FEASIBILITY OF HIGH-INTENSITY FUNCTIONAL STRENGTH TRAINING AT HOME FOR POSTINJURY OLDER ADULTS
Bibliographic record
Abstract
Abstract Older adults who experience a slip, trip, or fall may experience preclinical mobility limitation (PCML), where individuals report modifications but not difficulty in mobility tasks and are at increased risk for future functional decline. Functional decline may be prevented with exercise. The purpose of this pilot trial was to determine the feasibility (adherence, recruitment, retention, safety) and preliminary outcomes (physical functioning, cognitive functioning, enjoyment) of home-based 12-week high-intensity functional strength training (HIFST) for community-dwelling older adults (≥ 55 years) with PCML who have had an injury from a slip, trip, or fall in the previous year. The trial is currently underway (target completion spring 2023). Participants are stratified by sex and randomized (1:1) to HIFST or a stretching program, both delivered by a physiotherapist via videoconferencing. Feasibility will be assessed based on predetermined criteria and reported using descriptive statistics. Preliminary effects will be reported as between group differences. To date, 20 participants (11 HIFST, 9 stretch) have enrolled (target n=24). Six participants have completed the HIFST intervention, and 2 have withdrawn before completion (reasons: mental health crisis and acute knee pain episode) with a total adherence rate of 85.8% of sessions completed (97.7% for the 6 participants who did not withdraw) which exceeds our threshold for feasibility (≥ 70%). No serious intervention-related adverse events have been reported. The results of this pilot will provide essential information for future research regarding the process, resources, and potential effects of home-based HIFST in a PCML post-injury older adult population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".