Effectiveness of lower limb rehabilitation protocol using mobile health on quality of life, functional strength and functional capacity among knee osteoarthritis patients who were overweight and obese: A randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Objective This study aimed to investigate the effectiveness of the lower limb rehabilitation protocol (LLRP) using mobile health (mHealth) on quality of life (QoL), functional strength, and functional capacity among knee OA patients who were overweight and obese. Materials and Methods In the current trial, 114 patients were recruited and randomized into either the rehabilitation group with mobile health (RGw-mHealth) receiving reminders by using mHealth to carry on the strengthening exercises of LLRP and instructions of daily care (IDC), the rehabilitation group without mobile health (RGwo-mHealth) following the strengthening exercises of LLRP and instructions of daily care (IDC) and control group (CG) only following the IDC for duration of 12-weeks. The reminders for using mHealth were provided two times a day for three days a week. Primary outcome measures were QoL assessed by the Western Ontario and McMaster Universities Osteoarthritis Index summary score, and functional strength by Five-Repetition Sit-To-Stand Test. Secondary outcome measure was functional capacity assessed by the Gait Speed Test. The assessments of QoL, functional strength, and functional capacity were taken at baseline and posttest after 12-weeks of intervention. Results After 12 weeks of intervention, patients in all three groups had statistically significant improvement in QoL within groups (p < 0.05). Furthermore, patients in the RGw-mHealth and RGwo-mHealth had statistically significant improvement in functional strength and walking gait speed within groups (p < 0.05). The pairwise between-group comparisons (Bonferroni post hoc test) of the mean changes in QoL, functional strength, and functional capacity at posttest assessments revealed that patients in the RGw-mHealth had statistically significant greater mean change in QoL, functional strength and functional capacity relative to both the RGwo-mHealth and CG (p < 0.001). Conclusion Improvement in QoL, functional strength, and functional capacity was larger among patients in the RGw-mHealth compared with the RGwo-mHealth or CG. TRIAL REGISTRATION Chinese Clinical Trial Registry: ChiCTR1900028600 Date of registration: 28-12-2019 Registration Status: Prospective URL: http://www.chictr.org.cn
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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) | 0.006 | 0.004 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".