Psychological eHealth Interventions for Patients With Cardiovascular Diseases: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Psychological distress is recognized as an independent risk factor for cardiovascular diseases (CVDs), contributing to increased morbidity and mortality. While eHealth is increasingly used to deliver psychological interventions, their effectiveness for patients with CVDs remains unclear. Objective This meta-analysis aimed to evaluate the effects of eHealth psychological interventions for patients with CVDs. Methods Eligible studies were retrieved from 5 databases (Embase, Medline, PubMed, CINAHL, and Cochrane Library), covering the period from database inception to December 2024. Randomized controlled trials (RCTs) investigating the effect of evidence-based psychological eHealth interventions to improve psychosocial well-being and cardiovascular outcomes for people with CVDs were included. The Cochrane Risk of Bias tool (version 2) was used to judge the methodological quality of reviewed studies. RevMan (version 5.3) was used for meta-analysis. Results A total of 12 RCTs, comprising 2319 participants from 10 countries, were included in the review. The results demonstrated significant alleviation of depressive symptoms for patients receiving psychological eHealth intervention compared to controls (number of paper included in that particular analysis, n=7; standardized mean difference=–0.30, 95% CI –0.47 to –0.14; I2=57%; P<.001). More specifically, in 6 trials where internet-based cognitive behavioral therapy was delivered, a significant alleviation of depressive symptoms was achieved (standardized mean difference=–0.39, 95% CI –0.56 to –0.21; I2=53%; P<.001). There was no significant change in anxiety or quality of life. Synthesis without meta-analysis regarding stress, adverse events, and cardiovascular events showed inconclusive findings. Conclusions Psychological eHealth interventions, particularly internet-based cognitive behavioral therapy, can significantly reduce depressive symptoms among patients with CVDs. A multidisciplinary approach is crucial for comprehensively improving psychological and cardiovascular outcomes. Future studies should explore integrating persuasive design features into eHealth and involving mental health professionals for intervention delivery. Trial Registration PROSPERO CRD42023452276; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023452276
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".