The Effects Of Power Training Frequency On Functional Performance In Healthy, Older, Untrained Women.
Bibliographic record
Abstract
Power training (PT) in older adults can improve muscle power and functional performance. The majority of studies have utilized higher intensities for training (≥ 60% of maximum strength) and have included combined results for older men and women. Less is known about the effects of low-intensity PT on muscle performance and function in older, healthy women. In addition, the dose-response of PT on power and function with 1, 2, or 3 days/week in older adults has not been determined. PURPOSE: The purpose of this study was to investigate the impact of different weekly frequencies of low-intensity PT on muscle strength, power, and function in healthy, older, untrained women. METHODS: Older women (n = 54) were randomized to PT 1 (n = 14), 2 (n = 17), or 3 (n = 17) days/week or wait-control, C (n = 15). Participants undertook 12 weeks of PT using lower-body resistance training machines at an intensity of 40% of the 1-repetition maximum (1RM), and performed the concentric phase of the exercises ‘as fast as possible’. The primary outcome was functional performance (Short Physical Performance Battery, stair climb, 30 second chair stands, and 400-meter walk) and secondary outcomes were strength (leg-press 1RM) and power (knee-extension power at 40% of maximal isometric strength). RESULTS: Within-group analyses (pre-post time points) indicated that strength improved in all PT groups (p < 0.05) with a 23.7%, 23.3%, 34.8%, and 9.8% increase from baseline for PT1, PT2, PT3 and C, respectively. Pre-post power improved significantly in PT2 and PT3 (p < 0.05) by 9.6% and 12.2%, respectively. For pre-post function, all PT groups improved in 3 of 4 functional tests (p < 0.05) with improvements ranging from 4.0 - 21.7% and with no differences observed between groups. Although the control group showed small but significant improvement in some aspects of function over the course of the study, effects sizes for all PT groups suggest small to large improvements above that observed in the controls. The large intra-individual variability in the data might have limited statistical power to detect differences between the groups. CONCLUSIONS: PT of 2 days/week or more is recommended for improving muscle power, however, 1 session weekly might be sufficient for improving functional performance.
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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) | 0.000 | 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) | 0.002 | 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".