CARDIOVASCULAR RISK MODERATES THE EFFECT OF RESISTANCE TRAINING ON PHYSICAL PERFORMANCE IN OLDER ADULT WOMEN
Bibliographic record
Abstract
Abstract We aimed to examine whether the Framingham Cardiovascular Risk Profile Score (FCRP) moderates the effect of progressive resistance training (RT) on mobility in older adult women. This is an exploratory analysis of a single-blind, 12-month randomized controlled trial in 155 omen, aged 65 to 75 years old, who were randomized to: 1x/week progressive RT; or 2x/week progressive RT program; or 2x/week balance and tone (BAT). At baseline and trial completion, mobility was measured using the Short Physical Performance Battery (SPPB). The SPPB is a composite measure of usual gait speed, standing balance, and sit to stand performance; scores < 9/12 are indicative of functional decline. Baseline 10-year cardiovascular risk was calculated using the FCRP. Participants were classified as either low risk (<16.5% FCRP score; LCVR) or high risk ≥16.5% FCRP score; HCVR). A complete case analysis (n=126) was conducted using a two-way analysis of covariance (ANCOVA) to evaluate the interaction effect of group by FCRP risk on SPPB scores at trial completion; baseline SPPB scores and age in years were entered as covariates. There was a significant interaction effect (F(1,126)=3.74, p=0.027). At trial completion, both 1x/RT and 2x/RT participants with HCVR demonstrated greater SPPB scores than those with LCVR (11.59 vs. 11.38 for 1x/week; 11.86 vs 11.46 for 2x/week). In contrast, BAT participants with HCVR demonstrated worse SPPB scores than those with LCVR (11.18 vs 11.66). Our data suggest that RT may be more efficacious for improving mobility in older women with higher cardiovascular risk than women with lower risk.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".