Feasibility and Preliminary Effectiveness of a Mobile App–Based Personalized Exercise Program in Older Patients With Chronic Knee Osteoarthritis: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background: Knee osteoarthritis is a prevalent cause of disability among older adults, emphasizing the need for effective and accessible self-management strategies. Mobile app-based personalized exercise programs predominantly overcome the barriers associated with traditional approaches. Objective: This study aimed to evaluate the feasibility and preliminary efficacy of a 6-week mobile app-based self-exercise program that incorporates a multimonitoring system, weekly progress tracking, and dynamic exercise adjustments used by physiotherapists, and compares them with those of a conventional paper-based self-exercise program in older patients with chronic knee osteoarthritis. Methods: A total of 29 participants aged ≥60 years with chronic knee pain and radiographic evidence of osteoarthritis were randomized at a 2:1 ratio to either the intervention (19/29, 66%; mobile app-based program) or control (10/29, 34%; paper-based program) group. The mobile app delivered a personalized exercise program, which was tailored by physiotherapists based on remote monitoring of patient-reported symptoms. Feasibility outcomes included retention, adherence, and satisfaction rates, as well as safety. Preliminary clinical outcomes included changes from baseline to 6 weeks in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score, Numeric Rating Scale (NRS) pain, and other functional measures. Results: A total of 26 participants (n=16, 62% intervention and n=10, 38% control) completed the 6-week assessment, with retention rates of 84% and 100%, respectively. No adverse events were reported. Adherence was high in the intervention group, with 69% exercising ≥5 days per week and 88% reporting high satisfaction. The intervention group exhibited significant reductions in the WOMAC total score (median change -11.00, 95% CI -23.00 to -2.50; P=.01) and NRS pain score (mean change -2.12, 95% CI -3.13 to -1.11; P<.001). Conclusions: The mobile app-based personalized exercise program was feasible, safe, and well-accepted among older patients with knee osteoarthritis. High adherence and satisfaction support the practicality of this approach, and preliminary improvements in pain and function suggest potential clinical benefit. A larger, adequately powered trial is warranted to confirm the effectiveness of digital self-exercise interventions for knee osteoarthritis management.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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) | 0.006 | 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".