Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease
Bibliographic record
Abstract
BACKGROUND: The CardioFit Internet-based expert system was designed to promote physical activity in patients with coronary heart disease (CHD) who were not participating in cardiac rehabilitation. DESIGN: This randomized controlled trial compared CardioFit to usual care to assess its effects on physical activity following hospitalization for acute coronary syndromes. METHODS: A total of 223 participants were recruited at the University of Ottawa Heart Institute or London Health Sciences Centre and randomly assigned to either CardioFit (n = 115) or usual care (n = 108). The CardioFit group received a personally tailored physical-activity plan upon discharge from the hospital and access to a secure website for activity planning and tracking. They completed five online tutorials over a 6-month period and were in email contact with an exercise specialist. Usual care consisted of physical activity guidance from an attending cardiologist. Physical activity was measured by pedometer and self-reported over a 7-day period, 6 and 12 months after randomization. RESULTS: The CardioFit Internet-based physical activity expert system significantly increased objectively measured (p = 0.023) and self-reported physical activity (p = 0.047) compared to usual care. Emotional (p = 0.038) and physical (p = 0.031) dimensions of heart disease health-related quality of life were also higher with CardioFit compared to usual care. CONCLUSIONS: Patients with CHD using an Internet-based activity prescription with online coaching were more physically active at follow up than those receiving usual care. Use of the CardioFit program could extend the reach of rehabilitation and secondary-prevention services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.011 | 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".