Occupational sleepiness in suburban metro drivers: association with fatigue, occupational balance and work functioning
- 1Faculty of Health Sciences, Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, High Performance Research Group in Assessment and Evaluation of Capacity, Functioning and Disability, Rey Juan Carlos University
- 2Department of Health Sciences, Faculty of Health Sciences, Area of Developmental and Educational Psychology, TECTO Research Group (Technology and Occupational Therapy), University of Burgos
Abstract
The study examined levels of daytime sleepiness, occupational fatigue, perceived work functioning and occupational balance among suburban metro train drivers, and identified independent predictors of daytime sleepiness. A cross-sectional study was conducted with 191 public transport train drivers who completed validated self-report measures assessing sleep-related functional outcomes (FOSQ), daytime sleepiness (ESS), occupational fatigue (SOFI), work role functioning (WRFQ) and occupational balance (OBQ-E). Descriptive analyses and Pearson correlations were performed, and a multiple linear regression model adjusted for work shift identified independent predictors of daytime sleepiness. Clinically relevant levels of daytime sleepiness (ESS median 11, IQR 7–15) and occupational fatigue were observed. Sleepiness showed a moderate positive correlation with occupational fatigue (r = .544, p < .001) and negative correlations with occupational balance (r = − .419, p < .001) and work role functioning (r = − .434, p < .001). The regression model adjusted for work shift was significant (F(6,173) = 17.00, p < .001) and explained 37.1% of the variance. Occupational fatigue (β = 0.298, p = .002), occupational balance (β = −0.210, p = .007) and work role functioning (β = −0.169, p = .045) were significant predictors of daytime sleepiness, whereas work-shift categories were not significant after adjustment. Interventions targeting fatigue management and occupational balance may help reduce sleepiness and enhance operational safety.
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BibTeX
@article{serradatejeda2026occupational,
title = {{Occupational sleepiness in suburban metro drivers: association with fatigue, occupational balance and work functioning}},
author = {Sergio Serrada-Tejeda and Marta Pérez-de-Heredia-Torres and Lucía Hernández-Hernández and Paula Obeso-Benítez and Lucía Simón-Vicente and Rosa M. Martínez-Piédrola and Patricia Sánchez-Herrera-Baeza},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63733-3},
url = {https://doi.org/10.1038/s41598-026-63733-3},
note = {CC BY},
}
RIS
TY - JOUR
TI - Occupational sleepiness in suburban metro drivers: association with fatigue, occupational balance and work functioning
AU - Sergio Serrada-Tejeda
AU - Marta Pérez-de-Heredia-Torres
AU - Lucía Hernández-Hernández
AU - Paula Obeso-Benítez
AU - Lucía Simón-Vicente
AU - Rosa M. Martínez-Piédrola
AU - Patricia Sánchez-Herrera-Baeza
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63733-3
UR - https://doi.org/10.1038/s41598-026-63733-3
AB - The study examined levels of daytime sleepiness, occupational fatigue, perceived work functioning and occupational balance among suburban metro train drivers, and identified independent predictors of daytime sleepiness. A cross-sectional study was conducted with 191 public transport train drivers who completed validated self-report measures assessing sleep-related functional outcomes (FOSQ), daytime sleepiness (ESS), occupational fatigue (SOFI), work role functioning (WRFQ) and occupational balance (OBQ-E). Descriptive analyses and Pearson correlations were performed, and a multiple linear regression model adjusted for work shift identified independent predictors of daytime sleepiness. Clinically relevant levels of daytime sleepiness (ESS median 11, IQR 7–15) and occupational fatigue were observed. Sleepiness showed a moderate positive correlation with occupational fatigue (r = .544, p < .001) and negative correlations with occupational balance (r = − .419, p < .001) and work role functioning (r = − .434, p < .001). The regression model adjusted for work shift was significant (F(6,173) = 17.00, p < .001) and explained 37.1% of the variance. Occupational fatigue (β = 0.298, p = .002), occupational balance (β = −0.210, p = .007) and work role functioning (β = −0.169, p = .045) were significant predictors of daytime sleepiness, whereas work-shift categories were not significant after adjustment. Interventions targeting fatigue management and occupational balance may help reduce sleepiness and enhance operational safety.
SN - 2045-2322
ER -