Anthropometric profiles, adiposity, and physical fitness in long-haul truck drivers: implications for chronic disease risk
Bibliographic record
Abstract
Long-haul truck drivers (LHTD) face unique occupational challenges, including prolonged sedentary periods, irregular meal patterns, and limited access to physical activity, contributing to high levels of adiposity and elevated risks for chronic diseases. This study examined relationships between anthropometric measures, body composition, physical fitness, and occupational factors among Canadian LHTD and compared field-testing equipment for assessments of body composition. One hundred ninety-seven LHTD underwent assessments of body anthropometry (body mass index, skinfold thickness, bioelectrical impedance analysis, segment circumference), grip strength, and completed a survey on physical activity and the work environment. Over a fifth (22%) reported not engaging in any moderate to vigorous activity in the past week. Grip strength was negatively correlated with body fat percentage ( p < 0.001), waist-to-height ratio ( p < 0.001), waist circumference ( p = 0.03), work ( p = 0.021) and leisure-time ( p = 0.01) physical activity, and years worked in the industry ( p = 0.003). Increased waist circumference positively correlated with hours driven continuously ( p = 0.007) and years worked in the industry ( p = 0.004), while waist-to-height ratio was positively correlated with years as an LHTD ( p < 0.001). All body composition assessment methods exhibited large coefficients of variation, though the measurement error was generally low except for the Taylor Body Analyzer, and agreement between tools tended to decrease with higher levels of body fat. These findings highlight the high rates of adiposity and reduced physical fitness in LHTD. Tailored interventions focusing on dietary improvements, physical activity, and strength training should be prioritized to mitigate chronic disease risk in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".