Fall arrest strategy training improves upper body response time compared to standard fall prevention exercise in older women: A randomized trial
Bibliographic record
Abstract
Introduction Exercise can decrease fall risk in older adults but less is known about training to reduce injury risk in the event a fall is unavoidable. The purpose of this study was to compare standard fall prevention exercises to novel Fall Arrest Strategy Training ( FAST); exercises designed to improve upper body capacity to reduce fall-injury risk in older women. Method Forty women (mean age 74.5 years) participated in either Standard (n = 19) or FAST (n = 21) twice per week for 12 weeks. Both interventions included lower body strength, balance, walking practice, agility and education. FAST added exercises designed to enhance forward landing and descent control such as upper body strengthening, speed and practice of landing and descent on outstretched hands. Results Both FAST and Standard significantly improved strength, mobility, balance, and fall risk factors from pre to post-intervention. There was a significant time by group interaction effect for upper body response time where FAST improved but Standard did not ( p = 0.038). Discussion FAST resulted in similar gains in factors that reduce fall risk as a standard fall prevention program; with the additional benefit of improving speed of arm protective responses; a factor that may help enhance landing position and reduce injury risks such as head impact during a forward fall.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".