# Depression and daytime dysfunction centralize the fatigue–sleep cascade in island firefighters: a symptom network and Bayesian DAG study

## TL;DR

This study explores how depression and daytime dysfunction connect sleep issues and fatigue in island firefighters using network and Bayesian modeling.

## Contribution

The study identifies depression and daytime dysfunction as central factors linking sleep and psychological distress in firefighters using novel network and Bayesian DAG approaches.

## Key findings

- Depression and daytime dysfunction are central nodes in the fatigue–sleep network among island firefighters.
- Sleep disturbance prevalence was 46%, with distinct network structures observed between sleep-disturbed and sleep-normal groups.
- Tenacity (C1) may act as an upstream protective factor influencing sleep and depression.

## Abstract

Sleep disturbances, fatigue, and psychological distress are prevalent among island-based firefighters, a high-risk occupational group. However, the interactions and mechanisms underlying these factors remain unclear. This study investigated relationships among fatigue, sleep disturbances, psychological distress, and psychological resilience using symptom network analysis and exploratory Bayesian Directed Acyclic Graph (DAG) modeling.

We surveyed 570 male island-based firefighters in China (cross-sectional). The PSQI, FSS, SCL-90, and CD-RISC were administered. Variables were residualized for demographic/behavioral covariates and z-standardized. We estimated an EBICglasso Gaussian Graphical Model (γ = 0.50) to quantify centrality (Strength, expected influence) and predictability (R²). Robustness was assessed via γ = 0.25–0.75 sensitivity, bootstrapping, and Network Comparison Tests across sleep status (sleep-disturbed [SD] vs sleep-normal [SN]) and work type (shift work [SW] vs non-shift [NS]). Exploratory Bayesian DAG modeling was conducted in SD using parallel Tabu/Hill-Climbing with BIC scoring and bootstrapped aggregation to derive a CPDAG.

Sleep disturbance prevalence was 46.0% (262/570). In the full network, depression (S4) and daytime dysfunction (P7) were among the most central nodes (EI = 1.938 and 1.613), and the fatigue total (F0) showed the highest predictability (R² = 0.176). In SD, hostility (S6, EI = 1.913) and anxiety (S5, EI = 1.462) emerged as potential affective hubs; tenacity (C1) was positioned upstream (Strength = 1.961; EI = −1.315) in relation to sleep and depression. Compared with SN, SD showed lower density and global strength (both P < 0.01). Between SW and NS, overall network structure differed (P = 0.014) whereas global strength did not (P = 0.694). Sensitivity analyses indicated high agreement of non-zero edges and minimal fluctuations in density/global strength across γ = 0.25–0.75. The DAG/CPDAG suggested a potential path from subjective sleep quality → fatigue → depression → hostility → somatization, with C1 potentially influencing sleep and depression; directionality warrants further longitudinal validation.

Depression (S4) and daytime dysfunction (P7) may serve as key nodes linking sleep and affective processes; fatigue may relate to psychological distress via sleep; and tenacity (C1) could play an upstream protective role. Sleep status and shift work may reorganize network structure without necessarily altering global connectivity. Targeted interventions may consider subjective sleep perception and psychological resilience in island-based firefighters.