Effects of Obesity on Seat Belt Fit
Bibliographic record
Abstract
INTRODUCTION: Obesity has been shown to increase the risks of some types of injury in crashes. One way in which obesity may increase injury risk is by changing the routing of the belt relative to the underlying skeletal structures. METHODS: Belt fit was measured in a laboratory study of 54 men and women, 48 percent of whom were obese, defined by body mass index (BMI) of 30 kg/m(2) or greater. Test conditions included a wide range of upper and lower belt anchorage locations and ranges of seat height, seat cushion angle, and seat back angle spanning the conditions in a large fraction of front and rear seats in passenger cars and light trucks. In some conditions, foot position was restricted to simulate the typical situation in the second row of a small sedan. RESULTS: Across individuals, an increase in BMI of 10 kg/m(2) was associated with a lap belt positioned 43 mm further forward and 21 mm higher relative to the anterior-superior iliac spines of the pelvis. Each 10 kg/m(2) increase in BMI was associated with an increase in lap belt webbing length of 130 mm. The worsening of lap belt fit with restricted foot position was slightly greater for obese participants. Obesity was associated with a more-inboard shoulder belt routing across a wide range of upper belt anchorage locations, and the shoulder belt webbing length between the D-ring and latch plate increased by an average of 60 mm with each 10 kg/m(2) increase in BMI. DISCUSSION AND CONCLUSIONS: The results suggest that obesity effectively introduces slack in the seat belt system by routing the belt further away from the skeleton. Particularly in frontal crashes, but also in rollovers and other scenarios, this slack will result in increased excursions and an increased likelihood and severity of contacts with the interior. The higher routing of the lap belt with respect to the pelvis also increases the likelihood of submarining in frontal crashes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".