Exercise, Processing Speed, and Subsequent Falls: A Secondary Analysis of a 12-Month Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Strength and balance retraining exercises reduce the rate of subsequent falls in community-dwelling older adults who have previously fallen. Exercise can also improve cognitive function, including processing speed. Given processing speed predicts subsequent falls, we aimed to determine whether improved processing speed mediated the effects of the Otago Exercise Program on the rate of subsequent: (i) total falls, (ii) non-injurious falls, (iii) moderate injurious falls, and (iv) serious injurious falls. METHOD: A secondary complete case analysis of a 12-month, single-blind, randomized clinical trial among 256 of 344 adults aged at least 70 years who fell in the previous 12 months. Participants were randomized 1:1 to receive usual care plus the Otago Exercise Program (n = 123) or usual care (n = 133), consisting of fall prevention care provided by a geriatrician. The primary outcome was self-reported number of falls over 12 months (ie, rate of falls). Processing speed was assessed at baseline and at 12 months by the Digit Symbol Substitution Test (DSST). Causal mediation analyses were conducted using quasi-Bayesian estimates and 95% confidence intervals. RESULTS: Exercise significantly reduced the rate of subsequent moderate injurious falls (IRR = 0.49; 95% CI: 0.31, 0.77; p = .002) and improved processing speed (estimated mean difference: 1.16 points; 95% CI: 0.11, 2.21). Improved DSST mediated the effect of exercise on the rate of subsequent moderate injurious falls (estimate: -0.06; 95% CI: -0.15, -0.001; p = .036). CONCLUSION: Improved processing speed may be a mechanism by which exercise reduces subsequent moderate injurious falls in older adults who fell previously. CLINICAL TRIALS REGISTRATION NUMBER: ClinicalTrials.gov Protocol Registration System:NCT01029171: https://clinicaltrials.gov/ct2/show/NCT01029171NCT00323596: https://clinicaltrials.gov/ct2/show/NCT00323596.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".