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Related Experiment Video

Updated: Oct 14, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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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.

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|November 3, 2021
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Summary
This summary is machine-generated.

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.

Keywords:
Clock genesgeneticsin-depth reviewpersonality traitsshift worktolerance

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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.