Night Shift Work, Diet, Meal Timing, and Cardiometabolic Risk: An Exploratory Cross-Sectional Study in Italian Cement Workers
Bibliographic record
Abstract
Rotating shift work, including night shifts, is reported by one-fifth of workers in the EU27 survey and is associated with an increased risk of cardiovascular disease. Irregular meal timing and poor dietary habits related to shift work contribute to metabolic disorders and may further elevate cardiovascular risk. In this pilot study, 14 rotating shift workers including night shifts (NSWs) and 14 regular daytime workers (DWs) underwent assessments of blood pressure, body mass index (BMI), waist (W) and hip (H) circumference, triglycerides, HDL, and LDL cholesterol. All participants also completed questionnaires evaluating nutrient quality, meal timing over a week, and lifestyle factors. In NSWs, a disrupted eating schedule was observed during both workdays and rest days, with frequent lunch skipping in favour of high-fat snacks. Weekly intake of junk food was higher (p < 0.01) and fresh vegetable consumption lower (p < 0.05) in NSWs compared to DWs. BMI, W/H ratio, and triglyceride levels were slightly higher in NSWs. Active smoking was more common among NSWs (50%) than DWs (21%, p < 0.01). Excess body weight, dyslipidemia, and higher smoking prevalence—combined with a long-standing pattern of unhealthy eating may, along with circadian misalignment, contribute to the elevated cardiometabolic risk observed in otherwise healthy NSWs. Implementing workplace policies to improve nutrient quality and meal timing may help reduce the risk of cardiometabolic disorders in this population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".