Home-based high-intensity functional strength training (HIFST) for community-dwelling older adults with preclinical mobility limitations after a slip, trip, or fall: a pilot randomized controlled trial
Bibliographic record
Abstract
PURPOSE: To determine the feasibility and preliminary effects of a home-based 12-week high-intensity functional strength training (HIFST) intervention for community-dwelling older adults (≥55 years) experiencing preclinical mobility limitations after a slip, trip, or fall. MATERIALS AND METHODS: Participants were randomized (1:1) to HIFST (interval-based strengthening using everyday movements) or a lower extremity stretching group. Both interventions were delivered virtually by a physiotherapist. Feasibility was assessed based on predetermined criteria for adherence, recruitment, retention, and safety. Preliminary effects on physical and cognitive functioning outcomes were assessed before and after the intervention. Exploratory analyses were also conducted to assess enjoyment throughout the intervention. RESULTS: Twenty-four participants (mean age 67.5 years, 21 females) were randomized (12 in each group). All feasibility criteria were met; 86.1% of HIFST sessions were completed, 82.8% of eligible participants were enrolled, 91.7% of participants completed follow-up assessments, and no serious adverse events occurred. Exploratory analyses suggested benefits for HIFST on a self-reported mobility status, the Oral-Trails Making Test-B, and higher self-reported exercise enjoyment levels at several time points. CONCLUSIONS: Home-based HIFST delivered virtually by a physiotherapist is feasible and results suggest beneficial effects which warrant further exploration in a larger fully powered 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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".