Front. Med. Frontiers in Medicine Front. Med. 2296-858X Frontiers Media S.A. 10.3389/fmed.2026.1770352 Review Global prevalence of poor sleep quality in hemodialysis patients: a systematic review and meta-analysis Shi Gui-Fen 1 2 Conceptualization Writing – original draft Visualization Methodology Formal analysis Zhou Xu-Hua 1 2 Data curation Validation Methodology Writing – original draft Chen Lin 1 2 * Methodology Supervision Writing – review & editing Validation Conceptualization Zhang Ying-Jun 1 2 Formal analysis Methodology Writing – original draft Validation Yu Wen-Wen 1 2 Writing – original draft Visualization Validation Methodology Zhang Jiao 1 2 Writing – original draft Visualization Validation Methodology He Li 1 2 Writing – original draft Visualization Validation Tang Si-Kai 1 2 Writing – original draft Methodology Validation Department of Nephrology, Hemodialysis Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China West China School of Nursing, Sichuan University, Chengdu, Sichuan, China Correspondence: Lin Chen, clhxxuetou@163.com 23 02 2026 2026 13 1770352 18 12 2025 26 01 2026 10 02 2026 Copyright © 2026 Shi, Zhou, Chen, Zhang, Yu, Zhang, He and Tang. 2026 Shi, Zhou, Chen, Zhang, Yu, Zhang, He and Tang 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

Poor sleep quality is associated with various adverse outcomes among hemodialysis (HD) patients. Although poor sleep quality is a widely recognized health issue in HD patients, the reported prevalence in the current literature are remarkably inconsistent. This study aimed to determine the global prevalence of poor sleep quality in HD patients.

 Methods

A comprehensive literature search was conducted across seven electronic databases (PubMed, Web of Science, Scopus, Embase, Cochrane Library, CINAHL, PsycINFO) from their inception to October 20, 2025. Data extraction was performed using a standardized form, and the methodological quality of included studies was evaluated with the Joanna Briggs Institute (JBI) critical appraisal checklist for prevalence studies. A random-effects model was applied to calculate the pooled prevalence of poor sleep quality, and the heterogeneity was quantified using the I2 statistic. Subgroup analyses and meta-regression were conducted to explore potential sources of heterogeneity.

 Results

A total of 69 studies involving 14,998 HD patients were included in the meta-analysis. The pooled global prevalence of poor sleep quality was 64.2% (95% CI: 60.5–67.8%). Based on the JBI critical appraisal tool, 55 studies were rated as having a low risk of bias, while 14 were considered to have a moderate risk of bias. Subgroup analysis revealed that the pooled prevalence varied significantly by the cut-off values. Meta-regression results indicated that prevalence was not significantly associated with sample size, mean age, dialysis duration, and proportion of females.

 Conclusion

Our findings demonstrate a high prevalence of poor sleep quality among HD patients. To mitigate the adverse effects of poor sleep quality on HD patients, healthcare providers should routinely conduct screenings and deliver evidence-based interventions.

 epidemiology hemodialysis meta-analysis poor sleep quality prevalence The author(s) declared that financial support was not received for this work and/or its publication. section-at-acceptance Nephrology Introduction

End-stage renal disease (ESRD) remains a serious global public health challenge (1). Patients with ESRD are dependent on renal replacement therapy to survive, of which hemodialysis (HD) is one of the most common treatments worldwide, supporting the lives of millions of individuals (2). As the number of ESRD patients continues to increase, the primary focus of clinical management has shifted from improving survival rates to enhancing the quality of life for long-term dialysis patients (3).

Sleep quality is generally defined as an individual’s subjective satisfaction with the sleep experience, encompassing sleep initiation, sleep maintenance, sleep depth, and feeling refreshed upon waking, all of which are equally crucial for ensuring a decent quality of life in HD patients (4, 5). Owing to the combined effects of uremic toxin-induced neurological symptoms, dialysis-related circadian rhythm disturbances, and comorbid emotional disorders, all contribute to the prevalent poor sleep quality observed in HD patients (6, 7). It is worth noting that poor sleep quality has a considerable and ongoing effect on the overall health of HD patients (5). Specifically, poor sleep quality is closely associated with numerous clinical adverse outcomes such as dialysis-related fatigue, cardiovascular events, cognitive impairment, infection, and mortality among HD patients (8–11). Therefore, a comprehensive understanding of the prevalence of poor sleep quality in HD patients and its associated factors is crucial for achieving better health outcomes for them.

An accurate estimation of the prevalence of poor sleep quality among HD patients is essential for informing the development of evidence-based prevention and treatment strategies. However, substantial heterogeneity exists in the reported prevalence of poor sleep quality among HD patients across existing studies, with estimates ranging from 31.5 to 93.8% (12–14). Potential resources that contribute to this significant heterogeneity may be attributed to several factors, including different demographic characteristics of the cohorts, instruments used to assess sleep quality, cut-off values, and study designs. To date, there has been no meta-analysis on the global prevalence of poor sleep quality among HD patients. Therefore, this study aimed to determine the global prevalence and its moderating factors of poor sleep quality in HD patients.

 Materials and methods

This meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (15). The protocol was registered with PROSPERO (CRD420251175694).

Search strategy

A comprehensive literature search was conducted across seven electronic databases, including PubMed, Web of Science, Scopus, Embase, Cochrane Library, CINAHL, PsycINFO for articles published from inception until October 20, 2025. The search strategy incorporated MeSH and key terms, including “dialysis,” “hemodialysis,” “hamedialysis,” “sleep quality,” “sleeping quality,” “Pittsburgh sleep quality index,” and “PSQI.” The search syntax for each database is detailed in Supplementary Table S1. Manual searches of the reference lists of included articles and relevant reviews were conducted to identify additional eligible studies.

 Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) participants were adult patients (aged ≥ 18 years) on hemodialysis; (2) a cross-sectional or cohort design was adopted (only baseline data included for longitudinal studies); (3) poor sleep quality was defined using a validated instrument such as the PSQI; (4) the prevalence of poor sleep quality was reported or calculable. The exclusion criteria were: (1) reviews, case reports, comments, editorials, or conference abstracts; (2) not published in English; (3) the cut-off values for poor sleep quality were not provided. In cases of duplicate publications, only the study with the largest sample size was retained for analysis.

 Data extraction

Data extraction was performed independently by two reviewers using a pilot-tested, standardized Excel form. The following data were extracted: first author, publication year, country, study design, sample size, mean age, dialysis duration, proportion of females, assessment tool, cut-off value, and prevalence of poor sleep quality. All extractions were cross-checked to ensure accuracy, with any discrepancies resolved by consensus.

 Risk of bias assessment

The risk of bias in each included study was independently evaluated by two reviewers using the Joanna Briggs Institute (JBI) critical appraisal tool for prevalence studies (16). This checklist includes nine domains, each scored as “yes,” “no,” or “unclear/not applicable.” Based on the proportion of “yes” responses, the overall risk for each study was then classified as low (≥ 70%), moderate (50–69%), or high (≤ 49%). Any discrepancies were referred to a third reviewer and resolved by consensus discussion.

 Data analysis

All analyses were conducted using Stata 15.0. The pooled prevalence of poor sleep quality among HD patients and its 95% confidence interval (CI) were derived using a random-effects model. Heterogeneity was evaluated using the I2 statistic, with substantial heterogeneity defined as I2 ≥ 50% (17). Potential sources of heterogeneity were explored via subgroup analyses based on study design, country income level, and the cut-off values applied. In addition, univariate meta-regression analyses were performed to identify moderators of the prevalence estimates, with sample size, mean age, dialysis duration, and proportion of females serving as covariates. Publication bias was examined visually by inspecting the funnel plot symmetry and statistically using Egger’s test. A leave-one-out sensitivity analysis was undertaken to assess the robustness of the pooled results. P < 0.05 was considered statistically significant (two-sided).

 Results Study selection

The initial search of electronic databases identified 7,731 records from databases and 7 from manual searches. After the removal of duplicates, 4,324 records were screened based on titles and abstracts. Of these, 165 full-text articles were assessed for eligibility. A total of 69 studies were ultimately incorporated into the meta-analysis. The study screening process is detailed in Figure 1.

Flow diagram of study selection.

PRISMA flow diagram displays study selection: 7,731 records identified from eight databases, 7 additional records found elsewhere, 4,324 after duplicates removed, 176 screened, 4,148 excluded, 165 assessed, 96 excluded for eligibility reasons, and 69 studies included in meta-analysis. Study characteristics

The detailed characteristics of the included studies are shown in Table 1. A total of 14,998 participants from 69 studies (7–11, 14, 18–76) were included, with mean ages ranging from 37.10 to 65.61 years. The included studies were conducted in 19 countries, including Iran (n = 11), China (n = 10), Turkey (n = 10), Pakistan (n = 5), Saudi Arabia (n = 4), Malaysia (n = 4), Somalia (n = 3), Canada (n = 3), USA (n = 3), India (n = 3), Brazil (n = 3), Palestine (n = 2), Korea (n = 2), Oman (n = 1), Egypt (n = 1), Bosnia and Herzegovina (n = 1), Italy (n = 1), Croatia (n = 1), and Serbia (n = 1). The sample sizes for individual studies varied from 46 to 1,281. All studies utilized the PSQI to assess sleep quality, employing different cut-off values: a PSQI score ≥ 5 was used in 26 studies, a score ≥ 6 in 41 studies, and a score ≥ 7 in 2 studies. The prevalence of poor sleep quality reported in the included studies varied considerably, with a range of 31.5–93.8%.

Characteristics of included studies.

Study Country Study design Sample size Mean age (years) Dialysis duration (years) Female (%) Assessment tool Cut-off value Prevalence (%)
Abforoushha et al. (18) Iran Cross-sectional 150 65.61 ± 4.07 3.37 ± 2.20 49.3 PSQI ≥ 6 54.1%
Al Naamani et al. (6) Oman Cross-sectional 123 NR NR 32.5 PSQI ≥ 5 56.9%
Almutary (7) Saudi Arabia Cross-sectional 116 50.66 ± 12.73 NR 54.3 PSQI ≥ 5 56.9%
Alshammari et al. (8) Saudi Arabia Cross-sectional 260 NR NR 42.7 PSQI ≥ 5 37.7%
Anwar and Mahmud (19) Pakistan Cross-sectional 113 NR NR 53.1 PSQI ≥ 5 72.6%
Araujo et al. (20) Brazil Cross-sectional 400 51.6 ± 15.5 5.9 ± 5.5 59.0 PSQI ≥ 6 56.7%
Badr et al. (14) Egypt Cross-sectional 81 47.2 ± 7.6 NR 48.1 PSQI ≥ 5 93.8%
Bastos et al. (21) Brazil Cross-sectional 100 46.1 ± 15.5 5.00 ± 4.58 41.0 PSQI ≥ 6 75.0%
Bilgic et al. (22) Turkey Cross-sectional 67 47.7 ± 11.4 8.64 ± 4.93 49.3 PSQI ≥ 6 44.8%
Carneiro et al. (23) Brazil Cross-sectional 48 48.32 ± 12.37 5.12 ± 3.82 45.8 PSQI ≥ 6 68.8%
Čengić et al. (24) Bosnia and Herzegovina Cross-sectional 200 56.8 ± 14.3 5.22 ± 4.75 39.0 PSQI ≥ 6 73.5%
Choudhary et al. (25) India Cross-sectional 66 NR NR 40.9 PSQI ≥ 6 74.2%
Daraghmeh et al. (26) Palestine Cross-sectional 250 54.9 ± 15.08 NR 37.2 PSQI ≥ 5 66.4%
Davison et al. (27) Canada Cross-sectional 205 60.0 ± 15.9 2.77 ± 4.12 42.0 PSQI ≥ 7 61.5%
D’Onofrio et al. (28) Italy Cross-sectional 103 NR NR 37.9 PSQI ≥ 5 56.3%
Erickson et al. (29) USA Cross-sectional 160 58 ± 14 4.13 ± 4.18 45.0 PSQI ≥ 5 90.6%
Eslami et al. (30) Iran Cross-sectional 190 NR NR 39.5 PSQI ≥ 5 85.8%
Firoz et al. (31) Iran Cross-sectional 310 59.64 ± 13.94 NR 47.7 PSQI ≥ 6 73.5%
Gencdal et al. (32) Turkey Cross-sectional 137 49.74 ± 12.49 3.45 ± 3.87 38.7 PSQI ≥ 6 63.5%
Han et al. (33) China Cross-sectional 141 59.7 ± 15.3 NR 39.0 PSQI ≥ 5 62.4%
Harris et al. (10) USA Cohort 128 57.3 ± 13.8 NR 39.8 PSQI ≥ 6 45.3%
Ho et al. (4) Malaysia Cross-sectional 184 54.3 ± 12.6 NR 39.1 PSQI ≥ 6 51.1%
Hosseini et al. (13) Iran Cross-sectional 175 51.6 ± 16.4 NR 36.4 PSQI ≥ 6 78.2%
Iliescu et al. (34) Canada Cross-sectional 89 60.1 ± 16.8 NR 38.2 PSQI ≥ 6 70.8%
Jeele et al. (35) Somalia Cross-sectional 299 56.65 ± 12 NR 45.8 PSQI ≥ 6 61.9%
Ji et al. (36) Korea Cross-sectional 175 56.9 ± 13.8 5.5 ± 5.5 44.0 PSQI ≥ 6 73.7%
Joshwa et al. (37) India Cross-sectional 47 37.1 ± 13.1 NR 49.0 PSQI ≥ 5 68.1%
Kang et al. (38) Korea Cross-sectional 101 57.3 ± 12.2 2.98 ± 3.00 45.1 PSQI ≥ 5 75.2%
Kaya et al. (39) Turkey Cross-sectional 232 60.9 ± 14.1 3.57 ± 3.63 43.5 PSQI ≥ 6 34.9%
Kir et al. (40) Turkey Cross-sectional 338 NR NR 47.6 PSQI ≥ 6 41.4%
Kose et al. (12) Somalia Cross-sectional 200 52.3 ± 14.13 NR 41.5 PSQI ≥ 6 31.5%
Lin et al. (41) China Cross-sectional 120 NR NR 46.7 PSQI ≥ 6 92.5%
Ling et al. (42) Malaysia Cross-sectional 184 54.3 ± 12.6 NR 39.1 PSQI ≥ 6 51.1%
Liu et al. (43) China Cross-sectional 201 51.1 ± 9.0 NR 44.3 PSQI ≥ 6 43.3%
Maung et al. (44) USA Cross-sectional 69 55.6 ± 16.6 NR 50.7 PSQI ≥ 6 58.0%
Mohamed et al. (45) Somalia Cross-sectional 200 52.29 ± 14.13 NR 41.5 PSQI ≥ 6 31.5%
Monfared et al. (46) Iran Cross-sectional 126 54.9 ± 16.1 NR 38.9 PSQI ≥ 6 44.6%
Mortazavi et al. (47) Iran Cross-sectional 160 60.01 ± 13.52 6.37 ± 6.02 33.7 PSQI ≥ 6 84.4%
Morvaridi et al. (48) Iran Cross-sectional 423 52.83 4.95 40.7 PSQI ≥ 6 60.5%
Naeem Alharbi (49) Saudi Arabia Cross-sectional 100 NR NR 58.0 PSQI ≥ 6 65.0%
Ng et al. (50) Malaysia Cross-sectional 217 57 ± 13 4.62 ± 4.06 50.2 PSQI ≥ 6 54.8%
Norozi Firoz et al. (51) Iran Cross-sectional 310 59.64 ± 13.94 3.32 ± 3.55 47.7 PSQI ≥ 6 59.7%
Ongan and Yuksel (52) Turkey Cross-sectional 103 59.19 ± 14.57 NR 51.5 PSQI ≥ 5 62.1%
Pai et al. (53) China Cross-sectional 164 57.9 ± 11.8 NR 53.0 PSQI ≥ 6 74.4%
Pan et al. (54) China Cross-sectional 178 62.9 ± 11.5 4.73 ± 3.35 42.0 PSQI ≥ 6 60.1%
Parvan et al. (55) Iran Cross-sectional 245 58.03 ± 14.03 NR 35.5 PSQI ≥ 6 83.3%
Pojatić et al. (56) Croatia Cross-sectional 170 NR NR 40.6 PSQI ≥ 5 68.8%
Ramezanzade et al. (57) Iran Cross-sectional 225 58.23 ± 13.50 NR 41.3 PSQI ≥ 5 72.0%
Rehman et al. (58) Pakistan Cross-sectional 354 NR NR 33.9 PSQI ≥ 6 74.0%
Sabbagh et al. (59) Canada Cross-sectional 46 61.9 ± 16.9 NR 34.0 PSQI ≥ 5 76.1%
Sabet et al. (60) Iran Cross-sectional 61 52.5 ± 18.0 NR 32.8 PSQI ≥ 6 73.8%
Samara et al. (61) Palestine Cross-sectional 167 57.6 ± 12.9 NR 47.9 PSQI ≥ 5 76.7%
Shen et al. (62) China Cross-sectional 68 61.75 ± 16.56 2.64 ± 2.45 36.8 PSQI ≥ 5 69.1%
Soleimani Damaneh et al. (63) Iran Cross-sectional 423 52.84 ± 14.63 4.12 ± 5.12 41.1 PSQI ≥ 6 60.5%
Taraz et al. (64) Iran Cross-sectional 72 56.68 ± 15.79 5.93 ± 5.36 41.7 PSQI ≥ 5 75.0%
Terzi et al. (65) Turkey Cross-sectional 50 64.46 ± 14.61 NR 56.0 PSQI ≥ 5 82.0%
Tian et al. (9) China Cohort 613 63.7 ± 7.8 NR 42.1 PSQI ≥ 5 77.0%
Tian et al. (11) China Cohort 595 57.40 ± 13.72 NR 41.2 PSQI ≥ 7 46.7%
Trbojević-Stanković et al. (66) Serbia Cross-sectional 222 57.3 ± 11.9 5.12 ± 5.03 40.5 PSQI ≥ 6 64.0%
Türk et al. (67) Turkey Cross-sectional 220 NR NR 50.9 PSQI ≥ 6 51.8%
Uysal et al. (68) Turkey Cross-sectional 102 NR NR 48.0 PSQI ≥ 5 59.8%
Velu et al. (69) India Cross-sectional 148 44 ± 14.5 NR 31.8 PSQI ≥ 5 68.2%
Xu et al. (70) China Cross-sectional 193 53.09 ± 11.68 NR 34.2 PSQI ≥ 6 63.7%
Yang et al. (71) China Cross-sectional 861 NR NR 55.2 PSQI ≥ 5 83.7%
Yavuz et al. (72) Turkey Cross-sectional 121 NR NR 54.5 PSQI ≥ 6 46.3%
Zhang et al. (73) China Cross-sectional 741 60 ± 14 NR 62.1 PSQI ≥ 6 62.2%
Zhang et al. (74) China Cross-sectional 1281 54.48 ± 13.09 NR 34.7 PSQI ≥ 6 58.5%
Zubair and Butt (75) Pakistan Cross-sectional 140 NR NR 27.9 PSQI ≥ 5 68.6%
Zubair and Butt (76) Pakistan Cross-sectional 137 NR NR 27.7 PSQI ≥ 5 66.4%
Risk of bias assessment of included studies

The risk of bias assessment results were detailed in Supplementary Table S2. Among the 69 evaluated responses, 55 (79.7%) were rated as having a low risk of bias, while 14 (20.3%) were considered to have a moderate risk of bias.

 Prevalence of poor sleep quality in HD patients

Owing to the substantial heterogeneity observed across studies, a random-effects model was adopted for the meta-analysis. As shown in Figure 2, the pooled global prevalence of poor sleep quality among HD patients was 64.2% (95% CI: 60.5–67.8%, I2 = 96.0%).

Forest plot of pooled prevalence of poor sleep quality in HD patients.

Forest plot summarizing effect sizes (ES) and ninety-five percent confidence intervals for numerous studies, with each study listed on the left and corresponding weights, ES, and intervals shown on the right. Combined overall effect size appears at the bottom. Subgroup analysis and meta-regression analysis

Subgroup analysis was conducted to examine the sources of heterogeneity between studies (Table 2). Subgroup analysis based on country income level (Supplementary Figure S1) revealed no significant difference in the pooled prevalence of poor sleep quality between high-income (64.0%, 95% CI: 54.0–73.9%) and low- and middle-income countries (64.2%, 95% CI: 60.3–68.2%). When stratified by study design (Supplementary Figure S2), cross-sectional studies (64.5%, 95% CI: 60.8–68.3%) and cohort studies (56.5%, 95% CI: 33.4–79.6%) demonstrated similar prevalence of poor sleep quality among HD patients. Subgroup analysis by PSQI cut-off value (Supplementary Figure S3) revealed a significant negative correlation (P < 0.05). Specifically, the pooled prevalence of poor sleep quality decreased from 70.5% (95% CI: 65.4–75.6%) at a cut-off of ≥ 5–60.8% (95% CI: 56.4–65.2%) at a cut-off of ≥ 6 and then to 53.8% (95% CI: 39.4–68.3%) at a cut-off of ≥ 7.

Subgroup analyses of the prevalence of poor sleep quality among HD patients.

Subgroups Number of studies Prevalence (95% CI) Heterogeneity P-values across subgroups
I2 P-values
Income levelHigh incomeLow and middle income   0.964
13 64.0% (54.0–73.9%) 95.2% P < 0.001
56 64.2% (60.3–68.2%) 96.2% P < 0.001
Study designCross-sectionalCohort   0.502
66 64.5% (60.8–68.3%) 95.8% P < 0.001
3 56.5% (33.4–79.6%) 98.6% P < 0.001
Cut-off values≥ 5≥ 6≥ 7   0.006
26 70.5% (65.4–75.6%) 94.0% P < 0.001
41 60.8% (56.4–65.2%) 95.4% P < 0.001
2 53.8% (39.4–68.3%) 92.8% P < 0.001

HICS, high-income countries; MICS, middle-income countries; AIS, Athens Insomnia Scale; ISI, Insomnia Severity Index.

The results of the univariate meta-regression (Table 3) demonstrated no significant associations between the pooled prevalence of poor sleep quality and the potential moderators, including sample size, mean age, dialysis duration, and female proportion (all P > 0.05).

Meta-regression results.

Variables Number of studies Coefficient Standard error 95%CI t–values P-values
Sample size 69 -5.07 × 10−5 −8.53 × 10−5 −0.0002–0.0001 −0.59 0.554
Mean age (years) 52 0.0023 0.0038 0.0053–0.0099 0.61 0.543
Dialysis duration (years) 20 0.0021 0.0217 −0.0434–0.0476 0.10 0.924
Female (%) 69 −0.0012 0.0024 −0.0059–0.0036 −0.50 0.620
Publication bias and sensitivity analysis

Neither visual inspection of the funnel plot (Figure 3) nor statistical evaluation by Egger’s test revealed any evidence of substantial publication bias (Egger’s test: t = 15.60, P = 0.055, Supplementary Figure S4). Furthermore, the sensitivity analysis confirmed that the pooled prevalence was robust (Supplementary Table S3).

Funnel plot of prevalence of poor sleep quality in HD patients.

Funnel plot displaying individual study points with effect size on the x-axis and standard error on the y-axis, including pseudo ninety-five percent confidence limit boundaries formed by dashed lines and a solid central line. Discussion

Given that poor sleep quality is a distressing symptom that significantly impairs quality of life in HD patients (8), this study pooled its prevalence in a global sample of 14,998 participants across 19 countries. To the best of our knowledge, this is the first meta-analysis to comprehensively estimate the global prevalence of poor sleep quality in this population. The pooled global prevalence of poor sleep quality among HD patients was 64.2%, which is substantially higher than rates reported in the general population and stroke survivors (77, 78). These findings are in line with expectations, since the various disease and treatment-related issues experienced by HD patients make them susceptible to sleep disturbances (23–26). Firstly, despite regular dialysis, the persistent accumulation of uremic toxins can impair central nervous system function, leading to conditions such as restless legs syndrome and periodic limb movements during sleep, which significantly disrupt sleep initiation in HD patients (24). In addition, the psychological burden of being dependent on dialysis, coupled with concerns about prognosis and socioeconomic issues, usually results in clinical anxiety and depression. These conditions have been demonstrated to be one of the primary contributors to sleep disturbances in these patients (33). Finally, the rigorous daytime schedule of hemodialysis, which generally requires a considerable time commitment several days a week for HD patients, interrupts their normal daily rhythms and is an important cause of sleep disturbances at night (45).

Subgroup analysis results indicated no significant difference in the pooled prevalence of poor sleep quality between high-income (64.0%, 95% CI: 54.0–73.9%) and low- and middle-income countries (64.2%, 95% CI: 60.3–68.2%). These findings may be attributed to several factors. First of all, the mechanisms leading to sleep disturbances in HD patients, such as the accumulation of uremic toxins, electrolyte imbalances, and the comorbidity burden, are inherently connected to renal failure itself (65). Furthermore, the widespread use of the PSQI as a standardized assessment tool ensures consistent measurement of outcomes, thereby reducing potential diagnostic variability that might otherwise result from economic disparities between countries (41–43). Lastly, economic status may be a less relevant indicator than specific factors such as dialysis adequacy, shift work patterns, or the level of family support, all of which have a more direct impact on sleep quality.

In subgroup analyses, the pooled prevalence of poor sleep quality was observed to be similar in both cross-sectional (64.5%, 95% CI: 60.8–68.3%) and cohort studies (56.5%, 95% CI: 33.4–79.6%). In our study, only the prevalence of poor sleep quality at baseline in cohort studies was included in the analysis. At baseline assessment, patients in longitudinal studies are essentially equivalent to those in cross-sectional studies, as they share comparable disease status, treatment exposure, and demographic characteristics before any specific follow-up occurs (79). The factors influencing sleep quality are therefore consistent across both study designs at this point. Therefore, for the purpose of estimating prevalence, information derived from baseline data in high-quality cohort studies should be considered as reliable.

In the stratified analysis based on cut-off values, significant differences were observed in the pooled prevalence of poor sleep quality among studies using different thresholds. Specifically, the pooled prevalence of poor sleep quality decreased from 70.5% (95% CI: 65.4–75.6%) at a cut-off of ≥ 5–60.8% (95% CI: 56.4–65.2%) at a cut-off of ≥ 6 and then to 53.8% (95% CI: 39.4–68.3%) at a cut-off of ≥ 7. Lower cut-off values inherently broaden the scope of case identification, encompassing individuals with milder clinical symptoms. Conversely, applying more stringent thresholds makes the diagnostic criteria for defining poor sleep quality more rigorous, restricting case designation to those exhibiting more pronounced clinical features. These results highlight the fact that choosing cut-off values has a crucial impact on both epidemiological estimates and the potential for developing targeted interventions to address the spectrum of sleep disorder severity in this population.

Given the high prevalence of poor sleep quality in HD patients, it is recommended to integrate sleep assessment into the routine clinical management of HD patients. Specifically, standardized tools should be used for screening during outpatient visits and follow-ups at the clinical screening level. Additionally, clinical nurses can incorporate sleep monitoring into daily assessments and provide sleep hygiene guidance. Finally, multidisciplinary teams integrating psychiatric and psychological services with rehabilitation therapy should be established to develop individualized intervention strategies.

 Limitations

Some limitations should be considered. Firstly, consistent with other meta-analyses conducted on prevalence studies, the included studies exhibited a high level of heterogeneity. However, the heterogeneity remained unexplained by our subgroup and meta-regression analyses. Secondly, despite the lack of a statistical association between prevalence and income level in our subgroup analysis, the overrepresentation of articles from low- and middle-income countries in the included studies limits the robustness of this conclusion for high-income countries. Thirdly, the measurement of poor sleep quality relies on self-reporting tools, which may introduce recall bias. Fourthly, we excluded articles published in any language other than English, which may introduce potential selection bias. Finally, the findings of this study should be interpreted with caution, as they are primarily derived from the pooled results of multiple small-sample observational studies, and the statistical power of the publication bias test is limited.

 Conclusion

In conclusion, this meta-analysis demonstrates that poor sleep quality is a common adverse symptom experienced by HD patients. Given the negative impact of poor sleep quality on HD patients, it is essential to routinely evaluate their sleep quality and implement evidence-based interventions.

 Author contributions

G-FS: Conceptualization, Writing – original draft, Visualization, Methodology, Formal analysis. X-HZ: Data curation, Validation, Methodology, Writing – original draft. LC: Methodology, Supervision, Writing – review & editing, Validation, Conceptualization. Y-JZ: Formal analysis, Methodology, Writing – original draft, Validation. W-WY: Writing – original draft, Visualization, Validation, Methodology. JZ: Writing – original draft, Visualization, Validation, Methodology. LH: Writing – original draft, Visualization, Validation. S-KT: Writing – original draft, Methodology, Validation.

 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.

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

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 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2026.1770352/full#supplementary-material

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Edited by: Davide Viggiano, University of Campania Luigi Vanvitelli, Italy

Reviewed by: Ika Yuni Widyawati, Faculty of Nursing Universitas Airlangga, Indonesia

Prabhjot Kaur, Baba Farid University of Health Sciences, India