Investigating the biomechanics of falls in older adults in long-term care using a video camera: a scoping review
Bibliographic record
Abstract
BACKGROUND: Falls are a worrying and growing phenomenon worldwide that especially affects the elderly. With the development of technology, one way of studying the real-life falls that occur in healthcare settings is by using video cameras. AIMS: To (a) map the patterns of the research on real-life falls among older adults in healthcare settings as assessed with digital video camera supports; and (b) highlight the advances, the evidence produced, and the gaps still present regarding the biomechanics of falls as assessed technologically. METHODS: A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. CINAHL, PubMed and Google Scholar were searched. All studies regarding falls investigated with video cameras among older individuals in healthcare settings published from 1st January 1990 to 1st January 2022 were eligible. Findings were summarised according to the Patterns, Advances, Gaps, Evidence and Recommendations framework for Scoping Reviews. RESULTS: In total, 1943 studies were retrieved, and 16 met the inclusion criteria. Studies were mostly conducted in Canada. They described the real-life falls that occurred mainly in common and living areas of long-term facilities among older individuals, mainly females. Thirteen studies investigated falls through biomechanics, while three provided advances in the reliability of the measures as collected with video cameras. Studies reported that the biomechanics of a fall, reflecting the direction of the fall and protective responses, increase or decrease the likelihood of serious impact. In addition, the direction of the landing after a fall has been determined as having a significant impact on the severity and outcome of the fall. CONCLUSION: The use of video cameras to investigate the biomechanics of falls is a well-established research area that offer interesting insight regarding (a) how to prevent falls and their injuries and (b) the direction of the research in the field of falls.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".