Caffeine Intake and Demographic Characteristics of Shift Workers: A Cross-sectional Analysis Using NHANES 2005–2010 Data (P14-001-19)
Bibliographic record
Abstract
Caffeine is the most widely consumed stimulant in the world and sociodemographic factors including occupation are associated with its intake. Non-standard work schedules are required in various occupations, and it is difficult to adapt to them. Shift work is associated with poor sleep, inadequate diet and numerous adverse health effects. We assessed whether caffeine intake differs in individuals working various shifts since it is assumed shift workers use more caffeine to cope with fatigue and disrupted circadian rhythms. The 24-h dietary recall data collected in NHANES 2005–2010 datasets (employed adults age 19–70 years, n = 8500) were used to estimate individual usual caffeine intake from caffeine-containing foods and beverages. Daily patterns of work were self-reported as: regular daytime shift; evening shift; night shift; rotating shift; or “other”. Regression analyses assessed associations of shift work with caffeine intake after adjustment for age, gender, ethnicity, smoking status, work hours, energy intake, and alcohol intake, all known to be associated with caffeine intake. Approximately 73.5% of employed adults were day shift workers and 26.5% were non-day shift workers. Day shift workers were more likely to be non-Hispanic white and of higher economic status compared to other shift workers. Mean 24-hour caffeine intake of day shift workers (204 ± 5 mg) was similar (P > 0.2) to that of evening, night, and rotating shift workers (209 ± 23, 184 ± 18, and 199 ± 15 mg, respectively). Regardless of work schedule, individuals consumed the most caffeine during morning hours. Evening and night shift workers consumed less caffeine during their work hours (76.8 ± 8.8 and 98.4 ± 18.5 mg, respectively) and more during non-work hours (131 ± 24 and 84.9 ± 9.5 mg, respectively) compared to day shift workers (157 ± 4 and 49.7 ± 3.4 mg during work hours and non-work hours, respectively; P < 0.01 for both). Unexpectedly, daily caffeine intake was similar across different types of shift workers after adjustment for age, gender, ethnicity, smoking, economic status and other factors. Opinions or assertions contained herein are private views of the authors and not to be construed as official or reflecting views of the Army or DoD. DMRP/MRMC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".