Improved Processing Speed Is a Potential Mechanism by Which Exercise Reduces Falls
Bibliographic record
Abstract
Abstract A 12-month trial demonstrated the Otago Exercise Program (OEP), a home-based exercise program of strength and balance retraining exercises, significantly reduced the rate of subsequent falls among 344 older adults receiving care after a fall (JAMA, 2019). A significant improvement in processing speed, as measured by the Digit Symbol Substitute Test (DSST), was also observed. Given the DSST is a predictor of falls, we conducted mediation analyses to determine whether improved DSST mediated the effects of OEP on rate of: 1) total falls; 2) non-injurious falls; 3) mild injurious falls; and 4) severe injurious falls over the 12-month trial. Our causal mediation analyses were conducted using the mediation package in R, using quasi-Bayesian estimates and 95% confidence intervals. Compared with usual care, OEP significantly reduced the rate of total falls (IRR= 0.64; 95% CI: 0.44, 0.91; p= 0.013) and mild injurious falls (IRR= 0.49; 95% CI: 0.31, 0.77; p= 0.002). Improved DSST score was also associated with lower mild injurious fall rates (IRR= 0.95; 95% CI: [0.91, 0.99]; p= 0.014). Formal mediation analyses showed that improved DSST was a significant mediator of the effect of OEP on the rate of mild injurious falls (95% CI: -0.15, 0.00; p= 0.036). Improved processing speed may be a mechanism by which exercise reduces mild injurious falls.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".