Impact attenuation provided by older adult protective headwear products during simulated fall-related head impacts
Bibliographic record
Abstract
Introduction While protective headwear products (PHP) are designed to protect older adults from fall-related head injuries, there are limited data on their protective capacity. This study’s goal was to assess the impact attenuation provided by commercially available PHP during simulated head impacts. Methods A drop tower and Hybrid III headform measured the decrease in peak linear acceleration ( g atten ) provided by 12 PHP for front- and back-of-head impacts at low (clinically relevant: 3.5 m/s) and high (5.7 m/s) impact velocities. Results The range of g atten across PHP was larger at the low velocity (56% and 41% for back and frontal impacts, respectively) vs. high velocity condition (27% and 38% for back and frontal impacts, respectively). A significant interaction between impact location and velocity was observed ( p < .05), with significantly greater g atten for back-of-head compared to front-of-head impacts at the low impact velocity (19% mean difference). While not significant, there was a modest positive association between g atten and product padding thickness for back-of-head impacts ( p = .095; r = 0.349). Conclusion This study demonstrates the wide range in impact attenuation across commercially available PHP, and suggests that existing products provide greater impact attenuation during back-of-head impacts. These data may inform evidence-based decisions for clinicians and consumers and help drive industry innovation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".