This paper is in the following e-collection/theme issue:

Digital Health Reviews (1285) Web-based and Mobile Health Interventions (3296) Innovations and Technology for Physical Activity Education (741)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46439, first published .
Benefits and Harms of Digital Health Interventions Promoting Physical Activity in People With Chronic Conditions: Systematic Review and Meta-Analysis

Benefits and Harms of Digital Health Interventions Promoting Physical Activity in People With Chronic Conditions: Systematic Review and Meta-Analysis

Benefits and Harms of Digital Health Interventions Promoting Physical Activity in People With Chronic Conditions: Systematic Review and Meta-Analysis

Authors of this article:

Graziella Zangger1, 2 Author Orcid Image ;   Alessio Bricca1, 2 Author Orcid Image ;   Behnam Liaghat2, 3 Author Orcid Image ;   Carsten B Juhl2, 4 Author Orcid Image ;   Sofie Rath Mortensen1, 5 Author Orcid Image ;   Rune Martens Andersen1, 6 Author Orcid Image ;   Camma Damsted1, 2 Author Orcid Image ;   Trine Grønbek Hamborg1 Author Orcid Image ;   Mathias Ried-Larsen5, 7 Author Orcid Image ;   Lars Hermann Tang1, 6 Author Orcid Image ;   Lau Caspar Thygesen8 Author Orcid Image ;   Søren T Skou1, 2 Author Orcid Image
Article Authors Cited by (14) Tweetations (133) Metrics

    Review

    1The Research Unit PROgrez, Department of Physiotherapy and Occupational Therapy, Næstved-Slagelse-Ringsted Hospital, Slagelse, Denmark

    2Research Unit for Musculoskeletal Function and Physiotherapy, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

    3Centre for Evidence-Based Orthopedics (CEBO), Department of Orthopaedic Surgery, Zealand University Hospital, Køge, Denmark

    4Department of Physiotherapy and Occupational Therapy, University Hospital of Copenhagen, Herlev and Gentofte, Denmark

    5Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

    6Department of Regional Health Research, University of Southern Denmark, Odense, Denmark

    7Centre for Physical Activity Research, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark

    8National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark

    Corresponding Author:

    Graziella Zangger, MHSc

    The Research Unit PROgrez

    Department of Physiotherapy and Occupational Therapy

    Næstved-Slagelse-Ringsted Hospital

    Fælledvej 2C, 1 sal

    Slagelse, 4200

    Denmark

    Phone: 45 26142377

    Email: gzangger@health.sdu.dk


    Background: Digital health interventions for managing chronic conditions have great potential. However, the benefits and harms are still unclear.

    Objective: This systematic review and meta-analysis aimed to investigate the benefits and harms of digital health interventions in promoting physical activity in people with chronic conditions.

    Methods: We searched the MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to October 2022. Eligible randomized controlled trials were included if they used a digital component in physical activity promotion in adults with ≥1 of the following conditions: depression or anxiety, ischemic heart disease or heart failure, chronic obstructive pulmonary disease, knee or hip osteoarthritis, hypertension, or type 2 diabetes. The primary outcomes were objectively measured physical activity and physical function (eg, walk or step tests). We used a random effects model (restricted maximum likelihood) for meta-analyses and meta-regression analyses to assess the impact of study-level covariates. The risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and the certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation.

    Results: Of 14,078 hits, 130 randomized controlled trials were included. Compared with usual care or minimal intervention, digital health interventions increased objectively measured physical activity (end of intervention: standardized mean difference [SMD] 0.29, 95% CI 0.21-0.37; follow-up: SMD 0.17, 95% CI 0.04-0.31) and physical function (end of intervention: SMD 0.36, 95% CI 0.12-0.59; follow-up: SMD 0.29, 95% CI 0.01-0.57). The secondary outcomes also favored the digital health interventions for subjectively measured physical activity and physical function, depression, anxiety, and health-related quality of life at the end of the intervention but only subjectively measured physical activity at follow-up. The risk of nonserious adverse events, but not serious adverse events, was higher in the digital health interventions at the end of the intervention, but no difference was seen at follow-up.

    Conclusions: Digital health interventions improved physical activity and physical function across various chronic conditions. Effects on depression, anxiety, and health-related quality of life were only observed at the end of the intervention. The risk of nonserious adverse events is present during the intervention, which should be addressed. Future studies should focus on better reporting, comparing the effects of different digital health solutions, and investigating how intervention effects are sustained beyond the end of the intervention.

    Trial Registration: PROSPERO CRD42020189028; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=189028

    J Med Internet Res 2023;25:e46439

    doi:10.2196/46439

    Keywords

    digital health; eHealth; mobile health; mHealth; wearables; physical activity; physical function; chronic conditions; randomized controlled trials; systematic review; meta-analysis 


    Background

    Chronic conditions are the most significant contributors to the global burden of disease, which is continuously increasing [1]. Living with ≥1 chronic conditions significantly impacts a person’s life, family, and society [1]. Physical inactivity is a major risk factor for at least 35 chronic conditions, which has become a global health concern [2]. Physical inactivity is expensive, constituting up to 4.6% of a country’s national health care expenditure [3]. In contrast, being physically active can benefit physical and psychosocial health [4]. In fact, following a structured exercise program can reduce the symptoms of chronic conditions with no additional risk of serious adverse events [5]. However, a high proportion of people living with chronic conditions do not meet the recommended levels of physical activity [6,7], possibly because of various reasons such as lack of access; travel distance; time inconvenience; cost; or internal factors such as pain, lack of motivation, or reluctance to engage in group activities [8].

    Regarding physical activity interventions, it is essential to understand and adapt care according to the individual’s needs, preferences, and abilities [9]. One way to deliver and make physical activity available, individually or as an add-on, is through digital health interventions [10]. Digital health involves multiple solutions, including telephone calls, SMS text messages, mobile apps, websites, videoconferencing, and wearables (eg, activity trackers). The potential advantages of digital health solutions are convenience in terms of reduced distance, time, and cost, thereby limiting some of the more traditional barriers to physical encounters [10]. Digital health solutions enable personalized and person-centered tailored health interventions. Such personalized care empowers people to manage their health, increases health care efficiency, and reduces health care costs [11]. Consequently, the focus on digital health interventions has steadily grown over the last 2 decades [12], accelerating further in response to the COVID-19 pandemic [13]. This has led to digital health interventions being initiated without fully knowing their effect, potentially introducing people to ineffective interventions [14].

    Several systematic reviews have investigated the effect of digital health interventions on health outcomes in people with individual chronic conditions (eg, chronic obstructive pulmonary disease [COPD], heart disease, and diabetes) [15-26]. Some reviews have focused on physical activity [19-21,23,25,27], but these included few studies, as they focused on a single condition, solution (eg, only telephone calls or apps), or outcome measure (eg, pain). This challenges the assessment of the benefits and harms of digital health intervention. Furthermore, the proportion of people with >1 chronic condition is increasing. More than half of the people with chronic conditions have at least 2 conditions [28]; therefore, to increase the power of the results and better resemble real-life clinical practice, including studies on several chronic conditions is relevant. Therefore, a more comprehensive approach is needed to provide clinicians and decision makers with the best current evidence.

    Objectives

    With this systematic review and meta-analysis, we investigated the benefits and harms of digital health interventions promoting physical activity among people with ≥1 chronic conditions on objectively and subjectively measured physical activity and physical function, depression, anxiety, health-related quality of life (HRQOL), and nonserious and serious adverse events.


    Overview

    The study was preregistered at PROSPERO (CRD42020189028) and the study protocol (including amendments) is available in Multimedia Appendix 1 [4,26,29-44]. We followed the Cochrane Handbook for Systematic Reviews [45] and reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [46]. The PRISMA checklist is available in Multimedia Appendix 2 [46].

    Ethical Considerations

    As this research design only investigates aggregated data from published randomized controlled trials (RCTs) with no individual data, no ethics approval was required.

    Eligibility Criteria

    We included all peer-reviewed RCTs that compared an intervention with a digital health component in promoting physical activity to usual care or minimal intervention (eg, education or blinded wearables). Digital health interventions were defined as interventions in which any solution or technology (eg, website, app, and wearable) was used to deliver health information between health care providers and participants over a distance [47]. Studies were included if (1) physical activity (any activity that was up to the participants to choose), exercise (any prescribed activity), or exercise therapy (any planned and structured prescribed activity) was used or promoted; (2) the primary or secondary aim was physical activity promotion; and (3) digital health intervention was delivered with or without additional pharmacotherapy or other nondigital adjuvant interventions (eg, rehabilitation, education, or counseling).

    Moreover, studies had to include adults (aged ≥18 years) with ≥1 of the following chronic conditions: depression or anxiety (mental health condition), ischemic heart disease or heart failure (heart disease), COPD, osteoarthritis of the hip or knee, hypertension, or type 2 diabetes. The inclusion criteria focused on these specific conditions, as they are among the most common worldwide according to the global burden of disease [1]. Furthermore, they share the risk factors of physical inactivity and systemic low-grade inflammation, which can cause a chain of events that can lead to worse outcomes and other chronic conditions [4,48].

    Studies were excluded if they included angina, valvular heart disease, congenital heart disease, cardiomyopathy, hypertension if systolic blood pressure ≤139 mm HG or diastolic blood pressure ≤89 mm Hg, mixed populations of type 1 and 2 diabetes, tested the same digital health intervention but in different settings, or used the digital health solution for monitoring without any interaction with or feedback to the participants (ie, electronic health records or passive use of telemonitoring or a blinded actigraphy device).

    Information Sources and Search Strategy

    The MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials databases were searched via Ovid from inception to October 28, 2022, with no language restrictions. The search string was developed based on previous searches used in systematic reviews for people with chronic conditions [49,50], and validated RCT search filters were used if available and applicable in each database [51]. All search strings are presented in Multimedia Appendix 3 [52-191]. In addition, studies found during full-text screens were manually added.

    Selection and Data Collection Process

    Initially, search duplicates were removed using the reference manager tool EndNote (version 20; Clarivate) [192] and the management tool Covidence (Veritas Health Innovation;) [193]. In total, 5 authors (AB, NN, FD, AL, and GZ) independently screened titles and abstracts, and 2 authors (AB and GZ) independently screened the full texts. Discrepancies were solved during regular meetings and resolved by discussing with a third study team member (CBJ). Although no restrictions were applied to the search, we only included studies if available in 1 of the following languages: English, Danish, Norwegian, Swedish, or Italian. Covidence was used throughout the selection process.

    A standardized data extraction form (piloted in 5 studies) was used. A total of 4 authors (AB, BL, NN, and GZ; in pairs) independently extracted data at two time points: (1) immediately after the end of the intervention and (2) at follow-up, the time point closest to 12-month follow-up. Discrepancies were resolved by consensus or by referring to a third author (CBJ). WebPlotDigitizer (version 4.5; Ankit Rohatgi) was used to extract data from the figures [194]. The authors were contacted if the studies had missing or unclear information or data [195]. If the authors did not respond, the study eligibility was reassessed and consequently excluded.

    Outcomes

    Outcomes were hierarchically ordered (Table 1), with objectively measured outcomes prioritized over subjective and generic tests over disease-specific tests. The primary outcomes were objectively measured physical activity and physical function. The secondary outcomes were subjectively measured physical activity and physical function, depression, anxiety, HRQOL, and adverse events (nonserious and serious).

    Table 1. Outcome hierarchy.
    OutcomeHierarchy
    Primary outcomes

    		Objectively measured physical activity
    1. Accelerometer measures (eg, daily time spent in moderate to vigorous physical activity)
    2. Pedometer (eg, outcomes such as step counts)
    3. Any other outcome measure related to objectively measured physical activity

    		Objectively measured physical function
    1. The 6-minute walk test
    2. Incremental shuttle walk test
    3. Any other outcome measure related to daily function (eg, Chair stand test)
    Secondary outcomes

    		Subjectively measured physical activity
    1. The Global Physical Activity Questionnaire
    2. The Physical Activity Scale for the Elderly questionnaire
    3. The International Physical Activity Questionnaires, long form or short form
    4. Any other outcome measure related to subjectively measured physical activity

    		Subjectively measured physical function
    1. The 36-item Short-Form Health Survey, as the Physical Function subscale or the Role Function subscale
    2. Any other self-reported measure of physical function

    		Depression
    1. The Beck Depression Inventory
    2. Any other depression questionnaire (eg, HADSa depression subscale)
    3. Any other assessment of depression (eg, clinical assessment)

    		Anxiety
    1. State-Trait Anxiety Inventory questionnaire
    2. Any other anxiety questionnaire (eg, HADS anxiety)
    3. Any other assessment of anxiety (eg, clinical assessment)

    		Health-related quality of life
    1. The EQ-5D questionnaire
    2. Any other generic health-related quality-of-life questionnaires
    3. Disease-specific health-related quality-of-life questionnaires (eg, the Minnesota Living with Heart Failure questionnaire)

    		Adverse events (nonserious and serious)
    1. Extracted if reported in the included trials accordingly to the FDAb recommendations of nonserious and serious adverse events [29]. Adverse events were defined accordingly to FDA as “any unfavorable and unintended sign, symptom or disease temporally associated with the use of a medicinal [investigational] product whether or not considered related to the medicinal [investigational] product and grouped in serious adverse events such as death, hospitalization, disability or permanent damage, and nonserious adverse events such as pain, falls and fatigue” [29].

    aHADS: Hospital Anxiety and Depression Scale.

    bFDA: Food and Drug Administration.

    Data Extraction

    Extracted data included characteristics of the (1) trial—study design, study authors, publication year, country, and setting; (2) participants—number randomized, age, sex, BMI, socioeconomic status, and index conditions (if a study included participants based on them having ≥2 chronic conditions, the study was categorized as multimorbidity); and (3) intervention—type, frequency, duration, delivery method (categorized as mobile health [mHealth; ie, telephone calls, SMS text messages, and apps], eHealth [eg, internet based, website, and emails], or digital device [eg, wearables and Bluetooth-connected devices]), type of comparator, physical activity (categorized as physical activity [any activity that was up to the participants to choose] or exercise therapy [any planned and structured prescribed activity]), phase (categorized as without, with, or after any other rehabilitation), use of theory or framework, intervention components (categorized as digital only or combined in-person and digital [ie, other in-person adjuvant intervention, as rehabilitation, education, or counseling]), and adherence.

    Risk of Bias Assessment and Certainty Assessment

    The risk of bias was assessed for each outcome in pairs by 7 authors (CD, SRM, TGH, RMA, LHT, GZ, AB) independently using the Cochrane Risk of Bias 2 tool [30]. Disagreements were resolved by reaching a consensus or by including a third author (GZ or AB). The quality of evidence of each outcome was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach by 2 authors (GZ and AB) [31]. An agreement was reached through consensus.

    Synthesis of Results

    Meta-analysis was conducted using a random effects model, adjusted to Hedges g and using a restricted maximum likelihood method, as heterogeneity was expected. The effect of the intervention on continuous outcomes was expressed as standardized mean difference (SMD) for outcomes reported with different measures and with 95% CIs. The mean differences with 95% CIs were calculated for data on the same metric scale. Risk ratios were calculated with 95% CIs for dichotomous outcomes (ie, adverse events). Meta-analyses were visually presented as forest plots and were performed using the meta command in Stata (version 17.0; StataCorp LLC) [196]. The pooled SMD effect sizes were interpreted as 0.20, 0.50, and 0.80, representing small, moderate, and large effects, respectively [32]. The inconsistency of the results was evaluated using the I2 and adjusted R2 statistics and by visual inspection of the bubble plots and meta-regressions [52].

    Meta-Regression and Subgroup Analyses

    Univariable and multivariable meta-regression analyses were performed if ≥10 studies were included in the analyses to identify factors that could influence the effect estimates. For each outcome at the end of the intervention, the impact of (1) participant characteristics—age, sex, BMI, socioeconomic status, depression, and anxiety levels—and (2) intervention characteristics—number of digital and in-person sessions, frequency, duration, type of physical activity, phase, use of theory or framework, adherence, digital only, other in-person adjuvant intervention, and financial incentive—were assessed. Meta-regression analyses were performed using the meta regress command in Stata version 17.0 [196].

    Subgroup analyses investigated whether the effects of digital health interventions varied among the included index conditions on all outcomes. Moreover, the delivery method and methodological quality according to the Cochrane Risk of Bias 2 tool (high, some concern, or low risk of bias) were assessed by subgroups for the primary outcomes.

    Reporting Bias Assessment

    Small study bias was assessed using funnel plots and Egger test (or Harbord test for risk ratios) by 2 authors (GZ and AB), and agreement was reached by consensus.


    Study Selection and Characteristics

    The search retrieved 14,078 hits; 427 papers were full-text screened, and 140 papers were included, representing 130 RCTs (see Figure 1 for flowchart and refer to Multimedia Appendix 3 for the list of excluded studies). The included RCTs allowed for 136 comparisons, representing 20,094 participants (mean age 60.68, SD 7.17 years; 8440/20,094, 41.63% female). The interventions were conducted in 31 countries, mainly in the United States (24 RCTs) [53-75] and Australia (21 RCTs) [76-96]. Over half (91/140, 65%) of the RCTs were published between 2018 and 2022; however, the publication years ranged from 2003 to 2022. For study characteristics, refer to Multimedia Appendix 3.

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart of the study selection process.

    Intervention Components

    In total, 80 RCTs used a digital-only approach [53,54,56,59-62,65,66,68-74,76,79,80,83,85,86,88-93,95-144], whereas 56 RCTs combined digital solutions with nondigital interventions (eg, counseling or in-person guided exercise) [55,57,58,63,64,66,67,75,77,78,81,82,84,87,94,142,145-188]. Of the included studies, 7 offered a preintervention period as run-in exercise (a period before randomization and intervention start where all participants performed exercise) or rehabilitation [63,98,108,111,120,160,183,189]. Furthermore, 13 RCTs offered rehabilitation as part of the intervention [64,75,80,82,147,149,151,152,157,160,163,169,178].

    mHealth solutions (eg, telephone calls, text messages, and apps) were primarily used in 56 RCTs [53,56,57,59,63,67,69,78-80,​83-86,90,92,93,96,99,101,102,105,108,110,112-115,119,​120,124,128-130,134,136,140-142,146-148,150,155,157,161,163,​164,167,168,172,176,177,179,181,185], whereas 22 RCTs [58,60,61,65,70,81,87,111,116,117,126,133,134,145,149,151,154,156,​165,171,173,174] used mHealth combined with a device (eg, wearable, pedometer, activity tracker, or Bluetooth-connected pulse oximeter or glycosometer). Furthermore, 6 RCTs [75,89,118,158,169,183] combined mHealth and eHealth (eg, websites, web apps, and emails) technology. eHealth solutions were used by 29 RCTs [54,55,66,72-74,76,77,88,91,95,​97,100,103,104,106,122,125,127,131,132,138,143,144,152,159,175,184,190], whereas 6 RCTs [64,71,82,98,137,162,189] combined eHealth and a device. Digital devices alone were used in 14 RCTs [62,68,94,107,109,121,123,130,139,166,170,178,182,186,188,191]. Finally, 2 studies combined mHealth, eHealth, and a digital device [132,160].

    The RCTs had a mean intervention duration of 22.94 (SD 18.72; range 4-104) weeks, and 60.9% (81/136) RCTs were placed in the physical activity category. A theory or framework most often (71/136, 52.6%) guided the interventions. The mean number of digital sessions was 97.98 (SD 109.29; range 2-377), whereas in-person sessions had a mean of 3.26 (SD 2.93; range 0-18). Of the included RCTs, 31 [54,58,62,65,70,76,​77,82,84,86,88,92,94,95,100,101,112,121,132,135,143,​144,146,150,151,155,163,164,174,182,186,189-191,197] had follow-up data, and the mean follow-up time was 45.38 (SD 21.74; range 12-104) weeks.

    Chronic Conditions

    Type 2 diabetes [53,56,58,59,66,67,81,84,88,92,93,97,99,104,​107,114-116,119,122,123,126-128,130,131,140,145,146,155,157,159,165,168,​172,182,184] and heart disease [62,64,65,70,72,73,75,80,85,87,​96,98,102,106,109,111-113,121,132-135,139,142,147,154,160,161,169,171,​173-181,185,189] were the most frequent index conditions (41 RCTs each), followed by COPD (26 RCTs) [60,61,63,68,71,74,79,82,91,94,105,108,117,120,124,​136,141,149,151-153,156,163,164,167,170,183,186,190,191], osteoarthritis (18 RCTs) [54,55,57,76-78,86,89,90,100,110,​118,129,138,148,158,166,188], mental health (5 RCTs) [95,125,137,143,144,162,198], hypertension (3 RCTs) [83,101,103,197], and multimorbidity (2 RCTs) [69,150]. For the index conditions and comorbidities among the participants, refer to Multimedia Appendix 3.

    Effect on Primary Outcomes at the End of the Intervention

    At the end of the intervention, the digital health interventions, compared with usual care or minimal intervention, showed a small improvement in objectively measured physical activity SMD 0.29 (95% CI 0.21-0.37), corresponding to an average increase of 970.79 daily steps (95% CI 657.11-1284.47; I2=54.04%). Objectively measured physical function also showed a small improvement in favor of the digital health interventions (SMD 0.36, 95% CI 0.13-0.59) consistent with an improvement of 19.82 m (95% CI 9.45-30.02; I2=75.94%) on the 6-minute walk test (6MWT). There was no between-group difference in objectively measured moderate to vigorous physical activity (MVPA; SMD 0.03, 95% CI −0.31 to 0.37). However, objectively measured physical function and MVPA showed substantial heterogeneity. See Figure 2 for the overall forest plot and refer to Multimedia Appendix 4 [52-191] for a detailed forest plot of each outcome, including daily steps and 6MWT.

    ‎
    Figure 2. Overall forest plot of objectively measured physical activity, objectively measured physical function, and moderate to vigorous physical activity (MVPA) at the end of the intervention. Obj PA: objectively measured physical activity; Obj PF: objectively measured physical function.

    Effect on Secondary Outcomes at the End of the Intervention

    End-of-intervention effects were found in favor of the digital health interventions for subjectively measured physical activity and physical function, depression, anxiety, and HRQOL. Subjectively measured physical activity and HRQOL showed substantial heterogeneity (Figures 3 and 4; Multimedia Appendix 4).

    ‎
    Figure 3. Overall forest plot of subjectively measured physical activity, physical function, and health-related quality of life (HRQOL) at the end of the intervention. Subj PA: subjectively measured physical activity; Subj PF: subjectively measured physical function.
    ‎
    Figure 4. Overall forest plot of depression and anxiety at the end of the intervention. ANX: anxiety; DEP: depression.

    There was an increased risk of nonserious adverse events (eg, musculoskeletal problems, soreness, or pain) in the intervention groups (risk ratio 1.31, 95% CI 1.11-1.55), whereas the risk of serious adverse events did not differ (risk ratio 0.89, 95% CI 0.76 to 1.04; Multimedia Appendix 4).

    Effect on Primary Outcomes at Follow-Up

    At follow-up, objectively measured physical activity showed a small improvement (SMD 0.17, 95% CI 0.04-0.31; I2=50.85%) in favor of the digital health intervention groups but with no between-group difference in daily steps (mean daily steps 498.40, 95% CI −44.99 to 1041.80; I2=77.94%). For objectively measured physical function, a small improvement was found in favor of digital health interventions (SMD 0.29, 95% CI 0.01-0.57; I2=76.17%). The improvement in 6MWT was sustained (50.06 m, 95% CI 15.1-85.01; I2=71.28%). However, a small effect on objectively measured MVPA in favor of the intervention group was found at follow-up (SMD 0.23, 95% CI 0.06-.40, I2=29.45%). See Figure 5 for the overall forest plot and refer to Multimedia Appendix 4 for a detailed forest plot of each outcome, including daily steps and 6MWT.

    ‎
    Figure 5. Overall forest plot of objectively measured physical activity, physical function, and moderate to vigorous physical activity (MVPA) at follow-up. Obj PA: objectively measured physical activity; Obj PF: objectively measured physical function.

    Effect on Secondary Outcomes at Follow-Up

    Only the effect on subjectively measured physical activity was sustained at the follow-up of the secondary outcomes (SMD 0.36, 95% CI 0.05-0.66; Figures 6 and 7; Multimedia Appendix 4). However, in the follow-up period, neither nonserious (risk ratio 1.35, 95% CI 0.71-2.54) nor serious adverse events (risk ratio 0.76, 95% CI 0.56-1.03) showed any between-group differences (Multimedia Appendix 4).

    ‎
    Figure 6. Overall forest plot of subjectively measured physical activity, physical function, and health-related quality of life (HRQOL) at follow-up. Subj PA: subjectively measured physical activity; Subj PF: subjectively measured physical function.
    ‎
    Figure 7. Overall forest plot of depression and anxiety at follow-up. ANX: anxiety; DEP: depression.

    Risk of Bias

    For objectively measured physical activity, 6% (3/51) of the RCTs were classified as low risk of bias [58,84,160], 55% (28/51) of the RCTs had some concerns [53,61,​62,68,70,71,74,81,82,95,97,98,100,102,107,111,115,​117,136,143,144,147,148,155,156,166,180,182,183,188-191], and 39% (20/51) of the RCTs were classified as high risk of bias [60,65,69,81,91,94,99,104,125,130-132,145,146,149,151,​152,163,164,186] (Figure 8). For objectively measured physical function, 4% (2/47) of the RCTs were classified as low risk of bias [110,158], 70% (33/47) of the RCTs had some concerns [54,55,64,71,74,79,82,87,94,96,97,102,105,108,117,​118,120,121,124,133,136,147,156,159,167,171,175,178,183,​185,186,190], and 26% (12/47) of the RCTs were classified as high risk of bias [57,63,91,112,126,149,151-154,163,164,​170,176] (Figure 9). The risk of bias profiles were similar for the secondary outcomes (Multimedia Appendix 5 [52-191]).

    ‎
    Figure 8. Risk of bias for objectively measured physical activity given as percentages.
    ‎
    Figure 9. Risk of bias with objectively measured physical function given as percentages.

    Small study bias was judged as present at the end of the intervention for objectively measured physical function, subjectively measured physical activity, and HRQOL and at follow-up for subjectively measured physical activity (Multimedia Appendix 6 [52-191]).

    Certainty of Evidence

    The Grading of Recommendations Assessment, Development, and Evaluation assessment scores ranged from moderate to very low (Table 2). Downgrading was done owing to study limitations, imprecision of the findings (some conditions had a limited number of RCTs), and inconsistency (high statistical heterogeneity only partially explained by the meta-regression analyses).

    Table 2. Grading of Recommendations Assessment, Development, and Evaluation summary of findings.
    Outcome; time pointNumber of participants (studies)Relative effect (95% CI)Anticipated absolute effectsCertaintyWhat happens



    		Without digital health interventions, %With digital health interventions (95% CI)Difference

    Objectively measured physical activity; end of intervention: mean 20.7 (SD 16.6) weeks6207 (51 RCTsa)N/AbN/AN/ASMDc 0.29 SD higher (0.21 higher to 0.37 higher)⊕⊕⊕○
    Moderated,e    		Digital health interventions that promote physical activity likely slightly increase objectively measured physical activity. This may correspond to an average increase of 970.8 daily steps (95% CI 657.1 1284.5 steps; I2=54.0%; 30 RCTs; 3904 participants). However, there was no effect on objectively measured moderate to vigorous physical activity (SMD 0.03, 95% CI −0.31 to 0.37; I2=89.8%; 18 RCTs; 1921 participants).
    Objectively measured physical function; end of intervention: mean 17.5 (SD 18.0) weeks6056 (47 RCTs)N/AN/AN/ASMD 0.36 SD higher (0.12 higher to 0.59 higher)⊕○○○
    Very lowf,g,h,i    		Digital health interventions promoting physical activity may slightly increase objectively measured physical function, but the evidence is very uncertain. However, at the end of the intervention (mean 16.8 weeks), the effect may correspond to an average increase of 19.8 m (95% CI 9.5-30.0 m; I2=75.9%; 34 RCTs; 4725 participants) on the 6-minute walk test.
    Subjectively measured physical activity; end of intervention: mean 27.3 (SD 21.4)10,906 (58 RCTs)N/AN/AN/ASMD 0.29 SD higher (0.19 higher to 0.39 higher)⊕○○○
    Very lowd,f,g,i,j    		Digital health interventions promoting physical activity may slightly increase subjectively measured physical activity, but the evidence is very uncertain.
    Subjectively measured physical function; end of intervention: mean 18.5 (SD 10.5) weeks4065 (26 RCTs)N/AN/AN/ASMD 0.21 SD higher (0.13 higher to 0.29 higher)⊕⊕⊕○
    Moderated,e,g    		Digital health interventions that promote physical activity likely slightly increase subjectively measured physical function.
    Depression assessed with subjective measures; end of intervention: mean 20 (SD 15.4) weeks4604 (40 RCTs)N/AN/AN/ASMD 0.25 SD lower (0.35 lower to 0.16 lower)⊕⊕⊕○
    Moderated,e,g    		Digital health interventions that promote physical activity likely slightly reduce depression.
    Anxiety assessed with subjective measures; end of intervention: mean 16 (SD 11.1) weeks2934 (25 RCTs)N/AN/AN/ASMD 0.17 SD lower (0.26 lower to 0.09 lower)⊕⊕○○
    Lowd,g,i,j,k    		Digital health interventions that promote physical activity may slightly reduce anxiety.
    Health-related quality of life assessed with subjective measures; end of intervention: mean 24.3 (SD 24.3) weeks10,645 (74 RCTs)N/AN/AN/ASMD 0.18 SD higher (0.07 higher to 0.29 higher)⊕⊕○○
    Lowi,j,k    		Digital health interventions that promote physical activity may slightly increase health-related quality of life.
    Nonserious adverse events; end of intervention: mean 21.5 weeks6813 (45 RCTs)1.31 (1.11-1.55)l8.5%11.1% (9.4%-13.2%)2.6% more (0.9 more to 4.7 more)⊕⊕⊕○
    Moderated,e    		Digital health interventions that promote physical activity likely slightly increase nonserious adverse events.
    Serious adverse events; end of intervention: mean 26.2 weeks10,508 (61 RCTs)0.89 (0.76-1.04)l8.3%7.4% (6.3%-8.7%)0.9% fewer (2 fewer to 0.3 more)⊕⊕⊕○
    Moderated,e    		Digital health interventions that promote physical activity likely result in little to no difference in serious adverse events.

    aRCT: randomized controlled trial.

    bN/A: not applicable.

    cSMD: standardized mean difference.

    dSome conditions are not represented, or the results only included a few studies within each condition.

    eModerate certainty: we are moderately confident in the effect estimate; the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.

    fVery low certainty: we have very little confidence in the effect estimate; the true effect is likely to be substantially different from the estimate of effect.

    gStudies with low risk of bias reported lower effect on the outcome.

    hThe 95% CI ranges from no effect to high effect.

    iA possible presence of a small study bias from visual inspection of the funnel plot and Egger test.

    jResults are inconsistent and measured using the I2 statistics.

    kLow certainty: our confidence in the effect estimate is limited; the true effect may be substantially different from the estimate of the effect.

    lThe risk in the intervention group (and its 95% CI) was based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).

    Meta-Regression, Subgroup Analyses, and Sensitivity Analysis

    Univariate meta-regression analysis for objectively measured physical activity showed that increasing age was associated with a lower effect size (slope −0.01, 95% CI −0.03 to −0.00), suggesting an effect reduction of 0.01 SDs per increased year. For objectively measured physical function, a higher effect was found for interventions that combined exercise therapy and physical activity (coefficient [β]=1.40, 95% CI 0.42-2.36). A similar effect of age was found for subjectively measured physical activity (slope −0.02, 95% CI −0.03 to −0.00), suggesting an effect reduction of 0.02 SDs per increased year; however, BMI was also associated with a lower effect size (β=.04, 95% CI −0.07 to −0.01) for this outcome. Furthermore, for subjectively measured physical activity, shorter intervention duration showed a lower effect size (β=−.01, 95% CI −0.01 to −0.00), and the use of a theory or framework showed a decreased effect (β=−.31, 95% CI −0.50 to −0.11), which persisted in the multivariate model (adjusted for intervention session, duration, and frequency; β=−.28, 95% CI −0.46 to −0.09). For mental health conditions, the univariate analysis showed that participants with higher depression levels had a greater effect on depression symptoms (slope −0.01, 95% CI −0.02 to −0.00), which persisted in the multivariate model (adjusted for age and sex). There were no differences in the rest of the meta-regression analyses (Multimedia Appendix 7) or in the subgroup analyses stratified by conditions, delivery method, or methodology quality (Multimedia Appendix 8 [52-191]).

    In an unplanned sensitivity analysis of the primary outcomes of RCTs with a digital-only approach, the results at the end of the intervention were in line with the main analyses (Multimedia Appendix 8).


    Principal Findings

    Overall, digital health interventions had a small effect on physical activity and physical function in people with chronic conditions compared with usual care or minimal intervention at the end of the intervention and follow-up. Furthermore, subjectively measured physical function, depression, anxiety, and HRQOL showed a small improvement but only at the end of the intervention. The certainty of the findings was downgraded because of study limitations, imprecision, and inconsistency and was moderate to very low.

    Findings in Comparison With Prior Work

    Overview

    This systematic review was the first to focus primarily on physical activity in several major chronic conditions and across various digital health solutions. A previous systematic review found a large effect of eHealth interventions on physical activity in people with noncommunicable diseases but only included physical activity–integrated and diet-integrated interventions and combined objectively and subjectively physical activity outcomes, thereby limiting clinical interpretation and comparison [81]. Another systematic review focused on multimorbidity and the effect of digital telemedicine on health outcomes but did not report any physical activity outcomes [19]. A third review focused on exercise intervention in people with chronic conditions but solely assessed videoconferencing as the delivery method and found a moderate to small improvement in exercise capacity, which is not directly comparable with physical activity or physical function [25,199]. Finally, a fourth review assessed objectively and subjectively measured physical activity in people with chronic conditions and found a small effect in both outcomes but only significant for the subjective measured outcome [200]. However, this study was limited to the assessment of self-guided digital interventions.

    Primary Outcomes

    To ease the clinical interpretation of objectively measured physical activity, we pooled daily steps data and found an improvement (970.8 steps) comparable with findings in adults using physical activity monitors [201]. Clinically, a 1000-steps increase is considered important and showed a risk reduction in cardiovascular morbidity and all-cause mortality for each additional 1000 daily steps taken, even below the 10,000 daily recommended steps [202]. However, we did not find any improvement in MVPA at the end of the intervention. This intensity level may provide even greater health benefits compared with lower physical activity levels, but importantly, any increase in physical activity is associated with health benefits, regardless of the duration [203].

    Assessing objectively measured physical function by pooling 6MWT data, we found an improvement (19.8 m) comparable with previous systematic reviews [15,16] and within the clinically relevant limits of 14.0-30.5 m that are considered clinically meaningful for people across different chronic conditions [204].

    Secondary Outcomes

    Subjectively measured physical activity and physical function showed almost the same effect as objectively measured outcomes at the end of the interventions, but it is known that people tend to overestimate their physical activity level when using subjective outcome measures [205]. It is problematic that the study participants could not be blinded to the intervention, especially when using subjective outcome measures, which is reflected in the higher risk of bias profile of the studies in the subjectively measured outcomes compared with the objectively measured outcomes.

    The digital health intervention also showed end-of-intervention effects on other essential health parameters such as depression, anxiety, and HRQOL. Depression and anxiety often coexist with other chronic conditions but are often undetected and untreated [206]. However, digital health interventions may offer a dual effect, as we found both an effect on frequent physical conditions and mental health conditions. Furthermore, our meta-regression indicated that there might be a higher effect for people with higher levels of depression. A similar effect was found in a study that investigated in-person physical activity interventions [5].

    HRQOL is an important person-reported outcome measure and is associated with higher rates of hospitalization, morbidity, and mortality [207-209]. Our findings are comparable with those of previous research [210], which also found a small effect on HRQOL in people with physical disabilities (eg, musculoskeletal conditions); however, we have overlapping studies. There has been a focus on measuring HRQOL in health research [208], and HRQOL was by far the most assessed outcome but also showed a high level of heterogeneity, highlighting the differences between trials and within-outcome assessment. This may hide the actual effect because of the differences in the intervention and outcome measures.

    Although exercise therapy has proven safe and beneficial for people with multiple chronic conditions [5], safety is paramount when initiating a digital physical activity intervention. We found an increased risk of nonserious events in the intervention groups, commonly musculoskeletal problems, soreness, or pain, which can be expected after engaging in physical activity, even in face-to-face interventions [211]. The higher risk of nonserious events should also be seen in the light of including participants with COPD and heart failure, who often experience complications [212,213]. No increased risk of serious adverse events aligns with previous reviews of in-person physical activity interventions among people with chronic conditions and multimorbidities [5,211].

    Follow-Up

    Only objectively measured physical activity and physical function, including MVPA, effects were sustained at follow-up. Owing to the few studies assessing follow-up, more research is needed to address the long-term effects [214]. Nevertheless, for both subjectively measured physical activity and HRQOL, our meta-regression showed that a shorter intervention duration showed lower effect sizes for the digital health interventions. Although we prespecified our meta-regression analyses, they were exploratory and should be interpreted cautiously.

    Age and Digital Health

    It has been suggested that age and multimorbidity may affect the use of digital technologies [215]. Meta-regression analysis of objectively and subjectively measured physical activity showed that increasing age was associated with a lower effect size; however, the effect was negligible. Furthermore, the included studies mostly had an upper age limit, which is why the full effect of age is not known.

    Heterogeneity

    We expected heterogeneity in our meta-analyses, as there is a clinical diversity in including the different chronic conditions, but further methodological diversity is also present as outcome measures differed among each outcome. There is no consensus on how to measure physical activity best [216]. None of the meta-regression analyses could explain the substantial heterogeneity between studies.

    Digital Health Interventions

    Digital health solutions encompass many different technologies [217]. However, we did not find any difference in the effect of the interventions based on the delivery method. This could be owing to a wide variation across the included interventions. Although more than half of the interventions primarily involved solutions within 1 digital health category (ie, mHealth), commonly, the included studies incorporated >1 technology (eg, apps, telephone calls, and emails). Assessing the effect of different interventions is quite complex, and we encourage the use of reporting guidelines [218-220]. Furthermore, for our study, the interventions also applied different approaches to physical activity (ie, self-management of the physical intervention or structured exercise therapy), which further challenges cross-comparisons.

    Chronic Conditions

    The included studies were generally limited to 1 index condition or reported comorbidities very poorly, making investigations of the results across conditions challenging. Including only participants with 1 condition confines a silo-based approach. A more real-life approach allows participants with more chronic conditions to participate, reflecting that people often have >1 chronic condition [221]. It should also be emphasized that the World Health Organization’s guidelines on physical activity are not restricted to any single condition but apply to all people independent of health conditions [203].

    Strengths and Limitations

    This study had some limitations. The risk of selection bias within the RCTs, our inclusion criteria, which focused on 8 specific chronic conditions, thereby excluded, for example, cancer, and the fact that only a few RCTs targeted hypertension, depression, anxiety, and multimorbidity limits the generalizability of our results. Furthermore, few RCTs had follow-up data that induced uncertainty regarding whether the effect was maintained beyond the end of the interventions. In addition, the reporting of digital health solutions and the type and dose of physical activity were poor for many RCTs, preventing us from conducting further analyses on the intervention characteristics. Another limitation is the lack of measuring digital health literacy within the included studies, as it is known that low literacy (both health and digital) affects participation and health outcomes and deepens health inequities [222]. Therefore, we lack information on whether the participants in the included studies were the ones with high or low levels of digital health literacy, as we may suspect that in groups with lower literacy, the effect would become even smaller [223].

    The quality of the evidence had to be downgraded to low or very low for objectively measured physical function, subjectively measured physical activity, anxiety, and HRQOL, implying that the actual effect might be different from the estimated effect. In addition, a small study bias cannot be ruled out for some outcomes, although other factors, such as high heterogeneity and low quality of evidence, may contribute to funnel plot asymmetry.

    Nevertheless, the strengths of this systematic review are the meticulous, state-of-the-art design and methodology, following the Cochrane handbook and reporting using the PRISMA guidelines. In addition, we assessed the effects across several chronic conditions, breaking down the more silo-based thinking of chronicity.

    Future Research

    More high-quality research with better reporting, available protocols, blinding of assessors and study participants (when possible), better handling of missing data, and the use of common and clearly described measurement methods are needed for future research. In addition, such research should assess whether higher and more sustainable effects of digital health interventions that promote physical activity can be achieved. It should also focus on whether any digital health solution or features within these are more effective than others and whether a dose-response relationship exists. Studies focusing on the impact of comorbidities or multimorbidities are also warranted. Furthermore, if digital health solutions are to live up to their potential and be part of the solution to the health care shortage, the digital divide must be addressed by assessing if and how the participants’ digital health literacy levels affect digital health interventions.

    Conclusions and Implications

    Engaging people in physical activity is considered a polypill that provides high health gains with low risk and low costs [224]. Digital health solutions as a delivery method can provide effective physical activity interventions across people with chronic conditions and may help make these types of interventions available for more people at a lower cost. Therefore, given the potential health benefits, digital health interventions promoting physical activity in people with ≥1 chronic condition should be considered in clinical practice, especially if the overall aim is to improve physical activity levels. However, educating participants about the potential risk of nonserious adverse events is essential. The findings of this study are also relevant for policy makers because easy and affordable access to high-quality health care services is at the top of the political agenda. However, it should be noted that the effects were small and, for most outcomes, limited to the end of the intervention, with analyses showing substantial heterogeneity between studies and many studies with some concerns for the overall risk of bias.

    Acknowledgments

    The authors would like to extend their gratitude and acknowledgment to Dr Palmira Bernocchi, Associate Professor Heidi Holmen, Dr Stephan Potter, Professor Sonja Lippke, Dr Nills Haller, Dr Ewa Piotrowicz, and Dr Jomme Claes for providing additional data; Ida-Marie Marcussen for practical support; and Alexander Luijk, Frederik Buch Dalum, and Nikolaj Aagaard Nøhr for help during the screening process. This study received funding from Region Zealand (Exercise First), the Næstved-Slagelse-Ringsted Research Fund, and a 1-year faculty scholarship from the University of Southern Denmark. The funders had no role in the study design, data collection, analysis, interpretation, report writing, or submission.

    Conflicts of Interest

    All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare that STS is an associate editor of the Journal of Orthopaedic & Sports Physical Therapy; has received personal fees from Munksgaard and TrustMe-Ed outside the submitted work; and is a cofounder of GLA:D, a not-for-profit initiative hosted at the University of Southern Denmark aimed at implementing clinical guidelines for osteoarthritis in clinical practice. Furthermore, STS is currently funded by a program grant from Region Zealand (Exercise First) and 2 grants from the European Union’s Horizon 2020 Research and Innovation Program, one from the European Research Council (MOBILIZE, grant agreement 801790) and the other under grant agreement 945377 (ESCAPE). In addition, LHT was funded by a grant from the Danish Regions and the Danish Health Confederation through the Development and Research Fund for financial support (project 2703) and a grant from Region Zealand (Exercise First). None of the authors had financial relationships with any organizations that might have an interest in the submitted work or other relationships or activities that could appear to have influenced the submitted work.

    Multimedia Appendix 1

    Study protocol including amendments to protocol.

    PDF File (Adobe PDF File), 283 KB
    Multimedia Appendix 2

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

    PDF File (Adobe PDF File), 546 KB
    Multimedia Appendix 3

    Search string for each database, excluded studies, study characteristics of the included studies, and overview of index conditions and comorbidities in the included studies.

    PDF File (Adobe PDF File), 1402 KB
    Multimedia Appendix 4

    Meta-analyses: forest plots for each outcome of interest.

    PDF File (Adobe PDF File), 11151 KB
    Multimedia Appendix 5

    Risk of bias: Cochrane Risk of Bias 2 assessment of each outcome of interest.

    PDF File (Adobe PDF File), 1243 KB
    Multimedia Appendix 6

    Small study bias: funnel plot and Egger or Harbor test for each outcome of interest.

    PDF File (Adobe PDF File), 979 KB
    Multimedia Appendix 7

    Meta-regression analyses.

    PDF File (Adobe PDF File), 593 KB
    Multimedia Appendix 8

    Subgroup and sensitivity analyses.

    PDF File (Adobe PDF File), 7099 KB
    1. GBD 2019 Diseases Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. Oct 17, 2020;396(10258):1204-1222. [FREE Full text] [CrossRef] [Medline]
    2. Jayasinghe S, Byrne NM, Patterson KA, Ahuja KD, Hills AP. The current global state of movement and physical activity - the health and economic costs of the inactive phenotype. Prog Cardiovasc Dis. Jan 2021;64:9-16. [CrossRef] [Medline]
    3. Ding D, Kolbe-Alexander T, Nguyen B, Katzmarzyk PT, Pratt M, Lawson KD. The economic burden of physical inactivity: a systematic review and critical appraisal. Br J Sports Med. Oct 2017;51(19):1392-1409. [CrossRef] [Medline]
    4. Pedersen BK, Saltin B. Exercise as medicine - evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. Dec 2015;25 Suppl 3:1-72. [CrossRef] [Medline]
    5. Bricca A, Harris LK, Jäger M, Smith SM, Juhl CB, Skou ST. Benefits and harms of exercise therapy in people with multimorbidity: a systematic review and meta-analysis of randomised controlled trials. Ageing Res Rev. Nov 2020;63:101166. [FREE Full text] [CrossRef] [Medline]
    6. Ashe MC, Miller WC, Eng JJ, Noreau L, Physical ActivityChronic Conditions Research Team. Older adults, chronic disease and leisure-time physical activity. Gerontology. 2009;55(1):64-72. [FREE Full text] [CrossRef] [Medline]
    7. Salman A, Sellami M. Do older adults with multimorbidity meet the recommended levels of physical activity? An analysis of scottish health survey. Int J Environ Res Public Health. Oct 04, 2019;16(19):3748. [FREE Full text] [CrossRef] [Medline]
    8. Keating A, Lee A, Holland AE. What prevents people with chronic obstructive pulmonary disease from attending pulmonary rehabilitation? A systematic review. Chron Respir Dis. 2011;8(2):89-99. [FREE Full text] [CrossRef] [Medline]
    9. Tuso P. Strategies to increase physical activity. Perm J. 2015;19(4):84-88. [FREE Full text] [CrossRef] [Medline]
    10. Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, et al. Evaluating digital health interventions: key questions and approaches. Am J Prev Med. Nov 2016;51(5):843-851. [FREE Full text] [CrossRef] [Medline]
    11. Lettieri E, Fumagalli LP, Radaelli G, Bertele' P, Vogt J, Hammerschmidt R, et al. Empowering patients through eHealth: a case report of a pan-European project. BMC Health Serv Res. Aug 05, 2015;15:309. [FREE Full text] [CrossRef] [Medline]
    12. Labrique A, Agarwal S, Tamrat T, Mehl G. WHO Digital Health Guidelines: a milestone for global health. NPJ Digit Med. Sep 2020;3:120. [FREE Full text] [CrossRef] [Medline]
    13. Kendzerska T, Zhu DT, Gershon AS, Edwards JD, Peixoto C, Robillard R, et al. The effects of the health system response to the COVID-19 pandemic on chronic disease management: a narrative review. Risk Manag Healthc Policy. 2021;14:575-584. [FREE Full text] [CrossRef] [Medline]
    14. Guo C, Ashrafian H, Ghafur S, Fontana G, Gardner C, Prime M. Challenges for the evaluation of digital health solutions-a call for innovative evidence generation approaches. NPJ Digit Med. 2020;3:110. [FREE Full text] [CrossRef] [Medline]
    15. Cox NS, Dal Corso SD, Hansen H, McDonald CF, Hill CJ, Zanaboni P, et al. Telerehabilitation for chronic respiratory disease. Cochrane Database Syst Rev. Jan 29, 2021;1(1):CD013040. [FREE Full text] [CrossRef] [Medline]
    16. Cavalheiro AH, Cardoso JS, Rocha A, Moreira E, Azevedo LF. Effectiveness of tele-rehabilitation programs in heart failure: a systematic review and meta-analysis. Health Serv Insights. Jun 15, 2021;14:11786329211021668. [FREE Full text] [CrossRef] [Medline]
    17. Indraratna P, Tardo D, Yu J, Delbaere K, Brodie M, Lovell N, et al. Mobile phone technologies in the management of ischemic heart disease, heart failure, and hypertension: systematic review and meta-analysis. JMIR Mhealth Uhealth. Jul 06, 2020;8(7):e16695. [FREE Full text] [CrossRef] [Medline]
    18. Eland-de Kok P, van Os-Medendorp H, Vergouwe-Meijer A, Bruijnzeel-Koomen C, Ros W. A systematic review of the effects of e-health on chronically ill patients. J Clin Nurs. Nov 2011;20(21-22):2997-3010. [CrossRef] [Medline]
    19. Kraef C, van der Meirschen M, Free C. Digital telemedicine interventions for patients with multimorbidity: a systematic review and meta-analysis. BMJ Open. Oct 13, 2020;10(10):e036904. [FREE Full text] [CrossRef] [Medline]
    20. Carneiro L, Rosenbaum S, Ward PB, Clemente FM, Ramirez-Campillo R, Monteiro-Júnior RS, et al. Web-based exercise interventions for patients with depressive and anxiety disorders: a systematic review of randomized controlled trials. Braz J Psychiatry. 2022;44(3):331-341. [FREE Full text] [CrossRef] [Medline]
    21. Chen T, Or CK, Chen J. Effects of technology-supported exercise programs on the knee pain, physical function, and quality of life of individuals with knee osteoarthritis and/or chronic knee pain: a systematic review and meta-analysis of randomized controlled trials. J Am Med Inform Assoc. Feb 15, 2021;28(2):414-423. [FREE Full text] [CrossRef] [Medline]
    22. Hodkinson A, Kontopantelis E, Adeniji C, van Marwijk H, McMillian B, Bower P, et al. Interventions using wearable physical activity trackers among adults with cardiometabolic conditions: a systematic review and meta-analysis. JAMA Netw Open. Jul 01, 2021;4(7):e2116382. [FREE Full text] [CrossRef] [Medline]
    23. Patterson K, Davey R, Keegan R, Freene N. Smartphone applications for physical activity and sedentary behaviour change in people with cardiovascular disease: a systematic review and meta-analysis. PLoS One. Oct 11, 2021;16(10):e0258460. [FREE Full text] [CrossRef] [Medline]
    24. Choo YJ, Chang MC. Effects of telecardiac rehabilitation on coronary heart disease: a PRISMA-compliant systematic review and meta-analysis. Medicine (Baltimore). Jul 15, 2022;101(28):e29459. [FREE Full text] [CrossRef] [Medline]
    25. Brown RC, Coombes JS, Rodriguez KJ, Hickman IJ, Keating SE. Effectiveness of exercise via telehealth for chronic disease: a systematic review and meta-analysis of exercise interventions delivered via videoconferencing. Br J Sports Med. Jun 17, 2022;56(18):1042-1052. [CrossRef] [Medline]
    26. Barbabella F, Melchiorre MG, Quattrini S, Papa R, Lamura G, Richardson E, et al, editors. How Can eHealth Improve Care for People With Multimorbidity in Europe?. Copenhagen (Denmark). European Observatory on Health Systems and Policies; 2017.
    27. Fan I, Govil D, Semciw A. The effectiveness of exercise based digital health interventions (requiring internet) in management of hip and knee osteoarthritis: a systematic review and meta-analysis. Osteoarthritis Cartilage. Apr 2022;30:S402. [CrossRef]
    28. Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers. Jul 14, 2022;8(1):48. [FREE Full text] [CrossRef] [Medline]
    29. What is a serious adverse event? U.S. Food & Drug Administration. URL: https://www.fda.gov/safety/reporting-serious-problems-fda/what-serious-adverse-event [accessed 2020-05-01]
    30. Sterne JA, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. Aug 28, 2019;366:l4898. [FREE Full text] [CrossRef] [Medline]
    31. GRADE homepage. GRADE. URL: https://www.gradeworkinggroup.org/ [accessed 2020-03-05]
    32. Cohen J. Statistical Power Analysis for the Behavioral Sciences. New York, NY. Routledge; 1988.
    33. Eysenbach G. What is e-health? J Med Internet Res. Apr 2001;3(2):E20. [FREE Full text] [CrossRef] [Medline]
    34. Rechel B, Doyle Y, Grundy E, McKee M. How can health systems respond to population ageing? World Health Organization. URL: https:/​/apps.​who.int/​iris/​bitstream/​handle/​10665/​107941/​Policy-brief-10-1997-8073-eng.​pdf?sequence=9&isAllowed=y [accessed 2023-06-11]
    35. Hale TM, Chou WY, Cotten SR, Khilnani A. eHealth: Current Evidence, Promises, Perils and Future Directions. Volume 15. West Yorkshire, UK. Emerald Publishing; 2018.
    36. Stewart AL, Hays RD, Wells KB, Rogers WH, Spritzer KL, Greenfield S. Long-term functioning and well-being outcomes associated with physical activity and exercise in patients with chronic conditions in the medical outcomes study. J Clin Epidemiol. Jul 1994;47(7):719-730. [CrossRef] [Medline]
    37. Norman GJ, Zabinski MF, Adams MA, Rosenberg DE, Yaroch AL, Atienza AA. A review of eHealth interventions for physical activity and dietary behavior change. Am J Prev Med. Oct 2007;33(4):336-345. [FREE Full text] [CrossRef] [Medline]
    38. Sforzolini FS. Taking the pulse of eHealth in the EU. European Union. 2017. URL: https:/​/digital-strategy.​ec.europa.eu/​en/​library/​taking-pulse-ehealth-eu-study-and-round-table-discussion [accessed 2023-06-14]
    39. Skou ST, Pedersen BK, Abbott JH, Patterson B, Barton C. Physical activity and exercise therapy benefit more than just symptoms and impairments in people with hip and knee osteoarthritis. J Orthop Sports Phys Ther. Jun 2018;48(6):439-447. [CrossRef] [Medline]
    40. Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the global burden of disease study 2013. Lancet. Aug 22, 2015;386(9995):743-800. [FREE Full text] [CrossRef] [Medline]
    41. Xu X, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: an overview of systematic reviews. Ageing Res Rev. Aug 2017;37:53-68. [CrossRef] [Medline]
    42. Smith SM, Wallace E, Salisbury C, Sasseville M, Bayliss E, Fortin M. A core outcome set for multimorbidity research (COSmm). Ann Fam Med. Mar 2018;16(2):132-138. [FREE Full text] [CrossRef] [Medline]
    43. Multimorbidity: a priority for global health research. The Academy of Medical Sciences. 2018. URL: https://acmedsci.ac.uk/file-download/82222577 [accessed 2019-01-05]
    44. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane handbook for systematic reviews of interventions version 6.0. Cochrane. 2019. URL: https://www.training.cochrane.org/handbook [accessed 2023-06-14]
    45. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Hoboken, New Jersey. Wiley; 2022.
    46. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]
    47. Fatehi F, Samadbeik M, Kazemi A. What is digital health? Review of definitions. Stud Health Technol Inform. Nov 23, 2020;275:67-71. [CrossRef] [Medline]
    48. Gleeson M, Bishop NC, Stensel DJ, Lindley MR, Mastana SS, Nimmo MA. The anti-inflammatory effects of exercise: mechanisms and implications for the prevention and treatment of disease. Nat Rev Immunol. Aug 05, 2011;11(9):607-615. [CrossRef] [Medline]
    49. Bricca A, Jäger M, Johnston M, Zangger G, Harris LK, Midtgaard J, et al. Effect of in-person delivered behavioural interventions in people with multimorbidity: systematic review and meta-analysis. Int J Behav Med. Apr 2023;30(2):167-189. [FREE Full text] [CrossRef] [Medline]
    50. Cox NS, McDonald CF, Hill CJ, O'Halloran P, Alison JA, Zanaboni P, et al. Telerehabilitation for chronic respiratory disease. Cochrane Database Systematic Rev. 2018(6) [CrossRef]
    51. EMBASE RCT filter. ISSG Search Filters Resource. URL: https://sites.google.com/a/york.ac.uk/issg-search-filters-resource/home/rcts/embase-rct-filter [accessed 2022-06-22]
    52. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. Sep 06, 2003;327(7414):557-560. [FREE Full text] [CrossRef] [Medline]
    53. Agboola S, Jethwani K, Lopez L, Searl M, O'Keefe S, Kvedar J. Text to move: a randomized controlled trial of a text-messaging program to improve physical activity behaviors in patients with type 2 diabetes mellitus. J Med Internet Res. Nov 18, 2016;18(11):e307. [FREE Full text] [CrossRef] [Medline]
    54. Allen KD, Arbeeva L, Callahan LF, Golightly YM, Goode AP, Heiderscheit BC, et al. Physical therapy vs internet-based exercise training for patients with knee osteoarthritis: results of a randomized controlled trial. Osteoarthritis Cartilage. Mar 2018;26(3):383-396. [FREE Full text] [CrossRef] [Medline]
    55. Allen KD, Woolson S, Hoenig HM, Bongiorni D, Byrd J, Caves K, et al. Stepped exercise program for patients with knee osteoarthritis. Ann Intern Med. Mar 2021;174(3):298-307. [CrossRef]
    56. Anderson DR, Christison-Lagay J, Villagra V, Liu H, Dziura J. Managing the space between visits: a randomized trial of disease management for diabetes in a community health center. J Gen Intern Med. Oct 16, 2010;25(10):1116-1122. [FREE Full text] [CrossRef] [Medline]
    57. Baker K, LaValley MP, Brown C, Felson DT, Ledingham A, Keysor JJ. Efficacy of computer-based telephone counseling on long-term adherence to strength training in elderly patients with knee osteoarthritis: a randomized trial. Arthritis Care Res (Hoboken). Jul 2020;72(7):982-990. [CrossRef] [Medline]
    58. Bender MS, Cooper BA, Park LG, Padash S, Arai S. A feasible and efficacious mobile-phone based lifestyle intervention for Filipino Americans with type 2 diabetes: randomized controlled trial. JMIR Diabetes. Dec 12, 2017;2(2):e30. [FREE Full text] [CrossRef] [Medline]
    59. Benson GA, Sidebottom A, Hayes J, Miedema MD, Boucher J, Vacquier M, et al. Impact of ENHANCED (diEtitiaNs Helping pAtieNts CarE for Diabetes) telemedicine randomized controlled trial on diabetes optimal care outcomes in patients with type 2 diabetes. J Acad Nutr Diet. Apr 2019;119(4):585-598. [CrossRef] [Medline]
    60. Benzo RP, Ridgeway J, Hoult JP, Novotny P, Thomas BE, Lam NM, et al. Feasibility of a health coaching and home-based rehabilitation intervention with remote monitoring for COPD. Respir Care. Jun 2021;66(6):960-971. [FREE Full text] [CrossRef] [Medline]
    61. Benzo R, Hoult J, McEvoy C, Clark M, Benzo M, Johnson M, et al. Promoting chronic obstructive pulmonary disease wellness through remote monitoring and health coaching: a clinical trial. Annals ATS. Nov 2022;19(11):1808-1817. [CrossRef]
    62. Chokshi NP, Adusumalli S, Small DS, Morris A, Feingold J, Ha YP, et al. Loss‐framed financial incentives and personalized goal‐setting to increase physical activity among ischemic heart disease patients using wearable devices: the ACTIVE REWARD randomized trial. JAHA. Jun 19, 2018;7(12):136. [FREE Full text] [CrossRef]
    63. Coultas DB, Jackson BE, Russo R, Peoples J, Sloan J, Singh KP, et al. A lifestyle physical activity intervention for patients with chronic obstructive pulmonary disease. A randomized controlled trial. Annals ATS. May 2016;13(5):617-626. [CrossRef]
    64. Deka P, Pozehl B, Williams MA, Norman JF, Khazanchi D, Pathak D. MOVE-HF: an internet-based pilot study to improve adherence to exercise in patients with heart failure. Eur J Cardiovasc Nurs. Feb 2019;18(2):122-131. [CrossRef] [Medline]
    65. Felker GM, Sharma A, Mentz RJ, She L, Green CL, Granger BB, et al. A randomized controlled trial of mobile health intervention in patients with heart failure and diabetes. J Card Fail. Nov 2022;28(11):1575-1583. [CrossRef] [Medline]
    66. Glasgow RE, Kurz D, King D, Dickman JM, Faber AJ, Halterman E, et al. Twelve-month outcomes of an internet-based diabetes self-management support program. Patient Educ Couns. Apr 2012;87(1):81-92. [FREE Full text] [CrossRef] [Medline]
    67. Jiwani R, Wang J, Berndt A, Ramaswamy P, Joseph NM, Du Y, et al. Changes in patient-reported outcome measures with a technology-supported behavioral lifestyle intervention among patients with type 2 diabetes: pilot randomized controlled clinical trial. JMIR Diabetes. Jul 24, 2020;5(3):e19268. [FREE Full text] [CrossRef] [Medline]
    68. Moy ML, Collins RJ, Martinez CH, Kadri R, Roman P, Holleman RG, et al. An internet-mediated pedometer-based program improves health-related quality-of-life domains and daily step counts in COPD: a randomized controlled trial. Chest. Jul 2015;148(1):128-137. [FREE Full text] [CrossRef] [Medline]
    69. Piette JD, Richardson C, Himle J, Duffy S, Torres T, Vogel M, et al. A randomized trial of telephonic counseling plus walking for depressed diabetes patients. Med Care. Jul 2011;49(7):641-648. [FREE Full text] [CrossRef] [Medline]
    70. Radhakrishnan K, Julien C, Baranowski T, O'Hair M, Lee G, De Main AS, et al. Feasibility of a sensor-controlled digital game for heart failure self-management: randomized controlled trial. JMIR Serious Games. Nov 08, 2021;9(4):e29044. [FREE Full text] [CrossRef] [Medline]
    71. Robinson SA, Cooper JA, Goldstein RL, Polak M, Cruz Rivera PN, Gagnon DR, et al. A randomised trial of a web-based physical activity self-management intervention in COPD. ERJ Open Res. Jul 15, 2021;7(3):00158-2021. [FREE Full text] [CrossRef] [Medline]
    72. Southard BH, Southard DR, Nuckolls J. Clinical trial of an internet-based case management system for secondary prevention of heart disease. J Cardiopulm Rehabil. 2003;23(5):341-348. [CrossRef] [Medline]
    73. Tomita MR, Tsai BM, Fisher NM, Kumar NA, Wilding G, Stanton K, et al. Effects of multidisciplinary Internet-based program on management of heart failure. J Multidiscip Healthc. Dec 01, 2008;2009(2):13-21. [FREE Full text] [CrossRef] [Medline]
    74. Wan ES, Kantorowski A, Homsy D, Teylan M, Kadri R, Richardson CR, et al. Promoting physical activity in COPD: insights from a randomized trial of a web-based intervention and pedometer use. Respir Med. Sep 2017;130:102-110. [FREE Full text] [CrossRef] [Medline]
    75. Widmer RJ, Allison TG, Lennon R, Lopez-Jimenez F, Lerman LO, Lerman A. Digital health intervention during cardiac rehabilitation: a randomized controlled trial. Am Heart J. Jun 2017;188:65-72. [CrossRef] [Medline]
    76. Bennell KL, Nelligan R, Dobson F, Rini C, Keefe F, Kasza J, et al. Effectiveness of an internet-delivered exercise and pain-coping skills training intervention for persons with chronic knee pain: a randomized trial. Ann Intern Med. Apr 04, 2017;166(7):453-462. [CrossRef] [Medline]
    77. Bennell KL, Nelligan RK, Rini C, Keefe FJ, Kasza J, French S, et al. Effects of internet-based pain coping skills training before home exercise for individuals with hip osteoarthritis (HOPE trial): a randomised controlled trial. Pain. Sep 2018;159(9):1833-1842. [CrossRef] [Medline]
    78. Bennell K, Nelligan RK, Schwartz S, Kasza J, Kimp A, Crofts SJ, et al. Behavior change text messages for home exercise adherence in knee osteoarthritis: randomized trial. J Med Internet Res. Sep 28, 2020;22(9):e21749. [FREE Full text] [CrossRef] [Medline]
    79. Cameron-Tucker HL, Wood-Baker R, Joseph L, Walters JA, Schüz N, Walters EH. A randomized controlled trial of telephone-mentoring with home-based walking preceding rehabilitation in COPD. Int J Chron Obstruct Pulmon Dis. 2016;11:1991-2000. [FREE Full text] [CrossRef] [Medline]
    80. Chow CK, Redfern J, Hillis GS, Thakkar J, Santo K, Hackett ML, et al. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial. JAMA. 2015;314(12):1255-1263. [CrossRef] [Medline]
    81. Coombes JS, Keating SE, Mielke GI, Fassett RG, Coombes BK, O'Leary KP, et al. Personal activity intelligence e-health program in people with type 2 diabetes: a pilot randomized controlled trial. Med Sci Sports Exerc. Jan 01, 2022;54(1):18-27. [CrossRef] [Medline]
    82. Cox NS, McDonald CF, Mahal A, Alison JA, Wootton R, Hill CJ, et al. Telerehabilitation for chronic respiratory disease: a randomised controlled equivalence trial. Thorax. Jul 2022;77(7):643-651. [CrossRef] [Medline]
    83. Eakin E, Reeves M, Lawler S, Graves N, Oldenburg B, Del Mar C, et al. Telephone counseling for physical activity and diet in primary care patients. Am J Prev Med. Feb 2009;36(2):142-149. [CrossRef] [Medline]
    84. Eakin EG, Winkler EA, Dunstan DW, Healy GN, Owen N, Marshall AM, et al. Living well with diabetes: 24-month outcomes from a randomized trial of telephone-delivered weight loss and physical activity intervention to improve glycemic control. Diabetes Care. Aug 2014;37(8):2177-2185. [FREE Full text] [CrossRef] [Medline]
    85. Hawkes AL, Patrao TA, Atherton J, Ware RS, Taylor CB, O'Neil A, et al. Effect of a telephone-delivered coronary heart disease secondary prevention program (proactive heart) on quality of life and health behaviours: primary outcomes of a randomised controlled trial. Int J Behav Med. Sep 2013;20(3):413-424. [CrossRef] [Medline]
    86. Hinman RS, Campbell PK, Lawford BJ, Briggs AM, Gale J, Bills C, et al. Does telephone-delivered exercise advice and support by physiotherapists improve pain and/or function in people with knee osteoarthritis? Telecare randomised controlled trial. Br J Sports Med. Jul 2020;54(13):790-797. [CrossRef] [Medline]
    87. Indraratna P, Biswas U, McVeigh J, Mamo A, Magdy J, Vickers D, et al. A smartphone-based model of care to support patients with cardiac disease transitioning from hospital to the community (TeleClinical Care): pilot randomized controlled trial. JMIR Mhealth Uhealth. Feb 28, 2022;10(2):e32554. [FREE Full text] [CrossRef] [Medline]
    88. Jennings CA, Vandelanotte C, Caperchione CM, Mummery WK. Effectiveness of a web-based physical activity intervention for adults with Type 2 diabetes-a randomised controlled trial. Prev Med. Mar 2014;60:33-40. [CrossRef] [Medline]
    89. Nelligan RK, Hinman RS, Kasza J, Crofts SJ, Bennell KL. Effects of a self-directed web-based strengthening exercise and physical activity program supported by automated text messages for people with knee osteoarthritis: a randomized clinical trial. JAMA Intern Med. Jun 01, 2021;181(6):776-785. [FREE Full text] [CrossRef] [Medline]
    90. O'Brien KM, Wiggers J, Williams A, Campbell E, Hodder RK, Wolfenden L, et al. Telephone-based weight loss support for patients with knee osteoarthritis: a pragmatic randomised controlled trial. Osteoarthritis Cartilage. Apr 2018;26(4):485-494. [FREE Full text] [CrossRef] [Medline]
    91. Tsai LL, McNamara RJ, Moddel C, Alison JA, McKenzie DK, McKeough ZJ. Home-based telerehabilitation via real-time videoconferencing improves endurance exercise capacity in patients with COPD: the randomized controlled TeleR Study. Respirology. May 19, 2017;22(4):699-707. [FREE Full text] [CrossRef] [Medline]
    92. Varney JE, Weiland TJ, Inder WJ, Jelinek GA. Effect of hospital-based telephone coaching on glycaemic control and adherence to management guidelines in type 2 diabetes, a randomised controlled trial. Intern Med J. Sep 08, 2014;44(9):890-897. [CrossRef] [Medline]
    93. Waller K, Furber S, Bauman A, Allman-Farinelli M, van den Dolder P, Hayes A, et al. Effectiveness and acceptability of a text message intervention (DTEXT) on HbA1c and self-management for people with type 2 diabetes. A randomized controlled trial. Patient Educ Couns. Jul 2021;104(7):1736-1744. [CrossRef] [Medline]
    94. Wootton SL, Hill K, Alison JA, Ng LW, Jenkins S, Eastwood PR, et al. Effects of ongoing feedback during a 12-month maintenance walking program on daily physical activity in people with COPD. Lung. Jun 15, 2019;197(3):315-319. [CrossRef] [Medline]
    95. Young MD, Drew RJ, Kay-Lambkin F, Collins CE, Callister R, Kelly BJ, et al. Impact of a self-guided, eHealth program targeting weight loss and depression in men: a randomized trial. J Consult Clin Psychol. Aug 2021;89(8):682-694. [CrossRef] [Medline]
    96. Yudi MB, Clark DJ, Tsang D, Jelinek M, Kalten K, Joshi SB, et al. SMARTphone-based, early cardiac REHABilitation in patients with acute coronary syndromes: a randomized controlled trial. Coron Artery Dis. Aug 01, 2021;32(5):432-440. [CrossRef] [Medline]
    97. Akinci B, Yeldan I, Satman I, Dirican A, Ozdincler AR. The effects of Internet-based exercise compared with supervised group exercise in people with type 2 diabetes: a randomized controlled study. Clin Rehabil. Jun 2018;32(6):799-810. [CrossRef] [Medline]
    98. Avila A, Claes J, Goetschalckx K, Buys R, Azzawi M, Vanhees L, et al. Home-based rehabilitation with telemonitoring guidance for patients with coronary artery disease (Short-Term Results of the TRiCH Study): randomized controlled trial. J Med Internet Res. Jun 22, 2018;20(6):e225. [FREE Full text] [CrossRef] [Medline]
    99. Bailey DP, Mugridge LH, Dong F, Zhang X, Chater AM. Randomised controlled feasibility study of the MyHealthAvatar-diabetes smartphone app for reducing prolonged sitting time in type 2 diabetes mellitus. Int J Environ Res Public Health. Jun 19, 2020;17(12):4414. [FREE Full text] [CrossRef] [Medline]
    100. Bossen D, Veenhof C, Van Beek KE, Spreeuwenberg PM, Dekker J, De Bakker DH. Effectiveness of a web-based physical activity intervention in patients with knee and/or hip osteoarthritis: randomized controlled trial. J Med Internet Res. Nov 22, 2013;15(11):e257. [FREE Full text] [CrossRef] [Medline]
    101. Bozorgi A, Hosseini H, Eftekhar H, Majdzadeh R, Yoonessi A, Ramezankhani A, et al. The effect of the mobile "blood pressure management application" on hypertension self-management enhancement: a randomized controlled trial. Trials. Jun 24, 2021;22(1):413. [FREE Full text] [CrossRef] [Medline]
    102. Chan NP, Lai AY, Choy HK, Cheung DY, Wan AN, Cheng VY, et al. Feasibility and potential effectiveness of a smartphone zero-time exercise intervention for promoting physical activity and fitness in patients with coronary heart disease: a pilot randomized controlled trial. Front Public Health. Jul 14, 2022;10:865712. [FREE Full text] [CrossRef] [Medline]
    103. Cicolini G, Simonetti V, Comparcini D, Celiberti I, Di Nicola M, Capasso LM, et al. Efficacy of a nurse-led email reminder program for cardiovascular prevention risk reduction in hypertensive patients: a randomized controlled trial. Int J Nurs Stud. Jun 2014;51(6):833-843. [CrossRef] [Medline]
    104. Connelly J, Kirk A, Masthoff J, MacRury S. A website to promote physical activity in people with type 2 diabetes living in remote or rural locations: feasibility pilot randomized controlled trial. JMIR Diabetes. Oct 19, 2017;2(2):e26. [FREE Full text] [CrossRef] [Medline]
    105. de Sousa Pinto JM, Martín-Nogueras AM, Calvo-Arenillas JI, Ramos-González J. Clinical benefits of home-based pulmonary rehabilitation in patients with chronic obstructive pulmonary disease. J Cardiopulm Rehabil Prev. 2014;34(5):355-359. [CrossRef] [Medline]
    106. Duan YP, Liang W, Guo L, Wienert J, Si GY, Lippke S. Evaluation of a web-based intervention for multiple health behavior changes in patients with coronary heart disease in home-based rehabilitation: pilot randomized controlled trial. J Med Internet Res. Nov 19, 2018;20(11):e12052. [FREE Full text] [CrossRef] [Medline]
    107. Dyson PA, Beatty S, Matthews DR. An assessment of lifestyle video education for people newly diagnosed with type 2 diabetes. J Hum Nutr Diet. Aug 2010;23(4):353-359. [CrossRef] [Medline]
    108. Galdiz JB, Gómez A, Rodriguez D, Guell R, Cebollero P, Hueto J, et al. Telerehabilitation programme as a maintenance strategy for copd patients: a 12-month randomized clinical trial. Arch Bronconeumol (Engl Ed). Mar 2021;57(3):195-204. [CrossRef] [Medline]
    109. Gingele AJ, Ramaekers B, Brunner-La Rocca HP, De Weerd G, Kragten J, van Empel V, et al. Effects of tailored telemonitoring on functional status and health-related quality of life in patients with heart failure. Neth Heart J. Nov 14, 2019;27(11):565-574. [FREE Full text] [CrossRef] [Medline]
    110. Gohir SA, Eek F, Kelly A, Abhishek A, Valdes AM. Effectiveness of internet-based exercises aimed at treating knee osteoarthritis: the iBEAT-OA randomized clinical trial. JAMA Netw Open. Feb 01, 2021;4(2):e210012. [FREE Full text] [CrossRef] [Medline]
    111. Guiraud T, Granger R, Gremeaux V, Bousquet M, Richard L, Soukarié L, et al. Telephone support oriented by accelerometric measurements enhances adherence to physical activity recommendations in noncompliant patients after a cardiac rehabilitation program. Arch Phys Med Rehabil. Dec 2012;93(12):2141-2147. [CrossRef] [Medline]
    112. Lee H, Park G, Jin H, Chun KJ, Kim JH. The effects of nurse-led motivational interviewing on exercise and quality of life among koreans with heart failure: a randomized controlled trial. Korean J Adult Nurs. 2021;33(6):588. [CrossRef]
    113. Hanssen TA, Nordrehaug JE, Eide GE, Hanestad BR. Improving outcomes after myocardial infarction: a randomized controlled trial evaluating effects of a telephone follow-up intervention. Eur J Cardiovasc Prev Rehabil. Jun 2007;14(3):429-437. [CrossRef] [Medline]
    114. Hidrus A, Kueh YC, Norsaádah B, Chang YK, Hung TM, Naing NN, et al. Effects of brain breaks videos on the motives for the physical activity of Malaysians with type-2 diabetes mellitus. Int J Environ Res Public Health. Apr 06, 2020;17(7):2507. [FREE Full text] [CrossRef] [Medline]
    115. Höchsmann C, Müller O, Ambühl M, Klenk C, Königstein K, Infanger D, et al. Novel smartphone game improves physical activity behavior in type 2 diabetes. Am J Prev Med. Jul 2019;57(1):41-50. [FREE Full text] [CrossRef] [Medline]
    116. Holmen H, Torbjørnsen A, Wahl AK, Jenum AK, Småstuen MC, Arsand E, et al. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: one-year results from the Norwegian randomized controlled trial RENEWING HEALTH. JMIR Mhealth Uhealth. Dec 11, 2014;2(4):e57. [FREE Full text] [CrossRef] [Medline]
    117. Hornikx M, Demeyer H, Camillo CA, Janssens W, Troosters T. The effects of a physical activity counseling program after an exacerbation in patients with Chronic Obstructive Pulmonary Disease: a randomized controlled pilot study. BMC Pulm Med. Nov 04, 2015;15(1):136. [FREE Full text] [CrossRef] [Medline]
    118. Hsu YI, Chen YC, Lee CL, Chang NJ. Effects of diet control and telemedicine-based resistance exercise intervention on patients with obesity and knee osteoarthritis: a randomized control trial. Int J Environ Res Public Health. Jul 21, 2021;18(15):7744. [FREE Full text] [CrossRef] [Medline]
    119. Jayasree B, Stalin P. Efficacy of behavior change communication using mobile calls on glycemic control among Type 2 diabetic patients in an urban area of Pondicherry, South India: a randomized controlled trial. J Educ Health Promot. 2019;8:92. [FREE Full text] [CrossRef] [Medline]
    120. Jiménez-Reguera B, López EM, Fitch S, Juarros L, Cortés MS, Rodríguez Hermosa JL, et al. Development and preliminary evaluation of the effects of an mHealth web-based platform (HappyAir) on adherence to a maintenance program after pulmonary rehabilitation in patients with chronic obstructive pulmonary disease: randomized controlled trial. JMIR Mhealth Uhealth. Jul 31, 2020;8(7):e18465. [FREE Full text] [CrossRef] [Medline]
    121. Jaarsma T, Klompstra L, Gal TB, Avraham BB, Boyne J, Bäck M, et al. Effects of exergaming on exercise capacity in patients with heart failure: results of an international multicentre randomized controlled trial. Eur J Heart Fail. Jan 13, 2021;23(1):114-124. [FREE Full text] [CrossRef] [Medline]
    122. Kim CJ, Kang DH. Utility of a Web-based intervention for individuals with type 2 diabetes: the impact on physical activity levels and glycemic control. Comput Inform Nurs. 2006;24(6):337-345. [CrossRef] [Medline]
    123. Kooiman TJ, de Groot M, Hoogenberg K, Krijnen WP, van der Schans CP, Kooy A. Self-tracking of physical activity in people with type 2 diabetes: a randomized controlled trial. Comput Inform Nurs. Jul 2018;36(7):340-349. [CrossRef] [Medline]
    124. Kwon H, Lee S, Jung EJ, Kim SH, Lee J, Kim DK, et al. An mHealth management platform for patients with chronic obstructive pulmonary disease (efil breath): randomized controlled trial. JMIR Mhealth Uhealth. Aug 24, 2018;6(8):e10502. [FREE Full text] [CrossRef] [Medline]
    125. Lambert JD, Greaves CJ, Farrand P, Price L, Haase AM, Taylor AH. Web-based intervention using behavioral activation and physical activity for adults with depression (The eMotion Study): pilot randomized controlled trial. J Med Internet Res. Jul 16, 2018;20(7):e10112. [FREE Full text] [CrossRef] [Medline]
    126. Li J, Wei D, Liu S, Li M, Chen X, Chen L, et al. Efficiency of an mHealth app and chest-wearable remote exercise monitoring intervention in patients with type 2 diabetes: a prospective, multicenter randomized controlled trial. JMIR Mhealth Uhealth. Feb 09, 2021;9(2):e23338. [FREE Full text] [CrossRef] [Medline]
    127. Liebreich T, Plotnikoff RC, Courneya KS, Boulé N. Diabetes NetPLAY: a physical activity website and linked email counselling randomized intervention for individuals with type 2 diabetes. Int J Behav Nutr Phys Act. Mar 27, 2009;6:18. [FREE Full text] [CrossRef] [Medline]
    128. Pamungkas RA, Usman AM, Chamroonsawasdi K, Abdurrasyid. A smartphone application of diabetes coaching intervention to prevent the onset of complications and to improve diabetes self-management: a randomized control trial. Diabetes Metab Syndr. Jul 2022;16(7):102537. [CrossRef] [Medline]
    129. Pelle T, Bevers K, van der Palen J, van den Hoogen FH, van den Ende CH. Effect of the dr. Bart application on healthcare use and clinical outcomes in people with osteoarthritis of the knee and/or hip in the Netherlands; a randomized controlled trial. Osteoarthritis Cartilage. Apr 2020;28(4):418-427. [FREE Full text] [CrossRef] [Medline]
    130. Plotnikoff RC, Karunamuni N, Courneya KS, Sigal RJ, Johnson JA, Johnson ST. The Alberta Diabetes and Physical Activity Trial (ADAPT): a randomized trial evaluating theory-based interventions to increase physical activity in adults with type 2 diabetes. Ann Behav Med. Feb 2013;45(1):45-56. [CrossRef] [Medline]
    131. Poppe L, De Bourdeaudhuij I, Verloigne M, Shadid S, Van Cauwenberg J, Compernolle S, et al. Efficacy of a self-regulation-based electronic and mobile health intervention targeting an active lifestyle in adults having type 2 diabetes and in adults aged 50 years or older: two randomized controlled trials. J Med Internet Res. Aug 02, 2019;21(8):e13363. [FREE Full text] [CrossRef] [Medline]
    132. Reid RD, Morrin LI, Beaton LJ, Papadakis S, Kocourek J, McDonnell L, et al. Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease. Eur J Prev Cardiol. Dec 2012;19(6):1357-1364. [CrossRef] [Medline]
    133. Shi YJ, Liu Y, Jiang TT, Zhang HR, Shi TY. Effects of multidisciplinary exercise management on patients after percutaneous coronary intervention: a randomized controlled study. Int J Nurs Sci. Jul 2022;9(3):286-294. [FREE Full text] [CrossRef] [Medline]
    134. Snoek JA, Meindersma EP, Prins LF, van’t Hof AW, de Boer MJ, Hopman MT, et al. The sustained effects of extending cardiac rehabilitation with a six-month telemonitoring and telecoaching programme on fitness, quality of life, cardiovascular risk factors and care utilisation in CAD patients: the TeleCaRe study. J Telemed Telecare. Nov 23, 2019;27(8):473-483. [CrossRef]
    135. Snoek JA, Prescott EI, van der Velde AE, Eijsvogels TM, Mikkelsen N, Prins LF, et al. Effectiveness of home-based mobile guided cardiac rehabilitation as alternative strategy for nonparticipation in clinic-based cardiac rehabilitation among elderly patients in Europe: a randomized clinical trial. JAMA Cardiol. Apr 01, 2021;6(4):463-468. [FREE Full text] [CrossRef] [Medline]
    136. Spielmanns M, Gloeckl R, Jarosch I, Leitl D, Schneeberger T, Boeselt T, et al. Using a smartphone application maintains physical activity following pulmonary rehabilitation in patients with COPD: a randomised controlled trial. Thorax. May 2023;78(5):442-450. [FREE Full text] [CrossRef] [Medline]
    137. Ström M, Uckelstam CJ, Andersson G, Hassmén P, Umefjord G, Carlbring P. Internet-delivered therapist-guided physical activity for mild to moderate depression: a randomized controlled trial. PeerJ. 2013;1:e178. [FREE Full text] [CrossRef] [Medline]
    138. Tore NG, Oskay D, Haznedaroglu S. The quality of physiotherapy and rehabilitation program and the effect of telerehabilitation on patients with knee osteoarthritis. Clin Rheumatol. Mar 24, 2023;42(3):903-915. [FREE Full text] [CrossRef] [Medline]
    139. Utriyaprasit K, Moore SM, Chaiseri P. Recovery after coronary artery bypass surgery: effect of an audiotape information programme. J Adv Nurs. Aug 2010;66(8):1747-1759. [CrossRef] [Medline]
    140. Vinitha R, Nanditha A, Snehalatha C, Satheesh K, Susairaj P, Raghavan A, et al. Effectiveness of mobile phone text messaging in improving glycaemic control among persons with newly detected type 2 diabetes. Diabetes Res Clin Pract. Dec 2019;158:107919. [CrossRef] [Medline]
    141. Wang L, Guo Y, Wang M, Zhao Y. A mobile health application to support self-management in patients with chronic obstructive pulmonary disease: a randomised controlled trial. Clin Rehabil. Jan 2021;35(1):90-101. [CrossRef] [Medline]
    142. Wong EM, Leung DY, Chair S, Sit JW. Effects of a web-based educational support intervention on total exercise and cardiovascular risk markers in adults with coronary heart disease. Worldviews Evid Based Nurs. Aug 08, 2020;17(4):283-292. [CrossRef] [Medline]
    143. Drew RJ, Morgan PJ, Young MD. Mechanisms of an eHealth program targeting depression in men with overweight or obesity: a randomised trial. J Affect Disord. Feb 15, 2022;299:309-317. [CrossRef] [Medline]
    144. Drew RJ, Morgan PJ, Collins CE, Callister R, Kay-Lambkin F, Kelly BJ, et al. Behavioral and cognitive outcomes of an online weight loss program for men with low mood: a randomized controlled trial. Ann Behav Med. Oct 03, 2022;56(10):1026-1041. [CrossRef] [Medline]
    145. Alghafri TS, Alharthi SM, Al-Farsi Y, Alrawahi AH, Bannerman E, Craigie AM, et al. 'MOVEdiabetes': a cluster randomized controlled trial to increase physical activity in adults with type 2 diabetes in primary health in Oman. BMJ Open Diabetes Res Care. 2018;6(1):e000605. [FREE Full text] [CrossRef] [Medline]
    146. Alonso-Domínguez R, Patino-Alonso MC, Sánchez-Aguadero N, García-Ortiz L, Recio-Rodríguez JI, Gómez-Marcos MA. Effect of a multifactorial intervention on the increase in physical activity in subjects with type 2 diabetes mellitus: a randomized clinical trial (EMID Study). Eur J Cardiovasc Nurs. Jun 2019;18(5):399-409. [CrossRef] [Medline]
    147. Andrade GN, Umeda II, Fuchs AR, Mastrocola LE, Rossi-Neto JM, Moreira DA, et al. Home-based training program in patients with chronic heart failure and reduced ejection fraction: a randomized pilot study. Clinics (Sao Paulo). 2021;76:e2550. [FREE Full text] [CrossRef] [Medline]
    148. Bartholdy C, Bliddal H, Henriksen M. Effectiveness of text messages for decreasing inactive behaviour in patients with knee osteoarthritis: a pilot randomised controlled study. Pilot Feasibility Stud. 2019;5:112. [FREE Full text] [CrossRef] [Medline]
    149. Bentley CL, Powell L, Potter S, Parker J, Mountain GA, Bartlett YK, et al. The use of a smartphone app and an activity tracker to promote physical activity in the management of chronic obstructive pulmonary disease: randomized controlled feasibility study. JMIR Mhealth Uhealth. Jun 03, 2020;8(6):e16203. [FREE Full text] [CrossRef] [Medline]
    150. Bernocchi P, Vitacca M, La Rovere MT, Volterrani M, Galli T, Baratti D, et al. Home-based telerehabilitation in older patients with chronic obstructive pulmonary disease and heart failure: a randomised controlled trial. Age Ageing. Jan 01, 2018;47(1):82-88. [CrossRef] [Medline]
    151. Cerdán-de-Las-Heras J, Balbino F, Løkke A, Catalán-Matamoros D, Hilberg O, Bendstrup E. Effect of a new tele-rehabilitation program versus standard rehabilitation in patients with chronic obstructive pulmonary disease. J Clin Med. Dec 21, 2021;11(1):11. [FREE Full text] [CrossRef] [Medline]
    152. Chaplin E, Hewitt S, Apps L, Edwards K, Brough C, Glab A, et al. An interactive web-based pulmonary rehabilitation programme: a randomised controlled feasibility trial. Eur Respir J. 2016;48:PA2064. [CrossRef]
    153. Chaplin E, Barnes A, Newby C, Houchen-Wolloff L, Singh SJ. Comparison of the impact of conventional and web-based pulmonary rehabilitation on physical activity in patients with chronic obstructive pulmonary disease: exploratory feasibility study. JMIR Rehabil Assist Technol. Mar 10, 2022;9(1):e28875. [FREE Full text] [CrossRef] [Medline]
    154. Clays E, Puddu PE, Luštrek M, Pioggia G, Derboven J, Vrana M, et al. Proof-of-concept trial results of the HeartMan mobile personal health system for self-management in congestive heart failure. Sci Rep. Mar 11, 2021;11(1):5663. [FREE Full text] [CrossRef] [Medline]
    155. De Greef KP, Deforche BI, Ruige JB, Bouckaert JJ, Tudor-Locke CE, Kaufman JM, et al. The effects of a pedometer-based behavioral modification program with telephone support on physical activity and sedentary behavior in type 2 diabetes patients. Patient Educ Couns. Aug 2011;84(2):275-279. [CrossRef] [Medline]
    156. Demeyer H, Louvaris Z, Frei A, Rabinovich RA, de Jong C, Gimeno-Santos E, et al. Mr Papp PROactive study groupthe PROactive consortium. Physical activity is increased by a 12-week semiautomated telecoaching programme in patients with COPD: a multicentre randomised controlled trial. Thorax. May 2017;72(5):415-423. [FREE Full text] [CrossRef] [Medline]
    157. Döbler A, Herbeck Belnap B, Pollmann H, Farin E, Raspe H, Mittag O. Telephone-delivered lifestyle support with action planning and motivational interviewing techniques to improve rehabilitation outcomes. Rehabil Psychol. May 2018;63(2):170-181. [CrossRef] [Medline]
    158. Doiron-Cadrin P, Kairy D, Vendittoli PA, Lowry V, Poitras S, Desmeules F. Feasibility and preliminary effects of a tele-prehabilitation program and an in-person prehablitation program compared to usual care for total hip or knee arthroplasty candidates: a pilot randomized controlled trial. Disabil Rehabil. Apr 13, 2020;42(7):989-998. [CrossRef] [Medline]
    159. Duruturk N, Özköslü MA. Effect of tele-rehabilitation on glucose control, exercise capacity, physical fitness, muscle strength and psychosocial status in patients with type 2 diabetes: a double blind randomized controlled trial. Primary Care Diabetes. Dec 2019;13(6):542-548. [CrossRef] [Medline]
    160. Frederix I, Hansen D, Coninx K, Vandervoort P, Vandijck D, Hens N, et al. Medium-term effectiveness of a comprehensive internet-based and patient-specific telerehabilitation program with text messaging support for cardiac patients: randomized controlled trial. J Med Internet Res. Jul 23, 2015;17(7):e185. [FREE Full text] [CrossRef] [Medline]
    161. Furuya RK, Arantes EC, Dessotte CA, Ciol MA, Hoffman JM, Schmidt A, et al. A randomized controlled trial of an educational programme to improve self-care in Brazilian patients following percutaneous coronary intervention. J Adv Nurs. Apr 17, 2015;71(4):895-908. [CrossRef] [Medline]
    162. Haller N, Lorenz S, Pfirrmann D, Koch C, Lieb K, Dettweiler U, et al. Individualized web-based exercise for the treatment of depression: randomized controlled trial. JMIR Ment Health. Oct 12, 2018;5(4):e10698. [FREE Full text] [CrossRef] [Medline]
    163. Horton EJ, Mitchell KE, Johnson-Warrington V, Apps LD, Sewell L, Morgan M, et al. Comparison of a structured home-based rehabilitation programme with conventional supervised pulmonary rehabilitation: a randomised non-inferiority trial. Thorax. Jan 29, 2018;73(1):29-36. [CrossRef] [Medline]
    164. Horton EJ, Ruksenaite J, Mitchell K, Sewell L, Newby C, Singh SJ. A comparison of physical activity between home-based and centre-based pulmonary rehabilitation: a randomised controlled secondary analysis. Front Rehabil Sci. 2021;2:743441. [FREE Full text] [CrossRef] [Medline]
    165. Lee DY, Park J, Choi D, Ahn HY, Park SW, Park CY. The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders: a randomized, controlled, open-label study. Sci Rep. Feb 26, 2018;8(1):3642. [FREE Full text] [CrossRef] [Medline]
    166. Li LC, Feehan LM, Xie H, Lu N, Shaw CD, Gromala D, et al. Effects of a 12-week multifaceted wearable-based program for people with knee osteoarthritis: randomized controlled trial. JMIR Mhealth Uhealth. Jul 03, 2020;8(7):e19116. [FREE Full text] [CrossRef] [Medline]
    167. Li Y, Qian H, Yu K, Huang Y. The long-term maintenance effect of remote pulmonary rehabilitation via social media in COPD: a randomized controlled trial. COPD. May 2022;Volume 17:1131-1142. [CrossRef]
    168. Lim SL, Ong KW, Johal J, Han CY, Yap QV, Chan YH, et al. Effect of a smartphone app on weight change and metabolic outcomes in Asian adults with type 2 diabetes: a randomized clinical trial. JAMA Netw Open. Jun 01, 2021;4(6):e2112417. [FREE Full text] [CrossRef] [Medline]
    169. Ögmundsdóttir Michelsen H, Sjölin I, Bäck M, Garcia MG, Olsson A, Sandberg C, et al. Effect of a lifestyle-focused web-based application on risk factor management in patients who have had a myocardial infarction: randomized controlled trial. J Med Internet Res. Mar 31, 2022;24(3):e25224. [FREE Full text] [CrossRef] [Medline]
    170. Moore J, Fiddler H, Seymour J, Grant A, Jolley C, Johnson L, et al. Effect of a home exercise video programme in patients with chronic obstructive pulmonary disease. J Rehabil Med. Feb 2009;41(3):195-200. [FREE Full text] [CrossRef] [Medline]
    171. Nagatomi Y, Ide T, Higuchi T, Nezu T, Fujino T, Tohyama T, et al. Home-based cardiac rehabilitation using information and communication technology for heart failure patients with frailty. ESC Heart Fail. Aug 2022;9(4):2407-2418. [FREE Full text] [CrossRef] [Medline]
    172. Namjoo Nasab M, Ghavam A, Yazdanpanah A, Jahangir F, Shokrpour N. Effects of self-management education through telephone follow-up in diabetic patients. Health Care Manag. 2017;36(3):273-281. [CrossRef]
    173. Wienbergen H, Fach A, Meyer S, Meyer J, Stehmeier J, Backhaus T, et al. Effects of an intensive long-term prevention programme after myocardial infarction - a randomized trial. Eur J Prev Cardiol. Mar 18, 2019;26(5):522-530. [CrossRef] [Medline]
    174. Osteresch R, Fach A, Frielitz FS, Meyer S, Schmucker J, Rühle S, et al. Long-term effects of an intensive prevention program after acute myocardial infarction. Am J Cardiol. Sep 01, 2021;154:7-13. [CrossRef] [Medline]
    175. Peng X, Su Y, Hu Z, Sun X, Li X, Dolansky MA, et al. Home-based telehealth exercise training program in Chinese patients with heart failure: a randomized controlled trial. Medicine (Baltimore). Aug 2018;97(35):e12069. [FREE Full text] [CrossRef] [Medline]
    176. Piotrowicz E, Zieliński T, Bodalski R, Rywik T, Dobraszkiewicz-Wasilewska B, Sobieszczańska-Małek M, et al. Home-based telemonitored Nordic walking training is well accepted, safe, effective and has high adherence among heart failure patients, including those with cardiovascular implantable electronic devices: a randomised controlled study. Eur J Prev Cardiol. Nov 26, 2015;22(11):1368-1377. [CrossRef] [Medline]
    177. Piotrowicz E, Piotrowski W, Piotrowicz R. Positive effects of the reversion of depression on the sympathovagal balance after telerehabilitation in heart failure patients. Ann Noninvasive Electrocardiol. Jul 02, 2016;21(4):358-368. [FREE Full text] [CrossRef] [Medline]
    178. Piotrowicz E, Pencina MJ, Opolski G, Zareba W, Banach M, Kowalik I, et al. Effects of a 9-week hybrid comprehensive telerehabilitation program on long-term outcomes in patients with heart failure: the telerehabilitation in heart failure patients (TELEREH-HF) randomized clinical trial. JAMA Cardiol. Mar 01, 2020;5(3):300-308. [FREE Full text] [CrossRef] [Medline]
    179. Pitta NC, Furuya RK, Freitas ND, Dessotte CA, Dantas RA, Ciol MA, et al. Effect of an educational program on physical activity in individuals undergoing their first percutaneous coronary intervention: a randomized clinical trial. Braz J Phys Ther. 2022;26(5):100443. [FREE Full text] [CrossRef] [Medline]
    180. Reid RD, Wooding EA, Blanchard CM, Moghei M, Harris J, Proulx G, et al. A randomized controlled trial of an exercise maintenance intervention in men and women after cardiac rehabilitation (ECO-PCR Trial). Can J Cardiol. May 2021;37(5):794-802. [FREE Full text] [CrossRef] [Medline]
    181. Kavradim ST, Özer ZC. The effect of education and telephone follow-up intervention based on the Roy Adaptation Model after myocardial infarction: randomised controlled trial. Scand J Caring Sci. Mar 2020;34(1):247-260. [CrossRef] [Medline]
    182. van der Weegen S, Verwey R, Spreeuwenberg M, Tange H, van der Weijden T, de Witte L. It's LiFe! Mobile and web-based monitoring and feedback tool embedded in primary care increases physical activity: a cluster randomized controlled trial. J Med Internet Res. Jul 24, 2015;17(7):e184. [FREE Full text] [CrossRef] [Medline]
    183. Vasilopoulou M, Papaioannou AI, Kaltsakas G, Louvaris Z, Chynkiamis N, Spetsioti S, et al. Home-based maintenance tele-rehabilitation reduces the risk for acute exacerbations of COPD, hospitalisations and emergency department visits. Eur Respir J. May 25, 2017;49(5):1602129. [FREE Full text] [CrossRef] [Medline]
    184. Vluggen S, Candel M, Hoving C, Schaper NC, de Vries H. A web-based computer-tailored program to improve treatment adherence in patients with type 2 diabetes: randomized controlled trial. J Med Internet Res. Feb 23, 2021;23(2):e18524. [FREE Full text] [CrossRef] [Medline]
    185. Witham MD, Fulton RL, Greig CA, Johnston DW, Lang CC, van der Pol M, et al. Efficacy and cost of an exercise program for functionally impaired older patients with heart failure. Circ: Heart Failure. Mar 2012;5(2):209-216. [CrossRef]
    186. Wootton SL, McKeough Z, Ng CL, Jenkins S, Hill K, Eastwood PR, et al. Effect on health-related quality of life of ongoing feedback during a 12-month maintenance walking programme in patients with COPD: a randomized controlled trial. Respirology. Jan 30, 2018;23(1):60-67. [FREE Full text] [CrossRef] [Medline]
    187. Coombes J, Keating S, Mielke G, Fassett R, Cooes B, O?Leary K, et al. Personal activity intelligence e-health program in people with type 2 diabetes: a pilot randomized controlled trial. Med Sci Sports Exerc. 2023;54(1):27. [CrossRef]
    188. Li LC, Sayre EC, Xie H, Falck RS, Best JR, Liu-Ambrose T, et al. Efficacy of a community-based technology-enabled physical activity counseling program for people with knee osteoarthritis: proof-of-concept study. J Med Internet Res. Apr 30, 2018;20(4):e159. [FREE Full text] [CrossRef] [Medline]
    189. Avila A, Claes J, Buys R, Azzawi M, Vanhees L, Cornelissen V. Home-based exercise with telemonitoring guidance in patients with coronary artery disease: does it improve long-term physical fitness? Eur J Prev Cardiol. Mar 2020;27(4):367-377. [CrossRef] [Medline]
    190. Wan ES, Kantorowski A, Polak M, Kadri R, Richardson CR, Gagnon DR, et al. Long-term effects of web-based pedometer-mediated intervention on COPD exacerbations. Respir Med. Feb 2020;162:105878. [FREE Full text] [CrossRef] [Medline]
    191. Moy ML, Martinez CH, Kadri R, Roman P, Holleman RG, Kim HM, et al. Long-term effects of an internet-mediated pedometer-based walking program for chronic obstructive pulmonary disease: randomized controlled trial. J Med Internet Res. Aug 08, 2016;18(8):e215. [FREE Full text] [CrossRef] [Medline]
    192. EndNote 20. Clarivate. 2013. URL: https://support.clarivate.com/Endnote/s/article/EndNote-20-Updates?language=en_US [accessed 2020-06-01]
    193. Covidence systematic review software. Veritas Health Innovation. URL: https://www.covidence.org/ [accessed 2020-06-01]
    194. Rohatgi A. WebPlotDigitizer. Automeris. URL: https://apps.automeris.io/wpd/ [accessed 2020-08-01]
    195. Goossen K, Rombey T, Kugler CM, De Santis KK, Pieper D. Strategies for effective study author contact to leverage existing research data when preparing systematic reviews – a randomised study within a review (SWAR). In: Proceedings of the 1st Evidence-Based Research Conference (EBR). Presented at: EBR '20; Nov 16-17, 2020, 2020; Virtual Event. [CrossRef]
    196. Stata statistical software: release 17. StataCorp LLC. 2021. URL: https://www.stata.com/support/faqs/resources/citing-software-documentation-faqs/ [accessed 2023-06-13]
    197. Eakin E, Reeves M, Winkler E, Lawler S, Owen N. Maintenance of physical activity and dietary change following a telephone-delivered intervention. Health Psychol. Nov 2010;29(6):566-573. [CrossRef] [Medline]
    198. Wong V, Ho F, Shi NK, Tong J, Chung KF, Yeung WF, et al. Smartphone-delivered multicomponent lifestyle medicine intervention for depressive symptoms: a randomized controlled trial. J Consult Clin Psychol. Dec 2021;89(12):970-984. [CrossRef] [Medline]
    199. Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, et al. American Heart Association Physical Activity Committee of the Council on LifestyleCardiometabolic HealthCardiovascular‚ Exercise‚ Cardiac RehabilitationPrevention Committee of the Council on Clinical Cardiology‚Council. Guide to the assessment of physical activity: clinical and research applications: a scientific statement from the American Heart Association. Circulation. Nov 12, 2013;128(20):2259-2279. [CrossRef] [Medline]
    200. Stavric V, Kayes NM, Rashid U, Saywell NL. The effectiveness of self-guided digital interventions to improve physical activity and exercise outcomes for people with chronic conditions: a systematic review and meta-analysis. Front Rehabil Sci. 2022;3:925620. [FREE Full text] [CrossRef] [Medline]
    201. Larsen RT, Wagner V, Korfitsen CB, Keller C, Juhl CB, Langberg H, et al. Effectiveness of physical activity monitors in adults: systematic review and meta-analysis. BMJ. Jan 26, 2022;376:e068047. [FREE Full text] [CrossRef] [Medline]
    202. Hall KS, Hyde ET, Bassett DR, Carlson SA, Carnethon MR, Ekelund U, et al. Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. Int J Behav Nutr Phys Act. Jun 20, 2020;17(1):78. [FREE Full text] [CrossRef] [Medline]
    203. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. Nov 25, 2020;54(24):1451-1462. [CrossRef]
    204. Bohannon RW, Crouch R. Minimal clinically important difference for change in 6-minute walk test distance of adults with pathology: a systematic review. J Eval Clin Pract. Apr 2017;23(2):377-381. [CrossRef] [Medline]
    205. Silfee VJ, Haughton CF, Jake-Schoffman DE, Lopez-Cepero A, May CN, Sreedhara M, et al. Objective measurement of physical activity outcomes in lifestyle interventions among adults: a systematic review. Prev Med Rep. Sep 2018;11:74-80. [FREE Full text] [CrossRef] [Medline]
    206. Shah A, Hussain-Shamsy N, Strudwick G, Sockalingam S, Nolan RP, Seto E. Digital health interventions for depression and anxiety among people with chronic conditions: scoping review. J Med Internet Res. Sep 26, 2022;24(9):e38030. [FREE Full text] [CrossRef] [Medline]
    207. Brown DS, Thompson WW, Zack MM, Arnold SE, Barile JP. Associations between health-related quality of life and mortality in older adults. Prev Sci. Jan 2015;16(1):21-30. [FREE Full text] [CrossRef] [Medline]
    208. Megari K. Quality of life in chronic disease patients. Health Psychol Res. Sep 24, 2013;1(3):e27. [FREE Full text] [CrossRef] [Medline]
    209. Phyo AZ, Freak-Poli R, Craig H, Gasevic D, Stocks NP, Gonzalez-Chica DA, et al. Quality of life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health. Nov 06, 2020;20(1):1596. [FREE Full text] [CrossRef] [Medline]
    210. Dias JF, Oliveira VC, Borges PR, Dutra FC, Mancini MC, Kirkwood RN, et al. Effectiveness of exercises by telerehabilitation on pain, physical function and quality of life in people with physical disabilities: a systematic review of randomised controlled trials with GRADE recommendations. Br J Sports Med. Feb 2021;55(3):155-162. [CrossRef] [Medline]
    211. Niemeijer A, Lund H, Stafne SN, Ipsen T, Goldschmidt CL, Jørgensen CT, et al. Adverse events of exercise therapy in randomised controlled trials: a systematic review and meta-analysis. Br J Sports Med. Sep 28, 2020;54(18):1073-1080. [CrossRef] [Medline]
    212. Anzueto A, Miravitlles M. Chronic obstructive pulmonary disease exacerbations: a need for action. Am J Med. Sep 2018;131(9S):15-22. [CrossRef] [Medline]
    213. Ziaeian B, Fonarow GC. The prevention of hospital readmissions in heart failure. Prog Cardiovasc Dis. Jan 2016;58(4):379-385. [FREE Full text] [CrossRef] [Medline]
    214. Clarkson P, Stephenson A, Grimmett C, Cook K, Clark C, Muckelt PE, et al. Digital tools to support the maintenance of physical activity in people with long-term conditions: a scoping review. Digit Health. 2022;8:20552076221089778. [FREE Full text] [CrossRef] [Medline]
    215. Manning SE, Wang H, Dwibedi N, Shen C, Wiener RC, Findley PA, et al. Association of multimorbidity with the use of health information technology. Digit Health. 2023;9:20552076231163797. [FREE Full text] [CrossRef] [Medline]
    216. Kelly P, Fitzsimons C, Baker G. Should we reframe how we think about physical activity and sedentary behaviour measurement? Validity and reliability reconsidered. Int J Behav Nutr Phys Act. Mar 01, 2016;13:32. [FREE Full text] [CrossRef] [Medline]
    217. Nielsen P, Sahay S. A critical review of the role of technology and context in digital health research. Digit Health. Jun 22, 2022;8:20552076221109554. [FREE Full text] [CrossRef] [Medline]
    218. Eysenbach G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res. Dec 31, 2011;13(4):e126. [FREE Full text] [CrossRef] [Medline]
    219. Agarwal S, LeFevre AE, Lee J, L'Engle K, Mehl G, Sinha C, et al. WHO mHealth Technical Evidence Review Group. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ. Mar 17, 2016;352:i1174. [CrossRef] [Medline]
    220. Franck CP, Babington-Ashaye A, Dietrich D, Bediang G, Veltsos P, Gupta PP, et al. iCHECK-DH: guidelines and checklist for the reporting on digital health implementations. J Med Internet Res. May 10, 2023;25:e46694. [FREE Full text] [CrossRef] [Medline]
    221. Carlesso LC, Skou ST, Tang LH, Simonÿ C, Brooks D. Multimorbidity: making the case for an end to disease-specific rehabilitation. Physiother Can. 2020;72(1):1-3. [FREE Full text] [CrossRef] [Medline]
    222. Neter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res. Jan 27, 2012;14(1):e19. [FREE Full text] [CrossRef] [Medline]
    223. van Kessel R, Wong BL, Clemens T, Brand H. Digital health literacy as a super determinant of health: more than simply the sum of its parts. Internet Interv. Mar 2022;27:100500. [FREE Full text] [CrossRef] [Medline]
    224. Fiuza-Luces C, Garatachea N, Berger NA, Lucia A. Exercise is the real polypill. Physiology (Bethesda). Sep 2013;28(5):330-358. [FREE Full text] [CrossRef] [Medline]

    ‎
    6MWT: 6-minute walk test
    COPD: chronic obstructive pulmonary disease
    HRQOL: health-related quality of life
    mHealth: mobile health
    MVPA: moderate to vigorous physical activity
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    RCT: randomized controlled trial
    SMD: standardized mean difference

    Edited by A Mavragani; submitted 12.02.23; peer-reviewed by V Stavric, C Koehn, SQ Yoong; comments to author 30.03.23; revised version received 20.05.23; accepted 25.05.23; published 06.07.23.

    Copyright

    ©Graziella Zangger, Alessio Bricca, Behnam Liaghat, Carsten B Juhl, Sofie Rath Mortensen, Rune Martens Andersen, Camma Damsted, Trine Grønbek Hamborg, Mathias Ried-Larsen, Lars Hermann Tang, Lau Caspar Thygesen, Søren T Skou. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.07.2023.

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