PATHway I: design and rationale for the investigation of the feasibility, clinical effectiveness and cost-effectiveness of a technology-enabled cardiac rehabilitation platform

Applying Exercise Capacity and Physical Activity as Single vs Composite Endpoints for Trials of Cardiac Rehabilitation Interventions: Rationale, Use-case, and a Blueprint Method for Sample Size Calculation

OBJECTIVE

To conceptualize a composite primary endpoint for parallel-group RCTs of exercise-based cardiac rehabilitation (CR) interventions and to explore its application and statistical efficiency.

DESIGN

We conducted a statistical exploration of sample size requirements. We combined exercise capacity and physical activity for the composite endpoint (CE), both being directly related to reduced premature mortality in patients with cardiac diseases. Based on smallest detectable and minimal clinically important changes (change in exercise capacity of 15 W and change in physical activity of 10 min/day), the CE combines 2 dichotomous endpoints (achieved/not achieved). To examine statistical efficiency, we compared sample size requirements based on the CE to single endpoints using data from 2 completed CR trials.

SETTING

Cardiac rehabilitation phase III.

PARTICIPANTS

Patients in cardiac rehabilitation.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

Exercise capacity (Pmax assessed by incremental cycle ergometry) and physical activity (daily minutes of moderate to vigorous physical activity assessed by accelerometry).

RESULTS

Expecting, for example, a 10% between-group difference and improvement in the clinical outcome, the CE would increase sample size by up to 21% or 61%, depending on the dataset. When expecting a 10% difference and designing an intervention with the aim of non-deterioration, the CE would allow to reduce the sample size by up to 55% or 70%.

CONCLUSIONS

Trialists may consider the utility of the CE for future studies in exercise-based CR to reduce sample size requirements. However, perhaps surprisingly at first, the CE could also lead to an increased sample size needed, depending on the observed baseline proportions in the trial population and the aim of the intervention.

Safety and long-term outcomes of remote cardiac rehabilitation in coronary heart disease patients: A systematic review

Objective

To systematically review the safety and the long-term mortality and morbidity risk-rates of the remotely-delivered cardiac rehabilitation (RDCR) interventions in coronary heart disease (CHD) patients.

Methods

The protocol was registered in the International Prospective Register of Systematic Reviews (CRD42023455471). Five databases (Pubmed, Scopus, Cochrane Central Register of Controlled Trials in the Cochrane Library, Cinahl and Web of Science) were reviewed from January 2012 up to August 2023. Inclusion criteria were: (a) randomized controlled trials, (b) RDCR implementation of at least 12 weeks duration, (c) assessment of safety, rates of serious adverse events (SAEs) and re-hospitalization incidences at endpoints more than 6 months. Three reviewers independently performed data extraction and assessed the risk of bias using the Cochrane Risk of Bias tool.

Results

14 studies were identified involving 2012 participants and a range of RDCR duration between 3 months to 1 year. The incidence rate of exercise-related SAEs was estimated at 1 per 53,770 patient-hours of RDCR exercise. A non-statistically significant reduction in the re-hospitalization rates and the days lost due to hospitalization was noticed in the RDCR groups. There were no exercise-related deaths. The overall study quality was of low risk.

Conclusions

RDCR can act as a safe alternative delivery mode of cardiac rehabilitation (CR). The low long-term rates of reported SAEs and re-hospitalization incidences of the RDCR could enhance the uptake rates of CR interventions. However, further investigation is needed in larger populations and longer assessment points.

Physical Therapy Programs in Older Adults with Coronary Artery Disease: Preferences to Technology-Based Cardiac Physical Therapy Programs

(1) Background: Assessing preferences in technology-based cardiac physical therapy programs in older adults with coronary artery disease (CAD) is fundamental to promoting adherence to healthy lifestyles and healthy aging. This study aimed at analyzing preferences in technology-based cardiac physical therapy programs in older adults with CAD. Additionally, a comparison by sex was performed. (2) Methods: Cross-sectional study. 70 older adults with CAD (mean age 66.73 ± 0.77, 80% men) were evaluated. Technology use and preferences in technology-based cardiac physical therapy programs (Technology Usage Questionnaire) were assessed. (3) Results: 97.1% of the sample had Smartphones and 81.4% accessed the Internet every day, mostly with their Smartphones (75.5%). A total of 54.3% were interested in receiving rehabilitation via their Smartphone, and most of the sample considered ideas to manage stress (92.9%), healthy meal ideas and recipes (85.7%), exercise ideas (84.3%), exercise prompts (72.9%), setting goals (67.1%), exercise taught by a virtual therapist (65.7%), ideas to overcome cigarette cravings (62.9%), information on local exercise opportunities (60%), ideas to remember to take medications (57.1%), steps to achieve goals (54.3%) and eating tips for takeaways (51.7%) very useful. Additionally, men considered the technology-based advice about exercise prompts, healthy meal ideas and recipes, and ideas to manage stress more useful than women, and had more frequently a Smartphone, less frequently made phone calls, had more regular access to the Internet, and used the Internet more often. (4) Conclusions: Clinicians should encourage older adults to engage in cardiac technology-based physical therapy programs to provide meaningful exercise counselling, promote healthy lifestyle and healthy aging.

Toward a Digital Platform for the Self-Management of Noncommunicable Disease: Systematic Review of Platform-Like Interventions

BACKGROUND

Digital interventions are effective for health behavior change, as they enable the self-management of chronic, noncommunicable diseases (NCDs). However, they often fail to facilitate the specific or current needs and preferences of the individual. A proposed alternative is a digital platform that hosts a suite of discrete, already existing digital health interventions. A platform architecture would allow users to explore a range of evidence-based solutions over time to optimize their self-management and health behavior change.

OBJECTIVE

This review aims to identify digital platform-like interventions and examine their potential for supporting self-management of NCDs and health behavior change.

METHODS

A literature search was conducted in January 2020 using EBSCOhost, PubMed, Scopus, and EMBASE. No digital platforms were identified, so criteria were broadened to include digital platform-like interventions. Eligible platform-like interventions offered a suite of discrete, evidence-based health behavior change features to optimize self-management of NCDs in an adult population and provided digitally supported guidance for the user toward the features best suited to their needs and preferences. Data collected on interventions were guided by the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist, including evaluation data on effectiveness and process outcomes. The quality of the included literature was assessed using the Mixed Methods Appraisal Tool.

RESULTS

A total of 7 studies were included for review. Targeted NCDs included cardiovascular diseases (CVD; n=3), diabetes (n=3), and chronic obstructive pulmonary disease (n=1). The mean adherence (based on the number of follow-up responders) was 69% (SD 20%). Of the 7 studies, 4 with the highest adherence rates (80%) were also guided by behavior change theories and took an iterative, user-centered approach to development, optimizing intervention relevance. All 7 interventions presented algorithm-supported user guidance tools, including electronic decision support, smart features that interact with patterns of use, and behavior change stage-matching tools. Of the 7 studies, 6 assessed changes in behavior. Significant effects in moderate-to-vigorous physical activity were reported, but for no other specific health behaviors. However, positive behavior change was observed in studies that focused on comprehensive behavior change measures, such as self-care and self-management, each of which addresses several key lifestyle risk factors (eg, medication adherence). No significant difference was found for psychosocial outcomes (eg, quality of life). Significant changes in clinical outcomes were predominately related to disease-specific, multifaceted measures such as clinical disease control and cardiovascular risk score.

CONCLUSIONS

Iterative, user-centered development of digital platform structures could optimize user engagement with self-management support through existing, evidence-based digital interventions. Offering a palette of interventions with an appropriate degree of guidance has the potential to facilitate disease-specific health behavior change and effective self-management among a myriad of users, conditions, or stages of care.

A qualitative exploration of cardiovascular disease patients' views and experiences with an eHealth cardiac rehabilitation intervention: The PATHway Project

The aim of this study is to explore participants’ views and experiences of an eHealth phase 3 cardiac rehabilitation (CR) intervention: Physical Activity Towards Health (PATHway). Sixty participants took part in the PATHway intervention. Debriefs were conducted after the six-month intervention. All interviews were audio recorded and transcribed verbatim. Transcripts were analysed with Braun and Clarke’s thematic analysis. Forty-four (71%) debriefs were conducted (n = 34 male, mean (SD) age 61 (10) years). Five key themes were identified: (1) Feedback on the components of the PATHway system, (2) Motivation, (3) Barriers to using PATHway, (4) Enablers to using PATHway, and (5) Post programme reflection. There were a number of subthemes within each theme, for example motivation explores participants motivation to take part in PATHway and participants motivation to sustain engagement with PATHway throughout the intervention period. Participant engagement with the components of the PATHway system was variable. Future research should focus on optimising participant familiarisation with eHealth systems and employ an iterative approach to development and evaluation.

Prediction of the Adherence to a Home-Based Cardiac Rehabilitation Program

The incidence and prevalence of cardiovascular diseases (CVD) is increasing which is partly due to an increase in unhealthy lifestyles, including lack of physical activity. Therefore, following a cardiovascular event, patients are encouraged to participate in a supervised exercise-based cardiac rehabilitation (CR) program. However, uptake rates of these programs are low and compliance to adequate volumes of physical activity after the completion of such programs are even lower. An approach that has been proposed towards the increase of patient adherence to exercise, is the incorporation of technology-enabled solutions which are applied at patient's homes. However, different factors may affect patient engagement with such alternative solutions. In this work, we use diverse types of data, including baseline characteristics of the patient (i.e. physiological, behavioral, demographical data) as well as usage data of a tele-rehabilitation solution during a 4-week familiarization period, in order to predict the compliance of patients with CVD to a technology-supported physical activity intervention after completion of a supervised exercise program. Patients were clustered based on their use of a technology intervention during a previously conducted study. Following a feature selection approach, a support vector machine was trained to classify patients as adherent or non-adherent to the intervention. The performance of the classifier was assessed by means of the receiving operator curve (ROC). Bio-psycho-social baseline variables predicted adherence with a ROC of 0.86, but adding usage data of the platform during a 4-week familiarization period increased the ROC up to 0.94. Furthermore, the high sensitivity values (83.8% and 95.5% respectively) support the strength of the models to identify those patients with CVD that will be adherent to a technology-enabled, home-based CR program.

Feasibility, Acceptability, and Clinical Effectiveness of a Technology-Enabled Cardiac Rehabilitation Platform (Physical Activity Toward Health-I): Randomized Controlled Trial

Background

Cardiac rehabilitation (CR) is highly effective as secondary prevention for cardiovascular diseases (CVDs). Uptake of CR remains suboptimal (30% of eligible patients), and long-term adherence to a physically active lifestyle is even lower. Innovative strategies are needed to counteract this phenomenon.

Objective

The Physical Activity Toward Health (PATHway) system was developed to provide a comprehensive, remotely monitored, home-based CR program for CVD patients. The PATHway-I study aimed to investigate its feasibility and clinical efficacy during phase III CR.

Methods

Participants were randomized on a 1:1 basis to the PATHway (PW) intervention group or usual care (UC) control group in a single-blind, multicenter, randomized controlled pilot trial. Outcomes were assessed at completion of phase II CR and 6-month follow-up. The primary outcome was physical activity (PA; Actigraph GT9X link). Secondary outcomes included measures of physical fitness, modifiable cardiovascular risk factors, endothelial function, intima-media thickness of the common carotid artery, and quality of life. System usability and patients’ experiences were evaluated only in PW. A mixed-model analysis of variance with Bonferroni adjustment was used to analyze between-group effects over time. Missing values were handled by means of an intention-to-treat analysis. Statistical significance was set at a 2-sided alpha level of .05. Data are reported as mean (SD).

Results

A convenience sample of 120 CVD patients (mean 61.4 years, SD 13.5 years; 22 women) was included. The PATHway system was deployed in the homes of 60 participants. System use decreased over time and system usability was average with a score of 65.7 (SD 19.7; range 5-100). Moderate-to-vigorous intensity PA increased in PW (PW: 127 [SD 58] min to 141 [SD 69] min, UC: 146 [SD 66] min to 143 [SD 71] min; P_interaction=.04; effect size of 0.42), while diastolic blood pressure (PW: 79 [SD 11] mmHg to 79 [SD 10] mmHg, UC: 78 [SD 9] mmHg to 83 [SD 10] mmHg; P_interaction=.004; effect size of −0.49) and cardiovascular risk score (PW: 15.9% [SD 10.4%] to 15.5% [SD 10.5%], UC: 14.5 [SD 9.7%] to 15.7% [SD 10.9%]; P_interaction=.004; effect size of −0.36) remained constant, but deteriorated in UC.

Conclusions

This pilot study demonstrated the feasibility and acceptability of a technology-enabled, remotely monitored, home-based CR program. Although clinical effectiveness was demonstrated, several challenges were identified that could influence the adoption of PATHway.

Trial Registration

ClinicalTrials.gov NCT02717806; https://clinicaltrials.gov/ct2/show/NCT02717806

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2017-016781

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring.

A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature.

A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified.

Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device.

An evaluation of an intervention designed to help inactive adults become more active with a peer mentoring component: a protocol for a cluster randomised feasibility trial of the Move for Life programme

Background

There is overwhelming evidence to support the promotion of physical activity in adults in terms of benefits to well-being, physical and mental health. Physical activity guidelines suggest that adults should accumulate at least 150 min of moderate to vigorous physical activity per week. In Ireland, the majority of adults do not achieve these guidelines, with costs to health and economy. ‘Move for Life’ (MFL) employs behavioural change techniques delivered by an instructor and peer mentor, using a train-the-trainer (cascade) model. This study will conduct a feasibility cluster randomised controlled trial of the MFL intervention for modifying physical activity behaviours in inactive adults aged 45 years and older.

Methods

The trial is set in eight Local Sports Partnership (LSP) hubs that have structured physical activity programmes. The hubs are the units of randomisation (clusters), and individuals are the units of analysis (participants). Eligible participants will contact one of the hubs, with each hub running four physical activity programmes. Each programme requires between 12–15 inactive adults, resulting in 48–60 participants per hub. Allowing for 20% dropout rate, an additional 96 people will be recruited giving a maximum sample of 576. The hub will be randomised: true control, usual programme or MFL intervention. The true control group will be given information about physical activity but will not be included in a programme for the duration of the trial; the intervention will involve the instructor training one (or more) of the participants to be a peer mentor using an educational toolkit; and usual care groups will have physical activity classes delivered as normal. Baseline data will collect physical activity measures and follow-up measurements will be obtained at 3 and 6 months. All participants will be asked to wear a device for measuring activity on the thigh (activPAL) for 7 days before commencing the programme and at 3 and 6 months. The primary objective of the study is to investigate if it is feasible to deliver the intervention and collect data on moderate to vigorous physical activity (MVPA) on all participants, thereby providing valuable information to guide sample size calculation for a future, more definitive trial.

Trial registration number

ISRCTN11235176

Electronic supplementary material

The online version of this article (10.1186/s40814-019-0473-y) contains supplementary material, which is available to authorized users.

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature

Background

Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.

Objective

Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.

Methods

We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).

Results

Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.

Conclusions

This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.

The development and codesign of the PATHway intervention: a theory-driven eHealth platform for the self-management of cardiovascular disease

Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.

Interventions to promote patient utilisation of cardiac rehabilitation

BACKGROUND

International clinical practice guidelines routinely recommend that cardiac patients participate in rehabilitation programmes for comprehensive secondary prevention. However, data show that only a small proportion of these patients utilise rehabilitation.

OBJECTIVES

First, to assess interventions provided to increase patient enrolment in, adherence to, and completion of cardiac rehabilitation. Second, to assess intervention costs and associated harms, as well as interventions intended to promote equitable CR utilisation in vulnerable patient subpopulations.

SEARCH METHODS

Review authors performed a search on 10 July 2018, to identify studies published since publication of the previous systematic review. We searched the Cochrane Central Register of Controlled Trials (CENTRAL); the National Health Service (NHS) Centre for Reviews and Dissemination (CRD) databases (Health Technology Assessment (HTA) and Database of Abstracts of Reviews of Effects (DARE)), in the Cochrane Library (Wiley); MEDLINE (Ovid); Embase (Elsevier); the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCOhost); and Conference Proceedings Citation Index - Science (CPCI-S) on Web of Science (Clarivate Analytics). We checked the reference lists of relevant systematic reviews for additional studies and also searched two clinical trial registers. We applied no language restrictions.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) in adults with myocardial infarction, with angina, undergoing coronary artery bypass graft surgery or percutaneous coronary intervention, or with heart failure who were eligible for cardiac rehabilitation. Interventions had to aim to increase utilisation of comprehensive phase II cardiac rehabilitation. We included only studies that measured one or more of our primary outcomes. Secondary outcomes were harms and costs, and we focused on equity.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the titles and abstracts of all identified references for eligibility, and we obtained full papers of potentially relevant trials. Two review authors independently considered these trials for inclusion, assessed included studies for risk of bias, and extracted trial data independently. We resolved disagreements through consultation with a third review author. We performed random-effects meta-regression for each outcome and explored prespecified study characteristics.

MAIN RESULTS

Overall, we included 26 studies with 5299 participants (29 comparisons). Participants were primarily male (64.2%). Ten (38.5%) studies included patients with heart failure. We assessed most studies as having low or unclear risk of bias. Sixteen studies (3164 participants) reported interventions to improve enrolment in cardiac rehabilitation, 11 studies (2319 participants) reported interventions to improve adherence to cardiac rehabilitation, and seven studies (1567 participants) reported interventions to increase programme completion. Researchers tested a variety of interventions to increase utilisation of cardiac rehabilitation. In many studies, this consisted of contacts made by a healthcare provider during or shortly after an acute care hospitalisation.Low-quality evidence shows an effect of interventions on increasing programme enrolment (19 comparisons; risk ratio (RR) 1.27, 95% confidence interval (CI) 1.13 to 1.42). Meta-regression revealed that the intervention deliverer (nurse or allied healthcare provider; P = 0.02) and the delivery format (face-to-face; P = 0.01) were influential in increasing enrolment. Low-quality evidence shows interventions to increase adherence were effective (nine comparisons; standardised mean difference (SMD) 0.38, 95% CI 0.20 to 0.55), particularly when they were delivered remotely, such as in home-based programs (SMD 0.56, 95% CI 0.37 to 0.76). Moderate-quality evidence shows interventions to increase programme completion were also effective (eight comparisons; RR 1.13, 95% CI 1.02 to 1.25), but those applied in multi-centre studies were less effective than those given in single-centre studies, leading to questions regarding generalisability. A moderate level of statistical heterogeneity across intervention studies reflects heterogeneity in intervention approaches. There was no evidence of small-study bias for enrolment (insufficient studies to test for this in the other outcomes).With regard to secondary outcomes, no studies reported on harms associated with the interventions. Only two studies reported costs. In terms of equity, trialists tested interventions designed to improve utilisation among women and older patients. Evidence is insufficient for quantitative assessment of whether women-tailored programmes were associated with increased utilisation, and studies that assess motivating women are needed. For older participants, again while quantitative assessment could not be undertaken, peer navigation may improve enrolment.

AUTHORS' CONCLUSIONS

Interventions may increase cardiac rehabilitation enrolment, adherence and completion; however the quality of evidence was low to moderate due to heterogeneity of the interventions used, among other factors. Effects on enrolment were larger in studies targeting healthcare providers, training nurses, or allied healthcare providers to intervene face-to-face; effects on adherence were larger in studies that tested remote interventions. More research is needed, particularly to discover the best ways to increase programme completion.

User Models for Personalized Physical Activity Interventions: Scoping Review

Background

Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.

Objective

This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.

Methods

A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.

Results

The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.

Conclusions

This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.

Computerized decision support for beneficial home-based exercise rehabilitation in patients with cardiovascular disease

BACKGROUND

Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes.

OBJECTIVES

We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs.

METHODS

The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems.

RESULTS

The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance.

CONCLUSIONS

Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.

Cardiac rehabilitation and physical activity: systematic review and meta-analysis

Objective

To undertake a systematic review and meta-analysis to assess the impact of cardiac rehabilitation (CR) on physical activity (PA) levels of patients with heart disease and the methodological quality of these studies.

Methods

Databases (MEDLINE, EMBASE, CENTRAL, CINAHL, PsychINFO and SportDiscus) were searched without language restriction from inception to January 2017 for randomised controlled trials (RCTs) comparing CR to usual care control in adults with heart failure (HF) or coronary heart disease (CHD) and measuring PA subjectively or objectively. The direction of PA difference between CR and control was summarised using vote counting (ie, counting the positive, negative and non-significant results) and meta-analysis.

Results

Forty RCTs, (6480 patients: 5825 CHD, 655 HF) were included with 26% (38/145) PA results showing a statistically significant improvement in PA levels with CR compared with control. This pattern of results appeared consistent regardless of type of CR intervention (comprehensive vs exercise-only) or PA measurement (objective vs subjective). Meta-analysis showed PA increases in the metrics of steps/day (1423, 95% CI 757.07 to 2089.43, p<0.0001) and proportion of patients categorised as physically active (relative risk 1.55, 95% CI 1.19 to 2.02, p=0.001). The included trials were at high risk of bias, and the quality of the PA assessment and reporting was relatively poor.

Conclusion

Overall, there is moderate evidence of an increase in PA with CR participation compared with control. High-quality trials are required, with robust PA measurement and data analysis methods, to assess if CR definitely leads to important improvements in PA.