Telehealth coaching to improve self-management for secondary prevention after stroke: A randomized controlled trial of Stroke Coach
Bibliographic record
Abstract
BACKGROUND: Stroke Coach is a lifestyle coaching telehealth program to improve self-management of stroke risk factors. AIMS: To examine the efficacy of Stroke Coach on lifestyle behavior and risk factor control among community-living stroke survivors within one-year post stroke. METHODS: Participants were randomized to Stroke Coach or an attention control Memory Training group. Lifestyle behavior was measured using the Health Promoting Lifestyle Profile II. Secondary outcomes included specific behavioral and cardiometabolic risk factors, health-related quality of life (HRQoL), cognitive status, and depressive symptoms. Measurements were taken at baseline, post-intervention (6 months), and retention (12 month). Linear mixed-effects models were used to test the study hypotheses (p < 0.05). All analyses were intention-to-treat. RESULTS: The mean age of the Stroke Coach (n = 64) and Memory Training (n = 62) groups was 67.2 and 69.1 years, respectively. The majority of participants (n = 100) had mild stroke (modified Rankin Scale = 1 or 2), were active, with controlled blood pressure (mean = 129/79 mmHg) at baseline. At post-intervention, there were no significant differences in lifestyle (b = -2.87; 95%CI - 8.03 to 2.29; p = 0.28). Glucose control, as measured by HbA1c (b = 0.17; 95%CI 0.17 to 0.32; p = 0.03), and HRQoL, measured using SF-36 Physical Component Summary (b = -3.05; 95%CI -5.88 to -0.21; p = 0.04), were significantly improved in Stroke Coach compared to Memory Training, and the improvements were maintained at retention. CONCLUSION: Stroke Coach did not improve lifestyle behavior; however, there were improvements to HbA1c and HRQoL among community-living stroke survivors with mild stroke-related disability. (ClinicalTrials.gov identifier: NCT02207023).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".