Self-Management-Centric Cardiac Rehabilitation for Acute Coronary Syndrome Patients During the COVID-19 Pandemic

Introduction

Cardiac rehabilitation (CR) has been proven to lower the risk of major adverse cardiac events (MACEs) as an essential evidence-based intervention for secondary prevention of acute coronary syndrome (ACS), earning a Class I recommendation in both European and American guidelines [1–3]. Low CR participation is associated with a 20–25% increase in 1-year mortality and approximately $10 000 higher healthcare costs per patient [4,5]. Center-based CR (CBCR) is a conventional mode in CR; however, the global participation rate is well below 50%. This can be attributed to the lack of suitable programs, patient scheduling conflicts, and traffic constraints [6,7]. The global outbreak of COVID-19, which began in late 2019 and rapidly spread in early 2020, further exacerbated these issues [8]. The sharp increase in cases, along with the necessary social distancing to reduce the spread of COVID-19, has hindered CBCR from proceeding normally.

Home-based CR (HBCTR) emerged as a viable alternative that was non-inferior to CBCR in reducing cardiac hospitalization, improving functional capacity, promoting positive behaviors, and managing risk factors [9–12]. However, its effects on MACEs remain controversial. While Ma et al reported a 7.4% reduction in 3-year composite MACEs with HBCTR [13], a systematic review found no mortality benefit compared to CBCR (relative risk 1.19, 95% CI 0.65–2.16) [14]. This discrepancy may stem from 2 critical limitations in current HBCTR implementations – insufficient intervention durations and inadequate emphasis on self-management sustainability – a finding supported by trials demonstrating that behavioral change techniques improve adherence and reduce MACEs [9,13,15,16]. Standard HBCTR models often focus primarily on structured exercise protocols and telemonitoring of physiological parameters, with limited emphasis on improving patients’ self-management skills for long-term behavioral sustainability [17,18]. This suggests that most interventions operate under clinician-driven paradigms, where patients passively follow prescribed regimens without acquiring the cognitive tools or confidence to independently manage their condition after the intervention. Crucially, the long-term effectiveness of CR relies on the patients’ abilities to maintain therapeutic lifestyle modifications beyond supervised phases. Current trial designs compound this limitation through short intervention periods (typically ≤6 months), failing to bridge the pivotal transition from supervised rehabilitation to lifelong self-management [9,13,15,16]. These shortcomings may explain why HBCTR has shown inconsistent effects on MACEs and mortality despite improving functional metrics.

In contrast, the present study evaluated a self-management-centric HBCTR (SMCCR) plan designed to overcome these gaps. While standard HBCTR emphasizes compliance with clinician-prescribed exercise plan, SMCCR integrates 2 novel components: (1) self-management training, delivered through a smartphone platform teaching patients goal setting, problem-solving, and relapse prevention strategies for diet [19], exercise, and medication adherence; and (2) a graduated autonomy-support framework that transitions patients from weekly telehealth coaching to long-term self-management of daily life and chronic diseases. This approach diverges fundamentally from conventional HBCTR by prioritizing patient empowerment over passive compliance. Additionally, while earlier studies relied on intermittent provider-patient interactions, SMCCR employs a design grounded in the gradual reduction of external support to foster autonomy: intensive support during the first month (daily coaching, weekly monitoring) tapers to monthly check-ins by month 12, enabling patients to transition from reliance on external feedback to dependence on intrinsic self-management capabilities, thereby reinforcing self-efficacy [20]. This plan partly addressed the situation observed in prior HBCTR trials, where benefits diminish after supervised care ends [21]. Furthermore, during the COVID-19 pandemic lockdown, SMCCR could help maintain continuity in CR and reduce the interruption of CR caused by the inability to access center-based care.

This retrospective cohort study aimed to evaluate whether patients who finished SMCCR plan could demonstrate: (1) reduced 3-year MACEs, and (2) enhanced self-perceived disease awareness, improved medication adherence, and better cardiovascular risk factor management compared to non-completers.

Material and Methods

STUDY DESIGN:

This retrospective cohort study was designed to evaluate the real-world implementation of finishing SMCCR in ACS patients treated during the COVID-19 pandemic (2019–2021) at a tertiary hospital in Xuzhou, China. Patients were consecutively screened and data were passively collected from 2 sources: the institution’s electronic data capture (EDC) system (established in 2019; http://edc.valtai.com/#/MemberManagement) and hospital electronic medical records. The dataset included demographic information, treatment details, imaging results, rehabilitation records, and follow-up data, with quality control measures implemented throughout.

Approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (Approval No. XYFY2023-KL202-01), this study adhered to the Declaration of Helsinki principles. The institutional review board granted a waiver for written informed consent due to the retrospective nature of data collection from existing medical records. Data analysis was conducted from May to August 2024.

STUDY POPULATION:

Inclusion criteria were: 1) first ACS episode (ST-segment elevation myocardial infarction (STEMI) or non-ST-segment elevation ACS (NSTEACS) confirmed by angiography, with or without percutaneous coronary intervention (PCI); 2) participation in the SMCCR plan with complete follow-up data recorded in the EDC system; and 3) mild COVID-19 infection not requiring hospitalization and recovery without sequela.

Exclusion criteria included: 1) maintenance dialysis; 2) severe or uncontrolled systemic dysfunction (eg, uncontrolled hypertension, poorly controlled diabetes mellitus, hepatic dysfunction, metabolic disorders, history of epilepsy or other central nervous system diseases, or severe psychiatric disorders); 3) active malignancy; 4) death within 30 days of ACS onset; and 5) incomplete data or loss to follow-up. For missing data, this study used a strict exclusion principle: all cases with any missing values in key variables (including primary and secondary endpoints and baseline characteristics) were excluded from the final analysis. We acknowledged that this conservative approach ensured high completeness of the analytical dataset but may affect the generalizability of the study results.

SMCCR PLAN DURING THE COVID-19 PANDEMIC:

Due to pandemic restrictions, the SMCCR plan adapted HBCTR into a telemedicine-based delivery model emphasizing self-management. As part of routine clinical practice, all ACS patients were uniformly informed about CR options and benefits, but the participation was voluntary without active recruitment. All patients completed Phase I in-hospital CR, including at least 1 supervised exercise session adhering to clinical guidelines to ensure safety, before transitioning to telemedicine-guided SMCCR at home [17,18,22].

The plan included guidance on multimorbidity, risk factors, medication adherence, exercise, nutrition, and psychosocial management. The implementation strategy involved: 1) daily engagement (first week): multimedia-supported tele-education (e.g., pictures, videos), and medication reminders via WeChat (Figure 2); 2) progressive supervision with weekly phone/text monitoring and guidance in the first month, transitioning to monthly intervals until month 12, then quarterly until year 3 (intensive initial support followed by gradual frequency reduction); and 3) periodic evaluations: in-person interviews at 1, 3, 6, and 12 months, transitioning to annual evaluation thereafter, focusing on education, medication optimization, target goals achievement, and reinforcement of self-management. The final target goals and measures for each SMCCR component are provided in detail in Table 1.

The final retrospective analysis was based on whether patients adhered to this plan for a long time. Patients were categorized as SMCCR-finished (SMCCRF) if they completed all scheduled interviews and assessments; otherwise, they were classified as SMCCR-unfinished (SMCCRNF).

STUDY OUTCOMES:

Primary and secondary outcomes were collected covering the period from the date of discharge following the first ACS event to December 31, 2023.

The primary outcome was MACEs, defined as cardiovascular (CV)-related mortality, non-fatal myocardial infarction (MI), unplanned revascularization, heart failure (HF) readmission, and non-fatal stroke. The definitions of each component of MACEs referred to the 2017 Cardiovascular and Stroke Endpoint Definitions for Clinical Trials [23], shown in Table 1.

The secondary outcomes included the management of risk factors, categorized into behavioral outcomes (self-perceived disease awareness, medication adherence, exercise adherence, and smoking cessation), physiological indices (blood pressure [BP], heart rate [HR], and body mass index [BMI]), and biochemical indices (low-density lipoprotein cholesterol [LDL-C] and glycated hemoglobin [HbA1c]). The definitions, targets, and measurement methods of each parameter are detailed in Table 1.

Self-perceived disease awareness was assessed using the 20-item Coronary Artery Disease Education Questionnaire-short Version (CADEQ-SV) [24], a True/False/I do not know format questionnaire divided into 5 domains, with 4 items per domain. Medication adherence was evaluated based on a Medication Possession Ratio (MPR) threshold of ≥75% for at least 3 of 4 prescribed drug classes: antiplatelet agents, statins, β-blockers, and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEI/ARBs/ARNIs). The MPR was calculated as the proportion of days covered by the prescribed medications within the observation period [25]. BP was measured 3 times in the right upper arm using a standard sphygmomanometer with participants in a seated position after 5 minutes of rest, and the readings were averaged. LDL-C and HbA1c levels, along with myocardial injury markers (high-sensitivity troponin T [hsTnT] and N-terminal pro-B-type natriuretic peptide [NT-proBNP]), were analyzed in fasting venous blood samples using standardized laboratory protocols. Left ventricular ejection fraction (LVEF) was assessed via two-dimensional echocardiography with Simpson’s biplane method.

STATISTICAL ANALYSIS:

To adjust for confounding variables, including PCI urgency (categorized as no PCI, emergent PCI, urgent PCI, or elective PCI) and baseline comorbidities, we performed 1: 1 propensity score matching (PSM) between the SMCCRF and SMCCRNF groups without replacement, with a calliper width set to 0.1 times the standard deviations (SD) of the logit-transformed propensity scores. Balance between groups was deemed acceptable if the standardized mean differences (SMDs) for all covariates were <0.2, as presented in Table 2 [26].

Continuous variables were summarized as mean (SD), or median (inter-quartile range, IQR) based on their distribution, assessed via the Shapiro-Wilk normality test. The t test was applied to normally distributed data, while the Wilcoxon rank-sum test was used for non-normally distributed data. Categorical variables are expressed as frequencies (%) and were analyzed with the chi-square test.

Kaplan-Meier curves were generated to visualize survival outcomes, and between-group difference in MACEs were assessed with the log-rank test. Cox proportional hazards models were employed to estimate hazard ratios (HRs). For outcomes violating the proportional hazards assumption (assessed via Schoenfeld residuals), we performed stratified Cox models. Additionally, landmark analyses were conducted for unplanned revascularization and non-fatal stroke at predefined timepoints.

Univariable and multivariable Cox regression analyses were performed using standardized data, with results expressed as HRs and 95% confidence intervals (CIs) to evaluate the association between SMCCR and MACEs after adjusting for covariates. Prespecified subgroup analyses were conducted based on diagnosis type, age categories (≤44, 45–64 vs ≥65 years), Killip classification (I vs >I), LVEF categories (≤40%, 41–49% vs ≥50%), hospital length of stay (HLOS, ≤5 vs >5 days), β-blockers (yes/no), and essential hypertension (EH, yes/no). A two-sided P value of <0.05 was considered statistically significant. To assess the robustness of our findings to potential unmeasured confounding, we performed sensitivity analyses using E-values. The E-value quantifies the minimum strength of association that an unmeasured confounder would need to have with both the exposure (SMCCR completion) and the outcome (MACEs) to fully explain away the observed effect. E-values were calculated for the HRs of primary outcomes, with higher E-values indicating greater robustness to unmeasured confounding. R statistical software (version 4.2.1; R Foundation for Statistical Computing) with packages including MatchIt for PSM, survival for Cox models, and ggplot2 for visualization.

Results

BASELINE CHARACTERISTICS:

A total of 1627 first-episode ACS patients (median age 66 years, 71.67% male) met the criteria, with 785 patients finishing the SMCCR plan, while 842 did not.

Table 2 presents the baseline characteristics of the SMCCRF and SMCCRNF groups before and after PSM. Prior to matching, significant differences (P<0.05) were observed in baseline demographics, comorbidities, cardiac function, medications, risk factors, and laboratory parameters between the 2 groups. Propensity scores were estimated via logistic regression including these indices. Nearest-neighbor matching with a calliper of 0.1 SD was applied to minimize bias. Following 1: 1 PSM, 691 matched pairs were retained, with SMDs <0.2 for all covariates, indicating adequate balance.

PRIMARY OUTCOME IN THE PSM COHORT:

Over a median follow-up of 23.39 months, the SMCCR group demonstrated striking clinical benefits.

MACES: As detailed in Table 3 and Figure 3, patients who finished the SMCCR plan exhibited a significantly lower incidence of MACEs compared to the SMCCRNF group (7.43% vs 18.74%; HR 0.43, 95% CI 0.30–0.60; P<0.001; Figure 3A), driven by notable reductions in CV mortality, non-fatal MI, HF readmission, and delayed-onset unplanned revascularization (divergence after 70 days, P=0.02). Stroke incidence was comparable between the groups. Multivariable Cox regression analyses identified finishing the SMCCR plan as an independent predictor of reduced MACEs risk, alongside 7 additional clinical variables (Table 4).

Subgroup analyses (Figure 4) consistently favored SMCCRF except in patients aged ≤44 years or those with LVEF ≤40%, likely due to limited sample sizes in these subgroups.

Patients in the SMCCRNF group missed a median of 3 sessions (IQR: 1–4). Notably, 71.89% missed 1–3 sessions and 28.11% missed ≥4 sessions; however, the number of missed sessions did not differ significantly across pandemic phases (pre-2021 [early pandemic] vs 2021–2023 [sustained pandemic], P=0.365). A dose-dependent relationship between missed sessions and adverse outcomes emerged: patients missing ≥4 sessions had a significantly higher risk of MACEs compared to those who missed 1–3 sessions (HR 1.94, 95% CI 1.34–2.81; Figure 3A), as well as 3-fold greater CV mortality and 71% increased HF readmission (all P<0.05). No significant difference was observed for other CV events and pandemic phases did not modify outcomes (P=0.668).

ASSESSMENT OF SELF-PERCEIVED DISEASE AWARENESS: Baseline CADEQ-SV scores did not differ significantly between the SMCCRF and SMCCRNF groups (mean [SD]: 9.12 [3.01] vs 9.00 [3.05], P=0.488). However, patients in the SMCCRF group demonstrated higher scores compared to the SMCCRNF group at 12 months and 36 months (+1.0 and +2.0 points respectively, P<0.05 for both), but within-group improvements did not reach clinically meaningful thresholds (Figure 5A).

MEDICATION ADHERENCE: Baseline medication adherence was near-perfect in both groups (99.03% vs 98.55%, P>0.05). From month 6 onward, adherence declined progressively in both groups, but the SMCCRF group continued to have consistently higher rates than the SMCCRNF group at all subsequent timepoints (all P<0.001). This is possibly due to SMCCR transitions from weekly to monthly monitoring during this period. By 36 months, patients in the SMCCRF group retained 76.92% adherence versus 49.35% in the SMCCRNF group, which is a 27.57% absolute difference (P<0.001; Figure 5B).

PHYSICAL EXERCISE AND RISK FACTORS:

Adherence to the plan-guided physical exercise was higher in the SMCCRF group compared to the SMCCRNF group, with between-group difference widening over time (Figure 5C).

The SMCCRF group also demonstrated superior control of risk factors, including BP, HR, BMI, LDL-C, HbA1c, and smoking cessation rates, with more patients achieving target thresholds at 12, 24, and 36 months (all P<0.01; Figure 6). Although both groups showed improvement from baseline, the SMCCRNF group exhibited greater variability in risk factor control over time.

The changes in cardiac function are noteworthy. LVEF and left ventricular end-diastolic dimension (LVEDD) improved during the first year in both groups but subsequently declined, with more deterioration in the SMCCRNF group. Patients in the SMCCRF group preserved better LVEF at 36 months (52.00% vs 50.00%; P<0.001) and earlier reverse remodeling benefits (LVEDD difference emerging within 3 months, P<0.001; Figure 6).

Additionally, all of the secondary outcomes, including CADE-Q SV, showed comparable trajectories over time between groups (P>0.05 for all intervals), while patients with ≥4 missed sessions demonstrated significantly lower CADE-Q SV scores at 24-month follow-up (P<0.001).

SENSITIVITY ANALYSES:

Despite rigorous PSM, potential unmeasured confounders (eg, socioeconomic status, patient motivation) may have influenced the results. Sensitivity analyses using E-values suggested that an unmeasured confounder would need to increase the odds of SMCCRF by 4-fold to negate the observed MACEs reduction (E-value=4.08).

Discussion

PRIMARY OUTCOMES:

The SMCCRF group exhibited a markedly reduced incidence of MACEs compared to the SMCCRNF group, particularly in terms of CV death and HF readmission rates. This result contrasts with previous studies, which reported that digital education programs improved disease awareness and self-management behaviors in ACS patients, but did not significantly reduce hospitalizations or mortality [33–35], likely due to the key difference: the tapering support model (intensive → self-directed) may sustain engagement better than fixed-duration programs. However, this association should be interpreted with caution. Although our results suggest a strong link between SMCCRF and reduced MACEs, causality cannot be definitively established due to the observational design of the study. The observed benefits may reflect both the direct effects of SMCCR and the intrinsic characteristics of patients who adhered to the plan. Remarkably, our definition of the SMCCRNF group (missing ≥1 session) aimed to identify patients with suboptimal engagement. However, the subgroup analysis revealed a dose-response relationship: those missing ≥4 sessions had nearly double the MACEs risk compared to those missing 1–3 sessions. This suggests that even modest reductions in SMCCR adherence can attenuate clinical benefits, consistent with prior studies [36,37]. The reasons for non-attendance and missed session types should be explored in future to better understand barriers to engagement.

Additionally, younger patients and those with severely reduced LVEF showed limited benefits, possibly due to the low proportion of young patients and the inherent challenges of managing advanced HF, respectively. This finding aligns with a study that reported a high drop-out rate among young adults in CR programs [38]. These results indicate the need for tailored interventions for specific patient subgroups, such as younger individuals and those with severe cardiac dysfunction, who need more attention or other individualized designed programs.

SECONDARY OUTCOMES:

The SMCCRF group exhibited consistently higher CADE-Q SV scores from the first month onward, with baseline scores being comparable between groups. This suggests that disease awareness improvements were driven by the plan rather than by pre-existing differences. The SMCCRF group also demonstrated better risk factor management compared to the SMCCRNF group, although fluctuations in medication adherence and smoking cessation were observed over time. The decline in medication adherence aligns with the phenomenon of “intervention fatigue” linked to sustained monitoring and reminders [39], while smoking relapse may reflect renewed exposure to stimuli (eg, stress and alcohol use) [40]. These findings suggest that the intensity and frequency of SMCCR support after several months may require adjusted intensity to maintain long-term behavioral changes.

The reduced incidence of MACEs in the SMCCRF group may be attributed not only to the plan itself but also to the synergistic effects of enhanced disease awareness, personalized goal setting, and behavior tracking. Cardiac function (LVEF and LVEDD) showed a slight improvement, likely due to near-normal baseline values in many patients. Additionally, while SMCCR may not have directly affected cardiac remodeling or function within the study period, its benefits on behavioral factors and clinical events are evident. These results suggest the importance of integrating self-management strategies into cardiac rehabilitation programs to optimize long-term outcomes.

In the context of the COVID-19 pandemic, SMCCR ensured continuity of CR, while minimizing the risk of virus transmission. Its applicability possibly extends beyond the pandemic [30,31,41,42], offering a scalable model for underserved populations, including those with limited access to CBCR, irregular working schedules, social phobia, women, and under-represented ethnic minorities [15,43]. SMCCR offers flexible implementation tailored to varying healthcare resource levels. In resource-rich settings, integration with electronic health records and wearable devices enables automated monitoring, while resource-limited regions can utilize text/voice messaging to deliver a simplified version of SMCCR, coupled with periodic community health worker visits. SMCCR serves as a cost-saving and accessible alternative to traditional rehabilitation that simultaneously reduces healthcare system burdens. This innovative plan achieves efficiency gains through 3 primary mechanisms: (1) significantly reducing direct staffing requirements through remote monitoring, (2) eliminating patients’ transportation needs, and (3) expanding service coverage without additional physical space. Additionally, the automated tele-education may further reduce waitlists by shifting appropriate patients to SMCCR. All these advantages contribute to enhanced system-wide efficiency and reduced hospitalization rates. While proven cost-effective in Poland [44], further research is needed to validate outcomes across different economic contexts, quantify long-term maintenance costs, and optimize protocols for the most resource-constrained settings. Even after the pandemic, SMCCR remains a valuable complement to CBCR, and can balance efficiency and effectiveness, particularly for healthcare systems facing workforce shortages.

The study has several limitations. First, the exclusive inclusion of mild COVID-19 cases may limit the generalizability of our results. Hospitalized or severe COVID-19 patients with complications may experience significantly impaired physical and spiritual well-being, and their disease trajectories and self-management challenges may differ substantially from those observed in our cohort [45]. Future studies should stratify interventions based on the severity levels of pandemic disease and proven hematological predictors to better address diverse clinical needs [46,47]. Second, smartphone-based intervention may exclude the elderly and patients with low education levels, who often need family member participation. This factor may limit generalizability to underserved populations, suggesting the need for hybrid technology-based approaches (eg, integrating low-cost wearable devices with healthcare worker support) to enhance applicability in resource-limited settings. Third, the lack of cardiopulmonary exercise testing (CPET) affected the precision of exercise prescriptions. During the pandemic, CPET was deemed high-risk due to its aerosol-generating nature, which could facilitate viral transmission [48,49], especially among patients with comorbidities. Finally, the retrospective design may have caused residual confounding. While PSM was used to reduce observed confounding, unmeasured confounders may still have biased the results. Our sensitivity analyses using E-values suggested moderate robustness to unmeasured confounding, though residual bias from factors such as patient motivation or socioeconomic status cannot be entirely ruled out.

Future research should prioritize multi-center RCTs to establish causal relationships between SMCCR adherence and clinical outcomes. To enhance generalizability, such trials should stratify enrolment by key variables identified in our study (eg, age ≤44 years, LVEF ≤40%) and include economic endpoints (eg, incremental cost per MACE prevented). Parallel qualitative studies exploring patient and provider barriers to engagement would further optimize intervention delivery. Only through rigorous RCT evidence can SMCCR transition from an observational association to a validated standard of care.

Conclusions

In summary, SMCCR reduces mortality, enhances disease awareness, and improves guideline-recommended risk factor control in ACS patients, demonstrating its potential as a complement or alternative to traditional rehabilitation. Its real-world applicability includes enabling CR accessibility in resource-limited settings by reducing direct clinical staffing and infrastructure costs, maintaining CR continuity during public health emergencies, and advancing health equity by extending service coverage to traditionally excluded populations, including ethnic minorities and female patients. To fully realize this potential, personalized approaches are needed to address subgroup disparities and sustained engagement requires adaptive interventions (eg, extended follow-ups, adaptive telemonitoring) to counter late-phase adherence declines. These refinements can further optimize SMCCR to deliver equitable, long-term benefits across diverse patient populations.

Data Availability Statement

To protect the privacy of participants, data analyzed in this study can be accessed from the corresponding authors (drluyuan329@163.com) only upon reasonable request.