Front. Public Health Frontiers in Public Health Front. Public Health 2296-2565 Frontiers Media S.A. 10.3389/fpubh.2025.1735543 Original Research Prevalence of sleep disturbance and associated factors among nurses in Chinese tertiary public hospitals: a national cross-sectional study Bin Chunhui 1 2 Writing – original draft Li Xuting 2 3 Project administration Writing – review & editing Methodology Tian Yusheng 2 4 5 Conceptualization Validation Supervision Investigation Writing – review & editing Chen Zengyu 2 6 Project administration Writing – review & editing Data curation Investigation Liu Yiting 2 7 Investigation Resources Writing – review & editing Project administration Ning Meng 1 2 Project administration Validation Supervision Data curation Software Writing – review & editing Resources Investigation Yang Jiaxin 2 5 8 Resources Validation Writing – review & editing Supervision Methodology Data curation Yu Qiang 2 Formal analysis Writing – review & editing Supervision Methodology Conceptualization Huang Chongmei 9 Writing – review & editing Zhang Dan 1 Formal analysis Writing – review & editing Project administration Methodology Yuan Jianghao 1 2 5 Investigation Writing – review & editing Methodology Data curation Ren Zhenhui 1 Writing – review & editing Investigation Software Data curation Project administration Li Yamin 10 * Conceptualization Writing – review & editing Funding acquisition Resources Xiangya Nursing School of Central South University, Changsha, Hunan, China Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China School of Nursing, University of Washington, Seattle, WA, United States Department of Emergency, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China School of Computer Science and Engineering, Central South University, Changsha, Hunan, China School of Nursing at Ningxia Medical University, Yinchuan, Ningxia, China Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China Correspondence: Yamin Li, aminny@csu.edu.cn 21 01 2026 2025 13 1735543 30 10 2025 13 12 2025 29 12 2025 Copyright © 2026 Bin, Li, Tian, Chen, Liu, Ning, Yang, Yu, Huang, Zhang, Yuan, Ren and Li. 2026 Bin, Li, Tian, Chen, Liu, Ning, Yang, Yu, Huang, Zhang, Yuan, Ren and Li https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Background

Sleep disturbance is a critical occupational health issue among nurses, jeopardizing their well-being and patient safety. Identifying its prevalence and modifiable factors is essential for developing effective interventions.

 Objectives

This study aimed to determine the national prevalence of sleep disturbance and explore associated demographic, occupational, and behavioral factors among Chinese nurses.

 Methods

In this nationwide cross-sectional study, we analyzed baseline data from the Nurses’ Mental Health Study (NMHS), a primary database established and maintained by the authors. A total of 132,910 nurses from 67 tertiary public hospitals in China were included between December 2023 and January 2024. Data were collected electronically via a structured questionnaire. Chi-square tests and binary logistic regression were used to identify factors associated with sleep disturbances.

 Results

The overall prevalence of sleep disturbances was 24.1%. Difficulty initiating sleep (13.3%), difficulty maintaining sleep (15.5%), and early morning awakening (15.4%) were most common, with higher rates in critical care, emergency, pediatrics, and obstetrics. Factors associated with increased odds of sleep disturbance included age (30–39 years), female sex, intermediate professional title, lower education, longer working experience, night shift work, smoking, alcohol use, and physical inactivity.

 Conclusion

Sleep disturbance is prevalent among Chinese nurses and associated with scheduling patterns and health behaviors. To mitigate risk, healthcare organizations should optimize shift schedules, enhance support for high-risk nurses, and allocate resources to high-burden clinical departments. Nurses are also encouraged to adopt healthier lifestyles to improve sleep quality and occupational safety.

 Clinical trial registration

Identifier ChiCTR2300072142.

 cross-sectional study nurse occupational health sleep disturbance workplace factors The author(s) declared that financial support was not received for this work and/or its publication. This work was supported by Chinese Nursing Association (Grant Number: ZHKY202306); Hunan Provincial People’s Hospital (Grant Number: KCTL202502). Special Funding for Leading Disciplines-Clinical Nursing (Grant Number: XK202003L). section-at-acceptance Occupational Health and Safety Background

Adequate sleep, beyond a physiological necessity, is a non-negotiable prerequisite for nursing competence and patient safety (1, 2). Sustained vigilance and emotional stability are essential for delivering high-quality care, yet these capacities are compromised by the high global prevalence of sleep disturbances among nurses (3).

Insomnia, the most common type of sleep disturbance, has serious implications for health and work performance (4). It is clinically defined by the DSM-IV as persistent difficulties with sleep initiation, duration, consolidation, or quality—despite adequate opportunity for sleep—resulting in daytime impairment, and is sub-classified into difficulty initiating sleep (DIS), difficulty maintaining sleep (DMS), and early morning awakening (EMA) (5). However, a significant methodological discrepancy exists: most studies on nurses’ sleep rely on self-report instruments (e.g., PSQI, ISI) rather than clinical diagnoses (1, 2). Consequently, we employ the broader term “sleep disturbance” to encompass all sleep-related complaints assessed in this study, which may not meet the full DSM-IV criteria for insomnia.

Nurses are at particularly high risk for sleep disturbance, primarily due to circadian rhythm disruption from shift work (3, 6). The consequences extend beyond impaired personal well-being to include reduced immunity, increased emotional distress, and higher morbidity (7). Crucially, sleep-related cognitive impairment elevates the likelihood of clinical errors, thereby jeopardizing patient safety (8, 9).

Despite these findings, evidence on the prevalence and risk factors lacks national representativeness and is subject to significant methodological limitations (3, 10). Although global and regional meta-analyses report high pooled prevalence rates [e.g., 34.8–61.0% globally (3, 11); 39.2–49.9% in China (12, 13)], these estimates are derived from studies often limited by single-center designs, small sample sizes, and convenience sampling. Research on risk factors is similarly constrained, as variable selection is often selective or data-driven rather than being guided by a comprehensive theoretical framework (10, 14). This narrow methodology introduces selection bias, severely limits generalizability, and potentially inflates prevalence estimates. A critical and unaddressed gap is whether risk factors differ across sleep disturbance sub-types (DIS, DMS, EMA)—an issue our study aims to address.

To overcome these gaps, a nationwide study targeting the nursing workforce within Chinese tertiary hospitals is essential. Within China’s unique hierarchical medical system, tertiary hospitals bear the core responsibility of treating severe cases (15, 16). Their nurses face higher workloads, more frequent shift work, and complex patient care demands, making their sleep disturbance patterns more typical of the professional group and helping to avoid the bias of single-institution samples (17, 18).

To analyze this complex interplay of factors, a comprehensive theoretical framework is required. We therefore adopted Krieger’s Eco-social Model, which conceptualizes health outcomes as emerging from the causal pathway of “social structure shaping environmental exposures, which in turn affect individual health” (19). This framework is uniquely suited to analyzing nurses’ sleep, as their sleep health is shaped by factors operating across macro, mezzo, and micro levels.

At the macro-level, organizational systems, such as the requirement for 24/7 shift work, directly disrupt circadian rhythms (20); at the mezzo-level, workplace environments characterized by high nurse-to-patient ratios and exposure to workplace violence increase psychological stress, indirectly impairing sleep (20, 21); at the micro-level, a constellation of individual factors is critical, including psycho-social and demographic traits (e.g., low social support, age, sex, education) (22–24), modifiable lifestyle behaviors and comorbidities (e.g., caffeine use, physical inactivity, chronic pain, and depression or anxiety) (23–26), and work–family conflict (27), which adds a significant layer of emotional strain.

This national, multi-center study aims to overcome previous limitations by recruiting a large, geographically dispersed sample from multiple tertiary hospitals. Our objectives are to: (1) determine the nationally representative prevalence of sleep disturbance and its sub-types (DIS, DMS, EMA); (2) identify key generalizable risk factors across demographic, occupational, and lifestyle domains; and (3) critically examine whether these risk factors operate differentially across the three insomnia sub-types. The results will provide a robust evidence base for developing targeted interventions and offer globally applicable insights for enhancing nurse well-being and patient safety in high-intensity healthcare settings.

 Methods Study design and participants

This cross-sectional study utilized primary data from the Nurses’ Mental Health Study (NMHS), a nationwide cohort initiated and conducted by the authors across 67 tertiary public hospitals in 31 Chinese provinces between December 2023 and January 2024. Ethical approval (LYF20230048) was obtained from the Institutional Review Board of the Second Xiangya Hospital, Central South University prior to initiation.

Using a two-stage cluster sampling approach, we first randomly selected 2–3 tertiary public hospitals per province from 31 Chinese administrative regions. All hospitals met predefined criteria: (1) general tertiary classification; (2) ≥ 2,000 beds or provincial top-5 ranking by bed capacity; (3) multidisciplinary service capacity [as prespecified in protocol (28)]. All full-time registered nurses (≥18 years) were eligible, excluding those on long-term leave, retired nurses, or interns.

 Sample size determination

The minimum sample size was 38,416 based on detecting events with 1% prevalence at ±0.1% absolute precision (two-sided α = 0.05). We recruited all eligible nurses at selected hospitals to ensure complete workforce coverage, issuing 147,832 invitations. This comprehensive approach enhanced representativeness and statistical power for subgroup analyses.

 Data collection

Trained research assistants administered questionnaires electronically via the Wenjuanxing® platform,1 a validated web-based survey platform widely implemented in Chinese clinical research (29, 30). Participants self-completed questionnaires (average duration: 15 min) with real-time logic checks to prevent invalid entries. From 147,832 invitations, 134,973 responses were received (91.3% response rate); 2,063 responses were excluded due to duplicate entries or ≥3 logical inconsistencies (e.g., conflicting age/working years), leaving 132,910 valid questionnaires for analysis (Figure 1).

Flowchart of the study participants. Invalid questionnaires (n = 2063) were excluded for duplicate responses or ≥3 logical inconsistencies (e.g., conflicting age/working years).

Flowchart illustrating the recruitment and enrollment process. Participants were recruited from sixty-seven tertiary hospitals in thirty-one provinces. One hundred forty-seven thousand eight hundred thirty-two were invited, yielding baseline responses from one hundred thirty-four thousand nine hundred seventy-three. Twelve thousand eight hundred fifty-nine declined. After excluding two thousand sixty-three invalid questionnaires, the final participant number was one hundred thirty-two thousand nine hundred ten. Recruitment

The Chinese Nursing Association coordinated hospital recruitment. Site nursing directors appointed liaisons to distribute encrypted access codes. Prior to participation, all nurses reviewed electronic informed consent documents detailing study objectives, risks, benefits, and confidentiality measures, then actively selected “Agree” or “Disagree” to indicate voluntary participation.

 Measurements

NMHS has developed a baseline questionnaire (see Additional file 1), which was derived from standardized questionnaires and scales.

Demographic and occupational variables

Basic demographic characteristics were gathered through standardized questions, which included age, sex, working experience, job title, education level, and marital status. Department and night shift status were determined through standard yes/no questions. The definition of night work referenced the legal guidelines of each country. For this study, night work was defined as working for a minimum of 3 h between 12:00 a.m. and 6:00 a.m.

 Lifestyle variables

Participants’ smoking status was assessed using a questionnaire designed based on Li-Ming Li′s publication, “Large Population Cohort Studies - Appropriate Techniques for Surveys,” specifically Chapter 3, Section 2. In addition, “non-smoking” was included as a distinct response option.

Drinking frequency was evaluated through the Alcohol Use Disorder Identification Test (AUDIT) developed by the World Health Organization (WHO), which asks, “In the past year, how often do you drink alcohol?”

Exercise frequency was measured with a question from the Medscape Physician Lifestyle & Happiness Report 2022: “How often do nurses exercise?”

Information on napping was gathered through standard yes/no questions. While naptime was categorized into no napping (0 min), 1–30 min, 31–60 min, 61–90 min, and over 90 min, as measured by the Insomnia Severity Index (ISI).

 Sleep disturbances

Information of three basic types of sleep disturbance symptoms occurring in the past month was ascertained by asking three standard questions (31): (1) DIS “Did you ever have difficulties in falling asleep?’; (2) DMS: “Did you ever have difficulties in maintaining sleep?”; (3) EMA: “Did you ever wake up in the middle of the night or early morning and have difficulties in falling asleep again?.” Respondents could choose from three options: “No,” “Sometimes” or “Often.” Patients who answered “Often” to any of these questions were classified as having “sleep disturbance.” This classification aligns with epidemiological studies capturing clinically significant symptoms (31).

 Perceived stress

Perceived stress was measured using the 4-item Perceived Stress Scale (PSS-4) (32). This scale assesses the degree to which situations in one’s life in the last month are appraised as stressful, based on an individual’s self-report of feelings and thoughts. The PSS-4 consists of four items rated on a 5-point Likert scale, ranging from 0 (Never) to 4 (Very Often). The total score ranges from 0 to 16. Specifically, items 2 and 3 are phrased positively (e.g., feeling confident about handling personal problems) and are reverse-scored during calculation. Higher total scores indicate a higher level of perceived stress.

 Data analysis

Data analysis was performed using SPSS (version 27.0; IBM Corp.) and R software (version 4.5.1). Chi-square tests were used to compare demographic characteristics between nurses with and without sleep disturbance. Continuous variables, such as PSS-4 scores, were presented as mean ± standard deviation, and independent samples t-tests were used for group comparisons. Missing values in independent variables (6.2%) were handled via multiple imputation by chained equations (MICE), creating 5 imputed datasets. Multicollinearity was assessed by variance inflation factors (VIF) and tolerance (VIF < 10; tolerance > 0.1), and no severe multicollinearity was detected. Multi-variable logistic regression (Forward: Likelihood Ratio method) was applied to identify predictors of sleep disturbance subtypes; only variables significantly contributing to the model were retained, adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were derived from pooled results across the imputed datasets. To capture the potential non-linear association between age and sleep disturbance, we employed a restricted cubic spline (RCS) with 5 knots within the logistic regression framework (using the rms package in R) and visualized the predicted probabilities. Statistical significance was set at a two-tailed p < 0.05. A detailed description of the methods is available in the Supplementary material.

 Results

A total of 147,832 registered nurses were recruited and 132,910 high-quality questionnaires were returned with a valid response rate of 89.9%. Supplementary Table S1 shows the total number of nurses in the 67 tertiary public hospitals and the percentage of the sample represented by the 67 hospitals.

Prevalence of sleep disturbances among nurses in China

Table 1 presents the seven mutually exclusive patterns of sleep disturbance. Strikingly, the simultaneous presence of all three symptoms (DIS + DMS + EMA) emerged as the most prevalent pattern (7.2%), surpassing any single isolated subtype (range: 3.2% ~ 4.5%). This finding underscores that sleep disturbance in this population is predominantly characterized by complex comorbidities rather than solitary symptoms.

Prevalence of seven mutually exclusive patterns of sleep disturbance.

Group N %
No sleep disturbance 100,933 75.9
Sleep disturbance 31,977 24.1
Isolated subtypes
Only DIS (difficulty initiating sleep) 4,290 3.2
Only DMS (difficulty maintaining sleep) 4,533 3.4
Only EMA (early morning awakening) 5,992 4.5
Comorbid subtypes
DIS + DMS 2,709 2.0
DIS + EMA 1,090 0.8
DMS + EMA 3,731 2.8
Combined type
DIS + DMS + EMA 9,632 7.2
Total 132,910 100
Characteristics of nurses with sleep disturbances

Table 2 shows a comparison of demographic characteristics between participants with and without sleep disturbance (DIS, DMS, and EMA). Individuals experiencing any of these symptoms are categorized as having sleep disturbances. Most nurses were female (93.5%), aged 30–39 (50.0%), married (68.9%), holding primary titles (51.6%), possessed bachelor’s degrees (87.1%), working night shifts (72.2%), had more than 10 years of working experience (53.7%), not taking naps (43.1%), had never smoked (96.3%) and drank (61.2%) and engaged in regular exercise (58.7%). However, each group also had a notable percentage of intermediate titles, secondary or higher vocational education, former smokers, and individuals who did not exercise. Those with sleep disturbances were more likely to be females, older, have more than 10 years of working experience, possess a lower education level, hold senior job titles, work night shifts, not take naps, smoke, drink alcohol, and report no regular exercise. Significant differences were also observed between the two groups in terms of marital status.

Demographic and clinical characteristics of the study sample (n = 132910).

Variables Sleep disturbance DIS DMS EMA
No (n = 100933) Yes (n = 31977) No (n = 115189) Yes (n = 17721) No (n = 112305) Yes (n = 20605) No (n = 112465) Yes (n = 20445)
n % n % n % n % n % n % n % n %
Demographic variables
Age group(yr)*
<30 34032 81.2 7885 18.8 37232 88.8 4685 11.2 37448 89.3 4469 10.7 37806 90.2 4111 9.8
30–39 49111 74.0 17292 26.0 57222 86.2 9181 13.8 54980 82.8 11423 17.2 55168 83.1 11235 16.9
≥40 17776 72.3 6795 27.7 20720 84.3 3851 15.7 19862 80.8 4709 19.2 19475 79.3 5096 20.7
p value <0.001 <0.001 <0.001 <0.001
χ2 948.548 300.025 1148.426 1663.217
Sex
Male 6753 77.9 1913 22.1 7560 87.2 1106 12.8 7587 87.5 1079 12.5 7397 85.4 1269 14.6
Female 94180 75.8 30064 24.2 107629 86.6 16615 13.4 104718 84.3 19526 15.7 105068 84.6 19176 15.4
p value <0.001 0.106 <0.001 0.049
χ2 19.98 2.612 65.92 3.891
Education level
Secondary or higher vocational education 8705 74.5 2972 25.5 9868 84.5 1809 15.5 9718 83.2 1959 16.8 9697 83.0 1980 17.0
Undergraduate 87655 75.7 28121 24.3 100306 86.6 15470 13.4 97670 84.4 18106 15.6 97847 84.5 17929 15.5
Bachelor's degree or above 4573 83.8 884 16.2 5015 91.9 442 8.1 4917 90.1 540 9.9 4921 90.2 536 9.8
p value <0.001 <0.001 <0.001 <0.001
χ2 200.256 176.525 147.076 152.789
Marital status
Never married 30156 79.4 7836 20.6 33006 86.9 4986 13.1 33602 88.4 4390 11.6 33809 89.0 4183 11.0
Married 68609 74.9 23008 25.1 79603 86.9 12014 13.1 76190 83.2 15427 16.8 76141 83.1 15476 16.9
Separated, widowed or divorced 2168 65.7 1133 34.3 2580 78.2 721 21.8 2513 76.1 788 23.9 2515 76.2 786 23.8
p value <0.001 <0.001 <0.001 <0.001
χ2 491.219 212.092 753.272 898.534
Occupational variables
Working experience (yr)**
<5 22520 83.2 4551 16.8 24375 90.0 2696 10.0 24597 90.9 2474 9.1 24768 91.5 2303 8.5
5–9 24792 77.1 7368 22.9 28048 87.2 4112 12.8 27590 85.8 4570 14.2 27914 86.8 4246 13.2
≥10 51900 72.7 19481 27.3 60827 85.2 10554 14.8 58220 81.6 13161 18.4 57883 81.1 13498 18.9
p value <0.001 <0.001 <0.001 <0.001
χ2 1211.148 415.757 1353.67 1789.404
Job titlea
Primary 53636 78.2 14957 21.8 59987 87.5 8606 12.5 59402 86.6 9191 13.4 59781 87.2 8812 12.8
Intermediate 42383 73.1 15569 26.9 49607 85.6 8345 14.4 47498 82.0 10454 18.0 47383 81.8 10569 18.2
High 4914 77.2 1451 22.8 5595 87.9 770 12.1 5405 84.9 960 15.1 5301 83.3 1064 16.7
p value <0.001 <0.001 <0.001 <0.001
χ2 443.015 102.212 517.122 710.442
Nightshift
No 28556 77.2 8419 22.8 32726 88.5 4249 11.5 31358 84.8 5617 15.2 31153 84.3 5822 15.7
Yes 72377 75.4 23558 24.6 82463 86.0 13472 14.0 80947 84.4 14988 15.6 81312 84.8 14623 15.2
p value <0.001 <0.001 0.051 0.023
χ2 78.968 173.918 31.846 24.667
Lifestyle variables
Nap time(min)
No 41848 73.0 15499 27.0 48330 84.3 9017 15.7 47272 82.4 10075 17.6 47452 82.7 9895 17.3
<30 15061 73.0 5577 27.0 17566 85.1 3072 14.9 16803 81.4 3835 18.6 16751 81.2 3887 18.8
30–60 36398 80.1 9025 19.9 40766 89.7 4657 10.3 39866 87.8 5557 12.2 39799 87.6 5624 12.4
>60 7626 80.3 1876 19.7 8527 89.7 975 10.3 8364 88.0 1138 12.0 8463 89.1 1039 10.9
p value <0.001 <0.001 <0.001 <0.001
χ2 917.631 793.057 804.405 806.856
Smoke
Never 97602 76.3 30346 23.7 111314 87.0 16634 13.0 108397 84.7 19551 15.3 108553 84.8 19395 15.2
Currently 3018 68.1 1412 31.9 3489 78.8 941 21.2 3540 79.9 890 20.1 3523 79.5 907 20.5
Used to 313 58.8 219 41.2 386 72.6 146 27.4 368 69.2 164 30.8 389 73.1 143 26.9
p value <0.001 <0.001 <0.001 <0.001
χ2 242.801 350.518 171.372 147.591
Drink
Never 63584 78.2 17711 21.8 71743 88.3 9552 11.7 69731 85.8 11564 14.2 69703 85.7 11592 14.3
<1/month 32645 73.5 11783 26.5 37843 85.2 6585 14.8 37030 83.3 7398 16.7 37200 83.7 7228 16.3
2–4/month 3807 66.9 1884 33.1 4525 79.5 1166 20.5 4461 78.4 1230 21.6 4494 79.0 1197 21.0
2–3/week 589 62.5 353 37.5 699 74.2 243 25.8 712 75.6 230 24.4 704 74.7 238 25.3
>4/week 308 55.6 246 44.4 379 68.4 175 31.6 371 67.0 183 33.0 364 65.7 190 34.3
p value <0.001 <0.001 <0.001 <0.001
χ2 850.495 800.12 495.425 468.18
Exercise
Never 40091 73.0 14837 27.0 46593 84.8 8335 15.2 45102 82.1 9826 17.9 45451 82.7 9477 17.3
Currently 60842 78.0 17140 22.0 68596 88.0 9386 12.0 67203 86.2 10779 13.8 67104 86.1 10968 14.1
p value <0.001 <0.001 <0.001 <0.001
χ2 465.728 285.641 420.454 325.868
PSS-4 1.12±0.92 1.64±0.94 1.17±0.93 1.72±0.96 1.16±0.93 1.70±0.95 1.17±0.93 1.67±0.97
p value <0.001 <0.001 <0.001 <0.001
t’ −85.668 −71.029 −75.796 −68.677

DIS, difficulty initiating sleep; DMS, difficulty maintaining sleep; EMA, early morning awakening. PSS-4, Perceived Stress Scale 4. *19 invalid data in the age group. **2298 invalid data in the working experience group.

China’s professional title system for nurses is hierarchical, consisting of three levels and five titles, with progressively increasing requirements for academic degree and working experience at each level. Primary title includes Nurse and Nurse Practitioner; Intermediate title includes Supervisor Nurse (Nurse in charge); High title includes Associate Chief Nurse and Chief Nurse.

Supplementary Table S2 displays the prevalence of sleep disturbances categorized by department. Notably, departments such as Critical Care (ICU) (25.9%, SE = 0.4), Emergency (26.5%, SE = 0.5), Pediatrics (25.7%, SE = 0.7), and Obstetrics (26.4%, SE = 0.5) exhibited relatively high prevalence rates of sleep disturbances. In contrast, psychiatry (17.8%, SE = 1.3) generally reported a lower prevalence compared to other departments.

 Influential factors of sleep disturbances among nurses

The results of the multi-variable logistic regression analyses (Table 3) showed associations between variables and sub-types of sleep disturbances among nurses after adjusting for age, sex, education and marital status.

Factors associated with sleep disturbance sub-types among Chinese nurses: a multi-variable adjusted logistic regression analysis.

Predictor Sleep disturbance DIS DMS EMA
95%CIa aOR p 95%CI aOR p 95%CI aOR p 95%CI aOR p
Demographic variables
Age group(yr)
<30 Ref. Ref. Ref.
30–39 1.167–1.288 1.226 <0.001*** 1.137–1.256 1.195 <0.001*** 1.255–1.418 1.334 <0.001*** 1.176–1.325 1.248 <0.001***
≥40 1.339–1.520 1.427 <0.001*** 1.362–1.540 1.448 <0.001*** 1.713–1.984 1.844 <0.001*** 1.435–1.659 1.543 <0.001***
Female 1.316–1.489 1.400 <0.001*** 1.271–1.441 1.353 <0.001*** 1.002–1.160 1.078 0.043* 1.397–1.631 1.510 <0.001***
Education level
Secondary or higher vocational education Ref. Ref. Ref. Ref.
Undergraduate 0.850–0.932 0.890 <0.001*** 0.856–0.940 0.897 <0.001*** 0.817–0.911 0.863 <0.001*** 0.826–0.921 0.872 <0.001***
Bachelor's degree or above 0.524–0.623 0.571 <0.001*** 0.501–0.596 0.546 <0.001*** 0.455–0.561 0.505 <0.001*** 0.468–0.577 0.519 <0.001***
Marital status
Never married Ref. Ref. Ref. Ref.
Married 0.905–0.980 0.942 <0.001*** 0.936–1.016 0.975 <0.001*** 1.049–1.158 1.102 <0.001*** 1.086–1.198 1.141 <0.001***
Separated, widowed or divorced 1.174–1.386 1.276 <0.001*** 1.108–1.316 1.208 <0.001*** 1.175–1.427 1.295 <0.001*** 1.241–1.507 1.368 <0.001***
Occupational variables
Working experience (yr)
<5 Ref. Ref. Ref. Ref.
5–9 1.272–1.399 1.334 <0.001*** 1.260–1.389 1.323 <0.001*** 1.199–1.355 1.275 <0.001*** 1.221–1.376 1.296 <0.001***
≥10 1.422–1.596 1.507 <0.001*** 1.416–1.592 1.502 <0.001*** 1.457–1.681 1.565 <0.001*** 1.378–1.585 1.478 <0.001***
Job titleb
Primary Ref. 0.607 0.755 0.434
Intermediate 1.017–1.087 1.051 0.003** – – –
High 0.802–0.928 0.863 <0.001*** – – –
Nightshift 1.192–1.271 1.231 <0.001*** 1.151–1.229 1.189 <0.001*** 1.097–1.183 1.139 <0.001*** 1.112–1.200 1.155 <0.001***
Pressure (PSS-4) 1.252–1.267 1.259 <0.001*** 1.252–1.267 1.259 <0.001*** 1.253–1.269 1.261 <0.001*** 1.270–1.287 1.279 <0.001***
Lifestyle variables
Nap time(min)
No nap Ref. Ref. Ref. Ref.
<30 0.935–1.007 0.970 0.113 0.945–1.021 0.982 0.358 0.994–1.085 1.038 0.094 0.981–1.070 1.024 0.280
30–60 0.641–0.681 0.661 <0.001*** 0.679–0.723 0.701 <0.001*** 0.672–0.723 0.697 <0.001*** 0.654–0.704 0.678 <0.001***
>60 0.627–0.699 0.662 <0.001*** 0.659–0.737 0.697 <0.001*** 0.581–0.668 0.623 <0.001*** 0.629–0.720 0.673 <0.001***
Smoke
Never Ref. Ref. Ref. Ref.
Currently 1.226–1.431 1.325 <0.001*** 1.226–1.431 1.325 <0.001*** 1.128–1.347 1.232 <0.001*** 1.218–1.458 1.333 <0.001***
Used to 1.543–2.243 1.860 <0.001*** 1.543–2.243 1.860 <0.001*** 1.336–2.020 1.643 <0.001*** 1.755–2.621 2.144 <0.001***
Drink
Never Ref. Ref. Ref.
<1/month 1.273–1.345 1.308 <0.001*** 1.238–1.310 1.273 <0.001*** 1.100–1.176 1.137 <0.001*** 1.153–1.232 1.192 <0.001***
2–4/month 1.650–1.863 1.753 <0.001*** 1.547–1.754 1.647 <0.001*** 1.344–1.552 1.445 <0.001*** 1.449–1.672 1.557 <0.001***
>=2/week 1.977–2.456 2.204 <0.001*** 1.769–2.218 1.980 <0.001*** 1.678–2.145 1.897 <0.001*** 1.658–2.128 1.879 <0.001***

DIS, difficulty initiating sleep; DMS, difficulty maintaining sleep; EMA, early morning awakening. PSS-4, Perceived Stress Scale 4. *p < 0.05, ***p < 0.001.

CI, Confidence Interval.

China’s professional title system for nurses is hierarchical, consisting of three levels and five titles, with progressively increasing requirements for academic degree and working experience at each level. Primary title includes Nurse and Nurse Practitioner; Intermediate title includes Supervisor Nurse (Nurse in charge); High title includes Associate Chief Nurse and Chief Nurse.

 Demographic variables

With respect to age, compared to the reference group (<30 years), nurses aged 30–39 years had an increased risk of sleep disturbance (aOR = 1.226, 95%CI: 1.167–1.288) and its sub-types (DIS: aOR = 1.195, 95%CI: 1.137–1.256; DMS: aOR = 1.448, 95%CI: 1.362–1.540; EMA: aOR = 1.248, 95%CI: 1.176–1.325). The risk was even higher for nurses≥40 years (sleep disturbance: aOR = 1.427, 95%CI: 1.339–1.520; DIS: aOR = 1.448, 95%CI: 1.362–1.540; DMS: aOR = 1.844, 95%CI: 1.713–1.984; EMA: aOR = 1.543, 95%CI: 1.435–1.659). Further exploration using restricted cubic splines delineated a non-linear age-dependent risk pattern (Figure 2). The predicted probability rose sharply in early adulthood, plateaued during midlife, and ascended again gradually in later adulthood. Consistent with multivariable regression results, Generalized Additive Models provided detailed insight into these associations.

Association between age and predicted probability of sleep disturbance among nurses using restricted cubic spline regression. The solid blue line represents the predicted probability of sleep disturbance derived from a restricted cubic spline model with 5 knots; the shaded area indicates the 95% confidence interval.

Graph showing the predicted probability of sleep disturbance increasing with age. The blue line represents the trend, with a shaded area indicating uncertainty. The probability rises from 10% at age 20 to near 30% by age 60. A dashed line marks the 30% probability.

Females exhibited a higher risk of sleep disturbance compared to males (aOR = 1.400, 95%CI: 1.316–1.489), with consistent elevated risks across sub-types (DIS: aOR = 1.353, 95%CI: 1.271–1.441; DMS: aOR = 1.708, 95%CI: 1.002–1.160; EMA: aOR = 1.510, 95%CI: 1.397–1.631). Separated, widowed or divorced marital status increased sleep disturbance risk (aOR = 1.276, 95%CI: 1.174–1.386) and its sub-types (DIS: aOR = 1.208, 95%CI: 1.108–1.316; DMS: aOR = 1.295, 95%CI: 1.175–1.427; EMA: aOR = 1.368, 95%CI: 1.241–1.507).

 Occupational variables

Regarding working experience, compared to the reference group (<5 years), nurses with 5–9 years of experience had elevated risks of sleep disturbance (aOR = 1.334, 95%CI: 1.272–1.399) and sub-types (DIS: aOR = 1.323, 95%CI: 1.260–1.389; DMS: aOR = 1.275, 95%CI: 1.199–1.355; EMA: aOR = 1.296, 95%CI: 1.221–1.376). Nurses with ≥10 years of experience showed further increased risks (sleep disturbance: aOR = 1.507, 95%CI: 1.416–1.592; DIS: aOR = 1.502, 95%CI: 1.416–1.592; DMS: aOR = 1.565, 95%CI: 1.457–1.681; EMA: aOR = 1.478, 95%CI: 1.378–1.585). night shift work was a risk factor (aOR = 1.231, 95%CI: 1.192–1.271), with consistent effects across sub-types.

Protective factors included a high job title [Associate Chief Nurse/Chief Nurse; reference: Primary title (Nurse/Nurse Practitioner)] (aOR = 0.863, 95%CI: 0.802–0.928) and higher education (bachelor’s degree or above: aOR = 0.571, 95%CI: 0.524–0.623 vs. secondary/vocational education).

 Lifestyle variables

Napping showed protective effects: napping 30–60 min (aOR = 0.661, 95%CI: 0.641–0.681) and >60 min (aOR = 0.662, 95%CI: 0.627–0.699) reduced sleep disturbance risk, compared to no napping. Risk factors included smoking (current smoking: aOR = 1.325, 95% CI: 1.226–1.431; used to smoke: aOR = 1.860, 95% CI: 1.543–2.243) and alcohol use (drinking ≥2/week: aOR = 2.204, 95% CI: 1.977–2.456).

 Discussion Principal findings

To our knowledge, this is the first multi-center survey after COVID-19 concerning sleep disturbances and associated risk factors of nurses in Mainland China. This study provides an updated and reliable prevalence of sleep disturbance at 24.1% based on baseline data from the NHMS. The results show that female nurses, night shift workers, smokers, and drinkers are more likely to develop sleep disturbance, whereas those who hold senior positions and participate in regular physical activity are less likely to suffer from sleep disturbance. The findings provide a reference for the development of targeted interventions to improve the well-being and performance of nurses.

 Conservative prevalence among nurses in public hospitals in China

A notable finding is that the overall prevalence of sleep disturbances was 24.1%, significantly lower than previous studies (11, 12). This number was 29.6% in the US (33), 42.2% in Sweden (34), 76% in France (35), 49.9% in China (13), 69.8% in Malaysia (36), 41.2% in Japan (37) and 75.5% in Ethiopia (10).

The variation can be attributed to three key factors: firstly, this study defined sleep disturbances using a more conservative criterion (“often” experiencing specific symptoms), differing from binary assessments or scales like the PSQI used in previous research (36). This methodological choice is supported by a large-scale study employing comparable symptom-frequency criteria, which documented an overall prevalence of 22.1% (31). Furthermore, the prevalence rates for the individual subtypes in our data demonstrate notable consistency with that report: difficulty initiating sleep (13.3% vs. 14.3%); difficulty maintaining sleep (15.5% vs. 16.2%); and early morning awakening (15.4% vs. 12.4%). This strong concordance suggests that our stringent definition effectively identified a population with clinically relevant sleep disturbance symptoms. Secondly, the study focused exclusively on nurses in tertiary public hospitals. These institutions typically feature more structured work environments, scientific shift scheduling, and comprehensive employee wellness programs, which may collectively mitigate the severity and prevalence of sleep issues compared to broader settings. Thirdly, the potential for social desirability bias, which is common in self-reported health surveys within professional settings, may have led to a conservative estimation (underreporting) of the true prevalence of sleep disturbances.

 High-risk groups for sleep disturbances among nurses

Our findings reveal several high-risk groups for sleep disturbances among nurses: female nurses, night shift workers, individuals aged 30 to 39 years, those with low education levels and intermediate professional titles, non-nappers, smokers and drinkers are particularly vulnerable. Consensus with prior research highlights that female nurses and those over 30 exhibit higher sleep disturbance rates (38). Notably, our analysis revealed a significant non-linear relationship between age and sleep risk, suggesting that the underlying drivers of sleep disturbance evolve across the career lifespan: from adaptation stress in early career, to persistent work–family conflict throughout midlife, and finally to age-related physiological changes in later years (14, 39, 40).

The refined age-dependent pattern provides specific guidance for interventions. The finding that high night shift exposure strongly intensifies the age-related increase in sleep disturbance risk, particularly among nurses aged 30–50, indicates that mid-career nurses with high night shift exposure are a critical subgroup for targeted intervention and occupational health support. Moreover, the distinct age-related patterns observed, particularly the sharp rise and high prevalence of Difficulty Maintaining Sleep (DMS) across much of the working age, underscores the importance of DMS as a central age-related sleep issue within this nursing population.

We posit that female nurses may be more susceptible due to hormonal fluctuations and societal roles (39). In China, cultural norms place significant demands on women aged 30–39 to balance marriage, childbirth and child-rearing while pursuing professional advancement. This dual burden creates work–family conflict, increasing sleep disturbances (41). Therefore, our findings advocate for targeted intervention strategies: structural support (such as flexible scheduling and childcare assistance) may prove more effective for middle-aged nurses in the plateau phase, while older nurses may benefit more from individual-level guidance like sleep hygiene education.

Some studies report poorer sleep quality among male ICU nurses due to physical demands and higher sleep disturbance prevalence among younger nurses (<30 years) due to rotation, interpersonal, and career pressures; however, these findings are largely mediated by job stress (14, 40). Notably, job stress surged during COVID-19, sharply increasing acute insomnia among nurses, and despite the pandemic’s end, persistent stress continues to drive global nursing workforce attrition (3, 42). In China, for example, the large population and low nurse-to-patient ratios (1,8.0 during day shifts; 1:23 at night) further exacerbate workloads (43). To effectively manage nurse stress, two evidence-based interventions are recommended for hospitals: (1) Implement dynamic workload allocation that considers individual factors, including physiological capacity, family obligations, and career development needs. For instance, younger nurses with less experience could be paired with senior staff during high-stress shifts. (2) Establish standardized staffing models based on patient acuity levels, ensuring that critical care units maintain optimal NTP ratios (e.g., 1:4 in ICUs) to reduce burnout risks.

 Impact of night shift work on sleep disturbances

In accordance with global evidence, night shift work has been demonstrated to increase the risk of sleep disturbance via circadian disruption (44). Shift systems have different effects on nurses’ work performance and occupational health (45). For example, the U.S. practice of long-term fixed night shifts minimizes circadian disruption but correlates with low job satisfaction, whereas rapid-rotating shifts in China, South Korea and Iran—while enhancing workforce flexibility—exacerbate sleep fragmentation and medical error risks (46, 47). These cross-cultural differences call for evidence-based shift design studies that balance physiological needs with work requirements. A study among 50 Italian nurses revealed that nurses working backward shifts experienced a higher prevalence of sleep disturbances and work-life imbalances compared to their forward-shift counterparts (48). The Dutch PerfectFit@Night program, which combines circadian-adaptive lighting, sleep hygiene education and personalized shift scheduling, has been proved to reduce sleep disturbances (49). This model could be adapted in collectivist settings like China by integrating group-based support to address cultural preferences for communal solutions, contrasting with individual-focused approaches in the U.S. Additionally, designating a “post-night shift sleep day” offers a scalable strategy to mitigate cumulative sleep debt, particularly relevant for countries with high nurse-to-patient ratios.

 Association of education level and department with sleep disturbances

The PerfectFit@Night intervention incorporates e-learning modules and sleep coaching at the individual level, aiming to enhance nurses’ sleep quality and coping skills (49). This objective aligns with our findings that nurses with higher education levels tend to experience lower rates of sleep disturbances, as educated nurses demonstrate greater psychological resilience and resource utilization for stress management (50). Therefore, education about sleep health should also be included in employee care programs.

For departments, this study identified emergency medicine, ICUs, pediatrics and obstetrics as high-prevalence departments for nurse sleep disturbances, consistent with global research showing these units experience intense patient acuity and workflow complexity (51, 52). The heightened prevalence in large departments like obstetrics may be attributed to the substantial patient caseload, complexity of medical conditions and environmental stressors (53). However, in contrast to previous studies, the rate of sleep disturbance among psychiatric nurses in this study was low (40). It may be a result of the low percentage of the department and the relatively small sample size. This is also supported by the large standard error (SE = 1.3). Special units such as ICUs and Emergency departments also exhibited elevated prevalence, likely influenced by the intense, urgent, and critical nature of nursing duties (54, 55). Departments with elevated prevalence rates should receive enhanced attention, such as additional nursing staff and double shifts at night (56). Targeted interventions can be implemented based on departmental characteristics. For example, obstetrics departments can optimize flexible scheduling and assign double the number of midwives during peak delivery periods; emergency departments can deploy AI triage systems to assist with manual triage; and ICUs can turn off non-essential alarms during daily lunch breaks.

 Lifestyle factors affecting sleep disturbances among nurses

Due to the influence of the human biological clock and circadian rhythm, the ability to remain fully awake is limited (57). Napping aids nurses in recovering from burnout and sleepiness (45). However, studies indicate that high workloads prevent many nurses from taking short breaks (naps) and obtaining sufficient rest between shifts (58). When nurses lack adequate rest, the risk to patient safety significantly increases (59). It is recommended that the scheduling system include nap time in the work routine of shift nurses. Alcohol and cigarettes have contrasting effects on mental activity but both negatively impact long-term sleep quality (60, 61).

Current intervention measures primarily target organizational factors. However, emerging evidence emphasizes the necessity of developing profession-specific exercise prescriptions for nurses’ sleep health management. Integration of moderate aerobic exercise, light strength exercise and mind–body exercise was more effective in improving sleep quality of shift nurses (62). Future research should prioritize: (1) tailoring exercise modalities to clinical specialty characteristics (e.g., low-impact yoga for obstetric nurses who stand for prolonged periods; high-intensity interval training for intensive care unit nurses working 12-h shifts); (2) culturally adapting interventions to national exercise guidelines; (3) Strict validation should utilize wearable sleep-exercise synchronized monitoring devices to establish a dose–response relationship between exercise patterns and improvements in sleep structure.

In conclusion, this study offers valuable insights into the correlates of sleep disturbances among nurses and serves as a significant reference for future research and health policy development. However, the factors influencing sleep disturbances in nurses differ based on individual fitness, socio-cultural background, and healthcare system (63). Therefore, international scholars should consider the findings of this study in conjunction with the specific contexts of their respective countries.

 Limitations

This study has several limitations. Firstly, the data relied on self-reported information, which may have introduced recall and self-reported biases, including the potential for social desirability bias in self-reported lifestyle factors (smoking, drinking) and sleep outcomes. To mitigate these biases, future research should employ objective measures. Secondly, the cross-sectional design used in this study limits the ability to establish causality between sleep disturbance and its correlates. Future studies should adopt longitudinal designs to explore these causal relationships. Thirdly, the generalizability of the findings is restricted as the sample was drawn exclusively from tertiary public hospitals. Consequently, the results may not apply to all nurses in China, particularly those working in rural settings. Fourthly, the study’s use of self-reported criteria to define sleep disturbance as a singular dimension may not fully capture its multifaceted nature. Future research could enhance validity by incorporating additional dimensions such as PSQI. Fifthly, the study did not capture detailed shift characteristics, such as rapid versus slow rotation, which introduces the potential for residual confounding related to complex shift work patterns.

 Conclusion

The significant prevalence of sleep disturbances among nurses in China demands urgent attention. Female nurses aged 30 to 39, holding intermediate titles with secondary or higher vocational education and engaging in unhealthy lifestyle habits such as smoking, drinking, and lack of exercise, face a heightened risk of sleep disturbance. It is imperative for nurses to proactively manage and regulate their well-being, while managers must earnestly consider strategies for identifying and intervening with high-risk groups.

 Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

 Ethics statement

The studies involving humans were approved by the Human Ethics Committee of the Second Xiangya Hospital of Central South University (Ethical approval number: LYF20230048). The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because Informed consent was obtained electronically from all participants prior to their completion of the anonymous online questionnaire.

 Author contributions

CB: Writing – original draft. XL: Project administration, Writing – review & editing, Methodology. YT: Conceptualization, Validation, Supervision, Investigation, Writing – review & editing. ZC: Project administration, Writing – review & editing, Data curation, Investigation. YiL: Investigation, Resources, Writing – review & editing, Project administration. MN: Project administration, Validation, Supervision, Data curation, Software, Writing – review & editing, Resources, Investigation. JY: Resources, Validation, Writing – review & editing, Supervision, Methodology, Data curation. QY: Formal analysis, Writing – review & editing, Supervision, Methodology, Conceptualization. CH: Writing – review & editing. DZ: Formal analysis, Writing – review & editing, Project administration, Methodology. YJ: Investigation, Writing – review & editing, Methodology, Data curation. RZ: Writing – review & editing, Investigation, Software, Data curation, Project administration. YaL: Conceptualization, Writing – review & editing, Funding acquisition, Resources.

 Acknowledgments

The researchers would like to express their gratitude to all the nurses in this study.

 Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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 Publisher’s note

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 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2025.1735543/full#supplementary-material

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Edited by: Carla Viegas, Instituto Politécnico de Lisboa, Portugal

Reviewed by: Yang Wang, Hebei Normal University, China

Bahia Galal Siam, University of Hail, Saudi Arabia

www.wjx.cn

NMHS, Nurses’ Mental Health Study; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders-IV; DIS, Difficulty Initiating Sleep; DMS, Difficulty Maintaining Sleep; EMA, Early Morning Awakening; AUDIT, Alcohol Use Disorders Identification Test; WHO, World Health Organization; PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; NTP, nurse-to-patient; PSS-4, Perceived Stress Scale 4; GAM, Generalized Additive Model.