Vol. 1, Issue 2, Part A (2025)

Background: Shift work is associated with circadian disruption, sleep loss, and increased risk of fatigue-related errors and injuries. Existing fatigue risk management systems focus largely on scheduling and self-reported fatigue, with limited use of objective physical fitness measures. This study examined whether baseline physical fitness screening predicts fatigue-related incidents among shift workers and improves risk prediction beyond conventional demographic, occupational, and sleep-related variables.

Methods: In a prospective cohort study, 402 healthcare, industrial, and logistics workers engaged in rotating or permanent night shifts underwent baseline assessment including questionnaires on demographics, job characteristics, sleep, fatigue, and work ability, alongside a standardised fitness battery (submaximal cardiorespiratory fitness [CRF; VO₂peak], handgrip strength, 60-s sit-to-stand, upper-limb endurance, flexibility, BMI, and waist-to-height ratio). Fatigue-related incidents (errors, near misses, minor injuries attributed primarily to tiredness or reduced alertness) were prospectively recorded over 12 months via organisational incident systems, supervisor reports, and monthly worker diaries. Negative binomial regression and Cox models estimated associations between fitness and incident risk, adjusting for potential confounders. Predictive performance of models with and without fitness variables was compared using area under the ROC curve (AUC), calibration statistics, and bootstrap internal validation.

Results: Over follow-up, 196 fatigue-related incidents occurred in 152 workers (51.4 events per 100 person-years). Incident rates decreased stepwise across CRF tertiles (77.2, 49.8, and 31.3 events per 100 person-years for low, medium, and high CRF). After multivariable adjustment, higher CRF, greater sit-to-stand performance, and higher work ability remained independently associated with lower incident counts, while higher BMI and short sleep on workdays were associated with increased risk. A base prediction model including demographics, job characteristics, adiposity, and sleep yielded an AUC of 0.68, which improved to 0.78 when fitness and work ability were added, with good calibration and minimal optimism.

Conclusion: Physical fitness screening—particularly assessment of CRF, muscular endurance, and work ability—provides independent and clinically meaningful predictive information for fatigue-related incidents among shift workers. Integrating simple, standardised fitness assessments into occupational health surveillance and fatigue risk management systems may enhance the identification of high-risk workers and support targeted, non-punitive interventions to improve safety in 24-hour work environments.