Delivery of Peer Support Through a Self-Management mHealth Intervention (Healing Circles) in Patients With Cardiovascular Disease: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is a leading cause of hospitalization and death around the world. The prevalence of CVD is increasing and, therefore, development and investigation of effective programs to help people better self-manage their CVD and prevent secondary complications are needed. OBJECTIVE: In this paper, we report on a protocol to evaluate Healing Circles-an evidence-based and patient-informed peer support mobile health program designed to facilitate self-management and support patients in their recovery from and management of CVD. We hypothesize that individuals with CVD who use Healing Circles will experience greater improvements to their self-management ability than individuals receiving usual care. METHODS: In this single-blinded (assessor) randomized controlled trial, 250 community-living individuals with CVD will be randomized on a 1:1 basis to either Healing Circles or Usual Care. The primary outcome of self-management will be measured using the Health Education Impact Questionnaire version 3.0. Secondary outcomes include self-efficacy with chronic disease management, health-related quality of life, health resource use and costs, and electronic health literacy. Measurements will be taken at the baseline and every 6 months for 24 months. RESULTS: The study started recruitment in September 2017. Individuals are currently being recruited for participation, and existing participants are currently on follow-up. Measurements will be taken every 6 months until the study end, which is anticipated in December 2019. CONCLUSIONS: Healing Circles is a novel program aimed toward improving self-management through peer support. Given our real-world study design, our findings will be readily translatable into practice. If the results support our hypothesis, it will indicate that Healing Circles is an effective intervention for improving self-management and reducing health care use. TRIAL REGISTRATION: ClinicalTrials.gov NCT03159325; https://clinicaltrials.gov/ct2/show/NCT03159325 (Archived by WebCite at http://www.webcitation.org/74DvxVKUd). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12322.
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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 | 0.039 | 0.039 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".