Effectiveness of Smartwatch Device on Adherence to Home-Based Cardiac Rehabilitation in Patients With Coronary Heart Disease: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Digital technologies have the potential to overcome many of the limitations associated with traditional center-based cardiac rehabilitation (CBCR), such as limited accessibility, transportation barriers, and low adherence. In this context, home-based cardiac rehabilitation (HBCR) has emerged as a promising alternative. However, maintaining adherence and providing continuous supervision in remote settings remain a major challenge. Smartwatch-based interventions may offer a novel solution to support and monitor patients in HBCR programs, yet robust clinical evidence is still limited. Objective: This study was designed to investigate the effectiveness of a smartwatch-facilitated HCBR model in improving exercise adherence and health-related outcomes in patients with coronary heart disease (CHD), aiming to improve adherence and other outcomes related to the secondary prevention of cardiovascular disease. Methods: We conducted a prospective, single-center, randomized, parallel-controlled, non-blinded trial. Eligible participants were adults (≥18 years) with a confirmed diagnosis of CHD, recruited from a tertiary hospital in Jilin Province, China. Participants were randomly assigned in a 1:1 ratio to either the intervention group (smartwatch-facilitated HBCR) or the control group (standard HBCR) for a duration of 3 months. The intervention group received a comprehensive program delivered via a smartwatch, including real-time feedback, remote supervision, physical activity monitoring, and educational content. The control group received conventional HBCR without technological assistance. The primary outcome was adherence to the HBCR program, assessed using the Home-Based Cardiac Rehabilitation Exercise Adherence Scale. Secondary outcomes included cardiopulmonary function (peak VO₂ measured via cardiopulmonary exercise testing), anxiety (Generalized Anxiety Disorder-7), depression (Patient Health Questionnaire-9), and health-related quality of life (36-Item Short Form Survey, SF-36), evaluated at baseline and at 3 months. Results: Between January 1 and December 30, 2023, a total of 62 patients (mean [SD] age 59.93 [10.06] years; 40.4% women [25/62]) were enrolled and randomized to the intervention group (n=32) or control group (n=30). Baseline characteristics were well balanced between the groups. At 3 months, participants in the smartwatch group demonstrated significantly higher adherence scores compared to the control group (P<.01). Additionally, the smartwatch group showed significant improvements in peak VO₂ (P<.01), anxiety (GAD-7, P<.01), depression (PHQ-9, P<.01), and selected domains of SF-36 (P<.05). No serious adverse events related to the intervention were reported, and user engagement with the smartwatch platform was high throughout the study period. Conclusions: This study demonstrates that a smartwatch-facilitated HBCR model is both feasible and effective in enhancing adherence and improving clinical outcomes among patients with CHD. These findings support the integration of wearable technology into routine HBCR and lay the groundwork for future large-scale, multicenter trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".