Interventions to Enhance Early Recognition and Management of Mental Health Symptoms in Patients with Cardiovascular Disease: A Rapid Scoping Review
Bibliographic record
Abstract
Background: Poor mental health is a recognized risk factor for adverse cardiovascular outcomes, yet depression, anxiety, and stress remain underdiagnosed and undertreated in individuals with cardiovascular disease (CVD). Effective strategies to promote early recognition and management of these conditions are not well established. We conducted a rapid scoping review to identify interventions aimed at improving mental health recognition and management in adult CVD population without diagnosed mental health conditions. Methods: We systematically searched MEDLINE, EMBASE, CENTRAL, PsycINFO, CINAHL, Web of Science, and Epistemonikos for articles published between January 1, 2014, and December 28, 2024. Results: Of 11,645 screened studies, 24 met inclusion criteria: 12 systematic reviews and meta-analyses, and 12 randomized controlled trials (RCTs). Most focused on coronary artery disease or stroke patients. Interventions included mindfulness interventions (n = 9), routine screening (n = 2), interactive mHealth education (n = 2), psychosocial interventions (n = 4), caregiver education (n = 4), self-care (n = 1), and integrated care (n = 1). Interventions were multimodal pairing patient education with structured clinical encounters. Reporting of delivery methods was inconsistent and fewer than half assessed adherence. Only two RCTs involved patients in intervention design. Primary outcomes included changes in psychological distress symptoms and quality-of-life measures. Conclusion: A variety of interventions target early recognition and management of mental health symptoms in CVD patients. The approach of combining self-management with clinician check-ins aligns with contemporary models of integrated care. Standardized reporting and greater interest-holder engagement are needed to improve intervention development, implementation, and evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".