Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jan 20;25(1):240.
doi: 10.1186/s12889-025-21357-8.

Elucidating the relationship between burnout and sleep disturbances among firefighters: a network analysis

Bin Liu #  1 , Lin Liu #  2   3 , Lei Ren  4   5 , Zhujing Ma  1 , Mingxuan Zou  1   6 , Yu Li  7 , Mengze Li  1 , Yuting Feng  1 , Zhongying Wu  1 , Tifei Yuan  8 , Yinchuan Jin  9 , Qun Yang  10
Affiliations

Elucidating the relationship between burnout and sleep disturbances among firefighters: a network analysis

Bin Liu et al. BMC Public Health. .

Abstract

Background: There exists an intricate relationship between burnout and sleep disturbances, especially among firefighters. Network analysis offers novel perspectives for understanding the interactions of psychopathological variables. This study aims to elucidate the relationship between burnout and sleep disturbances among firefighters through network analysis.

Methods: A total of 1,486 Chinese firefighters were included in this study. The Maslach Burnout Inventory-General Survey (MBI-GS) (Chinese version) and the Pittsburgh Sleep Quality Index (PSQI) were used to assess burnout and sleep disturbances among firefighters, respectively. Two network construction methodologies, the regularized partial correlation network (RPCN) and the directed acyclic graph (DAG), were employed to perform network analysis.

Results: Within the RPCN, "Subjective sleep quality" emerged as the central domain of firefighters' burnout and sleep disturbances, as well as "Emotional exhaustion" and "Daytime dysfunction" were influential bridge domains connecting the two. From the results pertaining to the DAG, "Subjective sleep quality" was the activation domain that triggered other burnout and sleep disturbance domains, with sleep disturbances serving as the potential cause of burnout.

Conclusions: Our findings offer some enlightenment into further understanding the relationship between burnout and sleep disturbances in firefighters. Furthermore, the aforementioned central, bridge, and activation domains may be potential targets for prevention and intervention.

Keywords: Burnout; Firefighters; Network analysis; Sleep disturbances.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: The present study adhered to the ethical standards outlined in the Declaration of Helsinki. Informed consents were obtained from all participants for this study. The present study has been reviewed and approved by the Ethics Committee of the First Affiliated Hospital of the Fourth Military Medical University (No. KY20202063-F-2). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The Spearman correlation heat map of domains. Notes. The upper right corner shows the specific correlation coefficient. The lower left corner indicates the significance level: *p < 0.05, **p < 0.01, ***p < 0.001
Fig. 2
Fig. 2
The RPCN of burnout and sleep disturbance domains. Notes. Blue and red edges represent positive and negative correlations, respectively. The thickness of the edges indicates the strength of the correlation. A thicker edge indicates a stronger correlation. The colored ring indicates the proportion of explained variance (that is, predictability)
Fig. 3
Fig. 3
The EI and BEI of burnout and sleep disturbances domains. Notes. A The EI of each node. B The BEI of each node. The specific EI values, BEI values, and abbreviation meanings of each node are shown in Table 2
Fig. 4
Fig. 4
The results of bootstrapped 95% CI and CS coefficients in the RPCN. Notes. A The bootstrapped 95% CI in the RPCN. B The CS coefficients of EI and BEI in the RPCN
Fig. 5
Fig. 5
The DAG of burnout and sleep disturbance domains. Notes. A The thickness of the edge represents that the BIC changes when this edge is removed from the DAG. B The thickness of the edge indicates the directional probability of each edge in the bootstrapped DAG
See this image and copyright information in PMC

References

    1. Maslach C. A Multidimensional Theory of Burnout. In:Oxford University Press; 1998:68–85.
    1. Maslach C, Jackson S, Leiter M. The Maslach Burnout Inventory Manual. In: The Scarecrow Press; 1997:191–218.
    1. Rodrigues H, Cobucci R, Oliveira A, Cabral JV, Medeiros L, Gurgel K, et al. Burnout syndrome among medical residents: A systematic review and meta-analysis. PLoS ONE. 2018;13: e0206840. - PMC - PubMed
    1. Gómez-Urquiza JL, De la Fuente-Solana EI, Albendín-García L, Vargas-Pecino C, Ortega-Campos EM, Cañadas-De la Fuente GA. Prevalence of Burnout Syndrome in Emergency Nurses: A Meta-Analysis. Crit Care Nurse. 2017;37:e1–9. - PubMed
    1. Agyapong B, Obuobi-Donkor G, Burback L, Wei Y. Stress, Burnout, Anxiety and Depression among Teachers: A Scoping Review. Int J Environ Res Public Health. 2022;19:10706. - PMC - PubMed

LinkOut - more resources