Associations Between Mental Health Conditions and Falls in Older Adults: An Umbrella Review of Systematic Reviews
Bibliographic record
Abstract
Abstract Falls are a leading cause of injury and reduced quality of life in older adults, with evidence suggesting a bidirectional relationship between falls and mental health conditions. Depression, anxiety, and psychosocial factors may influence fall risk and outcomes, yet findings across studies remain inconsistent. This umbrella review synthesizes evidence from systematic reviews to clarify these associations and identify potential moderating and mediating factors. We conducted a comprehensive search across MEDLINE, EMBASE, Web of Science, CINAHL, PsycINFO, LILACS, Cochrane, and KSR Evidence. Eligible reviews examined relationships between mental health conditions (e.g., depression, anxiety) and falls in adults aged ≥60 years. Dual independent screening and data extraction will be performed, with quality assessment using AMSTAR 2. Approximately 1780 articles were obtained from MeSH keyword searches. Preliminary findings suggest a strong association between depression and increased fall risk, with psychosocial factors, including loneliness, as potential moderators. Variability in mental health assessment tools and fall reporting methods contributed to heterogeneity in results. This review will synthesize the current body of evidence on the association between mental health and falls, assess methodological quality, and highlight gaps for future research. The findings will inform clinical guidelines and intervention strategies to improve fall prevention and mental health management in aging populations.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".