Physical Activity and Falls in Older Men
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) has many health benefits but may increase falls risk among older adults. We study how objectively measured habitual daily PA is related to falls by exploring the modifying effect of mobility limitations and the mediating roles of fitness and lower-limb strength. METHODS: One thousand six hundred fifty-five (53%) of 3137 surviving participants (men age 71-91 yr) in an ongoing UK-population-based cohort study wore an ActiGraph GT3x accelerometer over the hip for 1 wk in 2010-2012 to measure PA (exposure) and reported demographic and health status, including mobility limitations. One year later, 825 men reported falls history (outcome). RESULTS: Seven hundred of 825 men had ≥600 min·d of accelerometer wear for ≥3 d. Nineteen percent (n = 128) reported falls 1 yr later. Associations between PA and falls differed by presence of mobility limitations. Among 66% (n = 471) of men without mobility limitations, number of falls increased incrementally (for every 30 min of moderate to vigorous PA [MVPA]: incidence rate ratio [IRR], 1.50; 95% confidence interval [CI], 1.10-2.03, adjusted for falls risk factors). Step count was not related to number of falls below 9000 steps per day but was related to number of falls ≥9000 steps per day (for every additional 1000 steps per day: IRR, 1.59; 95% CI, 1.16-2.18). Among 33% (n = 229) of men with mobility limitations, falls risk declined with increasing activity (for every 1000 steps per day: IRR, 0.80; 95% CI, 0.70-0.91; for every 30 min of MVPA: IRR, 0.61; 95% CI, 0.42-0.89; for every additional 30 min of sedentary behavior ≥600 min·d: IRR, 1.22; 95% CI, 1.07-1.40). CONCLUSIONS: Interventions to promote MVPA in older men should incorporate falls prevention strategies. Among adults with mobility limitations, trials should investigate whether increasing MVPA levels can reduce falls risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".