Interactions during falls with environmental objects: evidence from real-life falls in long-term care captured on video
Bibliographic record
Abstract
Abstract Background Falls are the leading cause of injuries in older adults. Environmental objects (such as furniture, walls, and handrails) may act as hazards or facilitators to balance maintenance and safe landing. There is lack of objective evidence on how older adults interact with objects during falls. We addressed this gap by characterizing body part contacts with objects other than the floor during real-life falls in long-term care. Methods We analyzed videos of 1759 falls experienced by 584 residents to characterize the prevalence of contacts with objects before, during, and after fall initiation. Using generalized estimating equations, we compared the prevalence of falls with versus without contact to objects after fall initiation. Using linear mixed models, we tested for differences across body parts in the probability of contacting objects after fall initiation. Results In nearly one-third of falls, interactions with objects (e.g., trips over objects, loss of support with objects) or with other people (e.g., being pushed by another person) had a primary role in causing imbalance and initiating the fall. After fall initiation, participants contacted objects in 60% of falls, with intentional hand contacts to objects via reach-to-grasp or bracing being the most common type of interaction (Probability ± SE = 0.32 ± 0.01), followed by unintentional impacts to the torso (0.21 ± 0.01) and head (0.16 ± 0.01). Intentional hand contact to an object was more common during forward than backward falls (p < 0.001), while head and torso contacts to objects were more common during backward and sideways falls than forward falls (multiple p values ≤ 0.003). The hand most often contacted chairs, wheelchairs or couches, followed by tables or counters, walls, other people, walkers, and handrails. The head, torso, and shoulder most often contacted a wall. Conclusions Most falls in long-term care involved contacts with objects other than the ground, indicating that complex environments often accompany falls in long-term care. Higher probabilities of intentional hand contacts in forward falls, versus unintentional head and torso impacts in backward and sideways falls may reflect the influence of being able to visualize and adjust one’s falling patterns to nearby objects.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".