Using Mobile-Health to Connect Women with Cardiovascular Disease and Improve Self-Management
Bibliographic record
Abstract
Background/Introduction: Self-management approaches are regarded as appropriate methods to support patients with cardiovascular disease (CVD) and to prevent secondary complications and hospitalizations. Key to successful self-management is the ability of individuals to enlist peer supports to help sustain motivation and efforts to manage their condition. The purpose of this study was to investigate the proof of concept of a peer-support mobile-health (m-health) program, called Healing Circles, and explore the program's effect on self-management, social support, and health-related quality of life in women with CVD. MATERIALS AND METHODS: Healing Circles is a consumer m-health solution developed to facilitate peer support and self-management by connecting people with CVD in groups of five to nine people. Women with CVD (obstructive coronary artery disease) were included in this single group, pre/post study if they owned an iPhone/iPad with at least iOS 7.0. Participants (n = 21) used the Healing Circles program for a 10-week period. Self-management, social support, and health-related quality-of-life outcomes were assessed before and after the use of the program. User experiences and satisfaction were obtained during an exit interview. RESULTS: After 10 weeks of using the Healing Circles program, statistically significant improvements were observed in the participants' health behaviors (p = 0.04), self-monitoring (p = 0.04), social support (p = 0.01), and social integration (p = 0.002). As well, many women had a level of high satisfaction with the concept of using m-health for the delivery of peer support. CONCLUSION: The delivery of peer and self-management support using m-health technologies is well received and may improve self-management and social support. More research is needed to test hypotheses of the effect of the Healing Circles program on clinical outcomes.
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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 | 0.004 | 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 | 0.001 | 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".