The Effects of Power Training Frequency on Muscle Power and Functional Performance in Older Women: A Randomized Controlled Trial
Bibliographic record
Abstract
ABSTRACT: Katsoulis, K and Amara, CE. The effects of power training frequency on muscle power and functional performance in older women: a randomized controlled trial. J Strength Cond Res 37(11): 2289-2297, 2023-Low-intensity power training (PT) has emerged as an effective method for improving muscle power and functional performance in older adults. However, effects of low training frequencies are less understood and could expand the repertoire of exercise prescription, particularly in older women who experience greater functional disability with age compared with men. This study investigated the impact of frequency of low-intensity (40% of 1 repetition maximum, 1RM) PT on lower-body power and functional performance in healthy older women. Women (74 ± 4 years) were randomized to 12 weeks of PT of 1 (PT1, n = 14), 2 (PT2, n = 17), or 3 (PT3, n = 17) d·wk -1 or wait control (CON, n = 15). Measures included leg press 1RM, knee extension power (KEP), and functional performance (stair climb power, stair climb time, 30-second chair stands, 400-m walk, Short Physical Performance Battery). There were no differences between the frequency of training in changes in leg press 1RM, KEP, or functional performance after 12 weeks. Pre-post data for individual training groups revealed that leg press 1RM improved in all PT groups (20-33%, p < 0.05). Furthermore, KEP improved in PT2 and PT3 by 10 and 12%, respectively, and all PT groups improved in the 30-second chair stands and Short Physical Performance Battery (6-22%), whereas PT1 and PT3 improved in the 400-m walk and PT2 improved in stair climb power and stair climb time after training (4-7%, p < 0.05). One to 3 weekly low-intensity PT sessions can improve functional performance, although improvements in both functional performance and power might require 2 or 3 sessions per week in older healthy women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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) | 0.009 | 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".