Digital cardiac rehabilitation versus traditional cardiac rehabilitation in improving health parameters, patient satisfaction and adherence to guidelines—a systematic review and a meta-analysis

Introduction

The World Heart Report (WHR), carried out in 2023 by the World Heart Federation (WHF), revealed that cardiovascular diseases (CVDs) have become the leading cause of death worldwide as CVD-related deaths escalated from 12.1 million in 1990 to 20.5 million in 2021. Despite an overall increase in the last thirty years, the age-standardised death rate has globally decreased, therefore indicating unequal decline across different countries, especially in low- and middle-income ones. Having said this, governments and policymakers must address those inequalities (1,2).

Cardiac patients need more effective secondary prevention measures to mitigate the CVD burden (3), thereby reducing the risk of further cardiac events and hospital readmissions. Speaking of which, the multidisciplinary, well-structured, medically supervised, evidence-based cardiac rehabilitation (CR) programme has been shown to reduce cardiac death rates by at least 26% among patients who have already experienced cardiovascular (CV) events (3,4). Additionally, the European Society of Cardiology (ESC) Guidelines, the American Heart Association (AHA), and the American College of Cardiology (ACC) have defined CR as a Class Ia recommendation (5,6).

CR consists of three phases. The first one is the in-patient stage, followed by the out-patient phase, which entails a supervised ambulatory programme. Step three is the lifelong maintenance phase, which involves maintaining risk factor control, engaging in exercise training, and making lifestyle modifications (7). However, regardless of its established benefits, CR is still globally underused (8,9), according to estimates from the USA, ESC and the UK especially as impact of the coronavirus disease 2019 (COVID-19) pandemic (10) and other barriers to CR like low referral rates, lack of time, transportation limitations, geographical reasons, inability to drive, and frailty of specific patients (11).

Digital CR (DCR) has emerged as a supplementary approach to traditional CR (TCR) within medical practice in the recent digital health era, aiming to enhance acceptance rates and long-term outcomes, as well as extend care duration, particularly among older people (12,13). DCR can be provided online or remotely using various technologies, whereas TCR is offered within specialist rehab centres. Although DCR has been supported by the AHA, the British Heart Foundation, and the ESC, some organisations have emphasised the need for further research into eHealth interventions to ensure their efficacy and cost-effectiveness before widespread adoption in healthcare systems (4,6,10).

Over the last decade, there has been a significant surge in interest in telemedicine tools, particularly mobile applications (Apps), which are driving this field forward. Incorporating mobile Apps into clinical settings has garnered endorsement from the guidelines of cardiac societies. Numerous studies have evaluated its utility in managing cardiac patients, with many yielding promising results (13,14). This is particularly notable in response to the COVID-19 pandemic, given the significant momentum towards the digitalisation of medicine across various stakeholders (5,15,16).

Various studies have indicated that DCR might outperform TCR for patients with cardiac disease, reducing death, all-cause and cardiac-related hospitalizations, and emergency department visits (17,18) as well as improved diabetes, lipid, blood pressure (BP) managements, exercise capacity, adherence to lifestyle modifications, and enhanced quality of life (QoL) while maintaining long-term cost-effectiveness (19-21). Several randomised controlled trials (RCTs) have investigated the effectiveness of smartphone Apps in improving clinical outcomes beyond TCR (18,20,21). The rationale for this systematic review and meta-analysis is to compare the efficacy of DCR to TCR based on the available evidence, so CR through digital means may boost the overall use of such convalescence programmes as a steady resource, which can be delivered in a more tailored fashion, and to fill a gap in scientific literature, given the existence of scarce material on that matter in terms of systematic reviews and meta-analysis. Furthermore, this systematic review and meta-analysis aim to resolve the ambiguity around DCR and the inconsistencies in its efficacy across studies, which are needed for possible widespread adoption.

Objectives

This study reviewed existing research on the use of DCR compared to TCR.

Primary aims

• To assess if hospital readmission for all causes, cardiac-related readmissions, major adverse cardiovascular events (MACE), all-cause mortality, exercise capacity and CR adherence improve in DCR.

Secondary aims

• To investigate the impact of DCR on glycated haemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL-c), systolic BP (SBP), QoL, physical inactivity, ⁠⁠healthy diet, smoking status, ⁠⁠and medication adherence.

Definition of key terms

DCR

This is a home-based CR programme delivered virtually, primarily via technological devices such as mobile Apps and video conferencing.

TCR

This is a centre-based CR programme delivered primarily in person at healthcare facilities, such as hospitals and CR centres. We present this article in accordance with the PRISMA reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-404/rc) (22).

Methods

Study design

This systematic review investigated the use of DCR compared with TCR, following the population, intervention, comparison, outcome, and study design (PICOS) framework (23). The study was registered with the International Systematic Review Registry, PROSPERO, under registration number: CRD420251029747. There was no amendment to the original protocol; however, additional authors were added who contributed to the project.

Search strategy

A literature search was conducted following the PICO protocol, using a combination of key terms across the following databases: PubMed, Google Scholar, the Cochrane Central Register of Controlled Trials (CENTRAL), the WHO International Clinical Trials Registry Platform Search Portal, and ClinicalTrials.gov. Search tools, including Boolean operators (AND, OR), truncation, and wildcards, were also utilised, as were combining or excluding keywords, resulting in precise and relevant studies. Additionally, we also searched for grey literature on ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform and Google Scholar (23). The PICOS framework was used to define the scope of this review (23). Table 1 refers to PICO elements used to generate key terms for the database search.

The keywords used for search strategy were CR, digital technology, TCR, hospital readmission for all causes, cardiac-related readmissions, MACE, all-cause mortality, exercise capacity and CR adherence, HbA1c, LDL-c, BP, QoL, physical inactivity, ⁠⁠healthy diet, smoking status, ⁠⁠and medication adherence.

Study selection and eligibility

Three researchers independently conducted a literature search, and duplicates were removed using the Healthcare Databases Advanced Search programme. Subsequently, the titles and abstracts of the articles were screened to exclude any unrelated ones. The full texts were retrieved and critically assessed by three independent researchers, who also resolved discrepancies. Studies with unreported or missing data were also excluded. We also contacted the individual authors to request missing data; studies were excluded if the requested data were not provided or available.

We followed the PICO format for study selection, which consisted of patients undergoing CR (population), studies that investigated DCR (intervention), and studies that investigated DCR (comparison) (Table 1). Concerning outcomes, readmission for all causes, cardiac-related readmissions, MACE, all-cause mortality, improved exercise capacity, and CR adherence in patients undergoing DCR for primary ones and HbA1c, LDL-c, SBP, QoL improvement, ⁠⁠physical inactivity, ⁠⁠healthy diet, ⁠⁠current smoker, ⁠⁠and medication adherence in patients undergoing DCR for secondary ones.

Only RCTs and propensity-score matching (PSM) observational studies published in English-language and peer-reviewed journals from January 2010 to January 2025 were included in this meta-analysis. The literature search was performed in May 2025.

Selection of studies and quality assessment

Only 36 articles met the inclusion criteria in this systematic review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram (Figure 1). These studies were critically assessed and appraised using the ROB-2 Cochrane tool for RCTs and the ROBINS-I tool for non-randomised trials (22,24). All these studies are publicly available and accessible via the links provided.

Figure 1 PRISMA 2020 flow diagram for the systematic review and detailed breakdown of included and excluded studies. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Data collection and analysis

The data were independently extracted using a standardised extraction form in Microsoft Word, developed through Cochrane’s data collection form, which captured the study design, year of publication, and number of participants, as well as the country where the study was conducted, the digital technology intervention, and the outcomes measured. The collected data was stored on a laptop and in iCloud.

Data management and analysis

A general summary of the characteristics and findings of the included studies is provided, along with an evaluation of heterogeneity and risk of bias across them, and the use of the reviews’ effect sizes and their respective confidence intervals, as deemed appropriate by the researchers. The 36 studies selected were critically appraised to allow the reviewers to assess the studies’ reliability, validity, and relevance using the Critical Appraisal Skills Programme (CASP) checklist to evaluate the studies’ validity, relevance and reliability, as well as methodological quality and the possible impact on healthcare practice (25,26).

Statistical analysis

The statistical analysis was performed using the Review Manager (RevMan) software from Cochrane for systematic reviews and meta-analysis. Both primary and secondary outcomes were assessed using dichotomous data, the Mantel-Haenszel test, and the odds ratio (OR) as the effect measure, with a fixed-effects model. Sensitivity analysis was performed to assess heterogeneity across these studies, with I² values <25% considered low, 25–50% moderate, and >75% high. A fixed or random-effect model was used to assess robustness, and studies of low quality were excluded because they showed no significant variability. To assess potential publication bias, the Egger test was performed, and funnel plots were created to assess the distribution of study effects visually. The results suggested that publication bias is unlikely to be a significant issue in the study.

Bias

Bias assessment was carried out using the RevMan software on the Cochrane Library website, as noted in the forest plots for the selected studies presented in the results session. Still, the authors mitigated the risk of selection bias between patients who used the Well-Frame application and those in the TCR by employing propensity score-matching (27,28). Additionally, studies with missing data were excluded from this meta-analysis to minimise bias.

Results

A total of 7,257 patients from 36 selected RCTs were included in this study, with 3,340 in the DCR group and 3,917 in the TCR group, respectively. The meta-analysis showed DCR superiority over TCR for the following primary endpoints: hospital readmission for all causes, cardiac-related readmissions, and the secondary outcomes of physical inactivity, healthy diet, and smoking status. Table 2 provides the demographic information of the included studies. We provided the OR and its confidence interval for each outcome, along with the level of heterogeneity and the model type used.

Primary outcomes

The primary outcomes were as follows: hospital readmission for all causes, cardiac-related readmissions, MACE, all-cause mortality, exercise capacity, and adherence to CR in both groups.

Hospital readmission for all causes

Seven papers with moderate heterogeneity comprising 859 patients, DCR (n=392) and TCR (n=467) groups (P=0.02; I²=59%) compared this primary endpoint (14,29,32,47,53,58,59). The respective numbers of hospital readmissions for all causes were 68 and 132 for the DCR and TCR groups, respectively. The OR for hospital readmission in the DCR group vs. the TCR group was 0.37 [95% confidence interval (CI): 0.25–0.56; P<0.001], despite Maddison et al. (45) reporting a higher number of hospital readmissions in the DCR group (Figure 2). To address the moderate heterogeneity of studies related to this outcome, a sensitivity test was performed, and the analyses for the overall effect test Z and P values ranged from 2.39 to 5.05, with P<0.001 and P=0.02, respectively. The heterogeneity itself found Chi² range 3.96–15.27, P range 0.009–0.56, and I² range 0–67%. Figure 3 shows a small risk of publication bias for the studies included in this review.

Figure 2 Forest plot for hospital readmission for all causes (14,29,32,47,53,58,59). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Figure 3 Funnel plot for hospital readmission for all causes (14,29,32,47,53,58,59). OR, odds ratio; SE, standard error.

Cardiac-related readmissions

Nine studies (14,29,32,33,39,42,44,52,54) that included 1,072 patients evaluating cardiac-related readmissions showed that DCR (n=537) was beneficial compared with TCR (n=535). There were 67 and 128 events in each group, respectively (OR: 0.35; 95% CI: 0.23–0.51; P<0.001), and the forest plot analysis showed a moderate level of heterogeneity among the included papers (P=0.05; I²=48%) (Figure 4). To address the moderate heterogeneity of studies related to this outcome, a sensitivity test was performed, and the analyses for the overall effect test Z and P values ranged from 3.40 to 5.62, with P<0.00001 and P=0.0007, respectively. The heterogeneity itself found Chi² range 5.25–15.23, P range 0.03–0.63, and I² range 0–54%. Figure 5 shows a very low risk of publication bias for the included studies.

Figure 4 Forest plot for cardiac-related readmissions showing DCR to be superior over TCR (14,29,32,33,39,42,44,52,54). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Figure 5 Funnel plot for cardiac-related readmissions (14,29,32,33,39,42,44,52,54). OR, odds ratio; SE, standard error.

MACE

Four studies (32,33,52,54) evaluated 584 patients and assessed the effects of DCR (n=293) and TCR (n=291) on MACE prevention. Apart from the study by Yudi et al. (32), all other studies favored the efficacy of DCR over TCR in reducing the occurrence of MACE (low heterogeneity—P=0.81; I²=0%). However, the combined analysis did not show a statistical difference between the groups, despite 21 and 32 events in the DCR and TCR groups, respectively (OR: 0.60; 95% CI: 0.33–1.10; P=0.10) (Figure 6). Figure 7 shows no obvious publication bias risk for these studies.

Figure 6 Forest plot for MACE (32,33,52,54). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; MACE, major adverse cardiac event; TCR, traditional cardiac rehabilitation.

Figure 7 Funnel plot for MACE shows no obvious risk of publication bias (32,33,52,54). MACE, major adverse cardiac event; OR, odds ratio; SE, standard error.

All-cause mortality

Six studies compared the all-cause mortality between the two groups (35,36,42,44,52,58). The total number of events in both groups was 10 and 8 for the DCR and TCR groups, respectively; however, there was no statistically significant difference between the DCR (n=1,020) and TCR (n=1,086) groups. This is demonstrated by the pooled effect analysis in the forest plot as shown in Figure 8 with an OR: 1.40; 95% CI: 0.58–3.36; P=0.45—low heterogeneity (P=0.37; I²=7%). Figure 9 shows a funnel plot demonstrating a low risk of publication bias.

Figure 8 Forest plot for all-cause mortality (35,36,42,44,52,58). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Figure 9 Funnel plot for all-cause mortality (35,36,42,44,52,58). OR, odds ratio; SE, standard error.

Exercise capacity

The mean difference between the two groups was 3.26 (95% CI: −11.31 to 4.79), favoring the DCR group; however, there was high heterogeneity among the studies, and a random-effects model was used. The mean difference in PVO₂ between the groups was 0.36 (95% CI: 24.16–24.87), and the heterogeneity across studies was 1%. The 24-week between-groups analysis of exercise capacity favoured the DCR group (P<0.001) (31). This also applied to the post-6-minute walk test (6MWT) analysis (i.e., the 6MWT conducted at CR programme completion) and to the average time participants spent walking daily (P=0.01) (Figures 10,11).

Figure 10 Forest plot for exercise capacity determined by 6MWT (14,19,28,29,34). 6MWT, 6-minute walk test; CR, cardiac rehabilitation; CI, confidence interval; df, degrees of freedom; IV, inverse variance; SD, standard deviation.

Figure 11 Forest plot for exercise capacity determined by PVO₂ (30,32). CR, cardiac rehabilitation; CI, confidence interval; df, degrees of freedom; IV, inverse variance; SD, standard deviation.

Furthermore, in the trial by Akinosun et al. (20), both groups experienced significant enhancements in PVO₂ compared to their initial levels (TCR—pre: 18.7±4.9, post: 22.9±6.3, P<0.001; DCR—pre: 19.1±4.7, post: 27.3±5.6, P<0.001). However, DCR exhibited substantially higher physical activity levels than TCR (P=0.002). Additionally, Lear et al. (33) found that DCR participants had a greater increase in maximal treadmill time (45.7 seconds) than TCR participants (P=0.045).

In contrast, Kraal et al. (28) reported statistically insignificant results (P=0.40), despite improved PVO₂ maximal workload in both groups. Interestingly, self-reported leisure-time physical activity (P=0.05) and walking (P=0.02) were higher in the DCR group than in the TCR group. Authors attributed the lack of significant improvement in exercise capacity to either an exercise plan that may lack the intensity to enhance cardiorespiratory fitness or participants may not have fully grasped the intensity conveyed in the text messages, leading them to perceive their exertion levels as lower than necessary to make a meaningful impact on exercise capacity (34).

Adherence to CR

Four trials evaluated the adherence to CR (19,28,30,32). One study reported an adherence rate of 94% in the DCR group and 68% in the TCR group (30). The participants in the DCR group were 1.4 times more likely to adhere to the programme than those in the TCR arm (P<0.05). The OR for adherence to CR was 7.76 (95% CI: 4.60–13.8). A significant percentage (>70%) of the dropouts from the programme were observed in the TCR group. One study reported an adherence rate of 86% for the TCR group and 100% for the DCR group, respectively (P=0.049) (Figure 12). In the study by Varnfield et al. (30), four patients and three patients were lost to follow-up in the TCR and DCR group respectively and were excluded from the analysis. Another study reported higher adherence rates in the DCR (62%) compared to the TCR (19%) (P<0.001) (32). Another study showed that adherence was twice as high in the DCR group (80.4%) compared with the TCR group (42.0%) (19). Similar findings were also reported by Kraal et al. (28), showing an adherence rate of (82% for the DCR and 67% for the TCR), but as this was a prospective study, it was excluded from the forest plot graphic. Despite favoring results regarding the DCR approach, the statistical analysis showed superiority of the TCR (Figure 13). There was no risk of publication bias as demonstrated by the funnel plot (Figure 14). Sensitivity analysis for the included studies did not show any obvious heterogeneity (Table 3).

Figure 12 Forest plot showing adherence to CR showing superiority for TCR (19,28,30,32). CI, confidence interval; CR, cardiac rehabilitation; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Figure 13 Adherence to CR for included studies. CR, cardiac rehabilitation; DCR, digital cardiac rehabilitation; TCR, traditional cardiac rehabilitation.

Figure 14 Funnel plot for adherence to CR (19,28,30,32). CR, cardiac rehabilitation; OR, odds ratio; SE, standard error.

Secondary outcomes

The secondary outcomes were the following: HbA1c, LDL-c, SBP, QoL, physical inactivity, healthy diet, current smoking, ⁠⁠and medication adherence.

⁠Glycosylated HbA1c

No statistical significance was found between the DCR and TCR groups for HbA1c across the three studies (14,32,57) and the total sample size of 621 participants. The number of amelioration events in the DCR group was 15 in contrast with 13 in the TCR approach (low heterogeneity—P=0.90; I²=0%—OR: 1.19; 95% CI: 0.55–2.54; P=0.66) (Figure 15). The funnel plot did not show any publication bias (Figure 16).

Figure 15 Forest plot for glycated haemoglobin showing no obvious difference between the two groups (14,32,57). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Figure 16 Funnel plot for HbA1c showing no evidence of publication bias (14,32,57). HbA1c, glycated haemoglobin; OR, odds ratio; SE, standard error.

LDL-c

Five studies in this review evaluated LDL-C levels in both groups, targeting levels <1.4 mmol/L to prevent further atherosclerotic disease and CV events (11,29,30,32,33). Overall, there was no statistically significant difference between the two groups (Chi² value =5.43, P=0.25 and I²=26%), as shown in Figure 17. One study showed a significantly lower LDL-c level in the DCR group (P=0.02) (33). Two other trials (29,30) did not show a significant difference in LDL-c levels between the DCR and TCR groups. Similar findings were also observed in another study, in which no statistical difference was found between the two groups at baseline or at study completion (P=0.80) (32). Using the propensity score-matched models in the observational study (11), no statistically significant difference was observed between the two groups (P=0.87 and 0.29).

Figure 17 Forest plot for LDL-c showing no significant difference between the two groups (11,29,30,32,33). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; IV, inverse variance; LDL-c, low-density lipoprotein cholesterol; SD, standard deviation; TCR, traditional cardiac rehabilitation.

SBP

Six studies evaluating SBP showed no statistical significance for this outcome (14,29,30-32,60), whereas four studies (35,36,51,57) showed that DCR was beneficial compared to TCR (Figure 18). This effect, however, was statistically not significant. All six studies (14,29-32,60) showing no statistical significance had P values of 0.20, 0.78, 0.26, 0.67, 0.11, and 0.26, respectively, indicating non-significant differences. Additionally, Yudi et al. (32) reported that 25% of the participants did not reach the target BP (less than 130/80 mmHg) in both groups (Figure 18). Six studies (32,35,36,51,57,60) were included in the meta-analysis, whereas four studies (14,29-31) did not provide the number of patients in the event and control groups and were not included in the meta-analysis but were included in the systematic review. The funnel plot shows an unequal distribution of studies, suggesting a low risk of publication bias (Figure 19).

Figure 18 Forest plot for SBP showing no statistical difference between the two groups (32,35,36,51,57,60). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; SBP, systolic blood pressure; TCR, traditional cardiac rehabilitation.

Figure 19 Funnel plot for SBP showing no publication bias (32,35,36,51,57,60). OR, odds ratio; SBP, systolic blood pressure; SE, standard error.

QoL

Six studies evaluated QoL improvement using different tools; therefore, only three studies using the EuroQol-5 Dimension (EQ-5D)-Index were included in the forest plot analysis (30,32,34). Two studies from this group (32,34) also utilised the Short-Form 36 (SF-36) domain questionnaire. The other three studies utilised the MacNew questionnaire (28) and the 4-item Heart Qol questionnaire (14), while Widmer et al. (29) used the Dartmouth tool.

Varnfield et al. found that patients in the DCR group had improved QoL (P<0.001) compared to the TCR group (P=0.70), at the end of the 6-week cardiac rehab programme (P=0.01) (30). In contrast (34), demonstrated no statistical difference in the EQ-5D-Index (P=0.23). However, the SF-36 questionnaire used in this study showed substantial improvements in the general health domain (P=0.03) at 24 weeks.

Similarly, two other studies (14,29) reported improved QoL in the DCR group compared with the TCR group. It is worth noting that Widmer et al. (29) employed the Dartmouth tool. Finally, despite showing promising improvements in QoL across all questionnaire sections, the between-group differences were not statistically significant (P=0.49) in the study by Kraal et al. (28) (Figure 20).

Figure 20 Forest plot for QoL (30,32,34). CI, confidence interval; CR, cardiac rehabilitation; df, degrees of freedom; IV, inverse variance; QoL, quality of life; SD, standard deviation.

Physical inactivity/sedentary lifestyle

This meta-analysis revealed that patients in the DCR group were less likely to have a sedentary lifestyle compared to those in the TCR group. The total number of patients from the four studies included in this meta-analysis was 1,140 (36,38,51,56). The DCR group demonstrated higher physical activity than the TCR group, with 214 patients remaining sedentary in the former group and 374 in the latter. Figure 21 shows moderate heterogeneity (P=0.02) across these studies (I²=69%; OR: 0.32; 95% CI: 0.25–0.41; P<0.001). To address the moderate heterogeneity of studies related to this outcome, a sensitivity test was performed, and the analyses for the overall effect test Z and P values ranged from 4.23 to 9.26, with P<0.00001–P<0.0001. The heterogeneity itself found Chi² range 3.73–7.86, P range 0.02–0.15, and I² range 46–75% (Figure 21).

Figure 21 Forest plot for physical inactivity/sedentary lifestyle (36,38,51,56). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Healthy diet

Five studies (29,33,37,55,56) involving 565 patients evaluated healthy dietary habits in both groups. The data suggest that DCR is slightly superior to TCR, with 77 (total patients 247) and 102 patients (total patients 247) in the DCR and TCR groups, respectively, who adhered to the dietary advice during the follow-up programme (moderate heterogeneity—P=0.22; I²=35%—OR: 0.59; 95% CI: 0.39–0.90; P=0.01) (Figure 22). It is worth mentioning that 1 study (33) was excluded from the forest plot due to outcome for both groups being provided as mean instead of events to maintain uniformity. To address the moderate heterogeneity of studies related to this outcome, a sensitivity test was performed, and the analyses for the overall effect test Z and P values ranged from 1.15 to 2.77 and 0.006 to 0.25, respectively. The heterogeneity itself found Chi² range 0.53–3.05, P range 0.08–0.47, and I² range 0–67%.

Figure 22 Forest plot for a healthy diet showing slight superiority for DCR (29,37,55,56). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Current smoker

Based on data from 1,891 patients across nine studies included in the meta-analysis (29,33,36,37,41,51,55-57), DCR was superior to TCR for smoking cessation. A total of 214 vs. 289 patients successfully quit smoking in the DCR and TCR groups, respectively. Statistical analysis showed moderate heterogeneity, and the OR was 0.63, as shown in Figure 23 (moderate heterogeneity—P=0.006; I²=63%—OR: 0.65; 95% CI: 0.52–0.81; P<0.001). To address the moderate heterogeneity of studies related to this outcome, a sensitivity test was performed, and the analyses for the overall effect test Z and P values ranged from 0.55 to 4.48 and P<0.001 to P=0.58, respectively. The heterogeneity itself found Chi² range 12.01–21.46, P range 0.001–0.10 and I² range 47–67%.

Figure 23 Forest plot for smoking status showing superiority for DCR (29,33,36,37,41,51,55-57). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

Medication’s adherence

There was no statistically significant difference in medication adherence between the two groups, as reported in a meta-analysis of 2,265 patients across eight studies. Study 37 was excluded from the forest plot analysis due to outcomes presented as means instead of events. The remaining ones (29,36,42,48-50,60) demonstrated high heterogeneity (P<0.001; I²=78%; OR: 1.17; 95% CI: 0.96–1.42; P=0.01) (Figure 24). To address the high heterogeneity of studies related to this outcome, a sensitivity test was performed, and the analyses for the overall effect test yielded Z and P values ranging from 0.82 to 2.40 and from 0.02 to 0.41, respectively. The heterogeneity itself was found to have a Chi² range of 14.03–27.04, a P range of <0.00001 to 0.02, and an I² range of 42–82%.

Figure 24 Forest plot showing no difference between the groups for medication adherence (29,36,42,48-50,60). CI, confidence interval; DCR, digital cardiac rehabilitation; df, degrees of freedom; M-H, Mantel-Haenszel; TCR, traditional cardiac rehabilitation.

There was no major difference in the outcome across the sensitivity analyses, as shown in Table 4.

Discussion

This systematic review and meta-analysis aimed to compare the effectiveness of DCR with TCR based on selected primary and secondary endpoints. The primary endpoints included in this meta-analysis were hospital readmission for all causes, cardiac-related readmissions, MACE, all-cause mortality, exercise capacity, and adherence to the CR programme. In contrast, the secondary endpoints included HbA1c, LDL-c, SBP, QoL, physical inactivity, healthy diet, current smoking status, and medication adherence (61,62).

The DCR group had significantly fewer admissions than the TCR group, and the number of all-cause hospital readmissions was about half as many in the DCR group as in the TCR group. Similarly, cardiac-related readmissions were also significantly lower among the DCR group (63). However, the moderately high heterogeneity (I²=54%) observed among the included studies for all-cause readmission should be taken into account when interpreting these findings. This meta-analysis reaffirmed findings from previous RCTs and meta-analyses showing a reduction in all-cause and cardiac-related hospital admissions in the DCR group (18,64,65). Physical activity, smoking cessation, a healthy diet, and increased medication adherence in the DCR group could help reduce this significant difference.

Physical inactivity/sedentary lifestyle (66,67), smoking (68), and unhealthy diets (69,70) have all been associated with recurrent CV events and hospital readmission after a MACE. Furthermore, smoking cessation has been identified as the most important secondary preventive strategy in CR (71). Studies in this systematic review showed that DCR improved physical activity (21,72,73), healthy dietary habits (21), and outcomes related to nicotine dependence (74) compared to TCR. Thus, a structured DCR programme with a specific focus on these primary endpoints is likely to result in fewer all-cause and cardiac-related readmissions compared to TCR.

Exercise, a key factor in reducing future CVD events, was assessed primarily using PVO₂ and 6MWT in the studies included in this meta-analysis. This meta-analysis showed a statistically significant improvement in 6MWT and PVO₂ in the DCR group compared to the TCR group. Similarly, cardiac rehab and medication adherence were also higher in the DCR group, reaffirming findings from previous studies showing DCR to be either superior or non-inferior to TCR for cardiac rehab, medication adherence, and increased exercise capacity (21,72,74).

DCR has the potential to mitigate some of the challenges that are associated with TCR (21), such as unavailability of CR facilities, inaccessibility, unaffordability, inflexibility of schedules, time constraints, psychosocial factors (like anxiety, depression, and reduced sense of well-being), and patient preference (73,75-77). This could explain the increased cardiac rehab, medication adherence, and exercise capacity in the DCR group.

This meta-analysis also showed a lower incidence of MACE in the DCR group; however, this difference was not statistically significant. Furthermore, there was no statistically significant outcome difference in all-cause mortality between the two groups. These findings are consistent with results from previous studies showing similar MACE outcomes (18,64,73,74,78) and all-cause mortality in both groups (18,64).

There was no statistically significant difference between DCR and TCR for the secondary outcomes, including HbA1c, LDL-c, SBP, and QoL. This finding differs slightly from previous studies showing that DCR is superior to TCR for QoL (21,73); however, it affirms another meta-analysis showing similar outcomes for both groups on QoL (74). Other studies have reported similar findings for blood glucose, LDL-c, and BP with no significant difference between the two groups (21,74).

A major limitation of the DCR group was that they did not receive medication optimisation for the secondary outcomes, which could have led to better outcomes. Finally, the different assessment tools used to measure QoL might have significantly affected the outcomes. A key conceptual framework for the DCR would include human beings, environment, health, technology and outcomes (77). The human part would consist of healthcare providers and the patients. Higher motivation, self-efficacy, and improved mood are likely to lead to increased adherence to the CR programme. The key goal of the rehab programme is to promote patients’ motivation, leading to improved adherence and better outcomes. Several moderators could affect compliance with the cardiac rehab programme, including age, gender, social support, familiarity with the programme, trust in technology, and cognitive function. Older patients and males are less likely to have cybersickness compared to younger patients and women. Patients with better social support are more likely to adhere to the cardiac rehab programme (78).

Limitations

One major limitation of this study is the heterogeneity of the included outcomes. While heterogeneity was high for certain variables, such as SBP, QoL, physical inactivity, medication adherence, and 6MWT, it was low for other outcomes, including MACE, all-cause mortality, cardiac-related hospital readmissions, and PVO₂. To address this, sensitivity analyses were done, and studies with low quality were excluded. Additionally, digital technology might have posed recruitment challenges due to limited access or concerns about privacy and data security, thereby compromising sample sizes in some studies. Additionally, participant dropouts may have reduced the study’s statistical power, thereby introducing bias (64,65). Furthermore, limiting sample size to allow close monitoring of participants and minimise potential risks, despite the increased number of participants in DCR after the COVID-19 pandemic (66). The presence of cofounding variables is also a possible limitation to the study. We addressed confounding variables using a propensity score-matched design that included demographic, psychosocial, clinical, and functional factors. However, there remains the possibility of residual confounding from unaccounted factors. Changes in lipid levels could not be adequately assessed at entry and completion. Additional factors, such as cultural differences and socioeconomic status, may partially account for differences between individuals in the TCR and DCR groups (11).

Strengths

The systematic methodology used a robust approach to literature searches involving multiple databases, a combination of keywords and appropriate search tools tailored to each specific search strategy, and the meticulous use of the PRISMA checklist. Additionally, we included studies from the past 15 years to focus on the most up-to-date evidence. Moreover, most of the studies were RCTs, which represent the pinnacle of evidence hierarchy. Finally, this meta-analysis included both pre- and post-COVID-19 studies on DCR, enabling comparisons across both time points. Further, larger-sized RCTs are recommended to compare the effectiveness of DCR to TCR.

Conclusions

Overall, DCR has emerged as a reasonable option for improving the health status of patients with certain heart diseases and CV risk factors. This meta-analysis showed that DCR was more beneficial for primary outcome measures, such as cardiac-cause and all-cause hospital readmissions, exercise capacity, and adherence to a CR programme. However, there was no statistically significant difference in MACE between DCR and TCR. And all-cause mortality. Some secondary endpoints (physical inactivity, unhealthy diet, and smoking status) also significantly improved in the DCR group compared with the TCR group. In contrast, there was no statistical difference between the two groups for HbA1c, LDL-c, SBP, medication adherence, and QoL. The insignificant difference observed for some secondary endpoints may have been because many patients in the DCR group did not undergo medication optimisation, compared with those in the TCR group. Finally, larger RCTs are recommended to assess the efficacy of DCR vs. TCR. Also, studies on optimising medication intake during DCR are needed.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-404/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-404/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-404/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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