Polypharmacy, Gait Performance, and Falls in Community‐Dwelling Older Adults. Results from the Gait and Brain Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Polypharmacy, defined as the use of five or more medications, has been repeatedly linked to fall incidence, and recently it was cross-sectionally associated with gait disturbances. Our objectives were to evaluate cross-sectional and longitudinal associations between polypharmacy and gait performance in a well-established clinic-based cohort study. We also assessed whether gait impairments could mediate associations between number of medications and fall incidence. DESIGN: Prospective cohort of community-dwelling older adults, with 5 years of follow-up. SETTING: Geriatric clinics in an academic hospital in London, ON, Canada. PARTICIPANTS: Community-dwelling older adults aged 65 and older (n = 249; 76.6 ± 8.6 y; 63% women). MEASUREMENTS: Number of medications, quantitative spatiotemporal gait parameters, and fall incidence during follow-up. RESULTS: The number of medications was cross-sectionally associated with poor gait performance (slow gait, speed p < .001; higher variability, p < .001; and higher stride, p < .001; step, p = .013, and double support times, p < .001). Prospectively, the number of medications was associated with overall gait decline (odds ratio = 1.23; 95% confidence interval [CI] = 1.13-1.33; p < .001), faster gait decline (hazard ratio = 4.62; 95%CI = 1.82-11.73; p < .001), and higher falls incidence (p = .006). These associations remained true after adjusting for age, sex, and accounting for "confounding by indication bias" by using a comorbidity propensity score adjustment. Each additional medication taken, significantly increased gait decline risk by 12% to 16% and fall incidence risk by 5% to 7%. Mediation analyses revealed that gait impairments in stride length, step length, and step width mediated the strength of the association between medications and fall incidence. CONCLUSION: Polypharmacy was cross-sectionally associated with poor gait performance and longitudinally associated with gait decline and fall incidence. Despite our use of propensity matching, confounding by indication could have influenced the results. Quantitative spatial gait parameters performance mediated the strength of the association between medications and falls, suggesting a role of gait disturbances in the medication-related falls pathway.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".