvement in PAEE after 1 year between the CT and HT group in this study is 20%, 36 experimental and 36 control subjects need to be studied to be able to test the null hypothesis that the population means are equal (power = 0.8 and alpha = 0.05). Accounting for a 20% loss to follow-up after 1 year (i.e. no assessment at 1 year follow-up), 45 subjects need to be included in both groups.

Statistical analysisData are analysed on an intention-to-treat basis. Multivariate analysis of variance (ANOVA) will be used to assess intra- and intergroup differences as well as their interactions, for PAEE, peak VO2, health related quality of life, patient satisfaction and training adherence. Analyses will be carried out in the statistical programming language R (version 2.13.1).

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Discussion

Aims of the studyThe presented study is designed to evaluate the relative (cost-)effectiveness of home-based exercise training in combination with telemonitoring guidance in low to moderate risk patients entering CR, compared to regular, centre-based exercise training. We hypothesize that early-onset home-based training with online coaching will increase motivation and self-efficacy in CR patients, resulting in improved long-term physical fitness and higher activity levels than after regular centre-based exercise training. The first results of this study are expected in 2014.

Strengths and limitationsPrevious studies indicated that home-based exercise training is equally safe and effective as centre-based exercise training. However, our study distinguishes itself from other studies that have been conducted previously by the use of telemonitoring guidance during home-based exercise training, its long (one year) follow-up period, and by the use of accurate measurement of physical activity energy expenditure in the home environment. To date, the combination of accelerometry and heart rate for the calculation of PAEE has rarely been used in clinical studies. In the last few years, prices for wearable heart rate and accelerometry measurement devices have dropped while their accu-racy has increased. With these devices, we can avoid self-reported outcomes based on personal diaries and questionnaires that do not allow for calculation of energy expenditure and have ques-tionable reliability for a combination of different exercise modalities [44]. Our intervention focuses on early activation of patients’ motivation, appealing to their own responsibility and stimulating self-efficacy concerning lifestyle changes. Patients are encouraged to change their lifestyle in their home environment from the onset of the rehabilitation process. This can prevent relapse to an inactive lifestyle, which is often observed after the completion of the typical 12-week supervised centre-based training [11]. In addition, we use telemonitoring for the assessment of training inten-sity during home-based training. Therefore, coaching by telephone is based on objective training data and is tailored to the individual patient. A study by Aamot et al. [16] showed that home-based high-intensity training, monitored by a HR monitor was equally effective as centre-based high-in-tensity training with respect to short-term improvement in exercise capacity. Other home-based studies did not monitor training intensity during home-based exercise training [45] or use self-re-ported perceived exertion rate during training [18,20].

Our study also has some limitations. By design, we are unable to blind participants for allocation. In addition, the personnel supervising the cardiopulmonary exercise tests are not blinded for alloca-tion. During the five days of continuous measurement of physical activity, a bias due to Hawthorne effects is expected. We expect patients to be more active during this period, because they are aware that their daily activity is measured. However, as both groups will experience this Hawthorne effect we expect that this will have no significant effect on the study outcome. The coaching of HT patients is dependent on the Garmin Connect software. Therefore, all patients are required to

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have Internet access from home and basic computer skills to install and use the Garmin Connect software. Although instruction manuals and expert guidance are available, a lack of basic computer skills could occasionally lead to problems at the start of the home-based programme. In addition, when a problem with Garmin Connect emerges, or when HT patients forget to upload training data to the Garmin Connect application, effectiveness of the coaching reduced.

Potential implications for practiceIn most countries, only centre-based CR is offered to the vast majority of CR patients. If the results of this study indicate that home-based training with telemonitoring guidance is more effec-tive than centre-based exercise training with respect to long-term physical fitness and phys-ical activity levels, there are compelling reasons to make home-based training equally acces-sible as centre-based training for low to moderate risk patients, and to stimulate these patients to train at their homes. Also, from a macro-economical point of view it may be beneficial to implement telemonitoring guided home-based CR. Due to underutilisation of CR [8], the Dutch Health Inspection has demanded CR centres to increase CR uptake in 2013. However, the current restrictions on healthcare budgets impose great difficulties for Dutch hospitals and CR centres to expand their CR services. A similar trend is visible in other developed countries: despite rising healthcare expenses, uptake of CR is low and budget for expansion of CR is not available [46,47]. Therefore, there is an urgent need for innovative, more cost-effective CR strategies. If home-based training with telemonitoring guidance meets these requirements, it can replace regular centre-based training in CR patients with a low to moderate risk of further events. This will allow a larger number of patients to receive CR without increasing the total costs. This strategy is in line with the 2012 Dutch government budget plans, which include a shift of basic non-complex healthcare services from the hospital towards the patients’ home environment, preferably by large-scale implementation of e-health services.

Competing interestThe FIT@Home study is executed in collaboration with Philips Research; the heart rate monitors and accelerometers used during the assessment of PAEE and during home-based training are provided by Philips Research.

Authors’ contributionsNP and HK had the basic idea for this study and were involved in the development of the protocol. HK, NP, MA and JK drafted the manuscript. All authors were involved in the critical revi-sion of the paper for intellectual content and its final approval before submission.

AcknowledgementsThis study is funded by ZonMw, the Netherlands Organisation for Health Research and Develop-ment. ZonMw project number 837001003.

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Jos J Kraal, Niels Peek, M Elske Van den Akker-Van Marle,

Hareld MC Kemps.

European Journal of Preventive Cardiology. 2014;21(25):26-31

Chapter 6

Effects of home-based training with telemonitoring guidance in

low to moderate risk patients entering cardiac rehabilitation:

Short-term results of the FIT@Home study

118

Abstract

Background: Home-based exercise training in cardiac rehabilitation (CR) has the poten-tial to improve CR uptake, decrease costs and increase self-management skills. The FIT@Home study evaluates home-based CR with telemonitoring guidance using coaching inter-ventions including strategies for behavioural changes with the aim to maintain adherence to a healthy lifestyle and to improve long-term effects. In this interim analysis we provide short-term results on exercise capacity, quality of life and training adherence of the first 50 patients included in the FIT@Home study.

Design: Randomised Controlled Trial

Method: Low to moderate risk CR patients were randomised to a 12-week home-based training (HT) programme or a 12-week centre-based training (CT) programme. In both groups, training was performed at 70-85% of maximal heart rate (HRmax) for 45-60 minutes, 2-3 times per week. The HT group received three supervised training sessions, before commencing training with a heart rate monitor in their home environment. These patients received individual coaching by telephone weekly, based on training data uploaded on Internet. The CT programme was performed under direct supervision of a physical therapist. Exercise capacity and health-related quality of life were assessed at baseline and at 12 weeks.

Results: CT (n=25) and HT (n=25) both showed a significant improvement in peak VO2 (10% and 14% respectively) and quality of life after 12 weeks of training, without significant between-group differences. The average training intensity of the HT group was 73.3 ± 3.5% of HRmax. Training adherence was similar between groups.

Conclusion: This analysis shows that HT with telemonitoring guidance has similar short-term effects on exercise capacity and quality of life as CT in CR patients.

119

Introduction

BackgroundAlthough mortality due to cardiovascular disease is decreasing, it remains one of the leading causes of death worldwide placing a large social and economic burden on society. Recently, total annual costs for cardiovascular disease in the EU were estimated at €196 billion [1]. Cardiac rehabilitation (CR) is a multidisciplinary intervention aiming to accelerate physical and psycho-social recovery and to reduce the risk for future cardiac events. Numerous studies showed that CR can lead to a substantial decrease in morbidity and mortality and is therefore highly recom-mended in clinical guidelines [2,3]. Exercised training, one of the crucial components of multidis-ciplinary CR, has proven benefits on exercise capacity, quality of life and mortality [2]. However, despite these benefits, participation and adherence to exercise-based CR is low. Previous studies showed that only 20-30% of all eligible cardiac patients participate in such a programme [4,5]. Especially in younger patients, work and social obligations as well as reluctance to take part in group sessions form important barriers to participate in CR [4,6]. Therefore, there is a need for innovative rehabilitation methods aiming at an increase of CR uptake.

Home-based CRAn intervention that has been proposed as an alternative for patients that are unwilling or unable to attend the hospital for supervised CR is home-based exercise training [7–9]. A meta-analysis of 12 studies showed comparable short-term improvement in exercise capacity and mortality rates after home-based as compared to centre-based CR [10]. However, long-term effects of home-based CR are not well established. While most studies report only direct results of CR, others show a decline in exercise capacity after termination of home-based CR [11,12] similar to long-term results of centre-based CR [13,14]. A possible explanation for these disappointing results may be a lack of direct objective feedback on training progression for patients. When patients do receive feedback of their actual training data and are able to monitor their training goals by assessment of training intensity, frequency and duration, they can develop self-management skills for maintaining an active lifestyle. In this way, skills like action planning, problem solving and deci-sion-making can be developed in the early stages of CR, so that self-efficacy for performing inde-pendent exercise at home can be improved [15]. Another explanation for the long-term deterio-ration of exercise capacity in these studies may be that the coaching interventions did not include strategies aimed at behavioural change (e.g. motivational interviewing or cognitive therapy).

The purpose of the FIT@Home study is to investigate the effects of a home-based CR strategy consisting of motivational interviewing in the initial CR phase in combination with on-going objective feedback on training progression in low to moderate risk CR patients. This paper provides short-term results of the FIT@Home strategy on exercise capacity, training adherence and quality of life.

120

Methods

Study designWe conducted a randomised controlled trial among low to moderate risk patients entering the CR at Máxima Medical Centre Veldhoven in the Netherlands. All patients provided written informed consent before entering the study. The study protocol was approved by the Institu-tional Review Board of the Máxima Medical Centre Veldhoven in the Netherlands (reference number 1243) and registered at ClinicalTrials.gov with registration number NCT01732419. The protocol is described in detail elsewhere [16].

Population and randomisationPatients were eligible for participation in the study when they entered CR after hospitalisation for myocardial infarction, unstable angina, or a revascularisation procedure (percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG)). Only patients with a low to moderate risk of future cardiac events according to the Dutch CR guidelines were included [17]. Finally, patients were required to have Internet access and a PC at home. During the CR intake proce-dure, patients were informed about the study by a specialised nurse practitioner or sport physician. After one week, patients were asked to participate by the coordinating investigator. After written consent, patients were randomly allocated to home-based training (HT) or centre-based training (CT). All treatment modalities other than exercise training (i.e. lifestyle change therapy, relaxation and stress management and/or education) took place as usual at the outpatient clinic.

Exercise protocolExercise training in both groups was prescribed according to the current recommendations of the European Society of Cardiology.[18] Both groups participated in a 12-week exercise programme with at least two training sessions per week. Patients were instructed to exercise for 45 to 60 minutes per session at 70-85% of their maximal heart rate. The CT group performed group-based training sessions on a treadmill or cycle ergometer, supervised by physical thera-pists and exercise specialists. Patients in the HT group received three initial supervised training sessions. During these three sessions, patients received instructions on how to use a wearable heart rate monitor (Garmin Forerunner 70) and how to upload the recorded exercise data to a web application (Garmin Connect) through Internet. The web application was used to review the training data by the patient, the physical therapist and the exercise specialist. During the first sessions, the patients were also familiarized with the training programme (duration, intensity) and their preferred training modality in the home environment was discussed. After three super-vised training sessions, patients in the HT group started training in their home environment. They received feedback on training frequency, duration and intensity from the physical thera-pist once a week via telephone. After 12 weeks, the telephonic feedback was terminated and the patients were advised to continue their training with the heart rate monitor.

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Telemonitoring GuidanceTo guide behavioural change, we used principles from goal setting theory and motivational interviewing during the home-based intervention. At the start of the rehabilitation programme patients defined personal training goals together with their physical therapist. After three super-vised training sessions, patients in the HT group started training at home and received coaching from their therapist through weekly telephone calls. During this phone call the therapist gave feedback on training parameters that were measured during the preceding week, and discussed progress with respect the personal training goals with the patient. In addition, based on the prin-ciples of Motivational Interviewing, they discussed barriers and facilitative factors in adhering to the exercise training protocol. Motivational Interviewing aims at resolving problems and barriers that occur in the process of change by means of activating and providing direction. Both Motiva-tional Interviewing and goal setting appear effective in the enhancement of motivation to main-tain behavioural change in various fields [19,20].

Outcomes measuresExercise capacity was determined at baseline and after 12 weeks, by a maximal exercise test with respiratory gas analysis. Exercise capacity was defined as the average peak oxygen uptake during the final 30 seconds of exercise (peak VO2). Secondary endpoints were health-related quality of life and training adherence. Health-related quality of life was assessed at baseline and after 12 weeks with a Dutch translation of the MacNew questionnaire [21]. Training adherence was defined as the number of training sessions during the 12-week CR programme either attended at the outpatient clinic (CT group), or performed at home (HT group). Training duration, exercise intensity and exercise time in the prescribed heart rate zone in the HT group were assessed via self-recorded heart rate measurements.

Statistical analysisPrimary endpoints of the FIT@Home study are exercise capacity and physical activity level, assessed at baseline, after 12 weeks and after one year. Besides health-related quality of life and training adherence as described above, other secondary endpoints in the FIT@Home study are patient satisfaction after 12 weeks and cost-effectiveness after one year. Sample size calculations were performed for physical activity levels after one year, based on data from Bonomi et al. [22]. We expect to include 45 patients in each group in the FIT@Home study. For the analysis of short-term results, linear regression analysis was used to assess differences in peak VO2, quality of life, and training adherence between the two groups. Data were analysed on an intention-to-treat basis and all analyses were performed with the statistical software package R (version 3.0.3) [23].

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Results

Between March 2013 and March 2014 a total of 55 patients agreed to participate in the study, of which 26 were allocated to the CT group and 29 to the HT group. Of these patients, 34 received a PCI, 10 patients underwent CABG, and 11 patients were only treated with medication during their hospitalisation. One patient in the CT group and one in the HT group were not able to perform an exercise test at 12 weeks, due to co-morbidities. Three patients in the HT group dropped out due to either technical difficulties (n=2) or due to a change in health status (n=1). There were no adverse events in either group. Baseline characteristics of the remaining 50 patients are described in Table 1.

Table 1: Baseline characteristics

Centre-Based Home-Based

n=25 n=25

Age (years) 56.1 ± 8.7 60.6 ± 7.5

Men, n (%) 21 (84%) 22 (88%)

BMI 27.9 ± 3.7 28.3 ± 3.3

Diagnosis

ACS with PCI, n (%) 10 (40%) 14 (56%)

ACS without PCI, n (%) 5 (20%) 4 (16%)

AP with PCI, n (%) 4 (16%) 2 (8%)

AP without PCI, n (%) 0 (0%) 2 (8%)

CABG, n (%) 6 (24%) 3 (12%)

Medication

Beta-blocker, n (%) 22 (88%) 23 (92%)

Statines, n (%) 25 (100%) 24 (96%)

Anti-platelets, n (%) 24 (96%) 25 (100%)

ACE-i / ARB, n (%) 20 (80%) 16 (64%)

Values are presented as mean (SD) unless stated otherwise.

BMI= body mass index; ACS= acute coronary syndrome; PCI= percutaneous coronary intervention; AP= angina pectoris;

CABG= coronary artery bypass graft; ACE-i= angiotensin converting enzyme inhibitors; ARB= angiotensin receptor blockers.

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Exercise dataTable 2 shows the outcome measures assessed at baseline and after 12 weeks. Peak VO2 improved significantly in both groups, while no significant between-groups difference was observed (p=0.40). In addition, maximal workload improved significantly in both groups while maximal heart rate remained unchanged.

Table 2: Effect of the FIT@Home intervention at 12 weeks

Centre-based (CT) Home-based (HT) p-value

n = 25 n = 25 Between groups

Baseline 12 weeks Baseline 12 weeks

Exercise data

Peak VO2 (ml/min/kg) 23.7 ± 6.4 26.1 ± 7.6** 22.8 ± 4.2 26.0 ± 5.9*** 0.401

Maximal workload (Watt) 179.6 ± 53.9 202.4 ± 68.2*** 181.1 ± 49.5 200.2 ± 53.3*** 0.546

HRmax (beats/min) 142.2 ± 19.8 147.2 ± 25.3 140.0 ± 17.4 142.7 ± 17.4 0.512

RER at peak VO2 1.3 ± 0.1 1.2 ± 0.1 1.3 ± 0.1 1.2 ± 0.1* 0.482

Quality of Life

Physical scale 5.0 ± 0.8 5.7 ± 0.8** 5.4 ± 0.8 6.1 ± 0.6*** 0.163

Emotional scale 5.1 ± 1.0 5.6 ± 0.9* 5.8 ± 0.8 5.9 ± 0.8* 0.880

Social scale 5.5 ± 0.8 6.1 ± 0.7*** 6.0 ± 0.8 6.4 ± 0.6** 0.257

Total score 5.2 ± 0.8 5.8 ± 0.7** 5.7 ± 0.7 6.1 ± 0.5*** 0.498

Training data

Frequency (sessions) 20.5 ± 4.5 24.0 ± 7.2 0.049

Duration (min/session) 60 ± 0.0 60.7 ± 16.1 0.690

Zone (min/session) 41.8 ± 10.9

Intensity (% HRmax) 73.2 ± 3.5

Values are presented as mean (SD) unless stated otherwise.

Peak VO2= peak oxygen uptake; RER= respiratory exchange ratio; HRmax maximal heart rate

Significant change from baseline: *P<0.05, **P<0.01, ***P<0.001

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Training dataOn average, patients in the CT group attended 20.5 supervised training sessions (adherence: 86%, range: 6-25), while the HT group performed an average of 24.0 training sessions in 12 weeks (adherence: 100%, range: 13-41). Patients in the HT group exercised 61 minutes per training session, of which they exercised 42 minutes in the prescribed exercise intensity zone (70-85% of maximal heart rate). In the CT group a regular 60-minute training session consisted of a warming up and cooling own phase and two 20-minute bouts on a treadmill or cycle ergometer.

Quality of lifeHealth-related quality of life improved significantly in both groups, without any between-group differences (Table 2). The improvement was present in all subscales of the quality of life questionnaire.

Discussion

The short-term results of the present study demonstrate two important findings. First, home-based exercise training with telemonitoring guidance results in similar improvement of exer-cise capacity and health-related quality of life compared to an intensive regular centre-based training. Second, training frequency, exercise duration and training intensity in the home-based training group was comparable with training adherence of the supervised centre-based training. This implicates that patients are able to independently execute a 12-week training programme in their home environment when they receive adequate coaching and objective feedback.

Although previous studies on home-based CR training using objective variables to set training intensity showed favourable effects, improvements in peak VO2 were somewhat lower than in our study (8%-10% versus 14%) [9,24]. This may be explained by a lack of feedback on training progression for patients. Aamot et al. [9] instructed home-based CR patients to use a heart rate monitor during exercise training, but these data were not used for coaching during the CR period. In the study of Oerkild et al. [24] patients received home-visits during the CR, but patients had no insight in their training data. Yet, the ability for patients to monitor progression of an intervention has been shown to increase motivation and self-management skills, and may therefore result in superior improvements in exercise capacity and physical activity levels [25]. As patients in the FIT@Home study are enabled to continue to use the heart rate monitor and soft-ware application after the initial CR phase, we expect that the favourable short-term results can be sustained on the long-term.

A limitation of this study is that the intervention may not be suitable for all patients, as it requires basic computer and Internet skills to install and use the software platform. Yet, in the present study only two patients were not able to independently work with the software. Furthermore,

125

the average age of the study participants was comparable to that of other studies investigating effects of supervised CR [2,10], suggesting that the applicability of this intervention is not limited to younger patients only. The prescribed training intensity for HT patients was calculated from the maximal heart rate assessed during the exercise test at baseline. However, changes in the prescribed beta-blocker dosage affects heart rate considerably. Although changes in medica-tion during the CR occurred only rarely (twice), we did not perform an additional exercise test for these cases. Instead, exercise intensity was adjusted based on previous training experience, similar training data and the rate of perceived exertion assessed by the Borg scale.[26] Therefore, we expect this has little impact on the presented results.

In conclusion, the results of this study show that home-based CR with telemonitoring guidance can be an effective alternative for regular centre-based CR. In addition, implementation of this strategy may lead to an increase in CR participation. Furthermore, early development of self-management skills by home-based CR with objective feedback for patients may increase long-term results of CR. Therefore, a long-term (cost-) effectiveness analyse will be performed on all FIT@Home data.

Conflicts of interestThe FIT@Home study is executed in collaboration with Philips Research; the heart rate monitors used during home-based training were provided by Philips Research.

AcknowledgementsThis study was funded by ZonMw, the Dutch Organisation for Health Research and Develop-ment (project number 837001003).

126

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127



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Jos J Kraal, M Elske Van den Akker-Van Marle, Ameen Abu-Hanna, Wim Stut, Niels Peek, Hareld MC Kemps.


Chapter 7Clinical and cost-effectiveness

of home-based cardiac rehabilitation compared to conventional, centre-based

cardiac rehabilitation: Results of the FIT@Home study

130

Abstract

Background: Although cardiac rehabilitation (CR) improves physical fitness after a cardiac event, many eligible patients do not participate in CR and beneficial effects of CR are often not maintained over time. Home-based training with telemonitoring guidance could improve participation rates and enhance long-term effectiveness. However, long-term effects and cost-effectiveness of home-based CR are not well established.

Methods: We randomised 90 low-to-moderate cardiac risk patients entering CR to three months of either home-based training with telemonitoring guidance or conventional, centre-based training. Outcome measures were physical fitness, physical activity levels, training adherence, patient satisfaction, health-related quality of life, and costs. Outcomes were assessed at baseline, at discharge from CR, and at one year after inclusion.

Results: Although training adherence was similar between groups, patients in the home-based group were more satisfied with the CR programme (p=0.02). For both groups physical fitness was improved at discharge (p<0.01) and at one-year follow-up (p<0.01), without differ-ences between groups (p=0.31 and p=0.87 respectively). In both groups, physical activity levels did not change during the one-year study period (centre-based p=0.10, home-based p=0.08). Physical and social subscales of health-related quality of life improved at follow-up in both groups, without differences between groups. Home-based training resulted in statis-tically non-significantly lower healthcare costs (€110 per patient, 95% confidence interval -669 to 888, p=0.78). From a broader societal perspective, a statistically non-significant difference of €2,894 per patient in favour of home-based CR was found (95% confidence interval -1220 to 7007, p=0.17) and the probability that it was more cost-effective varied between 97% (willing-ness-to-pay of €0 per QALY) and 75% (willingness-to-pay of €100,000 per QALY).

Discussion: We found no differences between home-based training with telemonitoring guidance and centre-based training on physical fitness, physical activity level or health-re-lated quality of life. However, home-based training was associated with a higher patient satisfaction and appears to be more cost-effective than centre-based training. We conclude that home-based training with telemonitoring guidance can be used as an alternative to centre-based training for low-to-moderate cardiac risk patients entering CR.

131

Introduction

Although cardiovascular disease remains a major cause of mortality and results in approximately 17 million annual deaths worldwide, the number of deaths decreases each year [1]. This is caused, among others, by high quality cardiac rehabilitation after an initial cardiac event [2,3]. Cardiac rehabilitation (CR) is a multidisciplinary treatment aiming at physical and psychosocial recovery after an acute coronary syndrome or cardiac intervention and prevention of future events. Exer-cise-based CR has shown to reduce mortality, prevent hospital readmission and improve quality of life[3–5]. Nonetheless, two persistent barriers limit the effectiveness of CR. First, participation in centre-based CR is low because a substantial number of patients do not participate or drop out at a later stage. This is partly due to health system barriers such as lack of referral to CR [6]. But there are also important practical barriers such as travelling time to the outpatient CR clinic and limited availability due to work resumption. In addition, personal barriers such as reluctance to participate in group-based therapy and individual training preferences that deviate from what is offered in centre-based CR limit participation rates [7,8]. Second, long-term effectiveness of CR is low [9]. Although it is well established that low physical activity levels (i.e. physical inactivity) are a major health risk [10], health systems struggle to incorporate physical activity enhancement in (secondary) prevention and intervention programmes. Exercise-based CR is often aimed at short-term improvement of physical fitness rather than inducing long-term lifestyle changes. In fact, patients in centre-based CR are often not sufficiently prepared for independent exercise in the home environment. Therefore, the beneficial effects of CR tend to decrease after supervised training in the outpatient clinic has completed [9,11,12].

We hypothesize that if CR were tailored to the patients’ preferences and CR programmes were aimed at preparing patients for independent exercise and physical activity, uptake could be improved, dropout rates reduced and beneficial effects of exercise-based CR sustained. Wear-able sensor technologies and ubiquitous connectivity have introduced new possibilities to deliver physical fitness and physical activity interventions in the home environment at low cost [13]. Previous studies demonstrated that home-based CR is a safe alternative to centre-based CR and short-term effectiveness is similar [14]. However, home-based CR has the potential to improve participation in CR programmes, especially for patients who are unable to participate in the conventional centre-based CR due to work resumption or other scheduling problems [7,15]. In addition, exercise training in the home environment with remote support may aid patients in developing self management skills for improving and maintaining their physical fitness levels after completion of CR, thereby enhancing long-term effectiveness of CR [16]. Previous studies showed beneficial effects of telerehabilitation interventions after completion of centre-based CR [17] or in combination with centre-based CR [18]. However, those interventions were an addition to usual care, and therefore require additional costs. If a telerehabilitation intervention replaces usual care, these beneficial effects may be achieved without additional costs. Unfortu-nately, data on cost-effectiveness of telerehabilitation interventions are scarce [14]. Furthermore,

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when the intervention uses telemonitoring guidance to provide patients the opportunity to develop self-management skills for maintaining an active lifestyle after CR, beneficial effects can be sustained over time [19]. If the telemonitoring guidance is focused on behavioural change using coaching strategies (i.e. motivational interviewing and cognitive behaviour therapy) and objective feedback, patients will be sufficiently prepared for independent exercise in the home environment [20,21].

To study the long-term effects and costs of home-based CR, we developed a home-based exercise training intervention with telemonitoring guidance for low-to-moderate cardiac risk patients entering CR. The telemonitoring guidance was aimed at improving exercise behaviour by providing feedback on exercise data using motivational interviewing principles. We addressed the following research questions: What is the effect of home-based exercise training with tele-monitoring guidance compared to regular centre-based exercise training on physical fitness and physical activity levels in low-to-moderate cardiac risk patients entering CR? How does home-based exercise training compare to regular centre-based exercise training regarding training adherence, health-related quality of life, and psychological status? Finally, is it a cost-effective alternative compared to centre-based exercise training?

Methods

Study designWe performed a randomised controlled trial among cardiac patients entering CR at Máxima Medical Centre. All subjects provided informed consent before enrolment in the study. The study protocol was approved by the Institutional Review Board of Máxima Medical Centre, and is registered at ClinicalTrials.gov with registration number NCT01732419. The study protocol is described in detail elsewhere [22].

Population and randomisationWe included patients that entered CR at Máxima Medical Centre after an ACS (myocardial infarc-tion or unstable angina) or a revascularisation procedure (percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG)). Patients were eligible for participation when they were classified as low to moderate risk for further events by a cardiologist, based on the criteria described in the Dutch CR practice guideline [23]. In addition, patients were required to have Internet access and a PC at home. After baseline measurements patients were randomised to either centre-based training (CB) or home-based training (HB). Allocation was performed with dedicated computer software and concealment was assured by using numbered and sealed opaque envelopes.

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InterventionIn both groups, all treatment components of CR other than exercise training were performed in the outpatient clinic as usual. Exercise training was prescribed according to the current national and international guidelines [24,25]. Both groups participated a training programme of 12 weeks with at least two training sessions a week. Session duration was 45-60 minutes and all sessions were based on continuous training with an intensity of 70-85% of the maximal heart rate (HRmax) assessed during the cardiopulmonary exercise test at baseline.

Patients in the CB group received group-based training in the outpatient clinic, supervised by two physical therapists specialized in CR. All patients received an individually tailored training programme on a cycle ergometer and treadmill. During the final sessions of the training programme, the physical therapist encouraged the participants to continue their physical activ-ities in their home environment.

Patients in the HB group received three supervised training sessions in the outpatient clinic, before they continued their training programme in their home environment. During these sessions, patients were familiarized with training duration and intensity and their preferred training modality was discussed with a physical therapist and exercise specialist. In addition, they were instructed how to use a heart rate monitor with a chest strap (Garmin FR70) and how to upload recorded heart rate data to a web application (Garmin Connect) through Internet. After the three training sessions, patients started their training programme in the home environ-ment. The heart rate monitor was used to record the exercise data and to evaluate training dura-tion and intensity during the training. The web application was used by the patient, the physical therapist and the exercise specialist to review the data. Once a week the patient received feed-back on training frequency, duration and intensity via telephone by the physical therapist. Moti-vational interviewing principles were used to enhance patients’ motivation and encourage the development of self-management skills. After 12 weeks, the feedback was terminated, but the patients were encouraged to continue their training programme with the heart rate monitor and web application.

Outcome measuresPhysical fitness was determined as peakVO2 assessed during a maximal exercise test with respi-ratory gas analysis on a cycle ergometer (Lode Corrival, Groningen). During the assessment a 12-lead electrocardiogram was monitored continuously. PeakVO2 was defined as the average oxygen uptake during the final 30 seconds of the individualized ramp protocol. Ventilatory anaerobic threshold (VAT) was assessed using the V-slope method [26] by two independent physicians who were blinded for patient allocation. When the VAT of an exercise test deviated over 10% between the two physicians, a third physician was asked for an additional assessment of the VAT. An average of the two nearest values was selected.

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Physical activity was determined as physical activity level (PAL), estimated from data of a tri-axial accelerometer worn at the hip (ActiGraph wGT3X+ monitor) and heart rate monitor with chest strap (Garmin FR70) with a chest strap. Patients were instructed to wear both sensors contin-uously during daytime for a period of five subsequent days. Accelerometry (ACC) data was recorded with a sample frequency of 40Hz and was time-aligned with the heart rate monitor (HR, beats per minute). For analysis, ACC data was resampled into 20Hz epochs and filtered using a band pass filter (0.5 - 3Hz) and a median filter (window size 5) before counts per minute were calculated. In order to determine the PAL, total energy expenditure was divided by resting meta-bolic rate, calculated by the Harris-Benedict equation [27]. Physical activity energy expenditure was calculated from ACC-, HR data and patient characteristics using a multivariate regression model for beta-blocker medicated cardiac patients. To develop the model, HR and ACC data of sixteen CR patients were measured during a resting metabolic rate assessment and activity protocol. Simultaneously, energy expenditure was measured using breath-by-breath pulmonary gas exchange measurement (Cosmed K4, b2 portable system). Results of the model develop-ment and validation will be published elsewhere. A physical activity assessment was consid-ered successful when at least one day with at least eight hours of useful ACC and HR data was recorded. PAL is an accepted parameter to express a person’s daily energy expenditure, which allows comparing physical activity measurements for subjects with different body size and composition. When PAL is used to classify the intensity of an activity, PAL < 3, PAL < 6, and PAL ≥ 6 are characterised as light, moderate, and vigorous intensity activities, respectively [28]. An average daily PAL of 1.2 represents the activity level of a bed-bound subject, while the average PAL for the adult population is 1.7 [29].

Health-related quality of life (HRQoL) was assessed using the SF-36 and the MacNew question-naire. Results of the SF-36 questionnaire were used to calculate the health utility scores for the cost-utility analyses [30]. The total score and scores within three separate domains of the MacNew questionnaire were used to calculate the health-related quality of life (physical, emotional and social score) [31]. The MacNew score ranges from 1 to 7, where a high score indicates better quality of life. Patient satisfaction was measured directly after completion of CR (either home based or centre based) using the Consumer Quality index. The Consumer Quality index is a stan-dardized patient survey method developed by the Dutch Center for Consumer Experience in Health Care, combining the inventory of patient experiences with an assessment of their priority [32]. Psychological status was assessed using the hospital anxiety and depression (HADS) and patient health questionnaire (PHQ) [33].

Outcome measures were assessed at baseline, after CR and one year after the start of CR. SF-36)questionnaires were also sent three months after completion of CR (six months after inclusion).

Training adherence in the CB group was determined as the number of attended training sessions at the outpatient clinic. Patients in the HB group recorded their training sessions in the web

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application using the heart rate monitor. These data were used to determine training adher-ence, training frequency, training intensity and time spent in the prescribed training zone. Other healthcare resource use, medication use and productivity losses from paid and unpaid work were recorded by means of a cost questionnaire at three, six and 12 months after inclusion. When the questionnaire was completed, but answers concerning hospital resource use or medication use were missing or unclear, validation was performed via the electronic patient record.

Cost analysisHealthcare costs were obtained by multiplying health care resource use by the unit costs obtained from the Dutch manual for costing in economic evaluations [34] and converted to 2015 price levels with the general Dutch consumer price index [35]. Estimated costs of the CR inter-vention were based on adherence data. For the CB group, the total training duration (session frequency * session duration) supervised by a physical therapist was divided by the average number of patients participating in group-based CR (four patients per physical therapist). This was multiplied by the hourly cost of a physical therapist (€66.08, which includes overhead). For the HB group, costs for the supervised sessions were calculated similarly. Additional costs were incurred for the heart rate monitor (€75) and the additional time spent to introduce home-based training (30 minutes, €33), the preparation and conduction of the ten weekly telephone calls (5 minutes preparation and 9 minutes calling, €55 and €99 respectively). Medication costs were based on actual Dutch standard costs [36]. Costs of absenteeism were calculated with the fric-tion cost method [37]. Presenteeism costs, the costs for productivity loss due to health issues while at work, were calculated by weighing the number of working days impaired by the effi-ciency score on these days as indicated by the patients. Cost of unpaid labour were obtained by multiplying the number of hours carers and other people performed household tasks that would normally be performed by the patient with the hourly rate of unpaid labour (€14) [34].

Statistical analysisWe compared physical fitness, physical activity levels, health-related quality of life, and patient satisfaction between groups at discharge from CR and after one year using independent Student t-tests. Differences within groups over time were assessed with paired t-tests. Differences in baseline characteristics between participants and dropouts were compared using independent Student t-tests. We imputed missing cost and effect data using the Multivariate Imputation by Chained Equations (MICE) algorithm with 20 iterations [38]. The primary analysis was conducted on an intention-to-treat basis, while a sensitivity analysis compared primary outcomes between "as treated" groups. Data are presented by mean ± standard deviation (SD) unless stated other-wise, with a p-value <0.05 considered as significant. Analyses were carried out using the statis-tical programming language R (version 3.0.3) and SPSS for Windows (version 22.0). For the analysis of sensor data we used the Lisa Compute Cluster (www.surfsara.nl) to parallelize computation. Specifically, we used the Portable Batch System (PBS) to submit a compute job requiring two Lisa compute nodes, each with 16 cores. We hence obtain a 32x speedup in computation.

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In the economic evaluation, the effects of both interventions were compared to their difference in costs. A cost-utility analysis with quality-adjusted life-years (QALYs) as outcome measure was performed. The QALYs were estimated based on the health utility scores at 0, 3, 6 and 12 months, by applying the area-under-the-curve method. The evaluation was performed from a societal perspective, making a distinction between healthcare and non-healthcare costs (absenteeism from paid and unpaid work). In a sensitivity analysis presenteeism was also included in the societal perspective. Non-parametric bootstrapping was used, involving 1000 replications, to calculate uncertainty around the costs and effects estimates. Based on these results a cost-ef-fectiveness acceptability curve was constructed by plotting the proportion of costs and effects pairs for which HB is cost-effective compared to CB, for a range of values of the willingness to pay for a QALY (λ).

Results

Between March 2013 and December 2014 we included 90 CR patients (10 female / 80 male, mean age 59.2 ± 8.5 years), of which 78 completed the study. Patients received PCI (n=44), CABG (n=22) or only medication (n=12) during the hospital admission before entering CR. One patient randomised to CB was erroneously allocated to the HB group and one patient randomised to HB was allocated to the CB group by the clinical staff. Baseline characteristic are provided in Table 1. A total of 12 patients were lost to follow-up (HB group: n=8; CB group: n=4). Reasons for loss to follow-up were withdrawal from the study (n=8), comorbidities (n=3) and death (n=1). While their demographic characteristics were similar (1 female/11 male, mean age 58.9 ± 10.7), the patients that were lost to follow-up had significant lower physical fitness at baseline compared to those who completed the intervention (-3.75 O2 ml.min-1.kg-1, p=0.02). Figure 1 provides a flow-chart of the randomisation and follow-up during the study. During the one-year study period, eight CB patients (3 PCI, 2 angina pectoris, 3 coronary angiography) and two HB patients (1 PCI, 1 CABG) were hospitalized for cardiac reasons.

AdherencePatients in the CB group attended 20.6±4.3 training sessions (86% of the expected 24 sessions, ranging from 6 to 25) during CR at the outpatient clinic. After the three introductory sessions in the hospital, patients in the HB group performed 22.0±6.8 sessions at home in the first 12 weeks (ranging from 13 to 41). Session duration, including warming up and cooling down, in the HB group was 64.0±21.1 minutes, of which 43.0±14.8 minutes in the prescribed training zone of 70-85% of HRmax. Average training intensity was 74.0±3.6% of HRmax. Heart rate data of one person could not be used due to incorrect values of the heart rate monitor caused by prema-ture ventricular contractions. No serious adverse events were recorded during centre-based and home-based training. Patients in the HB group were more satisfied with their CR programme compared to patients in the CB group (HB: 8.7/10, CB: 8.1/10, p=0.02).

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Table 1: Patient characteristics at baseline

Centre-based CR Home-based CR

n = 45 n = 45

Male/Female (n) 40/5 40/5

Age (years) 57.7 ± 8.7 60.5 ± 8.8

Length (cm) 178.5 ± 8.1 176.7 ± 7.5

BMI 28.2 ± 3.9 27.8 ± 4.8

Diagnosis

ACS with PCI, n (%) 19 (42%) 24 (53%)

ACS without PCI, n (%) 5 (11%) 5 (11%)

AP with PCI, n (%) 4 (10%) 3 (7%)

AP without PCI, n (%) 0 (0%) 4 (10%)

CABG, n (%) 17 (38%) 9 (20%)

Medication

Beta-blocker, n (%) 42 (93%) 40 (89%)

Statines, n (%) 45 (100%) 44 (98%)

Anti-platelets, n (%) 44 (98%) 45 (100%)

ACE-i / ARB, n (%) 37 (82%) 29 (64%)

BMI= body mass index, ACS= acute coronary syndrome, PCI= percutaneous coronary intervention, AP= angina

pectoris, CABG= coronary artery bypass graft, ACE-i= angiotensin converting enzyme inhibitor, ARB= angiotensin

receptor blockers

Physical fitnessPatients in both groups improved their peakVO2 from baseline to discharge from CR (CB +11% p<0.01, HB +15% p<0.01) without significant between-group differences (p=0.25). After one year, there was a significant improvement from baseline in peakVO2 in both groups without between-group difference (CB +16%, p<0.01, HB +17%, p<0.01, between groups p=0.89). Similarly, both groups improved VO2 uptake at VAT, maximum workload and workload per kg after discharge from CR and maintained those values at follow-up, similar to peakVO2 (Table 2).

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Table 2: Main outcome measures at baseline, discharge and follow-up after home-based and centre-based CR

Centre-based CR Home-based CR Between groups, p-value

Baseline Discharge CR Follow-up Baseline Discharge CR Follow-up 0-12 weeks 0-52 weeks

Physical fitness

Peak VO2 (ml min-1 kg-1) 24.0 ± 5.6 26.5 ± 7.1a 27.5 ± 8.1a 24.4 ± 6.7 27.9 ± 7.5a 27.7 ± 6.9a 0.308 0.865

Peak VO2 (ml min-1) 2115.8 ± 477.5 2336.5 ± 598.4a 2441.1 ± 643.6a 2072.8 ± 616.9 2324.6 ± 641.8a 2380.0 ± 606.3a 0.640 0.826

VAT at VO2 (ml min-1) 1293.4 ± 250.3 1459.1 ± 328.5a 1454.6 ± 303.0a 1244.5 ± 317.4 1432.1 ± 442.4a 1380.2 ± 304.1a 0.173 0.619

Peak Workload (Watt) 183.4 ± 47.6 203.7 ± 61.8a 208.9 ± 61.8a 178.9 ± 52.4 200.9 ± 52.5a 202.1 ± 54.3a 0.747 0.719

Workload/kg 2.08 ± 0.57 2.31 ± 0.68a 2.38 ± 0.75a 2.03 ± 0.58 2.33 ± 0.61a 2.34 ± 0.62a 0.305 0.938

HRmax (beats min-1) 142.6 ± 16.7 146.7 ± 22.4 149.6 ± 23.8a 140.4 ± 17.6 142.2 ± 15.5 141.4 ± 16.5 0.428 0.069

RER 1.24 ± 0.11 1.20 ± 0.13 1.17 ± 0.15a 1.24 ± 0.11 1.21 ± 0.15 1.17 ± 0.18a 0.891 0.816

BMI 28.0 ± 3.6 28.0 ± 3.8 28.1 ± 4.0 28.4 ± 5.1 27.8 ± 4.7 27.9 ± 4.2a 0.093 0.275

Physical activity

PAL 1.95 ± 0.86 2.38 ± 1.02a 2.13 ± 1.00 2.09 ± 0.92 2.22 ± 0.99 2.14 ± 1.06 0.311 0.650

Questionnaires

HRQoL total 5.45 ± 0.14 5.90 ± 0.13a 5.43 ± 0.12 5.62 ± 0.20 6.00 ± 0.13 a 5.75 ± 0.10 0.792 0.609

HRQoL physical 5.25 ± 0.14 5.85 ± 0.15a 5.72 ± 0.18a 5.38 ± 0.22 5.93 ± 0.15a 6.20 ± 0.14a 0.866 0.271

HRQoL social 5.67 ± 0.15 6.33 ± 0.10a 6.16 ± 0.15a 6.03 ± 0.21 6.36 ± 0.12 6.50 ± 0.11a 0.237 0.928

HRQoL emotional 5.50 ± 0.15 5.72 ± 0.14 4.75 ± 0.13a 6.20 ± 0.14 5.85 ± 0.16 5.07 ± 0.10a 0.498 0.929

HADS anxiety 3.98 ± 3.36 3.20 ± 2.80 2.85 ± 2.62a 3.70 ± 3.09 3.10 ± 2.39 2.31 ± 1.90a 0.776 0.728

HADS depression 2.01 ± 2.33 2.33 ± 2.52 3.80 ± 2.90a 2.23 ± 2.80 2.14 ± 2.61 3.24 ± 2.35 0.525 0.256

PHQ 3.09 ± 3.07 2.89 ± 2.99 3.33 ± 4.06 3.16 ± 3.73 2.21 ± 3.00 2.18 ± 2.79 0.314 0.139

Values are presented mean ± standard deviation.a = significant difference compared with baseline, p<0.05;

CR= cardiac rehabilitation; RER= respiratory exchange ratio, BMI= body mass index; PAL= physical activity level;

HRQoL= health-related quality of life; HADS= hospital anxiety and depression scale; PHQ= patient health questionnaire

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Table 2: Main outcome measures at baseline, discharge and follow-up after home-based and centre-based CR

Centre-based CR Home-based CR Between groups, p-value

Baseline Discharge CR Follow-up Baseline Discharge CR Follow-up 0-12 weeks 0-52 weeks

Physical fitness

Peak VO2 (ml min-1 kg-1) 24.0 ± 5.6 26.5 ± 7.1a 27.5 ± 8.1a 24.4 ± 6.7 27.9 ± 7.5a 27.7 ± 6.9a 0.308 0.865

Peak VO2 (ml min-1) 2115.8 ± 477.5 2336.5 ± 598.4a 2441.1 ± 643.6a 2072.8 ± 616.9 2324.6 ± 641.8a 2380.0 ± 606.3a 0.640 0.826

VAT at VO2 (ml min-1) 1293.4 ± 250.3 1459.1 ± 328.5a 1454.6 ± 303.0a 1244.5 ± 317.4 1432.1 ± 442.4a 1380.2 ± 304.1a 0.173 0.619

Peak Workload (Watt) 183.4 ± 47.6 203.7 ± 61.8a 208.9 ± 61.8a 178.9 ± 52.4 200.9 ± 52.5a 202.1 ± 54.3a 0.747 0.719

Workload/kg 2.08 ± 0.57 2.31 ± 0.68a 2.38 ± 0.75a 2.03 ± 0.58 2.33 ± 0.61a 2.34 ± 0.62a 0.305 0.938

HRmax (beats min-1) 142.6 ± 16.7 146.7 ± 22.4 149.6 ± 23.8a 140.4 ± 17.6 142.2 ± 15.5 141.4 ± 16.5 0.428 0.069

RER 1.24 ± 0.11 1.20 ± 0.13 1.17 ± 0.15a 1.24 ± 0.11 1.21 ± 0.15 1.17 ± 0.18a 0.891 0.816

BMI 28.0 ± 3.6 28.0 ± 3.8 28.1 ± 4.0 28.4 ± 5.1 27.8 ± 4.7 27.9 ± 4.2a 0.093 0.275

Physical activity

PAL 1.95 ± 0.86 2.38 ± 1.02a 2.13 ± 1.00 2.09 ± 0.92 2.22 ± 0.99 2.14 ± 1.06 0.311 0.650

Questionnaires

HRQoL total 5.45 ± 0.14 5.90 ± 0.13a 5.43 ± 0.12 5.62 ± 0.20 6.00 ± 0.13 a 5.75 ± 0.10 0.792 0.609

HRQoL physical 5.25 ± 0.14 5.85 ± 0.15a 5.72 ± 0.18a 5.38 ± 0.22 5.93 ± 0.15a 6.20 ± 0.14a 0.866 0.271

HRQoL social 5.67 ± 0.15 6.33 ± 0.10a 6.16 ± 0.15a 6.03 ± 0.21 6.36 ± 0.12 6.50 ± 0.11a 0.237 0.928

HRQoL emotional 5.50 ± 0.15 5.72 ± 0.14 4.75 ± 0.13a 6.20 ± 0.14 5.85 ± 0.16 5.07 ± 0.10a 0.498 0.929

HADS anxiety 3.98 ± 3.36 3.20 ± 2.80 2.85 ± 2.62a 3.70 ± 3.09 3.10 ± 2.39 2.31 ± 1.90a 0.776 0.728

HADS depression 2.01 ± 2.33 2.33 ± 2.52 3.80 ± 2.90a 2.23 ± 2.80 2.14 ± 2.61 3.24 ± 2.35 0.525 0.256

PHQ 3.09 ± 3.07 2.89 ± 2.99 3.33 ± 4.06 3.16 ± 3.73 2.21 ± 3.00 2.18 ± 2.79 0.314 0.139

Values are presented mean ± standard deviation.a = significant difference compared with baseline, p<0.05;

CR= cardiac rehabilitation; RER= respiratory exchange ratio, BMI= body mass index; PAL= physical activity level;

HRQoL= health-related quality of life; HADS= hospital anxiety and depression scale; PHQ= patient health questionnaire

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Figure 1: Flow diagram of the study.

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Physical activityOut of the 249 scheduled PAL assessments, 190 assessments (76%) were completed. A total of 135 assessments (71%) were identified as successful (i.e. at least one day of at least 8 hours of useful HR and ACC data). Assessments were mostly unsuccessful because of insufficient useful HR data (38/55). The average duration of successfully recorded data was 35.3±17.5 hours per patient, divided over 3.3±1.4 days. Although patients in the CB group improved their PAL at discharge from CR (p=0.05), PAL was similar to the baseline level at 1-year follow-up (p=0.38). Patients in the HB group did not improve their PAL at discharge from CR (p=0.50) or at follow-up (p=0.80, Table 2). Both at discharge from CR and at follow-up there were no between-group differences (p=0.31, p=0.65 respectively).

The sensitivity analysis, which compared physical fitness and physical activity levels between "as treated" groups, showed no significant change in PAL after the three-month rehabilitation period among patients in the CB group (p=0.51). All other results were similar to the intention-to-treat analysis.

Health-related quality of life and psychological statusAlthough HRQoL in the CB group improved after the three-month rehabilitation period (p<0.01), HRQoL was similar to baseline at 1-year follow-up (p=0.94). HRQoL in the HB group was unchanged at discharge from CR (p=0.07) and at follow-up (p=0.56). There were no signifi-cant between-group differences at discharge and at follow-up (p=0.79 and p=0.61, respectively). HRQoL improved on the physical and social subscales at discharge and follow-up in both groups. The emotional subscore decreased at follow-up in both groups. On neither of the subscales there were between-group differences (Table 2). Anxiety scores decreased at follow-up in both groups (CB p<0.05, HB p=0.01) without differences between groups (p=0.73). Depression scores were similar between baseline and follow-up in both groups, without differences between groups (p<0.01).

Cost-effectiveness analysisThe QALYs calculated for the CB group (0.78 ± 0.08) were similar to the QALYs for the HB group (0.77 ± 0.13, p=0.73). The mean costs for the CR programme were similar between groups (Table 3), €336 per patient in the CB group and €314 per patient in the HB group. The total healthcare costs per patient were €437 lower for the HB group (95% CI -562 to 1436). However, this differ-ence was not significant. Costs for visits to the GP (mean difference €33, 95% CI 0 to 66, p<0.05) and visits to the specialist (mean difference €158, 95% CI 1 to 315, p<0.05) were lower for patients in the HB group.

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Table 3: Average healthcare and non-healthcare costs per patient for home-based or centre-based CR (in €, price level 2015)

Centre-based CR (n=45) Home-based CR (n=45) Mean difference

Volumea, % Costs, € Volumea, % Costs, € Costs, € p-value

Healthcare costs

Healthcare visits

General Practitioner 78.3 114 ± 93 81.8 80 ± 64 33 0.048

Specialist 95.5 537 ± 449 93.9 379 ± 295 158 0.048

Physical Therapist 43.5 250 ± 387 51.5 304 ± 464 -54 0.548

Psychologist 30.4 110 ± 266 21.2 61 ± 123 49 0.259

Dietician 21.7 23 ± 34 33.3 31 ± 51 -8 0.397

CR nurse 39.1 22 ± 36 27.3 19 ± 32 3 0.633

Other 13.0 31 ± 139 6.1 9 ± 28 22 0.297

Healthcare admission

A&E department 17.4 37 ± 64 23.5 49 ± 87 -12 0.452

Hospital admission 8.7 682 ± 2300 24.2 503 ± 1245 179 0.645

Day treatment 13.0 69 ± 164 14.7 47 ± 112 22 0.455

Other

Medication 645 ± 411 624 ± 441 21 0.817

CR programme costs 336 ± 68 314 ± 68 22 0.128

Total healthcare costs

Non-healthcare costs

2855 ± 2797

2419 ± 1968 437 0.392

Paid absenteeism 35.0 5980 ± 7823 27.3 3289 ± 8467 2691 0.117

Unpaid absenteeism 31.8 589 ± 869 16.1 557 ± 1314 32 0.893

Presenteeism 52.6 8433±10689 23.5 5507 ± 8601 2926 0.152

Total non-healthcare costs b 6569 ± 8170 3846 ± 8400 2723 0.119

Total non-healthcare costs with presenteeism 15002 ± 16120 9353 ± 15114 5649 0.121

Total societal costs b 9425 ± 8714 6265 ± 8813 3160 0.087

Total societal costs with presenteeism 17858 ± 16510 11772 ± 15349 6085 0.070

a Volume are percentages of patients who made costs for that item.b Costs are presented without presenteeism

Costs are presented in euros (price index of 2015) as mean ± standard deviation

A&E= accident and emergency department.

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Table 3: Average healthcare and non-healthcare costs per patient for home-based or centre-based CR (in €, price level 2015)

Centre-based CR (n=45) Home-based CR (n=45) Mean difference

Volumea, % Costs, € Volumea, % Costs, € Costs, € p-value

Healthcare costs

Healthcare visits

General Practitioner 78.3 114 ± 93 81.8 80 ± 64 33 0.048

Specialist 95.5 537 ± 449 93.9 379 ± 295 158 0.048

Physical Therapist 43.5 250 ± 387 51.5 304 ± 464 -54 0.548

Psychologist 30.4 110 ± 266 21.2 61 ± 123 49 0.259

Dietician 21.7 23 ± 34 33.3 31 ± 51 -8 0.397

CR nurse 39.1 22 ± 36 27.3 19 ± 32 3 0.633

Other 13.0 31 ± 139 6.1 9 ± 28 22 0.297

Healthcare admission

A&E department 17.4 37 ± 64 23.5 49 ± 87 -12 0.452

Hospital admission 8.7 682 ± 2300 24.2 503 ± 1245 179 0.645

Day treatment 13.0 69 ± 164 14.7 47 ± 112 22 0.455

Other

Medication 645 ± 411 624 ± 441 21 0.817

CR programme costs 336 ± 68 314 ± 68 22 0.128

Total healthcare costs

Non-healthcare costs 2855 ± 2797

2419 ± 1968 437 0.392

Paid absenteeism 35.0 5980 ± 7823 27.3 3289 ± 8467 2691 0.117

Unpaid absenteeism 31.8 589 ± 869 16.1 557 ± 1314 32 0.893

Presenteeism 52.6 8433±10689 23.5 5507 ± 8601 2926 0.152

Total non-healthcare costs b 6569 ± 8170 3846 ± 8400 2723 0.119

Total non-healthcare costs with presenteeism 15002 ± 16120 9353 ± 15114 5649 0.121

Total societal costs b 9425 ± 8714 6265 ± 8813 3160 0.087

Total societal costs with presenteeism 17858 ± 16510 11772 ± 15349 6085 0.070

a Volume are percentages of patients who made costs for that item.b Costs are presented without presenteeism

Costs are presented in euros (price index of 2015) as mean ± standard deviation

A&E= accident and emergency department.

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The average non-healthcare costs per patient, consisting of absenteeism from paid and unpaid work, were €2723 lower for patients in the HB group (95% CI -699 to 6145, p=0.12). This difference was mainly caused by the costs for absenteeism from paid work, which was €2691 per patient lower in the HB group (95% CI -676 to 6059, p=0.12). From a societal perspective (i.e. the sum of healthcare and non-healthcare costs), costs per patient were €3160 lower for patients in the HB group (95% CI -460 to 6780, p=0.09). Costs for presenteeism were €2926 per patient lower in the HB group (95% CI -1072 to 6924, p=0.15). Including these costs in the total costs from a societal perspective leads to a difference of €6084 in favour of the HB group (95% CI -76 to 3259, p=0.07)

Although there were no significant differences in societal costs between groups, almost all components were lower in the HB group. Furthermore, the non-significant differences in QALYs in favour of the CB group were small (Figure 2). This resulted in a higher probability of cost-effec-tiveness for HB training than CB training from a societal perspective (Figure 3), varying between 97% (willingness-to-pay of €0 per QALY) and 75% (willingness-to-pay of €100,000 per QALY). When presenteeism is included in the societal costs, this probability then varied between 95% and 90%. From a healthcare perspective, the probability that HB training was more cost-effective than CB training varied between 80% and 40% for the same willingness-to-pay levels.

For the accepted willingness-to-pay per QALY in The Netherlands (€20,000-€40,000) [39], we can conclude that HB training appears to be more cost-effective than CB training.

Discussion

Our study found no significant differences in the effects on physical fitness between home-based exercise training with telemonitoring guidance and centre-based exercise training in patients with low-to-moderate cardiac risk entering CR. Also, physical activity levels were unchanged at one-year follow-up in both groups. Whereas health-related quality of life and psychological status were similar in both groups, patient satisfaction was higher in the home-based group. Our cost-effectiveness analysis showed that HB training is likely to be more cost-effective than CB training. Our short-term results are in line with the conclusions of the systematic review by Taylor et al., who demonstrated that home-based and centre-based CR are equally effective in improving short-term physical fitness [14]. Long-term effectiveness of home-based CR is less well estab-lished and seems to be related to the content of the intervention. We demonstrated that with motivational coaching strategies and objective feedback on training data, patients in the home-based group are able to maintain their physical fitness levels at one year. This is consistent with the findings of Aamot et al., who employed a similar monitoring strategy during the study [40]. Interestingly, patients in our centre-based group were able to maintain their physical fitness levels

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Figure 2: Overview of incremental costs and effects (QALYs) of home-based CR compared with centre-based CR

‐10000

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5000

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∆ costs (€)

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Figure 3: Cost-effectiveness acceptability curves for home-based CR compared with centre-based CR

0

0.2

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Societal perspective

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as well, in contrast to prior centre-based CR studies [40,41]. This may be attributed to the fact that due to the nature of our intervention, with its focus on technology and individual training, mainly young and motivated patients participated in our study. These patients, randomised to either home-based or centre-based CR, were able to maintain their physical fitness levels inde-pendently. Therefore, based on this study we cannot conclude that home-based CR leads to superior long-term physical fitness. However, our data showed that for this patient population, home-based CR is effective to improve and maintain physical fitness levels similar to centre-based CR. Moreover, home-based CR was associated with high patient-satisfaction levels and low costs for absenteeism from work. Therefore, we postulate that implementation of home-based CR can increase uptake, especially among younger cardiac patients with the ambition to return to work as soon as possible.

Physical inactivity is associated with most chronic diseases [10], but CR is often focused on improvement of physical fitness rather than increasing physical activity levels and preventing physical inactivity. Although we monitored physical activity levels before and after CR, the tele-monitoring guidance was mainly focused on physical fitness improvement. As a result, neither the centre-based nor the home-based training influenced the physical activity levels. These results are in line with previous studies that showed that exercise interventions focused on phys-ical fitness improvement in cardiac patients did not result in an improvement in physical activity [42–44]. Yet, other studies that combined an exercise intervention with a physical activity inter-vention during CR, did improve physical activity levels [18,45]. This implies that an improve-ment in physical fitness is not necessarily associated with an improvement in physical activity levels. Therefore, we recommend that exercise interventions are complemented with physical activity coaching, to influence daily activity behaviour. Ideally, physical activity coaching is based on objective and accurate data to maximise effectiveness [46], and remains available after the completion of CR. On-demand coaching can identify a relapse into an inactive lifestyle, which is often observed after completion of CR. In this way, coaching is available when it is needed the most. Currently, physical activity in CR studies is often assessed by questionnaires or accel-erometers [47]. With the development of wearable technology, a more reliable and accurate assessment of physical activity levels is available [13,48]. The methodology used in this study, combining heart rate with accelerometer data to estimate physical activity energy expenditure, can be considered as a first step towards accurate physical activity monitoring using wearable sensors in cardiac rehabilitation patients.

Although cost-effectiveness of an innovative intervention is essential for wide scale imple-mentation, cost-effectiveness analyses on home-based CR programmes are scarce. Taylor et al. described four randomised controlled trials and concluded that although the costs included in the analyses varied between studies, the costs between home-based and centre-based CR appeared to be similar [14]. In two further studies Frederix et al. indicated that a comprehensive telerehabilitation programme was more cost-effective than regular CR [49], while Kidholm et al.

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showed that a cardiac telerehabilitation programme was not more cost-effective compared to regular centre-based CR [50]. However, the latter two studies provided additional telerehabil-itation services after conventional CR, resulting in additional healthcare costs, which hampers the likelihood of implementation. Our results indicate that a telerehabilitation programme with equal duration as a regular CR programme could be a cost-effective alternative without increasing costs associated with the CR intervention. In our study, the average medical costs per patient were in favour of home-based CR. In addition, from a societal perspective the average costs per patient were €3160 higher for patients in centre-based CR. However, this difference was not significant. This difference was mainly caused by absenteeism from paid work between groups. Whereas patients in the home-based group were able to schedule their training sessions in their own time, patients in the centre-based group were obliged to visit the outpatient clinic during office hours, twice a week.

LimitationsA first limitation of our study was the lack of blinding for the physician for patient-allocation during the assessments of physical fitness at discharge and follow-up. Therefore, knowledge of group allocation could have affected the assessments. However, data from the maximal exer-cise tests (i.e. maximum heart rate and respiratory exchange ratio) showed that exhaustion was similar between groups. Second, although physical activity levels were assessed accurately by combining a heart rate monitor with an accelerometer, several patients experienced discomfort while wearing the chest-strap of the heart rate monitor for five consecutive days. Consequently, some patients terminated the physical activity assessment prematurely, resulting in a lower reli-ability of data. We expect that with the development of wrist-based heart rate monitors, future studies can avoid this limitation. Third, the home-based intervention required patients to have basic PC- and Internet skills to install and use the software platform. Nonetheless, some patients experienced problems with installing the software platform and uploading exercise data on the platform. This could have hindered the use of the software platform, therefore limiting the effec-tiveness of the telemonitoring guidance.

As mentioned in the discussion, the patients included in our study were not representative for the general CR population. Due to our selection process, we included mainly young and moti-vated patients that preferred to participate in home-based training. This can explain why the CB group were able to maintain their long-term physical fitness levels, even when they started with CB training and had to make the transition to the home environment without guidance. In addi-tion, many patients expressed their preference for home-based training and were subsequently disappointed when randomised to the CB group. This was supported by the lower patient satis-faction score in the CB group. Furthermore, the external validity of the results are limited because patients with a strong preference for one trial arm were possibly not participating due to the risk of being randomised in the non-preferred trial-arm. If preference-based trial-arms are included in a study design, a more mixed cardiac rehabilitation population can be obtained. However, this

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study design has substantial consequences on the sample size and costs of the study. Similarly, a blended study design can prevent demoralisation after randomisation in the non-preferred trial-arm. In this design, patients that prefer home-based training can switch from supervised centre-based training to home-based training with telemonitoring guidance when it is consid-ered safe by the physicians. Subsequently, effectiveness can be compared between patients that complete centre-based training and patients that switched to home-based training during CR.

ConclusionThis study shows comparable results for home-based CR and centre-based CR with respect to improving physical fitness and health-related quality of life. Furthermore, exercise adherence of patients in the home-based CR was high and patient-satisfaction was significantly higher than patients in the centre-based group. Home-based CR, which has the potential to increase overall participation in exercise-based CR, appears to have lower costs and to be more cost-effective than centre-based CR. Therefore, we conclude that home-based training with telemonitoring guidance is a useful alternative to conventional centre-based training for young and motivated low-to-moderate cardiac risk patients entering CR.

Funding and conflict of interestThe FIT@Home study was performed in collaboration with Philips Research. Philips Research provided the heart rate monitors and accelerometers used during the intervention and assess-ment of PAL. This study received funding from ZonMw, the Netherlands Organisation for Health Research and Development (ZonMw project number 837001003).

AcknowledgementsWe would like to thank the patients and clinical partners of the Máxima Medical Center Veld-hoven and Eindhoven who participated in this study. We thank SURFsara (www.surfsara.nl) for the support in using the Lisa Compute Cluster for the analyses of physical activity data.

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Chapter 8Summary and

general discussion

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This thesis focused on the development and evaluation of a novel intervention of home-based exercise training with telemonitoring guidance for low-to-moderate cardiac risk patients entering cardiac rehabilitation. In the first chapters we investigated the opportu-nities to develop a home-based cardiac rehabilitation intervention, focussing on the deter-minants of physical fitness improvement and opportunities to accurately assess physical activity levels in the home environment. The two final chapters evaluate the randomised controlled trial that compared the effectiveness and cost-effectiveness of a home-based training intervention plus telemonitoring guidance with conventional centre-based training for low-to-moderate cardiac risk patients entering cardiac rehabilitation.

Summary

In Chapter 02, we performed a systematic review and meta-regression analysis to identify which training characteristics determine the improvement of physical fitness after exercise training in coronary artery patients. After the systematic literature search, a total of 13 randomised controlled trials (representing 693 patients) were included in this study. Patients in the exer-cise group improved their exercise capacity significantly compared to the non-exercise control group. The meta-regression analysis showed that four training characteristics (i.e. session dura-tion, session frequency, programme length and training intensity) and total energy expendi-ture were independently related to the improvement in exercise capacity. However, when total energy expenditure was added as covariate, no independent effect of any of the four training characteristics was established. Therefore, we concluded that total energy expenditure was the strongest determinant for improvement in physical fitness after centre-based cardiac rehabil-itation. Cardiac rehabilitation programmes should be aimed at high total energy expenditure, without preference for high training intensity, duration, frequency or length.

A similar systematic review and meta-regression analysis for chronic heart failure patients was performed in Chapter 03. A total of 17 randomised controlled trials (representing 2935 patients) were identified and included in the study. Because almost 75% of the included patients were from one study, all analyses were performed with and without this trial. The analyses including the large study showed that total energy expenditure of a training programme was the stron-gest determinant for improvement in physical fitness. This was confirmed when the large study was excluded in the second analysis. In addition, this second analysis showed that three distinct training characteristics (i.e. session frequency, session duration and training intensity) were inde-pendently associated with improvement in exercise capacity when corrected for total energy expenditure. Ranking of the training characteristics demonstrated the largest effect for session frequency and session duration, followed by training intensity and programme length.

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In Chapter 04, we developed an energy expenditure prediction model for beta-blocker medi-cated cardiac rehabilitation patients, based on body movement and heart rate data. Sixteen cardiac rehabilitation patients underwent a resting metabolic rate assessment, a maximal exercise test and a physical activity protocol with daily life activities. Heart rate and body movement data were recorded using wearable devices and energy expenditure was assessed by a portable indi-rect calorimeter, used as gold standard. The best performance was achieved with a multivariate regression model that included heart rate data, body movement data and subject characteristics (i.e. age, weight, height, gender, physical fitness, beta-blocker dose). In current practice, these data are available at the start of the rehabilitation programme when a symptom-limited exercise test is included in the cardiac rehabilitation intake. Therefore, the prediction model is highly applicable in the clinical setting for monitoring physical activity levels in the home environment.

We described the rationale and design of the FIT@Home trial in Chapter 05. We randomised 90 low-to-moderate cardiac risk patients entering cardiac rehabilitation to three months of either home-based training with telemonitoring guidance or regular centre-based training. After three introductory sessions in the hospital, patients in the home-based group were instructed to train with a heart rate monitor in their home environment. They received individual coaching by tele-phone once a week, based on the measured heart rate data shared through an Internet portal. Main endpoints were physical fitness and physical activity levels. Secondary endpoints were training adherence, quality of life, patient satisfaction and cost-effectiveness. All endpoints were assessed at baseline, after discharge and at one-year follow-up.

In Chapter 06, we described the short-term results of the first 50 patients participating in the FIT@Home trial. Training frequency, exercise duration and training intensity in the home-based training group were comparable to training adherence of the centre-based training group. In addition, physical fitness and health-related quality of life were similar between groups. There-fore, we concluded that the first patients participating in the FIT@Home trial were able to inde-pendently execute a 12-week training programme in their home environment when they received coaching and objective feedback through telemonitoring guidance.

We described the full results of the FIT@Home trial in Chapter 07. The study showed comparable results for home-based exercise training with telemonitoring guidance and centre-based exer-cise training, with respect to improving physical fitness in low-to-moderate cardiac risk patients entering cardiac rehabilitation. The physical activity levels were unchanged at one-year follow-up in both groups. Whereas health-related quality of life and psychological status were similar in the two groups, patient satisfaction was significantly higher in the home-based cardiac reha-bilitation group. The cost-effectiveness analysis showed that home-based training is likely to be more cost-effective than centre-based training. In conclusion, these results indicate that home-based exercise training is a valuable alternative for regular centre-based exercise training for low-to-moderate cardiac risk patients entering cardiac rehabilitation. However, the intervention

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did not result in increased physical activity levels, indicating that future studies should include physical activity coaching in the intervention to stimulate an active lifestyle.

General discussion

In this thesis we explored opportunities to make cardiac rehabilitation more appealing for cardiac patients, without losing its clinical effectiveness. Home-based training offers exercise-based cardiac rehabilitation for patients who are not able to participate in centre-based cardiac reha-bilitation due to work resumption or other logistical challenges, or personal preferences. The home-based training intervention with telemonitoring guidance showed similar clinical effec-tiveness and favourable cost-effectiveness compared to centre-based cardiac rehabilitation.

In the first part of this thesis we demonstrated that the effectiveness of an exercise programme is mainly determined by the total energy expenditure of the exercise protocol. This implies that if total energy expenditure of a training protocol remains the same, session frequency, session duration, training intensity and length of the training programme can be altered without losing effectiveness. As a result, a training programme can be effectively translated to a patients’ home environment when total energy expenditure of the programme is carefully considered. For instance, if high intensity training is not feasible in the home environment, additional sessions at moderate training intensity can be added to the programme to achieve equally high total energy expenditure. As long as total energy expenditure remains the primary focus, other factors that impact training outcome can be taken into account. Previous studies showed that when patients are able to determine their own training type and location, training adherence improves and the beneficial effects of cardiac rehabilitation are sustained [1,2]. Therefore, we recommend that the training programme be tailored to the preferences of the patient, while taking into account total energy expenditure, to optimise patients’ resulting physical fitness. With this in mind, we designed a exercise-based cardiac rehabilitation programme that is feasible for exer-cise training in the home environment.

We developed an energy expenditure estimation model to monitor and assess the physical activity levels of cardiac patients in the home environment. This model uses body movement and heart rate data assessed by wearable sensors. The method utilises objectively measured physical activity data rather than subjective self-assessments via questionnaires that were often used in previous studies to assess physical activity behaviour. In the final part of this thesis the estimation model was used to assess physical activity levels before and after cardiac rehabil-itation. We concluded that neither the centre-based nor the home-based cardiac rehabilita-tion programme affected the physical activity behaviour. During the intervention, we used the physical activity data only at the start and end of the programme to monitor physical activity behaviour, and the data was not used during the programme to optimise the intervention.

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Previous studies showed that a cardiac rehabilitation programme requires an explicit physical activity intervention to improve physical activity levels [3,4] and that a programme focused on physical fitness improvement does not automatically result in an improvement in physical activity behaviour [5,6]. Therefore, we recommend implementing both physical activity moni-toring and physical activity coaching in the cardiac rehabilitation programme. In recent exer-cise-based cardiac rehabilitation guidelines, extra emphasis was put on objective monitoring of physical activity data and implementing coaching and behavioural-change strategies in cardiac rehabilitation to improve and maintain an physical active lifestyle [7–9]. If physical activity data are included in the telemonitoring guidance using behavioural-change coaching techniques (e.g. motivational interviewing, relapse prevention), then we expect that the programme can induce sustainable changes in physical activity behaviour.

In the second part of this thesis we demonstrated that home-based cardiac rehabilitation with tele-monitoring guidance appears to be a cost-effective alternative for centre-based cardiac rehabilita-tion. Evidence concerning cost-effectiveness of telemonitoring cardiac rehabilitation programmes so far is scarce. In addition, evidence of telemonitoring programmes so far was scarce and contra-dictory. The sparse results were hard to compare across studies due to the high variation in tele-monitoring guidance delivered at home (e.g. exercise manual, telephone support, home-visits) and the cardiac rehabilitation population studied [10,11]. In addition, some programmes provide additional services after regular cardiac rehabilitation resulting in additional costs [12], while others provide a programme replacing usual care [13]. Finally, economic evaluations often fail to include all relevant healthcare- and societal costs, therefore making it difficult to compare the results across studies [11]. Because cost-effectiveness of an innovative intervention is essential for wide scale implementation, we recommend including high quality cost-effectiveness analyses in future trials studying the effects of novel telemonitoring cardiac rehabilitation interventions.

Moreover, with current budget limits and focus on cost savings in healthcare, cost-effectiveness is a key parameter for policy-makers in the decision to implement novel interventions. A shift in the reimbursement of care is necessary, because telemonitoring interventions change the delivery of care from hospital-based care to care at home. The current reimbursement model, in which healthcare providers get paid for the number of visits (fee-for-service), lacks incen-tives to implement interventions that reduces those hospital visits. Therefore, a shift towards a pay-for-performance reimbursement model is necessary. If the quality and efficiency of the treatments are linked to the reimbursement for care, healthcare organisations are encouraged to implement novel interventions that improve the quality of care [14].

Unanswered questions and future workThis thesis provides insight into various topics concerning innovative cardiac rehabilitation programmes, however, some questions remain unanswered and new questions have emerged. We showed that energy expenditure is the most important determinant for improvement

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in exercise capacity in exercise-based cardiac rehabilitation. However, we did not take the effect of training modality into account. In current literature, there is debate whether interval training should be preferred to continuous training for cardiac patients [15,16]. High inten-sity interval training has shown to be at least as effective as moderate intensity continuous training, it is safe to perform and often requires less exercise time [15,17]. Therefore, it could be a useful alternative to conventional continuous training. On the other hand, high-intensity interval training is more difficult to perform in the home environment considering that fitness equipment is often not available at home and a high training intensity is harder to reach and control during outdoor walking or biking. Nonetheless, it is important to understand the effect of training modality on physical fitness in the design of a cardiac rehabilitation programme. Therefore, we recommend that future studies comparing training modalities correct for total energy expenditure of the training programmes in their analyses, or perform an isocaloric comparison. Furthermore, as previous studies have shown, there can be a substantial gap between prescribed training protocol and the exercises that were actually performed by the patients, hampering the empirical comparison of different training protocols [18]. Therefore, we recommend reporting both the prescribed protocol and the actually performed training. With the current development in wearable sensors, heart rate monitors are able to track the performed training protocol accurately and are widely available.

The beneficial effects of cardiac rehabilitation often decrease after the transition from super-vised centre-based training to independent training at home [19]. We therefore hypothesised that training in the home environment with telemonitoring guidance would result in supe-rior long-term effects compared to centre-based training. However, patients in both groups of the FIT@Home study were able to maintain their physical fitness levels at one-year follow-up. The unexpected favourable results from the centre-based cardiac rehabilitation group could be explained by the content of the programme, and our recruitment and selection process. First, today’s cardiac rehabilitation guidelines are more focused on the maintenance of exer-cise behaviour than several years ago [7]. Therefore, the professionals involved in the centre-based programme may have concentrated more on the maintenance of exercise behaviour than professionals in previous studies. Second, the nature of our intervention, with its emphasis on technology and individual training, prompted mainly young and motivated patients to partic-ipate in our study. It is not surprising that they were able to maintain their long-term physical activity levels, even when they started with centre-based training and had to make the tran-sition to the home environment. We therefore suspect that the difference in long-term effects between the centre-based and home-based participants in our trial is biased towards zero. We recommend future studies to adopt innovative strategies to limit this selection bias. For instance, two additional non-randomised, preference-based trial-arms could be added to the conven-tional randomised controlled trial study design. Although this has substantial implications for the sample size and costs of the study, a more mixed cardiac rehabilitation population could be included, thus enhancing the external validity of the results [20].

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The rapid changes in the technological landscape will allow future studies to incorporate innova-tions that will improve the quality of cardiac rehabilitation. By fully utilising the potential of digital health and telemonitoring, cardiac rehabilitation can be improved even more than described in this thesis. First, wearable sensors, such as smart-watches and activity-trackers, are becoming more accurate and provide the opportunity to monitor more clinical parameters. In addi-tion, sensors are integrated in consumer-end devices and are released by popular non-health-care brands. As a result, the user-population increases and the concept of activity tracking is becoming more popular. With this in mind, home-based data gathering will increasingly be accepted by patients and clinicians, and subsequently incorporated in the delivery of care [20]. Second, the data generated by those sensors can be analysed and utilised to optimise feed-back and supervision during behavioural-change programmes like cardiac rehabilitation. As our results showed, future studies should use physical activity data in behavioural-change coaching techniques, such as Motivational Interviewing and on-demand relapse-prevention, during tele-monitoring guidance to improve physical activity levels. Furthermore, sharing data with relatives and other cardiac rehabilitation patients can induce social support and peer pressure, which can motivate patients during the training programme [21,22]. Thirdly, modern telecommunication methods can improve access to healthcare services and improve guidance during home-based interventions [23]. During our study we provided feedback on the exercise sessions by phone. However, video communication can provide crucial nonverbal information and provide a closer simulation of face-to-face contact than telephone contact. If we can fully exploit the potential of these technological developments in the next years, we can make cardiac rehabilitation more appealing and optimise the quality of care delivered.

Conclusion

In this thesis we have developed and evaluated the effectiveness of a home-based training intervention that accommodates the preferences and constraints of patients while retaining the effectiveness of conventional, centre-based cardiac rehabilitation. Our systematic reviews of the literature found that the effectiveness of centre-based programmes is mainly determined by their total energy expenditure. This means that adjustments to programme characteristics can be made freely, without impairing effects on exercise capacity. provided that energy expendi-ture as a whole is preserved. Therefore, training programmes can be tailored towards patients’ preferences and translated to the home environment. Furthermore, we conclude that home-based cardiac rehabilitation with telemonitoring guidance is a useful alternative to centre-based cardiac rehabilitation for young and motivated patients with low-to-moderate cardiac risks. Home-based training resulted in similar improvements in physical fitness and health-related quality life as centre-based training, with a trend towards lower costs. We showed that wearable sensors can accurately assess physical activity levels of beta-blocker medicated cardiac patients. However, physical activity levels did not improve during and after completion of the cardiac

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rehabilitation programme, suggesting that some form of physical activity guidance is essential to increase physical activity levels. We believe that our work is an important step towards the development of personalised cardiac rehabilitation, and we encourage future interventions to build upon our lessons learned.

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1) J.H. Kocsis, A.C. Leon, J.C. Markowitz, et al., Patient preference as a moderator of outcome for chronic forms of

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3) J. Oliveira, F. Ribeiro, H. Gomes, Effects of a home-based cardiac rehabilitation program on the physical

activity levels of patients with coronary artery disease., J. Cardiopulm. Rehabil. Prev. 28 (2008) 392–396.

4) I. Frederix, N. Van Driessche, D. Hansen, et al., Increasing the medium-term clinical benefits of hospital-based

cardiac rehabilitation by physical activity telemonitoring in coronary artery disease patients., Eur. J. Prev.

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6) M. Borland, a Rosenkvist, Ã. Cider, A group-based exercise program did not improve physical activity in patients

with chronic heart failure and comorbidity: A randomized controlled trial, J. Rehabil. Med. 46 (2014) 461–467.

7) R.J. Achttien, J.B. Staal, S. van der Voort, et al., Exercise-based cardiac rehabilitation in patients with coronary

heart disease: A practice guideline, Netherlands Hear. J. 21 (2013) 429–438.

8) R.J. Achttien, T. Vromen, J.B. Staal, et al., Development of evidence-based clinical algorithms for prescription

of exercise-based cardiac rehabilitation, Netherlands Hear. J. 23 (2015) 563–575.





11) R.S. Taylor, H. Dalal, K. Jolly, et al., Home-based versus centre-based cardiac rehabilitation, Cochrane Database

Syst. Rev. (2015).




13) R.S. Taylor, A. Watt, H.M. Dalal, et al., Home-based cardiac rehabilitation versus hospital-based rehabilitation:

a cost effectiveness analysis., Int. J. Cardiol. 119 (2007) 196–201.

14) F. Eijkenaar, M. Emmert, M. Scheppach, O. Sch??ffski, Effects of pay for performance in health care: A systematic

review of systematic reviews, Health Policy (New. York). 110 (2013) 115–130.

15) K. Liou, S. Ho, J. Fildes, S.-Y. Ooi, High Intensity Interval versus Moderate Intensity Continuous Training in

Patients with Coronary Artery Disease: A Meta-analysis of Physiological and Clinical Parameters, Hear. Lung

Circ. 25 (2015) 1–9.

16) M. Tschentscher, J. Eichinger, A. Egger, S. Droese, M. Schönfelder, J. Niebauer, High-intensity interval

training is not superior to other forms of endurance training during cardiac rehabilitation., Eur. J. Prev.

Cardiol. (2014) 1–7.

17) K.D. Currie, J.B. Dubberley, R.S. McKelvie, M.J. MacDonald, Low-volume, high-intensity interval training in

patients with CAD., Med. Sci. Sports Exerc. 45 (2013) 1436–1442.

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improve aerobic exercise capacity in patients with coronary artery disease : The SAINTEX-CAD study, Int. J.

Cardiol. 179 (2015) 203–210.

19) D. Hansen, P. Dendale, A. Raskin, et al., Long-term effect of rehabilitation in coronary artery disease patients:

randomized clinical trial of the impact of exercise volume., Clin. Rehabil. 24 (2010) 319–27.

20) A. Britton, M. McKee, N. Black, K. McPherson, C. Sanderson, C. Bain, Choosing between randomised and non-

randomised studies: a systematic review., Health Technol. Assess. 2 (1998).

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AppendicesNederlandse samenvatting

PhD portfolioList of publications

Curriculum VitaeDankwoord

166

Dit proefschrift richt zich op het ontwikkelen van een innovatief programma om de hartreva-lidatie aantrekkelijker te maken voor hartpatiënten die niet willen of kunnen deelnemen aan reguliere hartrevalidatie in het ziekenhuis, zonder dat dit ten koste gaat van de effectiviteit.

Nederlandse samenvatting

Inleiding Na een behandeling aan het hart of een cardiaal incident is hartrevalidatie voor iedereen beschik-baar bij een ziekenhuis of revalidatiecentrum. De behandeling wordt uitgevoerd door een multi-disciplinair team en bestaat uit meerdere componenten. De belangrijkste componenten van hartrevalidatie zijn educatie, fysieke training, psychosociale begeleiding en stress- en ontspan-ningstherapie. Onderzoeksresultaten tonen aan dat hartrevalidatie de kans op sterfte door hart-problemen vermindert, de kans op een nieuw incident verkleint, de kwaliteit van leven verbe-tert en de fitheid verhoogt. Dit proefschrift richt zich specifiek op de fysieke training binnen de hartrevalidatie.

Ondanks dat de voordelen van hartrevalidatie uitgebreid zijn aangetoond, zijn er twee belang-rijke barrières, die een optimale effectiviteit van hartrevalidatie in de weg staan. Ten eerste is de deelname van patiënten die in aanmerking komen voor hartrevalidatie laag. Patiënten worden niet altijd doorverwezen door de cardioloog, willen niet altijd meedoen met de groepstrai-ningen bij de fysiotherapeut of moeten te ver reizen van en naar de hartrevalidatie. Daarnaast kunnen patiënten niet altijd meedoen vanwege werkhervatting of verplichtingen als mantel-zorger. Ten tweede blijkt uit onderzoek dat de lange termijn resultaten van hartrevalidatie teleur-stellend zijn. Patiënten vallen na afronding van de hartrevalidatie vaak terug in oude gewoontes, waardoor de positieve effecten van hartrevalidatie na verloop van tijd verdwijnen. Het is met de huidige hartrevalidatie dus moeilijk om een duurzame gedragsverandering te bewerkstelligen, een gedragsverandering die op de lange termijn behouden blijft.

Om bovenstaande problemen te overkomen, hebben we gezocht naar een innovatieve hartre-validatie interventie die aansluit bij de persoonlijke situatie van de patiënt, waardoor de resul-taten mogelijk duurzamer zijn. Hartrevalidatie met fysieke training in de thuissituatie heeft veel voordelen voor de patiënt, zonder dat de kwaliteit van de hartrevalidatie achteruit hoeft te gaan, en waarbij bovengenoemde terugval mogelijk wordt voorkomen. Patiënten hoeven minder te reizen naar de hartrevalidatie, kunnen het moment van training zelf inplannen en hoeven niet aan de groepstrainingen deel te nemen. Bovendien zijn de patiënten vanaf de start van de reva-lidatie zelf verantwoordelijk voor het uitvoeren en inplannen van de trainingen, waardoor de kans op het ontwikkelen van zelfmanagement eigenschappen verbetert. Draagbare sensoren

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(hartslagmeters, bewegingsmeters) kunnen tijdens de interventie worden gebruikt om de dage-lijkse activiteit en fysieke trainingen te meten. Deze gegevens kunnen tijdens de begeleiding op afstand worden gebruikt om de trainingen te verbeteren. Als daarnaast tijdens de begeleiding Motivational Interviewing wordt toegepast (een evidence-based techniek om personen tijdens gedragsverandering te ondersteunen en motiveren), verwachten we dat patiënten na de thuis-training de actieve leefstijl beter vol kunnen houden. Eerdere studies hebben al aangetoond dat thuistraining bij hartpatiënten even veilig is als reguliere hartrevalidatie, en de fitheid en kwaliteit van leven vergelijkbaar verbetert. Lange termijn effecten zijn echter minder vaak onderzocht en de resultaten zijn niet eenduidig, omdat de inhoud van de interventies (het trainingsprogramma, de begeleiding, het type feedback) vaak verschilt.

We hebben ons in dit proefschrift gericht op drie onderzoeksvragen: 1) Wat is het effect van de individuele trainingseigenschappen (trainingsfrequentie, trainings-

duur, trainingsintensiteit en lengte van het trainingsprogramma) op de verbetering in fitheid na fysieke training bij hartrevalidatie patiënten?

2) Hoe kunnen we een model ontwikkelen dat de dagelijkse activiteit van hartrevalidatie pati-enten accuraat kan voorspellen in de thuissituatie?

3) Wat is het verschil in effectiviteit en kosteneffectiviteit tussen thuistraining met begeleiding via telemonitoring en reguliere training in het ziekenhuis bij hartrevalidatie patiënten met een laag tot matig risico op een nieuwe cardiaal incident?

ResultatenIn het eerste deel van dit proefschrift bespreken we het effect van trainingskarakteristieken op de verbetering van de fitheid en de mogelijkheid om bij hartpatiënten de dagelijkse fysieke acti-viteiten op afstand te monitoren. Hierna focussen we op de klinische effecten en kosteneffecti-viteit van thuistraining met begeleiding via telemonitoring, vergeleken met reguliere training in het ziekenhuis bij hartrevalidatie patiënten. In hoofdstuk 02 beschrijven we een systematische review van de literatuur, waarin we onderzoeken welke individuele trainingskarakteristieken de verbetering van fitheid na fysieke training bepalen bij patiënten met coronairlijden. We hebben daarbij gekeken naar de trainingsfrequentie, trainingsduur, trainingsintensiteit, de lengte van het trainingsprogramma, en het product van deze vier karakteristieken, het totale energieverbruik van het programma. In totaal zijn 13 gerandomiseerde studies geïncludeerd (in totaal 693 pati-enten) die de fitheid van patiënten na fysieke training vergeleken met die van patiënten zonder fysieke training. Patiënten in de trainingsgroep verbeterden significant in fitheid vergeleken met de controlegroep. Meta-regressie analyses op de data tonen aan dat de vier trainingskarakteris-tieken en het totale energieverbruik een individueel effect hadden op de verbetering in fitheid. Echter, na correctie voor het totale energieverbruik in de analyses verdween het effect van de vier individuele trainingskarakteristieken. Hieruit kunnen we concluderen dat het totale energie-verbruik van het programma de sterkste voorspeller is van de vooruitgang in fitheid bij patiënten met coronairlijden.

168

Een vergelijkbare systematische review is uitgevoerd voor hartfalen patiënten in hoofdstuk 03. Hierbij hebben we 17 gerandomiseerde studies geïncludeerd met een totaal van 2935 patiënten. Omdat bijna 75% van de patiënten was geïncludeerd in één studie, hebben we de analyses zowel met als zonder deze grote studie uitgevoerd. Beide analyses tonen aan dat energieverbruik de belangrijkste voorspeller is van de verbetering in fitheid. De analyses zonder de grote studie tonen tevens aan dat na correctie voor energieverbruik, trainingsfrequentie, trainingsduur en trai-ningsintensiteit een effect hebben op de vooruitgang van de fitheid bij hartfalen patiënten.In hoofdstuk 04 hebben we een model ontwikkeld die het energieverbruik van hartpatiënten voorspelt op basis van bewegingsdata, hartslagdata en patiëntkarakteristieken. Zestien hartpa-tiënten hebben in het ziekenhuis een maximale inspanningstest en rustmetabolisme meting gedaan, en een activiteiten protocol doorlopen. Tijdens het activiteiten protocol hebben we het energieverbruik gemeten met een indirecte calorimetriemeter, gebaseerd op ademgasana-lyse. Daarnaast hebben we de beweging en hartslag gemeten met draagbare sensoren. Met de gegevens van de sensoren hebben we een model ontwikkeld dat het energieverbruik nauw-keurig voorspelt. Dit model kan in de praktijk gebruikt worden om de dagelijkse fysieke activi-teiten van hartpatiënten in de thuissituatie te meten.

In hoofdstuk 05 beschrijven we het studieprotocol van de FIT@Home studie. In deze studie includeerden we 90 hartrevalidatie patiënten met een laag tot matig risico op een nieuw infarct. Op basis van loting werden de patiënten ingedeeld in een thuistraining groep met begelei-ding via telemonitoring, of in een controlegroep met reguliere training in het ziekenhuis. Beide groepen kregen drie maanden fysieke training. De thuistraining groep kreeg eerst drie trai-ningen in het ziekenhuis als introductie, en ging daarna zelfstandig thuis trainen. De patiënten trainden met een hartslagmeter, zette de gegevens van elke training op een internetaccount en kregen één keer per week telefonische coaching. Na drie maanden stopte beide groepen met trainen. De belangrijkste uitkomstmaten in de studie zijn de fitheid en het dagelijkse fysieke acti-viteitenniveau. Secundaire uitkomstmaten zijn patiënttevredenheid, kwaliteit van leven en de kosteneffectiviteit. Alle uitkomstmaten worden gemeten voor aanvang van deelname, na afloop van de hartrevalidatie (na drie maanden) en een jaar na deelname.

De korte-termijn resultaten van de eerste 50 deelnemers aan de FIT@Home studie worden beschreven in hoofdstuk 06. De trainingsfrequentie, trainingsduur en trainingsintensiteit waren vergelijkbaar tussen patiënten die thuis trainden en patiënten die in het ziekenhuis trainden. De fitheid en kwaliteit van leven van beide groepen was na drie maanden significant verbeterd. Dit geeft aan dat de deelnemers van de thuistraining groep met behulp van de begeleiding op afstand zelfstandig kunnen trainen.

De eindresultaten van FIT@Home studie worden beschreven in hoofdstuk 07. De trainings-duur, frequentie en intensiteit was vergelijkbaar in beide groepen. De beide groepen verbe-terden daarom significant in fitheid na drie maanden, zonder verschillen tussen de groepen.

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Na 12 maanden was de fitheid van beide groepen hetzelfde als na afloop van het hartrevali-datie programma. Het dagelijkse activiteitenniveau van patiënten in beide groepen was na 12 maanden hetzelfde als bij de start van de interventie. Hoewel er geen verschil was in kwaliteit van leven tussen de groepen, was de patiënttevredenheid significant hoger bij patiënten in de thuistraining groep. De kosteneffectiviteit analyse toont aan dat thuistraining meer kosteneffec-tief is dan reguliere ziekenhuistraining, vooral omdat de kosten voor betaald werkverzuim signifi-cant hoger waren bij patiënten in de controlegroep. Deze resultaten tonen aan dat thuistraining met begeleiding een klinisch effectief en kosteneffectief alternatief is voor reguliere training in het ziekenhuis voor hartpatiënten met een laag tot matig risico op een nieuw cardiaal incident.

DiscussieIn het eerste deel van dit proefschrift tonen we aan dat bij het trainingsprogramma van hart-patiënten het totale energieverbruik van het programma de belangrijkste voorspeller is van de verbetering in fitheid. Het is daarom mogelijk om de andere karakteristieken van het trai-ningsprogramma te wijzigen zonder dat het trainingsprogramma minder effectief wordt. Het is daarom mogelijk om een thuistraining programma samen te stellen op basis van de voorkeur van de patiënt en de mogelijkheden in de thuissituatie. Als het programma invloed heeft op de samenstelling van het programma, is de motivatie en therapietrouw van de patiënt vaak hoger, wat kan resulteren in een beter eindresultaat. Echter, we hebben in dit deel alleen gekeken naar trainingsprogramma’s die gefocust waren op duurtraining en hebben alle trainingsprogramma’s met interval training geëxcludeerd. Toch is dit een belangrijke eigenschap van het trainingspro-gramma, en toekomstige studies zullen dit aspect mee moeten nemen in de analyses. Ook is het voor toekomstige studies belangrijk om zowel de voorgeschreven trainingsparameters, als de werkelijk uitgevoerde trainingsparameters te rapporteren. Uit verschillende studies blijkt dat het voorgeschreven trainingsprogramma niet overeen komt met het uitgevoerde trainingspro-gramma. Om inzicht te krijgen in het werkelijke effect van de trainingskarakteristieken is daarom een nauwkeurige rapportage van het trainingsprogramma noodzakelijk.

In het tweede deel van dit proefschrift tonen we aan dat hartrevalidatie in de thuissituatie met begeleiding via telemonitoring klinisch- en kosteneffectief kan worden uitgevoerd. Patiënten die weinig tijd hebben vanwege werkhervatting, ver van het ziekenhuis wonen of niet willen deelnemen aan de groepstrainingen, kunnen hierdoor toch profiteren van de positieve effecten van hartrevalidatie. Wij hebben echter niet kunnen aantonen dat de lange-termijn effecten van thuistraining beter zijn dan na reguliere training in het ziekenhuis. Hoewel het effect van thuis-training op lange-termijn nog zichtbaar was bij de thuistraining patiënten, zagen we eenzelfde effect bij de controlegroep. We verwachten dat dit komt doordat voornamelijk jonge, gemo-tiveerde hartpatiënten meededen aan de studie. De effecten van thuistraining zullen daarom moeten worden gevalideerd in een grotere en gevarieerde populatie hartrevalidatie patiënten. Ook is het dagelijkse fysieke activiteitenniveau van onze patiënten niet verbeterd. Dit toont aan dat een interventie gericht op verbetering van fitheid niet direct geassocieerd is met een

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verbeterd dagelijks activiteitenniveau. Echter, met de huidige ontwikkelingen op het gebied van sensor technologie en met de ontwikkeling van het model om energieverbruik te voorspellen, wordt het eenvoudiger om de dagelijkse activiteiten tijdens de hartrevalidatie in de thuissituatie te meten. Het is echter noodzakelijk om deze gegevens actief te betrekken in de begeleiding van de patiënten tijdens het hartrevalidatie programma. Op die manier worden de patiënten gesti-muleerd en gemotiveerd voor een actieve leefstijl.

Ondanks dat telemonitoring interventies binnen wetenschappelijk onderzoek aan populariteit winnen, worden weinig innovatieve interventies daadwerkelijk geïmplementeerd in de dage-lijkse zorg. Het is daarom essentieel dat in de wetenschappelijke studies de klinische effectivi-teit wordt aangevuld met een uitgebreide kosteneffectiviteitsanalyse. Met de huidige budget-limieten en kostenbesparingen in de zorg, is kosteneffectiviteit een belangrijke parameter voor de beleidsmakers. Daarnaast zorgen telemonitoring interventies voor een verschuiving van de zorg, van zorg in het ziekenhuis naar zorg bij de patiënt in de thuissituatie. Als deze zorg buiten het ziekenhuis door de zorgverzekeraar niet gelijkwaardig wordt vergoed, wordt de implemen-tatie ervan erg lastig. Echter, als de zorg op afstand wel wordt vergoed, zal dit de implementatie van innovatie telemonitoring interventies alleen maar versnellen.

ConclusieIn dit proefschrift beschrijven we de mogelijkheden om de hartrevalidatie aantrekkelijker te maken voor hartpatiënten die niet kunnen deelnemen aan hartrevalidatie in het ziekenhuis, zonder dat de effectiviteit vermindert. We tonen aan dat trainingsprogramma’s kunnen worden aangepast aan de voorkeur en thuissituatie van de patiënt, zolang het totale energieverbruik van het trainingsprogramma behouden blijft. Daarnaast hebben we aangetoond dat voor patiënten met een laag tot matig risico op een nieuw incident, training in de thuissituatie met telemonito-ring begeleiding een alternatief is voor reguliere training in het ziekenhuis.

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PhD Portfolio

Name PhD student

PhD period

Promotor

Copromotores

J.J. Kraal

April 2012 – April 2016

Prof. dr. A. Abu-Hanna

dr. N.B. Peek and dr. H.M.C. Kemps

PhD training Year Workload (ECTS)

General courses

Basic Course Legislation and Organization for Clinical Research

Reference Manager

Developing a Systematic Review of Interventions

Computing in R

Scientific Writing

2012

2012

2012

2013

2015

1.0

0.1

0.7

0.4

1.5

Specific courses

Motivational Interviewing, Hilversum 2013 1.2

Seminars, workshops and master classes

Innovation in healthcare, Máxima Medisch Centrum Veldhoven 2015 0.1

Presentations

NVVC Hartrevalidatie Congres, Amersfoort (oral presentation)

EuroPRevent, Rome (two poster presentations)

1st European Congress e-Cardiology & eHealth, Bern (oral presentation)

Refereeravond Cardiologie, Veldhoven (oral presentation)

MMC Wetenschapsavond, Veldhoven (poster presentation)

AMC Promovendi-dag, Breukelen (oral presentation)

EuroPRevent, Amsterdam (poster presentation)

Landelijke Werkgroep CardioPsychologie, Utrecht (oral presentation)

2nd European Congress e-Cardiology & eHealth, Bern (oral presentation)

MMC Wetenschapsavond, Veldhoven (oral presentation)

AMC Promovendi-dag, Breukelen (oral presentation)

VSG Wetenschapsavond, Bilthoven (oral presentation)

EuroPRevent, Lisbon (poster presentation)

MMC Promovendi-avond, Veldhoven (oral presentation)

ESC Congress, London (poster presentation)

NVVC Hartrevalidatie Congres, Eede (oral presentation)

NVHVV, Continuing Nursing Education, Utrecht (oral presentation)

MMC Wetenschapsavond, Veldhoven (poster presentation)

AMC Promovendi-dag, Amsterdam (oral presentation)

Mallinckrodt Pharmaceuticals, Petten (oral presentation)

2012

2013

2013

2014

2014

2014

2014

2014

2014

2015

2015

2015

2015

2015

2015

2015

2016

2016

2016

2016

0.5

1.0

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

172

PhD training Year Workload (ECTS)

(Inter)national conferences

NVVC Hartrevalidatie Congres, Apeldoorn

Symposium Verstoord Bewegen, Amsterdam

EuroPRevent, Rome

1st European Congress on e-Cardiology & eHealth, Bern

MMC Wetenschapsavond, Veldhoven

AMC Promovendi-dag, Breukelen

EuroPRevent, Amsterdam

2nd European Congress on e-Cardiology & eHealth, Bern

MMC Wetenschapsavond, Veldhoven

AMC Promovendi-dag, Breukelen

EuroPRevent, Lisbon

2nd Conference of Wearable Technology in Healthcare, Amsterdam

MMC Promovendi-avond, Veldhoven

eHealth Convention, Innovation Cure, Care and Technology, Amsterdam

Medisch Informatica Congres, Veldhoven

NVVC Hartrevalidatie Congres, Ede

2012

2012

2013

2013

2014

2014

2014

2015

2015

2015

2015

2015

2015

2015

2015

2016

0.25

0.2

0.75

0.5

0.2

0.25

0.75

0.5

0.2

0.25

0.75

0.5

0.2

0.25

0.25

0.75

Teaching

APROVE – Back to the Future Project

Medische Informatiekunde Module 1.1: eHealth 2014

Medische Informatiekunde Module 1.1: eHealth 2015

2013

2014

2015

0.25

0.25

0.25

Tutoring, mentoring

Mentor two students of the Bachelor Medical Informatics (5 months)

Mentor one student of the Bachelor Medical Informatics (5 months)

Mentor one student of the Bachelor Physical Therapy of

Fontys Hogeschool Eindhoven (2.5 months)

Mentor two students of the Master Clinical Psychology of

Universiteit Leiden (2.5 months)

2013

2014

2014

2014

1.0

0.5

0.25

0.5

Prizes and awards

Best poster Award, Máxima Medisch Centrum Veldhoven

Best Research Abstract, European Congress in e-Cardiology & eHealth, Bern

2014

2014

173

List of publications

· Houdijk H, van Ooijen MW, Kraal JJ Wiggerts HO, Polomski W, Janssen TW, Roerdink M. Assessing gait adaptability in people with a unilateral amputation on an instrumented treadmill with a projected visual context. Phys Ther. 2012 Nov;92(11):1452-60

· Vromen T, Spee RF, Kraal JJ, Peek N, van Engen-Verheul MM, Kraaijenhagen RA, Gijsbers HJ, Kemps HM. Exercise training programs in Dutch cardiac rehabilitation centres. Neth Heart J. 2013 Mar;21(3):138-43

· Kraal JJ, Peek N, van den Akker-Van Marle ME, Kemps HM. Effects and costs of home-based training with telemonitoring guidance in low to moderate risk patients entering cardiac rehabilita-tion: The FIT@Home study. BMC Cardiovasc Disord. 2013 Oct 8;13:82

· Kraal JJ, Peek N, Van den Akker-Van Marle ME, Kemps HM. Effects of home-based training with telemonitoring guidance in low to moderate risk patients entering cardiac rehabilitation: short-term results of the FIT@Home study. Eur J Prev Cardiol. 2014 Nov;21(2 Suppl):26-31

· Bonomi AG, Goldenberg S, Papini G, Kraal J, Stut W, Sartor F, Kemps H. Predicting energy expen-diture from photo-plethysmographic measurements of heart rate under beta blocker therapy: Data driven personalization strategies based on mixed models. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:7642-6

· Vromen T, Kraal JJ, Kuiper J, Spee RF, Peek N, Kemps HM. The influence of training characteristics on the effect of aerobic exercise training in patients with chronic heart failure: A meta-regression analysis. Int J Cardiol. 2016 Apr 1;208:120-7

In preparation · Kraal JJ, Sartor F, Papini G, Stut W, Peek N, Kemps HM, Bonomi AG. Energy expenditure esti-

mation in beta-blocker medicated cardiac patients by combining heart rate and body movement data. Submitted for publication

· G Papini, AG Bonomi, W Stut, Kraal JJ, HMC Kemps, F Sartor. A 45-second self-test for cardiore-spiratory fitness: Part 2 - accelerometry-based estimation in post-myocardial infarction patients. Submitted for publication

· Kraal JJ, Vromen T, Kuiper J, Spee RF, Kemps HM, Peek N. The influence of training characteristics on the effect of exercise training in patients with coronary artery disease: a meta-regression anal-ysis. Submitted for publication

· Kraal JJ, Van den Akker-Van Marle ME, Abu-Hanna A, Stut W, Peek N, Kemps HM. Clinical and cost-effectiveness of home-based cardiac rehabilitation compared to conventional, centre-based cardiac rehabilitation: Results of the FIT@Home study. Submitted for publication

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Curriculum Vitae

Jos Kraal was born in Venhuizen, the Netherlands on September 13th, 1987. In 2005 he finished his Gymnasium (Nature and Health) at secondary school Het Martinuscollege in Grootebroek and started the Bachelor programme of Human Movement Sciences at the Vrije Universiteit in Amsterdam. During the final year of this Bachelor programme he developed great interest in two subjects very relevant for the rest of his education: rehabilitation and innovation. After finishing the Bachelor programme in 2009, he enrolled in the Master programme of Human Movement Sciences with a specialisation in Health and Physical Therapy. His Master thesis, focused on the gait adaptability and walking performance of people with a unilateral ampu-tation, using an innovative treadmill (ForceLink C-Mill) was a first experience with a novel inter-vention to improve rehabilitation. Although his Master programme was not officially finalised in September 2010, he enrolled in a second Master programme at Maastricht University: Public Health, Healthcare Policy, Innovation and Management. In September 2011 he graduated from both Master programmes. In February 2012, Jos had a job interview at the department of Medical Informatics of the Academic Medical Center in Amsterdam, but his application was rejected. However, two weeks later dr. Niels Peek and dr. Hareld Kemps were able to offer him the oppor-tunity to work as Phd-student on a different project at the same department. In the following four years, he worked on the FIT@Home project, studying the effect of home-based exercise training in low-to-moderate cardiac risk patients entering cardiac rehabilitation. Although he was employed by the Academic Medical Center, the trial was performed in The Máxima Medical Center in Veldhoven/Eindhoven.

During his studies he worked at a sports bar (Amsterdam) and restaurant (Maastricht) to afford a life next to his education. In his spare time, Jos enjoys both watching and performing sports. He actively competes in squash, likes cycling and fitness, and has great interest in several other sports. In the time left he spends his time by playing the piano or guitar.

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Dankwoord

Dit proefschrift had niet kunnen bestaan zonder de steun en hulp die ik de afgelopen 4 jaar van velen heb gehad. Ik heb helaas geen ruimte om iedereen bij naam te noemen, maar ik zal proberen de belangrijkste mensen te bedanken:

In eerste instantie wil ik alle patiënten bedanken die hebben meegedaan met de onderzoeken die in dit proefschrift zijn beschreven. Het staat vast dat zonder jullie hulp dit proefschrift er niet was geweest. We hebben veel van jullie gevraagd (veel extra onderzoeken, ziekenhuis bezoek en vragenlijsten), en ik ben erg dankbaar dat jullie in een voor hectische revalidatie periode toch een bijdrage wilden leveren aan de wetenschap.

Minimaal zo belangrijk, mijn promotor, prof. dr. Ameen Abu-Hanna en copromotores dr. Niels Peek en dr. Hareld Kemps. Beste Ameen, in het eerste deel van mijn promotietraject was onze samenwerking niet bijzonder intensief, maar dat hebben we in het laatste deel absoluut inge-haald. Jouw enthousiasme voor het onderzoek en voor programmeren en data-analyse werkt erg aanstekelijk. Jij neemt geen genoegen met een simpel maar langzaam script voor de analyse van de data. Jij zit liever avonden lang te puzzelen op een bijzonder ingewikkeld script (via Lisa Surfsara en multi-core nodes) dat uiteindelijk alle data in 5 minuten kan verwerken. Niels, jouw begeleiding was juist in de eerste twee jaar erg belangrijk voor mijn introductie in de weten-schap en voor het opstarten van het onderzoek. Ondanks dat het contact minder frequent werd na jouw overgang naar Manchester, heb ik dit op geen enkel moment als onprettig of onhandig ervaren. Jouw wetenschappelijke diepgang en kritische blik heeft de kwaliteit van dit proefschrift naar een hoger niveau gebracht, en ik ben heel blij dat we de afgelopen jaren hebben mogen samenwerken. In Manchester ben je ambitieuze en veelbelovende projecten aan het opstarten en uitvoeren, en ik denk dat we daar nog heel veel van gaan horen. Hareld, jouw bevlogen-heid en enthousiasme voor innovatie in de patiëntenzorg is indrukwekkend en geeft mij erg veel energie. Als we jouw stroom van nieuwe ideeën, wearables en sensoren, open data-plat-formen en at-home zorgpaden direct konden implementeren, was de gezondheidszorg nu niet zo enorm verouderd geweest. Samen met Laurence hebben jullie het indrukwekkende Centrum Flow opgezet. Volgens mij absoluut de juiste weg om de zorg van de toekomst te leveren. Ik ben erg blij dat ik daar een bijdrage aan kan leveren in de toekomst.

De overige leden van de promotiecommissie, prof. dr. Peters, prof. dr. Witkamp, prof. dr. Nollet, prof. dr. Hopman, dr. Dendale en dr. Cornelissen, hartelijk dank voor het beoordelen van mijn proefschrift en de bereidheid om plaats te nemen in mijn promotiecommissie.

Lieve (ex-)kamergenoten, en aangetrouwde kamergenoten van het AMC, Manon, Erik, Sabine, Gaby, Airin, Marjan, Ellen, Tom, Nick, Ilse, en paranimfen Wouter en Leonie, dank voor de gezel-ligheid op de soms eindeloze dagen achter de laptop. Ik mis de rondjes naar de Albert Heijn

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voor koffie nu al iedere dag. Het was een moeilijk moment toen ik mijn sleutel van J1b-109 moest inleveren.. Cardiogroep van MMC: Victor, Ruud, Danny, Thijs, Rutger, (en Tom en Hareld). Door jullie werden de congressen onvergetelijk en veel meer dan alleen leerzaam. We moeten snel weer een rondje fietsen in de bergen, of een schrijfweek plannen (en echt, er wordt gewoon gewerkt tijdens de schrijfweek..). Ik hoop velen van jullie nog tegen te komen in en om Eind-hoven. Mooeeh! Overige collega’s van het AMC en MMC: Ellen, Gita, Irene, Aaltje, Rob, Renaldo en alle poli-secretaresses en functielaboranten, jullie hebben me ontzettend geholpen met het opstarten van het onderzoek, de fietstesten, de planning van agenda’s, de inhoudelijke sessies, de begeleiding van patiënten, en het uitvoeren van extra onderzoeken. Ook alle dank aan de rest van het (Flow-) team die bij FIT@Home betrokken was: Laurence, Jolande, Mandy, Nicole, Tonnie en vele anderen: de enorm goede sfeer die op de poli aanwezig was (en is), maakte het een plezier om naar Eindhoven te komen. Daarnaast wil ik ook de samenwerkingspartners en coauteurs van Philips Research bedanken. Wim, Alberto, Fancesco, Gabriele and Sharon, I really enjoyed working with you, hopefully we will collaborate in many more projects in the (near) future.

Verder wil ik mijn vrienden en (schoon-) familie bedanken voor de steun, ontspanning en afleiding in de afgelopen 4 jaar. Ondanks dat jullie niet altijd direct betrokken waren bij het onderzoek, heeft jullie belangstelling mij ontzettend geholpen om het onderwerp beter onder de knie te krijgen. Lieve ouders, dank voor de onvoorwaardelijke steun de afgelopen 29 jaar. Ik ben erg dankbaar voor de vele kansen die jullie mij hebben geboden. Tegen de tijd dat jullie dit lezen, hoop ik eindelijk een échte baan te hebben .

Save the best for last: lieve Jacomien, wat ben ik blij dat jij al 6,5 jaar naast me staat. Met jou kan ik moeiteloos inhoudelijke discussies afwisselen met filosofische gedachtewisselingen over de (on)zin van het leven, en daar ben ik erg blij mee. Ik hoop dat we dit nog lang samen kunnen doen!

HOMEBASED


Jos Kraal










Paranimfen:


[email protected]

HOME-BASEDCARDIAC



HO

ME - BA

SED CA

RDIA

C REHA

BILITATION

Jos Kraal

HOMEBASED


Jos Kraal










Paranimfen:


[email protected]

HOME-BASEDCARDIAC


novel intervention with telemonitoringguidance and wearable sensorsHOME

BASEDCARDIAC

REHABILITATION

Jos Kraal










Paranimfen:


[email protected]

HOME-BASEDCARDIAC



JosKraal_offertenr407539_Cover_DUBBELzijdig-FULLcolor_Krasvastlaminaat.indd 2-6 5-10-2016 14:35:34

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