Effects of In-Person and Distance Exercise Training on Outcomes of Knee Injury and Osteoarthritis among Elderly Individuals with Limited Literacy
Bibliographic record
Abstract
Background: Osteoarthritis is a common chronic disease of the musculoskeletal system in older adults. Aim: This study aimed to compare the effects of in-person and distance exercise training on the outcomes of knee injury and osteoarthritis among the elderly with limited literacy. Method: In this two-group randomized clinical trial with a pretest-posttest design, 60 elderly patients with knee injuries and osteoarthritis selected from two public parks in Mashhad during 2017-2018 were assigned to two groups of In-person and Distance training. The educational content, which included two stretching and three strength exercises for knee injury and osteoarthritis, was presented to the Distance group using booklets and multimedia. In the In-person group, the exercises were trained in eight 30-minute sessions, two days a week. The Western Ontario and McMaster Universities Osteoarthritis questionnaire was completed before and two months after training the exercises. To analyze the data, Mann-Whitney U test and Wilcoxon signed-rank test were run in SPSS, version 16. Results: The mean ages of the In-person and Distance groups were 68.2±5.6 and 69.2±9.4 years, respectively. We found a significant difference in the outcomes of knee injury and osteoarthritis post-intervention between the In-person and Distance groups (13.8±14.0 vs. 5.0±2.6; P≤0.003). Implications for Practice: Both methods could affect the outcomes of knee osteoarthritis. The in-person method was superior to distance training. These exercises are recommended as safe and cost-effective methods that could be included in health promotion programs targeting older adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".