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Updated: Oct 14, 2025

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
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Shift work tolerance
J Degenfellner1, E Schernhammer1,2,3
1Department of Epidemiology, Centre for Public Health, Medical University of Vienna, 1090 Vienna, Austria.
Occupational Medicine (Oxford, England)
|November 3, 2021
Summary
Understanding individual shift work tolerance (SWT) is key for developing coping strategies. Recent research, including genetic studies, has advanced our knowledge of factors influencing SWT.
Area of Science:
- Chronobiology and Occupational Health
- Human Genetics
- Psychology
Background:
- Shift work is prevalent across numerous occupations, necessitating an understanding of individual differences in shift work tolerance (SWT).
- Variations in SWT impact the well-being and performance of shift workers, highlighting the need for targeted interventions.
Purpose of the Study:
- To provide a comprehensive qualitative review of research advances in shift work tolerance (SWT) architecture from 2011 to 2021.
- To synthesize findings from longitudinal studies and genome-wide association studies (GWAS) related to SWT.
Main Methods:
- Conducted a literature search across Google Scholar, PubMed, and Medline for terms related to SWT.
- Included genome-wide association studies (GWAS) from GWAS Central and GWAS Catalogue to investigate the genetic basis of SWT.
Main Results:
- Eleven new studies published since 2011, with a doubling of longitudinal SWT research.
- Identified consistent associations with hardiness, and new factors like resistance to change and job stress.
- Fifteen large-scale GWAS revealed gene overlaps (e.g., PER2/3, PAX8, LINGO1) for SWT-related traits using UK Biobank and 23andMe data.
Conclusions:
- Progress in longitudinal SWT studies is noted, but a standardized definition remains essential.
- Non-genetic factors identified in SWT research are increasingly being explored through GWAS.
- Genetic and trait-based insights offer potential for personalized risk prediction and disease prevention for night-shift workers.
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