Prevalence of post-stroke poor sleep quality: a meta-analysis of Pittsburgh Sleep Quality Index results

Objective: To assess the prevalence of post-stroke poor sleep quality using the Pittsburgh Sleep Quality Index (PSQI).

Methods: This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Following the PICO framework, a systematic search was conducted in PubMed, CINAHL, Cochrane Library, and Web of Science for relevant cohort, case-control, and cross-sectional studies published up to October 2024. The retrieved literature was then meta-analyzed using Stata 13.0 software.

Results: Eighteen studies were reviewed, showing a total poor sleep quality prevalence of 55% (95%CI = 0.47 to 0.62) and a PSQI score is 8.12 (95%CI = 6.71 to 9.53). Compared to normal people, stroke patients sleep onset latency (SL) was prolonged by 1.36 min (95%CI = 0.82 to 1.90), sleep efficiency (SE) decreased by 1.48% (95%CI = −0.20 to −0.92), and periodic leg movements per hour of sleep (PLMI) increased by 1.07 per hour (95%CI = 0.56 to 1.59). Subgroup analysis showed that, compared with hemorrhagic stroke patients, ischemic stroke patients had higher incidence of poor sleep quality at 52% (95%CI = 0.24 to 0.86); the incidence of poor sleep quality was 59% (95%CI = 0.49 to 0.70) higher in chronic stroke patients compared to acute and subacute stroke patients; the incidence of poor sleep quality was 61% (95%CI = 0.51 to 0.71) higher in community stroke patients than in hospitalized stroke patients; and the incidence of poor sleep quality was 59% (95%CI = 0.58 to 0.61) higher in stroke patients in developing countries than those in developed countries.

Conclusion: Current evidence suggests that quality of sleep worsens after a stroke, with symptoms being widespread. Factors such as stroke type, stroke phase, clinical setting, and research region can all impact sleep quality after stroke. These findings underscore the importance of monitoring sleep quality in these populations and implementing appropriate preventive and interventional strategies.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD420251161167.

1 Introduction

Stroke remains the primary cause of long-term functional impairment, exerting profound physical and psychological effects on survivors and their families. It ranks among the top three causes of death and premature disability worldwide (1–3). While stroke incidence has declined in developed countries, it has risen in low- and middle-income countries over the past two decades (4). Survivors often face chronic sequelae, including mobility, cognition, language, and communication impairments; emotional disturbances; poor sleep quality; difficulties in daily activities; and social isolation (5).

Up to 60% of stroke patients experience concomitant insomnia (6). These disorders significantly impact stroke rehabilitation and can worsen pre-existing conditions like hypertension and diabetes, thereby reducing patients’ quality of life (7–10). The pathophysiological mechanisms behind post-stroke poor sleep quality are not fully understood, but potential factors include: ① Due to damage or dysfunction of specific brain regions, leading to abnormalities in sleep–wake regulation, orexin/arousal pathways, and 5-hydroxytryptamine (5-HT) levels, insomnia occurs directly (11, 12); ② Abnormal upper airway movements cause sleep-disordered breathing, with upper airway muscle relaxation reaching its highest degree during rapid eye movement sleep, causing drooping of the jaw or hypopharyngeal region, increasing upper airway resistance, and reducing alveolar ventilation, resulting in sleep apnea (13); and ③ Post-stroke patients experience psychological factors such as excessive focus on their own condition and feelings of loss from leaving their work, which further aggravate their mental burden and exacerbate sleep problems (14).

Studies examining the relationship between poor sleep quality and cerebrovascular accidents have revealed a high prevalence of post-stroke poor sleep quality, with significant discrepancies in reported results due to varying measurement criteria (15). Currently, assessment tools such as the PSQI (16), Insomnia Severity Index (ISI) (17), Epworth Sleepiness Scale (ESS) (18), Athens Insomnia Scale (AIS) (19), and polysomnography (PSG) (20) are used to evaluate quality of sleep in patients, each with its own strengths and limitations. The PSQI is a frequently used sleep quality questionnaire that has been shown to be reliable and valid in many populations The PSQI is a subjective measure of sleep which assesses nighttime sleep quality in the past month, was used. It consists of 19 items, with the total score ranging from 0 to 21, where a score of >5 indicates poor sleep quality. This cut-off has been shown to have high specificity and sensitivity for distinguishing insomnia patients and controls (21). Many studies define a PSQI score >5 as poor sleep quality. Based on this, our study performs a meta-analysis of research using the PSQI to evaluate quality of sleep, aiming to investigate the prevalence of post-stroke poor sleep quality. The findings are expected to inform targeted prevention and management strategies for patients experiencing poor sleep quality post-stroke.

2 Methods

2.1 Literature search

This study was conducted according to the PRISMA guidelines. The search strategy followed the PICOS framework. Two researchers, systematically trained in evidence-based practice, independently conducted literature searches on PubMed, CINAHL, Cochrane, Web of Science, Embase, and PsycINFO. Using PubMed as a representative example (see Figure 1), the researchers utilized a combination of free-text terms and controlled vocabulary to ensure comprehensive retrieval. The search covered the entire database history up to October 1, 2024, supplemented by manual tracking of references from the retrieved articles.

Figure 1

Figure 1. PubMed search strategy.

2.2 Study selection

Inclusion criteria: (1) Study designs included cohort studies, cross-sectional studies, and case-control studies. (2) The study subjects were stroke patients who met the diagnostic criteria outlined in the Diagnostic Criteria for Cerebrovascular Diseases. (3) The studies which were considered utilized the Pittsburgh Sleep Quality Index to assess sleep quality. (4) The primary outcome measure was the prevalence rate of post-stroke poor sleep quality. (5) The literature review concentrated on peer-reviewed and published English-language articles.

Exclusion criteria: (1) Conference abstracts; (2) literature lacking complete data reports or full-text availability; (3) duplicate publications; and (4) studies involving transient ischemic attack or subarachnoid hemorrhage were excluded. Studies with a “mixed” population (e.g., stroke and TIA), where results had not been reported separately for stroke, were included if the overall stroke sample exceeded 80%.

2.3 Data extraction

Literature screening and data extraction were independently performed by two researchers with evidence-based training. Discrepancies were resolved through discussion or consultation with a third researcher. EndNote software was utilized for deduplication. Preliminary screening involved reviewing titles and abstracts to exclude clearly irrelevant literature, followed by a full-text review to determine final inclusion. Original authors were contacted when necessary. Extracted data included: literature characteristics (author, publication year, and country); demographic features (age, sex/gender, BMI, sample size, smoking history, and alcohol consumption history); follow-up duration; stroke characteristics (stroke type, lesion location, lesion count, stroke assessment tool, and stroke score); comorbidities (anxiety assessment tool/score, depression assessment tool/score, diabetes mellitus, heart disease, and hypertension); and sleep parameters (prevalence rate of poor sleep quality, PSQI score, and objective data from Multi-Dimensional Sleep Mapping).

2.4 Quality assessment

A quality assessment of the literature was independently conducted by two researchers trained in evidence-based practice, and discrepancies were resolved through discussion or adjudication by a third researcher. Appropriate quality assessment methods were selected based on study designs. For cross-sectional studies, the risk of bias was evaluated using the assessment criteria recommended by the Agency for Health Care Research and Quality (AHRQ) (22). This 11-point scale assigns 1 point for “Yes” responses and 0 points for “No” or “Unclear” responses. Studies scoring 8–11 points were classified as high quality, 4–7 points as moderate quality, and 0–3 points as low quality. Studies scoring below 4 points are considered low-quality literature. Case–control studies and cohort studies were assessed using the Newcastle-Ottawa Scale (NOS) (23), which primarily consists of three categories and eight assessment items. The evaluation covers three aspects: sample selection, comparability between groups, and exposure factors. Each item scores 1 point, except for the comparability between groups criterion, which carries 2 points. The total NOS score ranges from 0 to 9 points: studies scoring 0–4 points were considered low-quality literature, 5–6 points as medium-quality, and 7–9 points as high-quality literature, with higher scores indicating better methodological quality. The studies included in this research were all cross-sectional studies.

2.5 Statistical analysis

Statistical analysis was performed using Stata 13.0 software. Measurement data were expressed as standardized mean difference (SMD) and weighted mean difference (WMD). The proportion of poor sleep quality measured in all included studies was extracted. Before pooling prevalence estimates, the variance of the raw prevalence from each included study was stabilized by using the Freeman-Tukey double arc-sine transformation (24). When the number of studies included was less than five, a fixed-effects model was used. When the number of studies included was greater than or equal to five, heterogeneity among study outcomes was first assessed (25). In cases where p > 0.1 and I² < 50%, a fixed-effects model was applied; however, when p ≤ 0.1 or I² ≥ 50%, indicating high heterogeneity, a random-effects model was used.

Subgroup analyses were used to explore the source of potential heterogeneity and were calculated based on categorical variables (e.g., cut-off values, country, study region, clinical environment, stroke period, and stroke type). Moreover, these subgroup analyses were conducted when there were at least two studies in each subgroup.

Sensitivity analysis was performed using the one-by-one exclusion method for individual studies. If sources of heterogeneity could not be identified, descriptive analysis was conducted. Funnel plots and Egger’s test were used to assess publication bias, with p < 0.05 considered statistically significant.

3 Results

3.1 Literature search results

The initial search identified 4,074 relevant articles. After removing duplicates, 3,079 articles remained. Following title and abstract screening, 61 articles underwent full-text review (reasons for exclusion included irrelevance to research topic, mismatched study population, and mismatched assessment tools), and 19 were ultimately included in the data analysis. The study selection process is shown in Figure 2.

Figure 2

Figure 2. Literature screening flowchart.

3.2 Basic characteristics and methodological quality of included studies

Table 1 summarizes the characteristics of the included studies. Of the 19 cross-sectional studies, AHRQ scores ranged from 7 to 11 points, with 17 classified as high quality (26–41) and two as moderate quality (42, 43). Detailed quality assessments are provided in Table 1.

Table 1

Table 1. Basic characteristics of included studies.

3.3 Meta-analysis results

3.3.1 Evaluating post-stroke quality of sleep symptoms using PSG

Two studies using PSG analyzed sleep quality. Through fixed-effects model meta-analysis, compared to the control group, post-stroke patients showed: sleep onset latency (SL) prolonged by 1.36 min (95%CI = 0.82 to 1.90), sleep efficiency (SE) decreased by 1.48% (95%CI = −0.20 to −0.92), periodic leg movements per hour of sleep (PLMI) increased by 1.07 per hour (95%CI = 0.56 to 1.59), and wake after sleep onset (WASO) with no statistically significant difference at 0.44 min (95%CI = 0.03 to 0.90). Specific details are shown in Table 2.

Table 2

Table 2. Results of sleep symptoms after stroke.

3.3.2 Assessing the prevalence of poor sleep quality after stroke using the PSQI

Sixteen studies reported on the prevalence of post-stroke poor sleep quality. Considerable heterogeneity was observed among these studies (I² = 96.3%, p < 0.1). Using a random-effects model for pooling, the meta-analysis found an overall prevalence rate of 55% (95%CI = 0.47 to 0.62). A forest plot illustrating the prevalence rates of post-stroke poor sleep quality is shown in Figure 3.

Figure 3

Figure 3. Forest plot of the prevalence rate of post-stroke poor sleep quality.

3.3.3 Assessing post-stroke quality of sleep using the PSQI scale

Nine studies reported post-stroke PSQI scores. Significant heterogeneity was observed among these studies (I² = 93.7%, p < 0.1). A random-effects model was used for pooling. The meta-analysis showed a mean post-stroke PSQI score of 8.12 points (95%CI = 6.71 to 9.53). The forest plot for post-stroke PSQI score is presented in Figure 4.

Figure 4

Figure 4. Forest plot of post-stroke PSQI score.

3.3.4 The impact of stroke type on poor sleep quality prevalence

Studies have compared the impact of stroke types on the incidence of poor sleep quality. Subgroup analyses were conducted based on cerebrovascular accident type. Significant heterogeneity persisted across all subgroups (I² > 50%, p < 0.1), and a random-effects model was applied for pooling. The prevalence was 52% for ischemic stroke and 50% for hemorrhagic stroke. The incidence of poor sleep quality in cases of atherosclerotic cerebral infarction of the artery was 63%. For cardioembolic infarction, poor sleep quality incidence was 65%, while for other cases of acute ischemic stroke, it was 68%. Only one study evaluated the impact of lacunar and non-lacunar ischemic stroke on poor sleep quality incidence, finding that lacunar ischemic stroke patients had a poor sleep quality incidence of 21.43%, while non-lacunar ischemic stroke patients showed an incidence of 11.05%. Details are provided in Table 3.

Table 3

Table 3. Meta-analysis of stroke types on the prevalence rates of post-stroke poor sleep quality.

3.3.5 Impact of comorbidities on the prevalence of poor sleep quality after stroke

Subgroup analyses were conducted based on comorbidity. Significant heterogeneity persisted across all subgroups (I² > 50%, p < 0.1), and a random-effects model was applied for pooling. When combined with hypertension, the prevalence was 52%; with diabetes mellitus, 55%; and with heart disease, 57%. Details are provided in Table 4.

Table 4

Table 4. Meta-analysis of comorbidities on poor sleep quality prevalence after stroke.

3.3.6 Impact of demographic characteristics on the prevalence of poor sleep quality after stroke

Significant heterogeneity persisted across all subgroups (I² > 50%, p < 0.1), and a random-effects model was applied for pooling. The results indicated that the prevalence of post-stroke poor sleep quality was 59% in the chronic phase, 55% in the acute phase, and 45% in the subacute phase. Moreover, prevalence was found to be 61% in community environments compared with 53% in hospital environments, while being 59% in developing countries compared with 44% in developed countries (Table 5).

Table 5

Table 5. Meta-analysis of demographic characteristics on post-stroke poor sleep quality prevalence.

3.4 Sensitivity analysis and publication bias

Sixteen analyses were conducted by sequentially removing individual studies from the pooled data. The overall results remained largely consistent, demonstrating the robustness of the meta-analysis findings, as illustrated in Figure 5. Publication bias was assessed using a funnel plot analysis of poor sleep quality prevalence rates. Although the funnel plot displayed an asymmetrical distribution of scatter points (Figure 6), Egger’s test revealed no statistically significant difference (p = 0.186), indicating a low risk of publication bias.

Figure 5

Figure 5. Sensitivity analysis of post-stroke quality of sleep.

Figure 6

Figure 6. Funnel plot for post-stroke quality of sleep.

4 Discussion

This meta-analysis of two studies using PSG to assess post-stroke poor sleep quality symptoms found that post-stroke patients experienced prolonged sleep onset latency (patients: range 23.1–23.9, mean 23.43; vs. controls: range 9.2–15.9, mean 13.46); reduced sleep efficiency (patients: range 72–78.8, mean 76.0; vs. controls: range 79.1–88.7, mean 82.62); increased periodic leg movements per hour (patients: range 12.08–13.7, mean 13.04; vs. controls: range 2.2–8.07, mean 5.92); and a trend toward increased WASO 9 (patients: range 21.4–102.7, mean 54.89; vs. controls: range 21.1–72.1, mean 53.54). The data suggest that stroke patients have poor sleep continuity and reveal subtle changes in sleep structure, though these observations are not yet sufficient to draw definitive conclusions. These changes may result from alterations in awakening and cortical-thalamic-cortical oscillations, with slow oscillations from intact cortical networks serving as envelopes for other brain oscillations like slow-wave activity or sleep spindles. Cortical lesions might disrupt this process, primarily affecting sleep continuity (44). Furthermore, the psychological impact of stroke could potentially increase cognitive (psychosocial) arousal, thereby leading to even poorer sleep continuity. Future research is anticipated to combine PSG with structural neuroimaging studies.

This study conducted a meta-analysis of 16 studies which reported on the prevalence of post-stroke poor sleep quality, revealing a pooled prevalence rate of 55%, clearly higher than the conclusions drawn from a Hinz A survey of the general population (45). Additionally, this study performed a meta-analysis of nine studies reporting post-stroke PSQI scores, finding that the average PSQI score in post-stroke patients was 8.12 points. These findings highlight the prominence of post-stroke poor sleep quality, which significantly impacts the quality of life in patients with cerebrovascular accidents. This may be due to factors such as neural functional deficits, overall body functional status, negative emotions, environmental factors, or treatment factors which have affected the patient’s sleep quality. Compared with other complications, the subtle nature of poor sleep quality symptoms makes them more likely to be overlooked by healthcare professionals in clinical practice. However, post-stroke poor sleep quality has a significantly higher prevalence rate than other stroke complications such as delirium, aspiration pneumonia, and venous thromboembolism (46, 47). Therefore, it is recommended that future clinical practice place greater emphasis on poor sleep quality and promote screening initiatives to facilitate early intervention, thereby mitigating or improving patients’ sleep disturbances and reducing adverse outcomes.

The prevalence of post-stroke poor sleep quality is generally lower in ischemic stroke patients compared to those with cerebral hemorrhagic stroke. However, this study found a higher prevalence of poor sleep quality for individuals experiencing ischemic stroke (52%) than those with hemorrhagic stroke (50%). Considering the limited number of studies conducted on stroke subtypes, this finding is tentative and needs to be replicated in future research.

In addition, this study found that the prevalence of poor sleep quality after stroke in developing countries (59%) was higher than in developed countries (44%). This may be related to insufficient stroke healthcare resources and fewer trained healthcare professionals in developing countries compared to developed countries, all of which can lead to adverse health outcomes, including poor sleep quality (48). It was found in this study that the prevalence of poor sleep quality in the chronic phase after stroke was 59%, higher than in the acute phase (55%) and subacute phase (45%). This is inconsistent with previous research, which found the prevalence of poor sleep quality during the acute, subacute, and chronic phases to be 40.7, 42.6, and 35.9% (49), respectively. This discrepancy may be due to differences in the types of sleep problems investigated across different studies. The present study found a significant difference in the prevalence of poor sleep quality in different clinical settings, with a higher prevalence of sleep disorders in community patients (61%) than in hospitalized patients (53%). Stroke treatment and rehabilitation are significantly associated with the sleep quality of stroke survivors (50). Hospitalized patients have more timely and easier access to medical resources compared to community patients; thus, the incidence of sleep-related problems is relatively lower.

The prevalence of post-stroke poor sleep quality was higher in patients aged ≥60 years (62%) compared to those aged <60 years (52%) which may be related to sleep apnea. One study suggests that the prevalence of sleep apnea among people aged 30–70 increases with age (51). Sleep apnea may lead to intermittent hypoxia, increased sleep awakenings (52), shortened sleep time, and decreased sleep quality (53). Future research exploring the association between sleep apnea and stroke is anticipated. Patients with BMI ≥ 25 showed a higher prevalence of post-stroke poor sleep quality (68%) compared to those with BMI < 25 (31%). In overweight/obese individuals, increased tongue fat deposition narrows the pharyngeal airway, and abdominal obesity can alter sleep structure, leading to sleep deprivation. This further triggers increased growth hormone-releasing peptide secretion and decreased leptin, promoting increased appetite. Increased appetite and weight gain further exacerbate poor sleep quality, creating a vicious cycle (54). Therefore, in stroke patients with obesity, it is necessary to control patient healthy diet and physical activities.

4.1 Strengths and limitations of the study

This study adopted a comprehensive review protocol and followed the PRISMA guidelines. Multiple electronic databases were searched, and studies were screened, while data were extracted and evaluated using rigorous methods. Additionally, only research using the PSQI was included, to reduce research heterogeneity. However, this study still has the following limitations: (1) All of the literature included comprised cross-sectional studies. Due to differences in study design, research quality, sample sources, and sample sizes, substantial heterogeneity was observed among the studies. Prevalence is an average across highly heterogeneous studies and should not be interpreted as a precise estimate applicable to all settings. (2) The generalizability of the findings may be limited, as only English-language literature was included. (3) Since only some studies reported excluding patients with pre-existing poor sleep quality prior to enrollment, the potential influence of pre-existing poor sleep quality on our research outcomes could not be fully ruled out. (4) Due to the limited number of studies included, this research has not yet explored the relationships between post-stroke poor sleep quality prevalence rates, PSQI scores, and factors such as lesion location, lesion count, or stroke severity scores. (5) The PSQI is a self-reported sleep quality measurement method and may be less accurate than objective measurement methods.

5 Summary

This study found a high prevalence of poor sleep quality after stroke. The incidence of poor sleep quality is higher in developing countries, as well as higher for community-dwelling patients, patients in the chronic phase of stroke, those aged ≥60 years, and those with a BMI ≥ 25. Therefore, it is recommended that healthcare institutions at all levels pay attention to monitoring these populations and accelerate the implementation of comprehensive prevention and treatment strategies to reduce the prevalence of poor sleep quality in stroke patients and reduce adverse outcomes. Future research could further focus on the incidence and symptoms of poor sleep quality before stroke and investigate the impact of sleep apnea on new-onset stroke, recurrent stroke, and adverse stroke outcomes. Additionally, we look forward to future studies examining the differences in stroke subtypes from the perspective of quality of sleep, such as lacunar and non-lacunar ischemic strokes, atherosclerotic strokes, and cardiogenic embolic strokes, as well as exploring the relationship between post-stroke poor sleep quality and gastrointestinal symptoms and gut microbiota based on the brain-gut axis theory.

Data availability statement

The data analyzed in this study are not publicly available, but can be provided upon reasonable request. Requests to access these datasets should be directed to Yu Zhou, 2568963148@qq.com.

Author contributions

YZ: Writing – original draft, Formal analysis, Data curation, Validation, Methodology. BG: Project administration, Validation, Conceptualization, Writing – review & editing. RT: Writing – original draft, Formal analysis, Methodology. QZ: Writing – review & editing, Data curation, Visualization. LZ: Conceptualization, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This article was funded by Sichuan Provincial Hospital Association, Chengdu Science and Technology Bureau, Chengdu Municipal Health Commission, and Sichuan Provincial Nursing Association.

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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References

1. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. (2024) 403:2100–32. doi: 10.1016/S0140-6736(24)00367-2

Crossref Full Text | Google Scholar

2. Li, J, Fan, H, Yang, Y, Huang, Z, Yuan, Y, and Liang, B. Global burden of myocarditis from 1990 to 2021: findings from the Global Burden of Disease Study 2021. BMC Cardiovasc Disord. (2024) 24:720. doi: 10.1186/s12872-024-04402-z,

PubMed Abstract | Crossref Full Text | Google Scholar

3. GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. (2021) 20:795–820. doi: 10.1016/S1474-4422(21)00252-0

Crossref Full Text | Google Scholar

4. Krishnamurthi, RV, Feigin, VL, Forouzanfar, MH, Mensah, GA, Connor, M, Bennett, DA, et al. Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet Glob Health. (2013) 1:e259–81. doi: 10.1016/S2214-109X(13)70089-5,

PubMed Abstract | Crossref Full Text | Google Scholar

5. Sawadogo, W, Adera, T, Alattar, M, Perera, R, and Burch, JB. Association between insomnia symptoms and trajectory with the risk of stroke in the health and retirement study. Neurology. (2023) 101:e475–88. doi: 10.1212/WNL.0000000000207449,

PubMed Abstract | Crossref Full Text | Google Scholar

6. Jeffers, MS, Pittman, AC, Kendzerska, T, Corbett, D, Hayward, KS, and Chen, Y. Self-reported sleep disturbances among people who have had a stroke: a cross-sectional analysis. CMAJ. (2023) 195:E354–62. doi: 10.1503/cmaj.221063,

PubMed Abstract | Crossref Full Text | Google Scholar

7. Stadler, S, Jalili, S, Schreib, A, Jung, B, Zeman, F, Böger, CA, et al. Association of sleep-disordered breathing with severe chronic vascular disease in patients with type 2 diabetes. Sleep Med. (2018) 48:53–60. doi: 10.1016/j.sleep.2018.05.001,

PubMed Abstract | Crossref Full Text | Google Scholar

8. Matas, A, Pinto, N, Conde, B, and Vaz Patto, M. Exploring the insomnia-ischemic stroke nexus: a comprehensive review. J Clin Med. (2024) 13. doi: 10.3390/jcm13061622,

PubMed Abstract | Crossref Full Text | Google Scholar

9. Zhang, Y, Jiang, X, Liu, J, Lang, Y, and Liu, Y. The association between insomnia and the risk of metabolic syndrome: a systematic review and meta-analysis. J Clin Neurosci. (2021) 89:430–6. doi: 10.1016/j.jocn.2021.05.039,

PubMed Abstract | Crossref Full Text | Google Scholar

10. Chen, S, Song, X, Shi, H, Li, J, Ma, S, Chen, L, et al. Association between sleep quality and hypertension in Chinese adults: a cross-sectional analysis in the Tianning cohort. Nat Sci Sleep. (2022) 14:2097–105. doi: 10.2147/NSS.S388535,

PubMed Abstract | Crossref Full Text | Google Scholar

11. De Luca, R, Nardone, S, Grace, KP, Venner, A, Cristofolini, M, Bandaru, SS, et al. Orexin neurons inhibit sleep to promote arousal. Nat Commun. (2022) 13:4163. doi: 10.1038/s41467-022-31591-y,

PubMed Abstract | Crossref Full Text | Google Scholar

12. Bollu, P C, Pandeya, A, Pesala, S P, and Krishna, N. Sleepiness after stroke: case report and review of literature on hypersomnia as a result of stroke. Madridge J Neurosci. (2017) 1:4–6. doi: 10.18689/mjns-1000102

Crossref Full Text | Google Scholar

13. Cai, H X, Wang, X P, Yang, G Y. Poor quality of sleep in stroke: an update on management. Aging Dis. (2021) 12:570–585, doi: 10.14336/AD.2020.0707,.

PubMed Abstract | Crossref Full Text | Google Scholar

14. Hepburn, M, Bollu, PC, French, B, and Sahota, P. Sleep medicine: stroke and sleep. Mo Med. (2018) 115:527–32.

Google Scholar

15. Baylan, S, Griffiths, S, Grant, N, Broomfield, NM, Evans, JJ, and Gardani, M. Incidence and prevalence of post-stroke insomnia: a systematic review and meta-analysis. Sleep Med Rev. (2020) 49:101222. doi: 10.1016/j.smrv.2019.101222,

PubMed Abstract | Crossref Full Text | Google Scholar

16. Buysse, DJ, Reynolds, CF 3rd, Monk, TH, Berman, SR, and Kupfer, DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. (1989) 28:193–213. doi: 10.1016/0165-1781(89)90047-4,

PubMed Abstract | Crossref Full Text | Google Scholar

17. Chalder, T. Insomnia: psychological assessment and management. Psychol Med. (1996) 26:1096–7.

Google Scholar

18. Johns, MW. Reliability and factor analysis of the Epworth sleepiness scale. Sleep. (1992) 15:376–81. doi: 10.1093/sleep/15.4.376,

PubMed Abstract | Crossref Full Text | Google Scholar

19. Soldatos, CR, Dikeos, DG, and Paparrigopoulos, TJ. Athens insomnia scale: validation of an instrument based on ICD-10 criteria. J Psychosom Res. (2000) 48:555–60. doi: 10.1016/S0022-3999(00)00095-7,

PubMed Abstract | Crossref Full Text | Google Scholar

20. Zhang, X, Song, Z, Ye, J, Fu, Y, Wang, J, Su, L, et al. Polysomnographic and neuropsychological characteristics of rapid eye movement sleep behavior disorder patients. Brain Behav. (2019) 9:e01220. doi: 10.1002/brb3.1220,

PubMed Abstract | Crossref Full Text | Google Scholar

21. Dementia and Cognitive Disorders Group of the Chinese Society of Neurology. Expert consensus on neuropsychological assessment for mild cognitive impairment (2025 edition). Zhonghua Yi Xue Za Zhi. (2025) 105:204–18. doi: 10.3760/cma.j.cn112137-20240612-01322

Crossref Full Text | Google Scholar

22. Stang, A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. (2010) 25:603–5. doi: 10.1007/s10654-010-9491-z,

PubMed Abstract | Crossref Full Text | Google Scholar

23. Lo, CK, Mertz, D, and Loeb, M. Newcastle-Ottawa scale: comparing reviewers' to authors' assessments. BMC Med Res Methodol. (2014) 14:45. doi: 10.1186/1471-2288-14-45,

PubMed Abstract | Crossref Full Text | Google Scholar

24. Barendregt, JJ, Doi, SA, Lee, YY, Norman, RE, and Vos, T. Meta-analysis of prevalence. J Epidemiol Community Health. (2013) 67:974–8. doi: 10.1136/jech-2013-203104,

PubMed Abstract | Crossref Full Text | Google Scholar

25. Tufanaru, C, Munn, Z, Stephenson, M, and Aromataris, E. Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness. Int J Evid Based Healthc. (2015) 13:196–207. doi: 10.1097/XEB.0000000000000065,

PubMed Abstract | Crossref Full Text | Google Scholar

26. Ho, LYW, Lai, CKY, and Ng, SSM. Contribution of sleep quality to fatigue following a stroke: a cross-sectional study. BMC Neurol. (2021) 21:151. doi: 10.1186/s12883-021-02174-z,

PubMed Abstract | Crossref Full Text | Google Scholar

27. Zhang, S, Chang, C, Zhang, J, Song, B, Fang, H, and Xu, YM. Correlation analysis of sleep quality and youth ischemic stroke. Behav Neurol. (2014) 2014:246841. doi: 10.1155/2014/246841,

PubMed Abstract | Crossref Full Text | Google Scholar

28. Xie, H, Chen, J, Chen, Q, Zhao, Y, Liu, J, Sun, J, et al. The diagnostic value of gut microbiota analysis for post-stroke poor quality of sleep. Diagnostics (Basel). (2023) 13:2970. doi: 10.3390/diagnostics13182970

Crossref Full Text | Google Scholar

29. Sonmez, I, and Karasel, S. Poor sleep quality I related to impaired functional status following stroke. J Stroke Cerebrovasc Dis. (2019) 28:104349. doi: 10.1016/j.jstrokecerebrovasdis.2019.104349,

PubMed Abstract | Crossref Full Text | Google Scholar

30. He, W, and Ruan, Y. Poor sleep quality, vitamin D deficiency and depression in the stroke population: a cohort study. J Affect Disord. (2022) 308:199–204. doi: 10.1016/j.jad.2022.04.031,

PubMed Abstract | Crossref Full Text | Google Scholar

31. Rangel, MFA, Silva, LC, Gonçalves, EH, Silva, A, Teixeira-Salmela, LF, and Scianni, AA. Presence of self-reported sleep alterations after stroke and their relationship with disability: a longitudinal study. Neurorehabil Neural Repair. (2024) 38:518–26. doi: 10.1177/15459683241252826,

PubMed Abstract | Crossref Full Text | Google Scholar

32. Kim, KT, Moon, HJ, Yang, JG, Sohn, SI, Hong, JH, and Cho, YW. The prevalence and clinical significance of poor quality of sleep in acute ischemic stroke patients-a questionnaire study. Sleep Breath. (2017) 21:759–65. doi: 10.1007/s11325-016-1454-5

Crossref Full Text | Google Scholar

33. Kim, J, Kim, Y, Yang, KI, Kim, DE, and Kim, SA. The relationship between sleep disturbance and functional status in mild stroke patients. Ann Rehabil Med. (2015) 39:545–52. doi: 10.5535/arm.2015.39.4.545,

PubMed Abstract | Crossref Full Text | Google Scholar

34. Tayade, K, Vibha, D, Singh, RK, Pandit, AK, Ramanujam, B, Das, A, et al. Prevalence and determinants of post-stroke poor quality of sleep: a cross-sectional hospital-based study. Sleep Breath. (2023) 27:2429–33. doi: 10.1007/s11325-023-02850-z

Crossref Full Text | Google Scholar

35. Niu, S, Liu, X, Wu, Q, Ma, J, Wu, S, Zeng, L, et al. Sleep quality and cognitive function after stroke: the mediating roles of depression and anxiety symptoms. Int J Environ Res Public Health. (2023) 20. doi: 10.3390/ijerph20032410,

PubMed Abstract | Crossref Full Text | Google Scholar

36. Mansour, AH, Ayad, M, El-khayat, N, Sadek, EI, and Alloush, TK. Post-stroke poor quality of sleep in Egyptian patients by using simply administered questionnaires: a study from Ain Shams University. The Egyp J Neuro Psych Neuro. (2020) 56:13. doi: 10.1186/s41983-020-0148-x

Crossref Full Text | Google Scholar

37. Karaca, B. Factors affecting poststroke poor quality of sleep. J Stroke Cerebrovasc Dis. (2016) 25:727–32. doi: 10.1016/j.jstrokecerebrovasdis.2015.11.015,

PubMed Abstract | Crossref Full Text | Google Scholar

38. Iddagoda, MT, Inderjeeth, CA, Chan, K, and Raymond, WD. Post-stroke sleep disturbances and rehabilitation outcomes: a prospective cohort study. Intern Med J. (2020) 50:208–13. doi: 10.1111/imj.14372,

PubMed Abstract | Crossref Full Text | Google Scholar

39. Fan, XW, Yang, Y, Wang, S, Zhang, YJ, Wang, AX, Liao, XL, et al. Impact of persistent poor sleep quality on post-stroke anxiety and depression: a national prospective clinical registry study. Nat Sci Sleep. (2022) 14:1125–35. doi: 10.2147/NSS.S357536,

PubMed Abstract | Crossref Full Text | Google Scholar

40. Zhang, Y, Xia, X, Zhang, T, Zhang, C, Liu, R, Yang, Y, et al. Relationship between poor quality of sleep and the prognosis of neurological function after stroke. Front Neurol. (2022) 13:1036980. doi: 10.3389/fneur.2022.1036980

Crossref Full Text | Google Scholar

41. Wu, W, Cui, L, Fu, Y, Tian, Q, Liu, L, Zhang, X, et al. Sleep and cognitive abnormalities in acute minor thalamic infarction. Neurosci Bull. (2016) 32:341–8. doi: 10.1007/s12264-016-0036-7

Crossref Full Text | Google Scholar

42. Sterr, A, Kuhn, M, Nissen, C, Ettine, D, Funk, S, Feige, B, et al. Post-stroke insomnia in community-dwelling patients with chronic motor stroke: physiological evidence and implications for stroke care. Sci Rep. (2018) 8:8409. doi: 10.1038/s41598-018-26630-y,

PubMed Abstract | Crossref Full Text | Google Scholar

43. Wang, H, Huang, Y, Li, M, Yang, H, An, J, Leng, X, et al. Regional brain dysfunction in insomnia after ischemic stroke: a resting-state fMRI study. Front Neurol. (2022) 13:1025174. doi: 10.3389/fneur.2022.1025174,

PubMed Abstract | Crossref Full Text | Google Scholar

44. Gloor, P, Ball, G, and Schaul, N. Brain lesions that produce delta wavesin the EEG. Neurology. (1977) 27:326–33. doi: 10.1212/WNL.27.4.326,

PubMed Abstract | Crossref Full Text | Google Scholar

45. Roth, T. Insomnia: definition, prevalence, etiology, and consequences. J Clin Sleep Med. (2007) 3:S7–S10. doi: 10.5664/jcsm.26929,

PubMed Abstract | Crossref Full Text | Google Scholar

46. Zhang, GB, Li, HY, Yu, WJ, Ying, YZ, Zheng, D, Zhang, XK, et al. Occurrence and risk factors for post-stroke delirium: a systematic review and meta-analysis. Asian J Psychiatr. (2024) 99:104132. doi: 10.1016/j.ajp.2024.104132,

PubMed Abstract | Crossref Full Text | Google Scholar

47. Shaw, RC, Walker, G, Elliott, E, and Quinn, TJ. Occurrence rate of delirium in acute stroke settings: systematic review and meta-analysis. Stroke. (2019) 50:3028–36. doi: 10.1161/STROKEAHA.119.025015,

PubMed Abstract | Crossref Full Text | Google Scholar

48. Pandian, JD, Kalkonde, Y, Sebastian, IA, Felix, C, Urimubenshi, G, and Bosch, J. Stroke systems of care in low-income and middle-income countries: challenges and opportunities. Lancet. (2020) 396:1443–51. doi: 10.1016/S0140-6736(20)31374-X,

PubMed Abstract | Crossref Full Text | Google Scholar

49. Hasan, F, Gordon, C, Wu, D, Huang, H-C, Yuliana, LT, Susatia, B, et al. Dynamic prevalence of sleep disorders following stroke or transient ischemic attack: systematic review and meta-analysis. Stroke. (2021) 52:655–63. doi: 10.1161/STROKEAHA.120.029847

Crossref Full Text | Google Scholar

50. Khot, SP, and Morgenstern, LB. Sleep and stroke. Stroke. (2019) 50:1612–7. doi: 10.1161/STROKEAHA.118.023553,

PubMed Abstract | Crossref Full Text | Google Scholar

51. Javaheri, S, Peker, Y, Yaggi, HK, and Bassetti, CLA. Obstructive sleep apnea and stroke: the mechanisms, the randomized trials, and the road ahead. Sleep Med Rev. (2022) 61:101568. doi: 10.1016/j.smrv.2021.101568,

PubMed Abstract | Crossref Full Text | Google Scholar

52. Zapater, A, Sánchez-de-la-Torre, M, Benítez, ID, Targa, A, Bertran, S, Torres, G, et al. The effect of sleep apnea on cardiovascular events in different acute coronary syndrome phenotypes. Am J Respir Crit Care Med. (2020) 202:1698–706. doi: 10.1164/rccm.202004-1127OC,

PubMed Abstract | Crossref Full Text | Google Scholar

53. Frangopoulos, F, Zannetos, S, Nicolaou, I, Economou, NT, Adamide, T, Georgiou, A, et al. The complex interaction between the major sleep symptoms, the severity of obstructive sleep apnea, and sleep quality. Front Psych. (2021) 12:630162. doi: 10.3389/fpsyt.2021.630162,

PubMed Abstract | Crossref Full Text | Google Scholar

54. Pennings, N, Golden, L, Yashi, K, Tondt, J, and Bays, HE. Sleep-disordered breathing, sleep apnea, and other obesity-related poor quality of sleep: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS). (2022) Obes Pillars. 15;4:100043.

Google Scholar