Risk for Diabetes From Long Working Hours and Night Work in the
United States: Prospective Associations and Machine Learning
Techniques

Risk for Diabetes From Long Working Hours and Night Work in the
United States: Prospective Associations and Machine Learning
Techniques

diabetes
long working hours
machine learning
night work
prospective cohort

Diabetes contributes significantly to death in the U.S., with many working-age individuals
affected. This research determined the independent and joint associations of long working hours and
night work with diabetes risk in U.S. workers, and their contribution to risk prediction.
Methods: This prospective study included 1,454 workers from the Midlife in the United States (MIDUS)
study with 9-year follow-up. Long working hours included those working 55 or more hours per week.
Night work involved those working 16 or more nights per year. Diabetes was determined by selfreported diagnosis or treatment. Multivariable Poisson regression analysis was applied to examine
the prospective association of these work-related factors at baseline with incident diabetes. A gradient
boosting machine learning model was used to investigate the contributions of both factors in predicting
incident diabetes.
Results: Long working hours (RR and 95% CI = 1.60 [1.04, 2.46], p < 0.05) and night work (RR and 95%
CI = 1.66 [1.05, 2.62], p < 0.05) were independently associated with the risk for diabetes, while controlling for baseline covariates. Gradient boosting analysis suggested long working hours and night work
facilitated diabetes incidence. Exposure to both long working hours and night work increased the risk
for diabetes (RR and 95% CI = 3.02 [1.64, 5.58], p < 0.001), suggesting additive interaction.
Conclusion: Organizations may consider reducing hours on duty and improving shift systems for primary prevention of diabetes.

Elizabeth Keller 1
, Liwei Chen 2
, Feng Gao 1,3
, Jian Li 1,2,4,5,*

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Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, United States 2Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, United States 3Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States 4 School of Nursing University of California Los Angeles, Los Angeles, United States 5Department of Public Health Nursing, Faculty of Public Health, Mahidol University, Bangkok, Thailand

28 May 2025

Elizabeth Keller 1
, Liwei Chen 2
, Feng Gao 1,3
, Jian Li 1,2,4,5,

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