Chronobiology internationalOct 22, 2026

Measuring eating schedule shifts in female healthcare shift workers using time intervals

Circadian Biology Weekly Brief ↗PubMed ↗DOI ↗

Updated Oct 24, 2026

Abstract

A cross-sectional study of 290 female healthcare professionals indicates that a novel method for assessing meal timing variability may capture important details about eating patterns.

Circadian rhythms influence physiological and metabolic functions, with disruptions linked to health issues like obesity and cardiovascular disease.
Meal timing variability, referred to as eating jet lag, is a potential indicator of circadian misalignment.
The new method assesses eating jet lag by comparing food intake intervals, rather than relying solely on single midpoint values.
Findings suggest that this interval-based method reveals temporal overlaps that the traditional midpoint approach may miss.
Bland-Altman analysis indicates that the two methods show systematic disagreement, particularly at lower alignment levels.
This approach may enhance the evaluation of eating-window alignment and its association with health outcomes.

Simplified

Circadian rhythms regulate physiological and metabolic functions, and their disruption, particularly from shift work, is associated with adverse health outcomes such as obesity or cardiovascular disease. Meal timing variability, or eating jet lag, has emerged as a valuable marker of circadian misalignment. This study introduces a novel method to assess eating jet lag through direct comparison of food intake intervals, addressing limitations of traditional midpoint-based approaches reducing intervals to single values, potentially overlooking meaningful overlap. We conducted a cross-sectional study on 290 female healthcare professionals in Asturias, Spain, from a larger cohort of 679 participants with complete temporal data for meals across morning, afternoon, and night shifts. The proposed method calculates the degree of embedding between intake intervals during work shifts and rest days, and a global index is derived by aggregating alignment values across shifts. Results show that the interval-based method captures temporal overlap not represented by the midpoint-based approach, especially where the midpoint method underestimates alignment. Bland-Altman analysis showed systematic and proportional disagreement between the two approaches, with discrepancies greater at lower levels of alignment. This approach provides complementary information for evaluating eating-window alignment and provides a basis for future studies between eating-window alignment and health-related outcomes.

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Measuring eating schedule shifts in female healthcare shift workers using time intervals

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