Cardiac Self-Efficacy Improvement in a Digital Heart Health Program: Secondary Analysis From a Feasibility and Acceptability Pilot Study
Bibliographic record
Abstract
Background: Lifestyle modification programs play a critical role in preventing and managing cardiovascular disease (CVD). A key aim of many programs is improving patients' self-efficacy. In-person lifestyle modification programs can enhance self-efficacy in managing CVD risk, also known as cardiac self-efficacy (CSE). However, such programs are typically staffing and resource intensive. Digital lifestyle modification programs may offer a scalable and accessible way to improve CSE, but this has not been shown in prior research. Objective: This study examined changes in CSE among individuals using a digital lifestyle modification program for cardiovascular health. Evaluation of improvement in CSE was a secondary goal of a feasibility and acceptability pilot study of a digital program for Heart Health. Methods: Participants were individuals with elevated risk for CVD who enrolled in a 90-day pilot study that involved mobile app-based, artificial intelligence-powered health coaching and educational lessons focused on behaviors that promote cardiovascular health. Participants completed the 9-item CSE Scale at baseline and in month 2. Changes in confidence in participants' ability to manage their cardiovascular health were assessed. Results: The sample included 273 (n=207, 61.2% female; mean age 59.3, SD 10.1 years) participants who submitted a complete CSE Scale at baseline and in month 2. The total CSE Scale score increased by 12.9% (P<.001) from baseline to month 2. Additionally, there were significant increases in mean score on each of the 9 individual CSE Scale items (all P<.001), with the largest increases in confidence "in knowing when to call or visit the doctor for your heart disease" (17% increase; P<.001), "in knowing how much physical activity is good for you" (16.3% increase; P<.001), and "that you can get regular aerobic exercise" (19% increase; P<.001). Conclusions: The present analyses indicate that participants in a digital lifestyle modification program for cardiovascular health showed significant improvements in CSE within 2 months. This work adds to the growing literature examining ways to improve health-related self-efficacy and scalable access to programs for prevention and management of CVD.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".