Mobile app to support home exercise adherence in knee osteoarthritis: A randomized clinical trial
Bibliographic record
Abstract
OBJECTIVE: Determine if a mobile app alongside a home strength program improves exercise adherence and/or function in knee osteoarthritis. DESIGN: 2-arm superiority randomized clinical trial in adults with knee osteoarthritis and physical dysfunction. Both groups received physiotherapist-prescribed strength exercises (prescribed thrice weekly). Intervention group also received the 'My Exercise Messages' app (provides exercise tracking, goal-setting and behavior change messages). Two self-reported primary outcomes measured: i) number of days exercises were performed over prior fortnight (ordinal outcome dichotomized as "complete adherence" [score 6 or more] or "incomplete adherence" [scores 0-5]) and ii) change in function (Western Ontario and McMaster Universities Osteoarthritis Index, 0-68), at 14 weeks (secondary end-point) and 26 weeks (primary end-point). Secondary outcomes included knee pain; sport/recreation function; knee-related quality of life; physical activity; exercise self-efficacy; global change; satisfaction; another exercise adherence measure; and joint replacement willingness. RESULTS: Of 182 people (mean (SD) age 63.5 (8.1) years, 126 (69%) women) randomized, 171 (94%) and 173 (95%) provided primary outcomes at 14 and 26 weeks respectively. At 26 weeks, the exercise plus app group were more likely to be completely adherent (n=31 (36%) versus n=14 (16%) in exercise only) to prescribed exercise frequency (relative risk [95% CIs], 2.1 [1.4, 3.2], p<0.001) but function was not different (mean difference [95% CIs], -0.9 [-3.9, 2.12], p=0.52). Results were similar at 14 weeks. Secondary outcomes did not differ, except Exercise Adherence Rating Scale, favoring exercise plus app. CONCLUSION: The app improved exercise adherence but this did not translate into improved function.
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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 | 0.003 | 0.004 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".