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Frontiers | Associated factors and a risk prediction model for comorbid circadian rhythm sleep-wake disorders in patients with chronic fatigue syndrome: implications for early screening and sleep health management
ObjectiveTo characterize comorbid circadian rhythm sleep-wake disorders (CRSWDs) in patients with chronic fatigue syndrome (CFS), identify factors independen...
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ORIGINAL RESEARCH article
Front. Psychiatry, 20 July 2026Sec. Sleep Disorders
Volume 17 - 2026 | https://doi.org/10.3389/fpsyt.2026.1826328
Associated factors and a risk prediction model for comorbid circadian rhythm sleep-wake disorders in patients with chronic fatigue syndrome: implications for early screening and sleep health management
Xiao Shao 1†- F
Fan Yang 1†
Le-le Qin 1†
Jia-ning Shi 1†- M
Min Chen 1 - W
Wen-jin Ge 1 - T
Tian-jun Jiang 1 - J
Jing-wen Yue 2 - W
- J
- +2 more
- Z
Zhen-xian Zhang 1*
- 1. Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- 2. Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- See more
Abstract
Objective:To characterize comorbid circadian rhythm sleep-wake disorders (CRSWDs) in patients with chronic fatigue syndrome (CFS), identify factors independently associated with CRSWDs, and develop a risk prediction model for early screening and sleep health management.
Methods:
This retrospective study included 610 patients with CFS, who were randomly divided into a training cohort (n = 427) and a validation cohort (n = 183) in a 7:3 ratio. Demographic characteristics, lifestyle factors, and sleep-related variables were analyzed using univariable and multivariable logistic regression. A nomogram was developed on the basis of the final model. Its discriminative ability, calibration, and public health usefulness were evaluated using receiver operating characteristic (ROC) analysis, the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA), respectively.
Results:
Among the 427 patients with CFS in the training cohort, 208 (48.7%) had comorbid CRSWDs. Univariable analysis showed that gender, BMI, fatigue severity (Fatigue Scale-14, FS-14), slow-wave sleep (SWS, N3%), duration of screen use before bedtime and shift-work schedule type were significantly associated with CRSWDs (P ≤ 0.05). Multivariable logistic regression analysis identified 12-hour rotating shifts (OR = 6.981, 95% CI: 2.603-18.720), 24-hour shifts (OR = 5.316, 95% CI: 2.197-12.863), 8-hour rotating shifts (OR = 3.982, 95% CI: 1.698-9.339), FS-14 (OR = 1.984, 95% CI: 1.442-2.728) and female sex (OR = 1.892, 95% CI: 1.178-3.037) as independently associated with an increased incidence of CRSWDs. By contrast, an increase in SWS (N3%) was independently associated with decreased odds of CRSWDs (OR = 0.982, 95% CI: 0.971-0.992). The AUCs in the training and validation cohorts demonstrated good model performance (AUC: 0.808, 0.767, respectively), and the Hosmer-Lemeshow test revealed a satisfactory fit.
Conclusion:
Female sex, greater fatigue severity, reduced N3 sleep, and rotating shift schedules were independently associated with comorbid CRSWDs in patients with CFS. The model may be useful for early screening and risk stratification and may help inform sleep hygiene education and schedule-related health management in individuals at elevated risk.