Rethinking the relationship between ambulatory activity and falls in long-term care: risk versus reward
Bibliographic record
Abstract
BACKGROUND: Ambulatory older residents in long-term care (LTC) have the highest risk of falling. However, the relationship between ambulatory activity (steps per day) and fall risk in LTC is unclear. This study examined whether baseline daily step count, functional capacity and cognitive function predicted falls in LTC residents, and whether functional capacity modified the relationship between step count and fall risk. METHODS: Two hundred seventy-six LTC residents from New Zealand-based Staying UpRight randomized controlled trial were included (age: 84 ± 7 years; 61% female). Baseline daily step count was derived from a lumbar-based accelerometer (3589 ± 2379 steps). Fall rates were calculated from facilities' falls reports (6 ± 18 falls). Residents were categorized as Moderate (n = 71) or Low functional capacity (n = 205) based on Short Physical Performance Battery scores. The Montreal Cognitive Assessment assessed cognition (15 ± 6). Quasi-Poisson generalized linear models explored associations between steps, cognition, and functional capacity with fall rates, including interactions between capacity and steps. The relative risk of falling and fall-related injuries was estimated between activity levels. RESULTS: Key results showed a significant interaction (P = .036), indicating that only the Moderate functional capacity group had a positive association between steps and fall rates. The Moderate group had a ∼23%-24% and ∼6% higher relative risk of falls and fall-related injuries, respectively, with higher activity, while the Low group showed a lower risk of falls (∼2.7%-3.9%) and falls-related injuries (2%-4%). Cognitive function was not associated with falls. CONCLUSIONS: Findings suggest that higher exposure to ambulatory activity is related to greater falls risk but not falls-related injuries only among residents with moderate functional capacity. This stratification should be considered when shaping falls prevention policies.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".