Effect of high‐speed power training on muscle performance, function, and pain in older adults with knee osteoarthritis: A pilot investigation
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of high-speed power training (HSPT) on muscle performance, mobility-based function, and pain in older adults with knee osteoarthritis. METHODS: Thirty-three participants (mean ± SD age 67.6 ± 6.8 years) were randomized to HSPT (n = 12), slow-speed strength training (SSST; n = 10), or control (CON; n = 11) for a 12-week intervention. HSPT performed 3 sets of 12-14 repetitions at 40% of the 1-repetition maximum (1RM) "as fast as possible," SSST performed 3 sets of 8-10 repetitions at 80% of the 1RM slowly, and CON performed stretching and warm-up exercises. Outcome measures included leg press (LP) 1RM and LP peak power (PP) from 40-90% of the 1RM and the corresponding PP velocity (PPV) and PP force; 400-meter walk, Berg Balance Scale, and timed chair rise; and self-reported function and pain using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Analysis of variance models were used to compare changes from baseline to 12 weeks. Statistical significance was accepted at P < 0.05. RESULTS: LP PP improved in both HSPT and SSST compared to CON (P = 0.04). LP PPV improved only in HSPT (P = 0.01). There were also improvements in timed chair rise (P = 0.002), WOMAC function (P = 0.004), and WOMAC pain (P = 0.02) across all of the groups. CONCLUSION: HSPT was effective at improving function and pain, but no more so than either SSST or CON. Because HSPT improved multiple muscle performance measures (strength, power, and speed), it is a more effective resistance training protocol than SSST and may increase safety in this population, especially when high-speed movements are required during daily tasks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".