Multi-Component Exercise with High-Intensity, Free-Weight, Functional Resistance Training in Pre-Frail Females: A Quasi-Experimental, Pilot Study
Bibliographic record
Abstract
BACKGROUND: No study has performed an exercise intervention that included high-intensity, free-weight, functional resistance training, and assessed frailty status as an inclusion criteria and outcome measure via original, standardized tools, in pre-frail females. OBJECTIVES: Determine if the intervention strategy is not only feasible and safe, but can also improve frailty status, functional task performance, and muscle strength. DESIGN: Pilot, quasi-experimental. SETTING: Community. PARTICIPANTS: 20 older-adults with pre-frailty characteristics. INTERVENTION: 12-weeks (3 days/week, 45-60 minutes/session) of multi-component exercise, inclusive of aerobic, resistance, balance and flexibility exercises. The crux of the program was balance and resistance exercises, the latter utilized high-intensity, free-weight, functional resistance training. The control group maintained their usual care. MEASUREMENTS: 1) Feasibility and safety (dropout, adherence, and adverse event); 2) Frailty (Frailty Phenotype, Clinical Frailty Scale, and gait speed); 3) Functional task performance (grip strength and sit-to-stand time); and 4) Isometric and isotonic strength of the knee extensors and elbow flexors. RESULTS: No participants dropped out of the intervention or experienced an adverse event, and adherence averaged 88.3%. The exercise group became less frail, whereas the control group became more frail. There was a significant within-group improvement in exercise participants gait speed (p ≤ 0.01, +0.24 m/sec), grip strength (p ≤ 0.01, +3.9 kg), and sit-to-stand time (p ≤ 0.01, -5.0 sec). There was a significant within-group improvement in exercise participants knee extension isometric torque (p ≤ 0.05, +7.4 Nm) and isotonic velocity (p = ≤ 0.01, +37.5 ˚/sec). Elbow flexion isotonic velocity significantly declined within the control group (p ≤ 0.01, -20.2 ˚/sec) and demonstrated a significant between-group difference (p ≤ 0.05, 40.73 ˚/sec) post-intervention. CONCLUSIONS: The intervention strategy appears to be feasible and safe, and may also improve frailty status, functional task performance, and muscle strength. These results help calculate effect size for a future randomized controlled trial.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".