Fatigue in Wildland Firefighting: Relationships Between Sleep, Shift Characteristics, and Levels of Stress and Cognitive Function.
Bibliographic record
Abstract
Rationale: With climate change rising, the impact of wildfires is expected to increase. Wildland firefighting requires constant attention while exposed to harsh working conditions, including long working hours and sub-optimal sleep. These stressors may contribute to heightened stress and impaired cognitive function, which poses a risk to worker health and safety, respectively. Purpose: The current study’s objective was to investigate the associations between sleep, shift characteristics and levels of stress and cognitive function in Canadian wildland firefighters. Methods: Employing a within-subject observational study design, we recruited a geographically diverse sample of 25 wildland firefighters from the British Columbia Wildfire Service (BCWS). Remote data collection occurred between June and September of the 2021 and 2022 fire seasons, including in participants’ homes and at their work respective location. Wrist-worn actigraphy, heart rate variability (HRV), and the psychomotor vigilance task served as objective, mobile measures of sleep, stress, and cognitive function, respectively. Web-based methods were used to collect shift information, as well as subjective reports of stress and fatigue. Linear mixed effects modelling was used to statistically control for inter-individual differences. The influence of participant-factors such as age, biological sex, and years of firefighting experience was also explored. Results: Average sleep and shift durations on fire suppression days were 6.7 and 13.8 hours, respectively (SD: 66 mins; 108 mins). Polar sleep score was found to be the best sleep-related predictor of every outcome measure, except HRV. Poor sleep, according to sleep score, was significantly associated with increased levels of stress and fatigue across all metrics (p<0.01). Later evening bedtimes were non-significantly related to reduced HRV (p<0.1). Shift duration was found to be the best shift-related predictor of every outcome measure. Longer shift durations were significantly associated with increased levels of stress and fatigue across all metrics (p<0.001). No shift characteristic predicted HRV. Cross-level interactions were indicated for two relationships involving shift duration. Physical activity and meditation experience were found to moderate the relationship between shift duration and heart rate such that the strength of association tended to be stronger in individuals without meditation experience and individuals with low physical activity. Trait morning-eveningness, physical activity, and meditation experience all moderated the relationship between shift duration and subjective fatigue such that the association was stronger in morning type individuals, individuals with low physical activity, and individuals with meditation experience. Conclusion: Our findings show that wildland firefighters are often exposed to sub-optimal sleep and long shifts. Importantly, poor sleep and long shift durations were associated with heightened levels of stress and impaired cognitive function, which have implications for worker heath and safety. We contribute novel findings to the field of research on occupational health and safety. We also provide insight and recommendations towards improved fatigue management policy within the BCWS by supporting the development, implementation, and continuous improvement of a practical and scientifically defensible fatigue risk management system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".