Sex differences in fall circumstances and injury biomechanics among older adults: A narrative review
Bibliographic record
Abstract
Falls are a leading cause of injury and disability among older adults, yet sex-specific differences in fall biomechanics and injury mechanisms remain underexplored. This narrative review synthesizes current evidence on how fall circumstances, intrinsic risk factors, and biomechanical responses differ between older males and females. A comprehensive literature search was conducted using PubMed, ScienceDirect, and Google Scholar, with search terms including "fall biomechanics,""sex differences,""older adults," and "injury risk." We screened peer-reviewed studies and included English-language, human-based research that examined sex-specific fall patterns, injury outcomes, and biomechanical factors. Our findings reveal that while males are more likely to fall from seated positions, females more commonly fall while walking and are prone to sideways and backward falls-patterns associated with increased hip and head injuries. In addition, biological differences such as lower injury thresholds, reduced muscle strength, and distinct soft tissue composition further elevate injury risk in females. Despite these differences, most injury models and prevention guidelines remain male-centric or do not consider sex differences. Our findings underscore the need to integrate sex-specific anatomical and functional characteristics into fall prevention strategies and injury prediction models to improve outcomes for both sexes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".