The effect of innovative smartphone application on adherence to a home-based exercise programs for female older adults with knee osteoarthritis in Saudi Arabia: a randomized controlled trial
Bibliographic record
Abstract
Purpose To examine the effects of an Arabic smartphone application on adherence to home exercise programs (HEPs) and the effectiveness of mobile-based HEPs on pain, physical function, and lower-limb muscle strength among older women with knee osteoarthritis (OA).Materials and methods This randomised control trial (ClinicalTrials.gov: (NCT04159883) enrolled 40 women aged ≥50 years with knee OA who were randomised into the app group (experimental; n = 20) receiving HEPs using an Arabic smartphone application called “My Dear Knee”, whereas the paper group (control; n = 20) receiving HEPs as hand-outs. Both groups had the same exercise program. Outcome measures were self-reported adherence, changes in the Arabic Numeric Pain Rating Scale, the Arabic version of the reduced Western Ontario, McMaster Universities Osteoarthritis Index-Physical Function subscale, and Five-Times Sit-To-Stand Test scores. All participants were assessed at baseline, at week 3 and week 6. Using completer-only analyses, the repeated measures ANOVA was used to compare the means of the outcome measures between the two groups.Results At the end of week 6, the app group reported greater adherence to HEPs (p = .002) and significant reduction in pain (p = .015).Conclusions A smartphone application with motivational and attractive features could enhance adherence to HEPs in this patient cohort.IMPLICATIONS FOR REHABILITATIONOlder adults with knee OA may face many obstacles that prevent or limit their adherence to the prescribed HEP.Smart device apps supported with attractive and motivational features could be an effective strategy to enhance adherence to HEPs among older adults with knee OA.Using such remote technology appears to overcome the barriers that may limit the ability of older women to receive supervised physical therapy in a clinical setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".