Effects of Protein-Rich Nutritional Composition Supplementation on Sarcopenia Indices and Physical Activity during Resistance Exercise Training in Older Women with Knee Osteoarthritis
Bibliographic record
Abstract
Older adults with knee osteoarthritis (KOA) are at high risk of sarcopenia. Protein-rich nutritional composition supplementation (PS) combined with resistance exercise training (RET) improves muscle gains and facilitates physical activity in older adults. However, whether PS augments the effects of RET on muscle mass and PA in patients with KOA remains unclear. Therefore, this study identified the effects of PS on sarcopenic indices and PA in older women with KOA subjected to an RET program. Eligible older women aged 60–85 years and diagnosed as having KOA were randomly assigned to either the experimental group (EG) or the control group (CG). Both groups performed RET twice a week for 12 weeks. The EG received additional PS during this period. Outcome measures included appendicular lean mass index, walking speed, physical activity, and scores on the Western Ontario and McMaster Universities Osteoarthritis Index—WOMAC). All measures were tested at baseline and after intervention. With participant characteristics and baseline scores as covariates, analysis of variance was performed to identify between-group differences in changes in all outcome measures after intervention. Statistical significance was defined as p < 0.05. Compared with the CG, the EG achieved greater changes in appendicular lean mass index (adjusted mean difference (aMD) = 0.19 kg/m2, p < 0.01), physical activity (aMD = 30.0 MET-hour/week, p < 0.001), walking speed (aMD = 0.09 m/s, p < 0.05), and WOMAC global function (aMD = −8.21, p < 0.001) after intervention. In conclusion, PS exerted augmentative effects on sarcopenic indices, physical activity, and perceived global WOMAC score in older women with KOA through 12 weeks of RET.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".