Development of video exercise-based mobile application to improve the clinical outcomes in patients with knee osteoarthritis: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to develop a video exercise-based mobile application and investigate its effectiveness in terms of pain, function, expectation, and satisfaction in patients with knee osteoarthritis (OA). METHODS: A randomized controlled trial was carried out with 52 individuals with knee OA. Participants were randomly allocated into two groups: the mobile application group (MAG) (n = 26) and the control group (CG) (n = 26). MAG received the two-month rehabilitation program via the developed application. CG was given paper-based exercise forms with the same protocol. Participants' pain, expectation, and satisfaction levels were assessed with the Visual Analog Scale (VAS), and function was assessed with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). All assessments were performed at baseline and after 8 weeks. RESULTS: Both MAG and CG showed statistically significant improvement in VAS-rest, VAS-activity, WOMAC-pain, WOMAC-stiffness, WOMAC-function, and WOMAC-total scores (p < 0.05). However, there was no significant difference between MAG and CG for all pain and function scores (p > 0.05). In addition, no difference was observed between the two groups regarding expectation-satisfaction changes (p > 0.05). Besides, expectation-satisfaction change was not significantly different within each group (p > 0.05). CONCLUSIONS: Rehabilitation presented with the mobile application was effective regarding pain and function. However, rehabilitation via mobile application did not provide additional contribution to pain, function, expectation-satisfaction compared to usual rehabilitation. Differences in change for function and satisfaction exceeded the minimal clinically important difference (MCID), favoring MAG. TRIAL REGISTRATION: ClinicalTrials.gov NCT06422910, 15 May 2024.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".