Post-stroke neuropsychiatric profiles and their association with functional independence: A latent profile analysis

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However, the patterns of co-occurring symptoms and their relationship with functional outcomes remain unclear. This study aimed to identify distinct neuropsychiatric profiles in acute stroke patients and examine their association with functional independence. Methods A total of 417 stroke patients hospitalized in Guangzhou and Changchun from October 2024 to September 2025 were recruited. Data were collected using a general information questionnaire, the Patient Health Questionnaire-9, the Athens Insomnia Scale, the Eight-item Informant Interview to Differentiate Aging and Dementia, and the Barthel Index. Latent profile analysis identified types of neuropsychiatric disturbance, and multiple linear regression examined their associations with functional independence. Results Eighty-four percent of participants had two or more types of neuropsychiatric disturbances. Four profiles were identified: 16% asymptomatic (Profile 1), 10% mild depression–sleep–cognitive impairment (Profile 2), 66% mild depression–sleep disorder (Profile 3), and 8% major depression–sleep–cognitive disorder (Profile 4). Barthel Index scores were significantly lower in Profiles 2, 3, and 4 compared with Profile 1 ( F = 7.55, P < 0.001). Patients in Profile 4 had the highest odds of functional dependence. Mean scores for bathing, stair climbing, and grooming in Profile 4 were 1.71, 2.71, and 3.00, respectively, the lowest among all groups. Sensitivity analysis confirmed poorer functional independence in patients with neuropsychiatric disturbances ( t = 7.59, P = 0.007). Conclusions This study identified distinct post-stroke neuropsychiatric profiles and their graded association with functional independence. Patients with moderate-to-severe depression, sleep disturbance, and cognitive impairment had the poorest performance in complex daily activities such as bathing, stair climbing, and grooming. Tailoring strategies to neuropsychiatric profiles may enhance functional recovery. Stroke Neuropsychiatric disturbances Daily Living Independence Figures Figure 1 Figure 2 Figure 3 Introduction Stroke is a leading cause of long-term disability worldwide 1 . Functional independence in daily activities, such as dressing and bathing, is a core metric of post-stroke recovery and delayed disability [ 2 ]. Yet, nearly 45% of stroke survivors remain dependent in daily activities five years after onset, which impacts their quality of life 3 . Recovery is further constrained by persistent neuropsychiatric disturbances, particularly cognitive impairment, depression, and sleep problems 4 . These symptoms affect 27% to 60% of stroke survivors [ 4 – 6 ] and exert a strong influence on rehabilitation participation, functional independence, and long-term outcomes [ 3 ]. Neuropsychiatric symptoms after stroke rarely occur in isolation. Cognitive impairment, depression, and sleep disturbances frequently co-occur and interact in complex ways. Sleep disturbances increase vulnerability to depression and anxiety, and depression may contribute to cognitive decline, indicating bidirectional relationships [ 3 ]. Abnormal sleep is also associated with diminished cognitive performance, underscoring the interconnected nature of these post-stroke symptoms [ 6 , 11 , 12 ]. Together, they form distinct clusters that can significantly influence recovery trajectories. Clinically, these coexisting symptoms have significant implications for stroke rehabilitation. Early cognitive impairment increases mortality, dependency, and institutionalization, while depression and sleep disturbances are linked to slower functional gains [ 6 ]. Cognitive deficits reduce independence in daily tasks, including mobility activities such as stair climbing and ambulation that may be affected by neglect or attention-related impairments; depression diminishes engagement, and sleep problems exacerbate cognitive and emotional difficulties 13–15 . These overlapping effects collectively limit functional outcomes. Accordingly, current stroke guidelines emphasize the importance of evaluating and managing multiple neuropsychiatric symptoms simultaneously to optimize recovery 16 . Despite recognition of their interconnections, most research has examined these disturbances individually [ 13 , 17 , 18 ]. This gap limits understanding of how neuropsychiatric symptoms cluster among stroke patients and how distinct symptom patterns relate to functional independence. A person-centered approach is therefore needed. Latent profile analysis can identify subgroups of stroke survivors with similar symptom patterns, revealing meaningful profiles of cognitive impairment, depression, and sleep disturbance that traditional variable-centered methods may overlook 19 . This approach allows for a more nuanced understanding of post-stroke neuropsychiatric challenges and their impact on daily functioning. Therefore, this study aims to identify latent profiles of neuropsychiatric disturbances among stroke survivors, focusing on cognitive impairment, depression, and sleep disturbance, and to examine how these profiles relate to functional independence in activities of daily living. By clarifying these categorical patterns and their functional impact, the study seeks to guide more targeted and effective stroke rehabilitation. Methods Study participants Between October 2024 and September 2025, stroke patients were recruited from two tertiary hospitals in China, using purposive sampling. A cross-sectional survey was conducted prior to discharge using an online questionnaire platform ( https://www.wjx.cn/ ). Eligible participants met the following criteria: (1) stroke confirmed by CT or MRI; (2) age 18 years or older; (3) intact consciousness; and (4) provided informed consent. Patients were excluded if they had a pre-stroke history of mental illness or antidepressant use, or severe cognitive or speech impairments. Based on prior research reporting a 60% prevalence of neuropsychiatric disturbances in stroke patients [ 4 , 20 ], with α = 0.05, δ = 0.1, and power = 0.9, the required sample size was calculated as 277 using PASS (V25.0.2, 2025). Allowing for a 20% potential attrition, the minimum sample size was set at 347 participants. Ethical considerations The study was approved by the Ethics Committees of the Fifth Affiliated Hospital of Sun Yat-sen University (K148-1) and the First Hospital of Jilin University (25K159-001), and all participants provided written informed consent. Assessment of neuropsychiatric disturbances Cognitive function The Eight-item Informant Interview to Differentiate Aging and Dementia (AD8) was used to screen cognitive function in stroke patients. AD8 is a brief questionnaire designed to assess cognitive changes over the past six months [ 21 ]. It consists of eight items, each scored binarily as 1 for “yes” and 0 for “no” or “don’t know.” Total scores range from 0 to 8, with a score above 2 indicating possible cognitive impairment. The Chinese version of AD8 demonstrates good reliability and validity, with a Cronbach’s alpha of 0.78 and a test-retest intraclass correlation coefficient (ICC) of 0.96 [ 22 ]. Depressive symptoms Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), a self-report instrument measuring symptom severity over the past two weeks 23 . The PHQ-9 comprises 9 items, each rated on a 4-point scale from 0 (“not at all”) to 3 (“nearly every day”), yielding a total score of 0–27. Scores of 5–9, 10–14, 15–19, and 20 or above indicate mild, moderate, moderately severe, and severe depression, respectively. Higher scores reflect greater symptom severity. The Chinese version of the PHQ-9 demonstrates good reliability, with a Cronbach’s alpha of 0.86 [ 24 ]. Sleep disorders Sleep disorders were assessed using the Athens Insomnia Scale (AIS), a self-report instrument based on the 10th Edition of the International Classification of Diseases criteria for insomnia, evaluating symptom severity over the past month [ 25 ]. The AIS comprises 8 items scored on a 4-point Likert scale from 0 (“no problem”) to 3 (“severe problem”), with total scores ranging from 0 to 24. The total score ranges from 0 to 24, with ≥ 6 commonly used as the diagnostic threshold for insomnia [ 26 ]. The Chinese version demonstrates good reliability, with a Cronbach’s alpha above 0.80 [ 27 ]. Functional independence in daily living The Barthel Index (BI) was used to evaluate functional independence in activities of daily living among stroke survivors [ 2 , 28 ]. Trained interviewers assessed difficulties across ten daily activities, including dressing, bathing, eating, mobility, toileting, and continence. The total score ranges from 0 to 100, with higher scores indicating greater functional independence. A BI ≥ 95 reflects basic independent living, whereas scores < 95 indicate dependence 29,30 . The Chinese version demonstrates good reliability, with a Cronbach’s alpha of 0.91 [ 31 ]. Covariates Covariates included demographic characteristics, health behaviors, and disease-related factors. A structured questionnaire was used to collect demographic variables (gender, age, marital status, income, primary caregiver), behavioral factors (smoking history, alcohol use, daily sleep duration), and clinical characteristics (stroke type, number of comorbid chronic conditions, stroke severity). Stroke severity was evaluated using the National Institutes of Health Stroke Scale (NIHSS), with total scores ranging from 0 to 42, with higher scores indicating greater neurological impairment [ 32 ]. Statistical analysis Data were analyzed using SPSS 26.0. Continuous variables with normal distribution were expressed as mean ± standard deviation (SD), and non-normally distributed variables as median (Q1, Q3). Categorical variables were presented as frequency (N) and percentage (%). Group comparisons were performed using the chi-square test or univariate analysis. Multiple linear regression was used to examine associations between neuropsychiatric disturbance profiles and BI scores. Latent profile analysis was conducted using Mplus 8.11. A single-class model was first specified, and the number of classes was sequentially increased. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (aBIC), Entropy, Lo-Mendell-Rubin adjusted likelihood ratio test (LMR), and Bootstrap Likelihood Ratio Test (BLRT). Lower AIC, BIC, and aBIC indicate a better fit. Entropy ranges from 0 to 1, with values > 0.8 indicating classification accuracy above 90% [ 19 , 33 ]. Models were also assessed for practical significance, ensuring that each class included at least 5% of the sample [ 34 ]. The significance level was set at α = 0.05, and p < 0.05 was considered statistically significant. A heatmap was generated using GraphPad Prism 10.6.1. Sensitivity analyses Sensitivity analyses were performed using propensity score matching (PSM). PSM was applied to balance baseline covariates, including age, smoking status, stroke type, sleep duration, and NIHSS score, between two groups with and without neuropsychiatric disturbances. After matching and adjusting for covariates, group differences in functional independence were evaluated using independent sample t -tests. Two-sided P < 0.05 was considered statistically significant. Results Characteristics of participants A total of 417 stroke patients were initially screened, of whom 17 did not meet the inclusion criteria (6 taking antidepressants, 8 with a history of depression, 3 diagnosed with vertebral-artery stenosis), resulting in 400 participants included in the analysis. Among these, 70% were male, with a mean age of 61.55 years (SD = 12.05). Most participants were married (89.3%), 73.0% reported a monthly household income below RMB 5,000, and 68.0% were primarily cared for by their spouses. The mean daily sleep duration was 6.66 hours (SD = 1.88). The majority had ischemic stroke (98.5%), and 42.8% had one comorbid chronic condition. Detailed characteristics are presented in Table S1 . Post-stroke neuropsychiatric disturbance profiles Depression, cognitive function, and sleep scores were used as indicators in the latent profile analysis. Models specifying one to six profiles were sequentially tested. Fit indices (AIC, BIC, aBIC) decreased with additional profiles; however, the six-profile model had a non-significant LMR ( P > 0.05), and the five-profile model included a class comprising less than 5% of participants. The four-profile model demonstrated the best fit and interpretability. Model fit indices are summarized in Table 1 . Table 1 Latent profile analysis fit indices for post-stroke neuropsychological disturbances (N = 400) Number of profiles Free parameters AIC BIC aBIC Entropy P Proportions LMR BLRT 1 6 6269.499 6293.448 6274.409 2 10 6105.676 6145.590 6113.860 0.894 0.001 <0.001 0.16/0.84 3 14 6014.713 6070.593 6026.170 0.909 0.021 <0.001 0.08/ 0.82/ 0.10 4 18 5970.591 6042.438 5985.323 0.829 0.012 <0.001 0.16/0.10/0.66/0.08 5 22 5931.305 6019.118 5949.310 0.851 0.008 <0.001 0.16/0.07/0.09/0.66/0.02 6 26 5894.337 5998.116 5915.616 0.835 0.206 <0.001 0.05/0.07/0.53/0.15/0.18/0.02 Note. The profile shown in bold was selected as the optimal model for this study. 84% of patients exhibited two or more neuropsychiatric symptoms before discharge. The four latent profiles demonstrated distinct patterns of post-stroke neuropsychiatric disturbances (Fig. 1 ). Profiles were labeled based on symptom characteristics: 16% asymptomatic, 10% mild depression-sleep-cognitive impairment, 66% mild depression-sleep disorder, and 8% major depression-sleep-cognitive disorder. Between-profile variations in sociodemographic and clinical characteristics Differences in sociodemographic and clinical characteristics across the neuropsychiatric disturbance profiles are summarized in Fig. 2 and Table S2. Gender, marital status, income, caregiver type, drinking history, and comorbidity count did not differ across profiles ( P > 0.05). In contrast, age ( F = 5.15, P = 0.002), sleep duration ( F = 7.42, P < 0.001), stroke severity ( F = 2.66, P = 0.048), prior smoking history ( χ2 = 13.10, P = 0.041), and stroke subtype ( χ2 = 14.27, P = 0.003) showed significant between-profile differences. Profile 1 included exclusively ischemic stroke patients (100%) with the lowest depression, sleep, and cognitive scores. Profile 2 included the oldest patients (67.67 ± 11.68 years) with the most severe cognitive impairment and mild depression and sleep disturbances. Profile 3 comprised the youngest patients (60.21 ± 11.77 years) with mild depression and sleep disturbances. Profile 4 patients exhibited major depression and sleep disturbances, less cognitive impairment than Profile 2. Profile 4 had the highest stroke severity (NIHSS 3.77 ± 3.88), the largest proportion of hemorrhagic strokes (20.0%), and the shortest sleep duration (6.25 ± 2.41 hours/day). Figure 2. Sociodemographic and clinical characteristics across neuropsychiatric profiles. (*) indicates significant group differences. Profile 1 = asymptomatic, Profile 2 = mild depression-sleep-cognitive impairment, Profile 3 = mild depression-sleep disorder, Profile 4 = major depression-sleep-cognitive disorder. Association between neuropsychiatric profiles and functional independence Functional independence, as measured by BI, differed significantly across neuropsychiatric profiles ( F = 7.55, P < 0.001). Profile 1 had the highest BI score (85.95 ± 21.36), followed by Profile 3 (73.84 ± 25.37), Profile 2 (72.02 ± 27.27), and Profile 4 the lowest (61.71 ± 27.19). Within Profile 4, the lowest mean scores were observed for bathing (1.71), stairs (2.71), and grooming (3.00), indicating greater dependence. Detailed BI scores are shown in Fig. 3 and Table S3. After adjusting for age, sleep duration, stroke severity, prior smoking, and stroke subtype, multiple linear regression was performed with neuropsychiatric profiles as the independent variable and BI scores as the dependent variable (asymptomatic Profile 1 as reference). Compared with the asymptomatic group, stroke patients in the mild depression-sleep-cognitive impairment (Profile 2), mild depression-sleep disorder (Profile 3), and major depression-sleep-cognitive disorder (Profile 4) profiles had significantly lower BI scores (− 10.36, − 7.58, and − 15.90, respectively; P = 0.023, 0.016, < 0.001). Greater severity and number of neuropsychiatric disturbances were associated with progressively poorer functional independence (Table 2 ). Table 2 Association of neuropsychiatric profiles with functional independence (N = 400) Model terms Model 1 B (95% CI) P Model 2 B (95% CI) P Profile Profile 1 Ref. Ref. Profile 2 -13.22 (-23.09, -3.36) 0.009 -10.36 (-19.28, -1.44) 0.023 Profile 3 -9.86 (-16.58, -3.13) 0.004 -7.58 (-13.72, -1.44) 0.016 Profile 4 -19.07 (-29.27, -8.86) <0.001 -15.90 (-25.17, -6.63) <0.001 Note. Bold indicates significant group differences. Profile 1 = asymptomatic, Profile 2 = mild depression-sleep-cognitive impairment, Profile 3 = mild depression-sleep disorder, Profile 4 = major depression-sleep-cognitive disorder. Model 1: no adjustment; Model 2 adjusted for age, sleep duration, severity, prior smoking history, and lesion type. Sensitivity analyses After propensity score matching (Table S4), 60 asymptomatic patients and 60 patients with neuropsychiatric disorders, all with ischemic stroke, were included in the t -test (Table S5). Patients with neuropsychiatric disorders exhibited significantly lower functional independence ( t = 7.59, P = 0.007). Discussion This study provides initial evidence linking post-stroke neuropsychiatric profiles with functional independence. We identified four distinct profiles, with BI scores declining as symptom complexity increased. Notably, 84% of patients exhibited two or more neuropsychiatric symptoms before discharge, higher than prior reports [ 35 ]. This likely reflects assessment during the acute hospitalization phase, when neuropsychiatric and functional changes are most evident [ 36 , 37 ]. The high prevalence of early, potentially modifiable symptoms underscores the importance of timely evaluation to understand their impact on functional outcomes. [ 38 ]. Profile-specific neuropsychiatric disturbances highlight distinct clinical priorities. Profile 1 included patients without neuropsychiatric disturbances. These exclusively ischemic stroke patients had consistently low depression, sleep disturbance, and cognitive scores, highlighting that the absence of neuropsychiatric symptoms aligns with better functional recovery. Profile 2 included older patients with mild depression, sleep disturbance, and notable cognitive impairment. Age-related decline in neuronal and hippocampal function likely contributes to vulnerability in cognitive domains after stroke, explaining moderate functional decline despite mild mood disturbance [ 39 ]. Profile 3 , the largest subgroup (66%), was characterized by mild depression and sleep disturbance and primarily included younger patients. Work, family responsibilities, and concerns about returning to normal life may exacerbate mood and sleep symptoms, making this the most common neuropsychiatric burden in acute stroke 40 . Profile 4 consisted of patients with major depression, sleep disturbance, and cognitive impairment. They showed the lowest functional independence, highest NIHSS scores, shortest sleep duration, and largest proportion of hemorrhagic strokes. Although hemorrhagic cases were limited, this pattern suggests that stroke subtype and acute severity may worsen neuropsychiatric symptoms and functional impairment, consistent with prior research [ 41 , 42 ]. Hemorrhagic stroke can cause extensive brain damage via hematoma, edema, and inflammation, increasing the risk of insomnia, mood disorders, and cognitive deficits [ 43 ]. Patients with moderate-to-severe depression, sleep disturbance, and cognitive impairment showed the poorest performance in complex daily activities such as bathing, stairs, and grooming. This aligns with prior evidence that interactions between mood, sleep, cognition, and motor function impair daily functioning [ 14 , 44 , 45 ]. Psychodynamic factors may explain variability in recovery[ 46 ]. Patients with positive pre-stroke coping adhere better to rehabilitation and maintain higher functional independence [ 47 , 48 ]. In contrast, maladaptive responses such as denial or emotional suppression can hinder engagement and delay recovery 46 . Differences from Assadi et al. 49 , who found no impact of post-stroke depression on daily activities, may be due to their focus on upper limb function rather than broader functional independence. The interaction of depression and insomnia further reduces energy, attention, and memory, worsening cognitive deficits like unilateral neglect, which impairs functional tasks [ 15 , 50 ]. Differences in prior smoking across profiles may link lifestyle risk with neuropsychiatric burden and functional recovery, underscoring the role of secondary prevention and behavioral support in post-stroke care. Limitations This study has several limitations. First, data were collected from hospitals in only two provinces without national stratified sampling, limiting generalizability across regions and healthcare settings. Second, reliance on self-reported questionnaires may introduce bias. Cognitive impairment can distort symptom perception, and depressive mood may exaggerate or mask sleep and cognitive deficits, affecting neuropsychiatric subtype classification. Objective and multi-domain neuropsychological testing would strengthen conclusions. Third, the cross-sectional design prevents causal inference and limits understanding of the “dose-response” relationship between neuropsychiatric symptoms and functional independence. The small number of hemorrhagic strokes and exclusion of patients on antidepressants further restricts generalizability. Future studies should use longitudinal designs to track profile transitions, predict recovery trajectories, and evaluate profile-informed interventions. External validation across diverse settings and instruments is needed to strengthen the evidence base. Implications Our findings show that post-stroke neuropsychiatric profiles are associated with functional independence, particularly in tasks such as bathing, stair climbing, and grooming. Functional independence declined progressively from asymptomatic to severe-profile patients, highlighting the importance of early screening and profile-specific monitoring. Conclusion This study identified distinct post-stroke neuropsychiatric profiles and demonstrated their graded association with functional independence. Patients with moderate-to-severe depression, sleep disturbance, and cognitive impairment showed the poorest functional independence, particularly in complex daily activities such as bathing, stair climbing, and grooming. Functional independence declined with increasing number and severity of neuropsychiatric symptoms. These findings highlight that patients with post-stroke depression, especially those with major depression-sleep-cognitive disorder, are a key population for targeted interventions. Tailoring interventions according to neuropsychiatric profiles may help improve functional recovery in stroke patients. Declarations CRediT authorship contribution statement Yan Liu: Writing-original draft, Writing-review & editing. Jiali Zhang: Data curation, Investigation. Qiuxia Deng: Data curation, Investigation. Qi Zhang: Project administration, Formal analysis, Writing-original draft, and funding acquisition. Each author contributed to the article and approved the version that was submitted. Declaration of Sources of Funding This study was supported by the National Natural Science Foundation of China (NSFC, No. 72204278), Natural Science Foundation of Guangdong Province (2024A1515011574), Youth S&T Talent Support Programme of Guangdong Provincial Association for Science and Technology (GDSTA), and Young Science and Technology Talent Support Program of Guangdong Precision Medicine Application Association (YSTTGDPMAA202502). Ethics approval and consent to participate This study complied with the Helsinki Declaration and was approved by the Ethics Committees of the Fifth Affiliated Hospital of Sun Yat-sen University (K148-1) and the First Hospital of Jilin University (25K159-001), and all participants provided written informed consent. Declaration of Conflicts of Interest The authors declare no conflicts of interest. Acknowledgements We thank all participants for their involvement and consent. Data availability Due to sensitivity reasons, the data supporting the results of this study are not publicly available. Applications with reasonable requirements can be sent to the corresponding author. Clinical trial number: not applicable. References Feigin VL, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P, F GM, Rautalin I: World Stroke Organization: Global Stroke Fact Sheet 2025 . INT J STROKE 2025, 20 (2):132-144. Edemekong PF, Bomgaars DL, Sukumaran S, Schoo C: Activities of Daily Living ; 2025. Mutai H, Furukawa T, Nakanishi K, Hanihara T: Longitudinal functional changes, depression, and health-related quality of life among stroke survivors living at home after inpatient rehabilitation . PSYCHOGERIATRICS 2016, 16 (3):185-190. 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Ma Y, He X, Yang T, Qin J, Nan J, Wang X, Cao J, Wang C, Han L: Cognitive function in older adults with stroke: A latent profile analysis . GERIATR NURS 2025, 62 (Pt B):269-275. Zeng YY, Cheng HR, Cheng L, Huang G, Chen YB, Tang WJ, He JC: Comparison of poststroke depression between acute ischemic and hemorrhagic stroke patients . INT J GERIATR PSYCH 2021, 36 (4):493-499. Unnithan AKA, Das JM, Mehta. P: Hemorrhagic Stroke .: Treasure Island (FL): StatPearls Publishing; 2025. Ihle-Hansen H, Hagberg G, Ihle-Hansen H, Munthe-Kaas R, Aam S, Aamodt EB, Lydersen S, Beyer MK, Luzum G, Saltvedt I et al : Impact of Insufficient or Excessive Sleep Duration on Cognitive Function After Stroke: The Norwegian Cognitive Impairment After Stroke Study . J AM HEART ASSOC 2025, 14 (8):e038125. Liu L, Marshall IJ, Bhalla A, Li X, Ayis S, Wolfe CDA, Wang Y, O'Connell MDL: Long‐Term Course of Depression After Stroke and Risk Factors for Symptoms With Poor Progression: A Population‐Based Study . J AM HEART ASSOC 2025, 14 (15):e041931. Costa A, Gullo S, Bivona U, Caltagirone C: Psychological Aspects of Recovery After Brain Injury: A Focus on Psychodynamic Factors . In Neurobiological and Psychological Aspects of Brain Recovery . Edited by Petrosini L. Cham: Springer International Publishing; 2023:367-390. Lim M, Tan J, Neo A, Ng B, Asano M: Acceptance of disability in stroke: a systematic review . ANN PHYS REHABIL MED 2024, 67 (2):101790. Xing F, Liu J, Mei C, Chen J, Wen Y, Zhou J, Xie S: Adherence to rehabilitation exercise and influencing factors among people with acute stroke: a cross-sectional study . FRONT NEUROL 2025, 16 :1554949. Assadi KS, Kim GJ, Rand D: Comparison of Upper Extremity Function and Daily Use in Individuals with and without Post Stroke Depression . NEUROREHAB NEURAL RE 2024, 38 (2):99-108. Fleming MK, Smejka T, Henderson SD, van Gils V, Garratt E, Yilmaz KE, Johansen-Berg H: Sleep Disruption After Brain Injury Is Associated With Worse Motor Outcomes and Slower Functional Recovery . NEUROREHAB NEURAL RE 2020, 34 (7):661-671. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Editor invited by journal 05 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 04 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8726224","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601948861,"identity":"587dfab7-973c-4172-9ee9-6659f16e1049","order_by":0,"name":"Yan Liu","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":601948862,"identity":"3e3d9d75-d0ec-4d79-8d02-8b0a92a278c7","order_by":1,"name":"Jiali zhang","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"zhang","suffix":""},{"id":601948863,"identity":"fd44fbd9-4619-445c-9719-801e7fc4d983","order_by":2,"name":"Qiuxia Deng","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Qiuxia","middleName":"","lastName":"Deng","suffix":""},{"id":601948864,"identity":"8be15b47-8ff8-4c78-8cf5-e351d97b8b38","order_by":3,"name":"Qi Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJklEQVRIiWNgGAWjYDACCTD5T46fvYGBIaEAJsxGUMsBY8meA0AtBiRoSTS4kQCkidEiP7v52cMvZXcSDG6+TvzwwIDB3uD8GQOGD2WHGfhnN2DVwjjnmLmxzLlneZK3czdLAB2WuOFGjgHjjHOHGSTuHMCqhVkiwUxaso25mO927gaQlgSDGzwGzLxthxkMJBKwamGTSP8G0pLYcPPs5h8JUIcx/8WjhUcix0zyY9vhxAk3eLeBbGHccCDHgJkRjxYJiZwyaYZzacBAzt1mkWAgkTjzRlrBwZ5z6TwSN7BrkZ+Rvk3yR5kNMCrPbr75o8LGnu/84Y0PfpRZy/HPwK4FHAQ8iFiQYFA4AIwmkJNxqgcCxh/IESffgE/tKBgFo2AUjEQAAEVOYSM4SwaTAAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":true,"prefix":"","firstName":"Qi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-01-29 02:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8726224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8726224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104396545,"identity":"d799adc4-ddc6-4a65-b92a-7190bdc42e3e","added_by":"auto","created_at":"2026-03-11 11:12:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51424,"visible":true,"origin":"","legend":"\u003cp\u003ePotential profilepatterns of neuropsychiatric disturbances in stroke patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8726224/v1/036ede0d9121b839dba1aef9.png"},{"id":104396561,"identity":"87fd1ff8-612f-464f-8ae9-8be1aac5d39d","added_by":"auto","created_at":"2026-03-11 11:12:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86938,"visible":true,"origin":"","legend":"\u003cp\u003eSociodemographic and clinical characteristics across neuropsychiatric profiles. (*) indicates significant group differences. Profile 1 = asymptomatic, Profile 2 = mild depression-sleep-cognitive impairment, Profile 3 = mild depression-sleep disorder, Profile 4 = major depression-sleep-cognitive disorder.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8726224/v1/9b94f85799e3514732cf690a.png"},{"id":104396533,"identity":"814fcf4b-f410-4491-9d30-4c366217f5dd","added_by":"auto","created_at":"2026-03-11 11:12:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53367,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of functional independence across neuropsychiatric profiles in stroke patients.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8726224/v1/5698716ef7aae48478ae510b.png"},{"id":104396611,"identity":"fa8c58e6-b80b-429d-9cc1-ba7c74925165","added_by":"auto","created_at":"2026-03-11 11:12:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3078029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8726224/v1/852abf8f-8e36-42f2-ba4f-9df29d220ef6.pdf"},{"id":104396565,"identity":"411157c2-1389-407f-8929-bed4ca6c0a3b","added_by":"auto","created_at":"2026-03-11 11:12:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39802,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-8726224/v1/2874a99e7ba265269d245052.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Post-stroke neuropsychiatric profiles and their association with functional independence: A latent profile analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is a leading cause of long-term disability worldwide \u003csup\u003e1\u003c/sup\u003e. Functional independence in daily activities, such as dressing and bathing, is a core metric of post-stroke recovery and delayed disability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Yet, nearly 45% of stroke survivors remain dependent in daily activities five years after onset, which impacts their quality of life \u003csup\u003e3\u003c/sup\u003e. Recovery is further constrained by persistent neuropsychiatric disturbances, particularly cognitive impairment, depression, and sleep problems \u003csup\u003e4\u003c/sup\u003e. These symptoms affect 27% to 60% of stroke survivors [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and exert a strong influence on rehabilitation participation, functional independence, and long-term outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeuropsychiatric symptoms after stroke rarely occur in isolation. Cognitive impairment, depression, and sleep disturbances frequently co-occur and interact in complex ways. Sleep disturbances increase vulnerability to depression and anxiety, and depression may contribute to cognitive decline, indicating bidirectional relationships [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Abnormal sleep is also associated with diminished cognitive performance, underscoring the interconnected nature of these post-stroke symptoms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Together, they form distinct clusters that can significantly influence recovery trajectories.\u003c/p\u003e \u003cp\u003eClinically, these coexisting symptoms have significant implications for stroke rehabilitation. Early cognitive impairment increases mortality, dependency, and institutionalization, while depression and sleep disturbances are linked to slower functional gains [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cognitive deficits reduce independence in daily tasks, including mobility activities such as stair climbing and ambulation that may be affected by neglect or attention-related impairments; depression diminishes engagement, and sleep problems exacerbate cognitive and emotional difficulties \u003csup\u003e13\u0026ndash;15\u003c/sup\u003e. These overlapping effects collectively limit functional outcomes. Accordingly, current stroke guidelines emphasize the importance of evaluating and managing multiple neuropsychiatric symptoms simultaneously to optimize recovery \u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite recognition of their interconnections, most research has examined these disturbances individually [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This gap limits understanding of how neuropsychiatric symptoms cluster among stroke patients and how distinct symptom patterns relate to functional independence. A person-centered approach is therefore needed. Latent profile analysis can identify subgroups of stroke survivors with similar symptom patterns, revealing meaningful profiles of cognitive impairment, depression, and sleep disturbance that traditional variable-centered methods may overlook \u003csup\u003e19\u003c/sup\u003e. This approach allows for a more nuanced understanding of post-stroke neuropsychiatric challenges and their impact on daily functioning.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to identify latent profiles of neuropsychiatric disturbances among stroke survivors, focusing on cognitive impairment, depression, and sleep disturbance, and to examine how these profiles relate to functional independence in activities of daily living. By clarifying these categorical patterns and their functional impact, the study seeks to guide more targeted and effective stroke rehabilitation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eBetween October 2024 and September 2025, stroke patients were recruited from two tertiary hospitals in China, using purposive sampling. A cross-sectional survey was conducted prior to discharge using an online questionnaire platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn/\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Eligible participants met the following criteria: (1) stroke confirmed by CT or MRI; (2) age 18 years or older; (3) intact consciousness; and (4) provided informed consent. Patients were excluded if they had a pre-stroke history of mental illness or antidepressant use, or severe cognitive or speech impairments.\u003c/p\u003e \u003cp\u003eBased on prior research reporting a 60% prevalence of neuropsychiatric disturbances in stroke patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with α\u0026thinsp;=\u0026thinsp;0.05, δ\u0026thinsp;=\u0026thinsp;0.1, and power\u0026thinsp;=\u0026thinsp;0.9, the required sample size was calculated as 277 using PASS (V25.0.2, 2025). Allowing for a 20% potential attrition, the minimum sample size was set at 347 participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Ethics Committees of the Fifth Affiliated Hospital of Sun Yat-sen University (K148-1) and the First Hospital of Jilin University (25K159-001), and all participants provided written informed consent.\u003c/p\u003e\n\u003ch3\u003eAssessment of neuropsychiatric disturbances\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCognitive function\u003c/h2\u003e \u003cp\u003eThe Eight-item Informant Interview to Differentiate Aging and Dementia (AD8) was used to screen cognitive function in stroke patients. AD8 is a brief questionnaire designed to assess cognitive changes over the past six months [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It consists of eight items, each scored binarily as 1 for \u0026ldquo;yes\u0026rdquo; and 0 for \u0026ldquo;no\u0026rdquo; or \u0026ldquo;don\u0026rsquo;t know.\u0026rdquo; Total scores range from 0 to 8, with a score above 2 indicating possible cognitive impairment. The Chinese version of AD8 demonstrates good reliability and validity, with a Cronbach\u0026rsquo;s alpha of 0.78 and a test-retest intraclass correlation coefficient (ICC) of 0.96 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepressive symptoms\u003c/h3\u003e\n\u003cp\u003eDepressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), a self-report instrument measuring symptom severity over the past two weeks \u003csup\u003e23\u003c/sup\u003e. The PHQ-9 comprises 9 items, each rated on a 4-point scale from 0 (\u0026ldquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every day\u0026rdquo;), yielding a total score of 0\u0026ndash;27. Scores of 5\u0026ndash;9, 10\u0026ndash;14, 15\u0026ndash;19, and 20 or above indicate mild, moderate, moderately severe, and severe depression, respectively. Higher scores reflect greater symptom severity. The Chinese version of the PHQ-9 demonstrates good reliability, with a Cronbach\u0026rsquo;s alpha of 0.86 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSleep disorders\u003c/h2\u003e \u003cp\u003eSleep disorders were assessed using the Athens Insomnia Scale (AIS), a self-report instrument based on the 10th Edition of the International Classification of Diseases criteria for insomnia, evaluating symptom severity over the past month [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The AIS comprises 8 items scored on a 4-point Likert scale from 0 (\u0026ldquo;no problem\u0026rdquo;) to 3 (\u0026ldquo;severe problem\u0026rdquo;), with total scores ranging from 0 to 24. The total score ranges from 0 to 24, with \u0026ge;\u0026thinsp;6 commonly used as the diagnostic threshold for insomnia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Chinese version demonstrates good reliability, with a Cronbach\u0026rsquo;s alpha above 0.80 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional independence in daily living\u003c/h3\u003e\n\u003cp\u003eThe Barthel Index (BI) was used to evaluate functional independence in activities of daily living among stroke survivors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Trained interviewers assessed difficulties across ten daily activities, including dressing, bathing, eating, mobility, toileting, and continence. The total score ranges from 0 to 100, with higher scores indicating greater functional independence. A BI\u0026thinsp;\u0026ge;\u0026thinsp;95 reflects basic independent living, whereas scores\u0026thinsp;\u0026lt;\u0026thinsp;95 indicate dependence \u003csup\u003e29,30\u003c/sup\u003e. The Chinese version demonstrates good reliability, with a Cronbach\u0026rsquo;s alpha of 0.91 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates included demographic characteristics, health behaviors, and disease-related factors. A structured questionnaire was used to collect demographic variables (gender, age, marital status, income, primary caregiver), behavioral factors (smoking history, alcohol use, daily sleep duration), and clinical characteristics (stroke type, number of comorbid chronic conditions, stroke severity). Stroke severity was evaluated using the National Institutes of Health Stroke Scale (NIHSS), with total scores ranging from 0 to 42, with higher scores indicating greater neurological impairment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS 26.0. Continuous variables with normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and non-normally distributed variables as median (Q1, Q3). Categorical variables were presented as frequency (N) and percentage (%). Group comparisons were performed using the chi-square test or univariate analysis. Multiple linear regression was used to examine associations between neuropsychiatric disturbance profiles and BI scores.\u003c/p\u003e \u003cp\u003eLatent profile analysis was conducted using Mplus 8.11. A single-class model was first specified, and the number of classes was sequentially increased. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (aBIC), Entropy, Lo-Mendell-Rubin adjusted likelihood ratio test (LMR), and Bootstrap Likelihood Ratio Test (BLRT). Lower AIC, BIC, and aBIC indicate a better fit. Entropy ranges from 0 to 1, with values\u0026thinsp;\u0026gt;\u0026thinsp;0.8 indicating classification accuracy above 90% [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Models were also assessed for practical significance, ensuring that each class included at least 5% of the sample [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The significance level was set at α\u0026thinsp;=\u0026thinsp;0.05, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. A heatmap was generated using GraphPad Prism 10.6.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses were performed using propensity score matching (PSM). PSM was applied to balance baseline covariates, including age, smoking status, stroke type, sleep duration, and NIHSS score, between two groups with and without neuropsychiatric disturbances. After matching and adjusting for covariates, group differences in functional independence were evaluated using independent sample \u003cem\u003et\u003c/em\u003e-tests. Two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of participants\u003c/h2\u003e \u003cp\u003eA total of 417 stroke patients were initially screened, of whom 17 did not meet the inclusion criteria (6 taking antidepressants, 8 with a history of depression, 3 diagnosed with vertebral-artery stenosis), resulting in 400 participants included in the analysis. Among these, 70% were male, with a mean age of 61.55 years (SD\u0026thinsp;=\u0026thinsp;12.05). Most participants were married (89.3%), 73.0% reported a monthly household income below RMB 5,000, and 68.0% were primarily cared for by their spouses. The mean daily sleep duration was 6.66 hours (SD\u0026thinsp;=\u0026thinsp;1.88). The majority had ischemic stroke (98.5%), and 42.8% had one comorbid chronic condition. Detailed characteristics are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePost-stroke neuropsychiatric disturbance profiles\u003c/h2\u003e \u003cp\u003eDepression, cognitive function, and sleep scores were used as indicators in the latent profile analysis. Models specifying one to six profiles were sequentially tested. Fit indices (AIC, BIC, aBIC) decreased with additional profiles; however, the six-profile model had a non-significant LMR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the five-profile model included a class comprising less than 5% of participants. The four-profile model demonstrated the best fit and interpretability. Model fit indices are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLatent profile analysis fit indices for post-stroke neuropsychological disturbances (N\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of profiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFree parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProportions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6269.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6293.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6274.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6105.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6145.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6113.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.16/0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6014.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6070.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6026.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.08/ 0.82/ 0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5970.591\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6042.438\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5985.323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.829\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.16/0.10/0.66/0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5931.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6019.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5949.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.16/0.07/0.09/0.66/0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5894.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5998.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5915.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05/0.07/0.53/0.15/0.18/0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. The profile shown in bold was selected as the optimal model for this study.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e84% of patients exhibited two or more neuropsychiatric symptoms before discharge. The four latent profiles demonstrated distinct patterns of post-stroke neuropsychiatric disturbances (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Profiles were labeled based on symptom characteristics: 16% asymptomatic, 10% mild depression-sleep-cognitive impairment, 66% mild depression-sleep disorder, and 8% major depression-sleep-cognitive disorder.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBetween-profile variations in sociodemographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eDifferences in sociodemographic and clinical characteristics across the neuropsychiatric disturbance profiles are summarized in Fig.\u0026nbsp;2 and Table S2. Gender, marital status, income, caregiver type, drinking history, and comorbidity count did not differ across profiles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, age (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), sleep duration (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stroke severity (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), prior smoking history (\u003cem\u003eχ2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), and stroke subtype (\u003cem\u003eχ2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) showed significant between-profile differences.\u003c/p\u003e \u003cp\u003eProfile 1 included exclusively ischemic stroke patients (100%) with the lowest depression, sleep, and cognitive scores. Profile 2 included the oldest patients (67.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68 years) with the most severe cognitive impairment and mild depression and sleep disturbances. Profile 3 comprised the youngest patients (60.21\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77 years) with mild depression and sleep disturbances. Profile 4 patients exhibited major depression and sleep disturbances, less cognitive impairment than Profile 2. Profile 4 had the highest stroke severity (NIHSS 3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88), the largest proportion of hemorrhagic strokes (20.0%), and the shortest sleep duration (6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41 hours/day).\u003c/p\u003e \u003cp\u003eFigure 2. Sociodemographic and clinical characteristics across neuropsychiatric profiles. (*) indicates significant group differences. Profile 1\u0026thinsp;=\u0026thinsp;asymptomatic, Profile 2\u0026thinsp;=\u0026thinsp;mild depression-sleep-cognitive impairment, Profile 3\u0026thinsp;=\u0026thinsp;mild depression-sleep disorder, Profile 4\u0026thinsp;=\u0026thinsp;major depression-sleep-cognitive disorder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between neuropsychiatric profiles and functional independence\u003c/h2\u003e \u003cp\u003eFunctional independence, as measured by BI, differed significantly across neuropsychiatric profiles (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Profile 1 had the highest BI score (85.95\u0026thinsp;\u0026plusmn;\u0026thinsp;21.36), followed by Profile 3 (73.84\u0026thinsp;\u0026plusmn;\u0026thinsp;25.37), Profile 2 (72.02\u0026thinsp;\u0026plusmn;\u0026thinsp;27.27), and Profile 4 the lowest (61.71\u0026thinsp;\u0026plusmn;\u0026thinsp;27.19). Within Profile 4, the lowest mean scores were observed for bathing (1.71), stairs (2.71), and grooming (3.00), indicating greater dependence. Detailed BI scores are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter adjusting for age, sleep duration, stroke severity, prior smoking, and stroke subtype, multiple linear regression was performed with neuropsychiatric profiles as the independent variable and BI scores as the dependent variable (asymptomatic Profile 1 as reference). Compared with the asymptomatic group, stroke patients in the mild depression-sleep-cognitive impairment (Profile 2), mild depression-sleep disorder (Profile 3), and major depression-sleep-cognitive disorder (Profile 4) profiles had significantly lower BI scores (\u0026minus;\u0026thinsp;10.36, \u0026minus;\u0026thinsp;7.58, and \u0026minus;\u0026thinsp;15.90, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, 0.016, \u0026lt;\u0026thinsp;0.001). Greater severity and number of neuropsychiatric disturbances were associated with progressively poorer functional independence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of neuropsychiatric profiles with functional independence (N\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eB (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eB (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfile\u003c/p\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.22 (-23.09, -3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.36 (-19.28, -1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.86 (-16.58, -3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.58 (-13.72, -1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19.07 (-29.27, -8.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-15.90 (-25.17, -6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Bold indicates significant group differences. Profile 1\u0026thinsp;=\u0026thinsp;asymptomatic, Profile 2\u0026thinsp;=\u0026thinsp;mild depression-sleep-cognitive impairment, Profile 3\u0026thinsp;=\u0026thinsp;mild depression-sleep disorder, Profile 4\u0026thinsp;=\u0026thinsp;major depression-sleep-cognitive disorder. Model 1: no adjustment; Model 2 adjusted for age, sleep duration, severity, prior smoking history, and lesion type.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eAfter propensity score matching (Table S4), 60 asymptomatic patients and 60 patients with neuropsychiatric disorders, all with ischemic stroke, were included in the \u003cem\u003et\u003c/em\u003e-test (Table S5). Patients with neuropsychiatric disorders exhibited significantly lower functional independence (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides initial evidence linking post-stroke neuropsychiatric profiles with functional independence. We identified four distinct profiles, with BI scores declining as symptom complexity increased. Notably, 84% of patients exhibited two or more neuropsychiatric symptoms before discharge, higher than prior reports [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This likely reflects assessment during the acute hospitalization phase, when neuropsychiatric and functional changes are most evident [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The high prevalence of early, potentially modifiable symptoms underscores the importance of timely evaluation to understand their impact on functional outcomes. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProfile-specific neuropsychiatric disturbances highlight distinct clinical priorities. \u003cb\u003eProfile 1\u003c/b\u003e included patients without neuropsychiatric disturbances. These exclusively ischemic stroke patients had consistently low depression, sleep disturbance, and cognitive scores, highlighting that the absence of neuropsychiatric symptoms aligns with better functional recovery. \u003cb\u003eProfile 2\u003c/b\u003e included older patients with mild depression, sleep disturbance, and notable cognitive impairment. Age-related decline in neuronal and hippocampal function likely contributes to vulnerability in cognitive domains after stroke, explaining moderate functional decline despite mild mood disturbance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. \u003cb\u003eProfile 3\u003c/b\u003e, the largest subgroup (66%), was characterized by mild depression and sleep disturbance and primarily included younger patients. Work, family responsibilities, and concerns about returning to normal life may exacerbate mood and sleep symptoms, making this the most common neuropsychiatric burden in acute stroke \u003csup\u003e40\u003c/sup\u003e. \u003cb\u003eProfile 4\u003c/b\u003e consisted of patients with major depression, sleep disturbance, and cognitive impairment. They showed the lowest functional independence, highest NIHSS scores, shortest sleep duration, and largest proportion of hemorrhagic strokes. Although hemorrhagic cases were limited, this pattern suggests that stroke subtype and acute severity may worsen neuropsychiatric symptoms and functional impairment, consistent with prior research [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Hemorrhagic stroke can cause extensive brain damage via hematoma, edema, and inflammation, increasing the risk of insomnia, mood disorders, and cognitive deficits [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with moderate-to-severe depression, sleep disturbance, and cognitive impairment showed the poorest performance in complex daily activities such as bathing, stairs, and grooming. This aligns with prior evidence that interactions between mood, sleep, cognition, and motor function impair daily functioning [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Psychodynamic factors may explain variability in recovery[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Patients with positive pre-stroke coping adhere better to rehabilitation and maintain higher functional independence [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In contrast, maladaptive responses such as denial or emotional suppression can hinder engagement and delay recovery \u003csup\u003e46\u003c/sup\u003e. Differences from Assadi et al. \u003csup\u003e49\u003c/sup\u003e, who found no impact of post-stroke depression on daily activities, may be due to their focus on upper limb function rather than broader functional independence. The interaction of depression and insomnia further reduces energy, attention, and memory, worsening cognitive deficits like unilateral neglect, which impairs functional tasks [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Differences in prior smoking across profiles may link lifestyle risk with neuropsychiatric burden and functional recovery, underscoring the role of secondary prevention and behavioral support in post-stroke care.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, data were collected from hospitals in only two provinces without national stratified sampling, limiting generalizability across regions and healthcare settings. Second, reliance on self-reported questionnaires may introduce bias. Cognitive impairment can distort symptom perception, and depressive mood may exaggerate or mask sleep and cognitive deficits, affecting neuropsychiatric subtype classification. Objective and multi-domain neuropsychological testing would strengthen conclusions. Third, the cross-sectional design prevents causal inference and limits understanding of the \u0026ldquo;dose-response\u0026rdquo; relationship between neuropsychiatric symptoms and functional independence. The small number of hemorrhagic strokes and exclusion of patients on antidepressants further restricts generalizability. Future studies should use longitudinal designs to track profile transitions, predict recovery trajectories, and evaluate profile-informed interventions. External validation across diverse settings and instruments is needed to strengthen the evidence base.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eOur findings show that post-stroke neuropsychiatric profiles are associated with functional independence, particularly in tasks such as bathing, stair climbing, and grooming. Functional independence declined progressively from asymptomatic to severe-profile patients, highlighting the importance of early screening and profile-specific monitoring.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified distinct post-stroke neuropsychiatric profiles and demonstrated their graded association with functional independence. Patients with moderate-to-severe depression, sleep disturbance, and cognitive impairment showed the poorest functional independence, particularly in complex daily activities such as bathing, stair climbing, and grooming. Functional independence declined with increasing number and severity of neuropsychiatric symptoms. These findings highlight that patients with post-stroke depression, especially those with major depression-sleep-cognitive disorder, are a key population for targeted interventions. Tailoring interventions according to neuropsychiatric profiles may help improve functional recovery in stroke patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYan Liu: Writing-original draft, Writing-review \u0026amp; editing. Jiali Zhang: Data curation, Investigation. Qiuxia Deng: Data curation, Investigation. Qi Zhang: Project administration, Formal analysis, Writing-original draft, and funding acquisition. Each author contributed to the article and approved the version that was submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Sources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (NSFC, No. 72204278), Natural Science Foundation of Guangdong Province (2024A1515011574), Youth S\u0026amp;T Talent Support Programme of Guangdong Provincial Association for Science and Technology (GDSTA), and Young Science and Technology Talent Support Program of Guangdong Precision Medicine Application Association (YSTTGDPMAA202502).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the Helsinki Declaration and was approved by the Ethics Committees of the Fifth Affiliated Hospital of Sun Yat-sen University (K148-1) and the First Hospital of Jilin University (25K159-001), and all participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants for their involvement and consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to sensitivity reasons, the data supporting the results of this study are not publicly available. Applications with reasonable requirements can be sent to the corresponding author. Clinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin VL, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P, F GM, Rautalin I: \u003cstrong\u003eWorld Stroke Organization: Global Stroke Fact Sheet 2025\u003c/strong\u003e. \u003cem\u003eINT J STROKE\u003c/em\u003e 2025, \u003cstrong\u003e20\u003c/strong\u003e(2):132-144.\u003c/li\u003e\n\u003cli\u003eEdemekong PF, Bomgaars DL, Sukumaran S, Schoo C: \u003cem\u003eActivities of Daily Living\u003c/em\u003e; 2025.\u003c/li\u003e\n\u003cli\u003eMutai H, Furukawa T, Nakanishi K, Hanihara T: \u003cstrong\u003eLongitudinal functional changes, depression, and health-related quality of life among stroke survivors living at home after inpatient rehabilitation\u003c/strong\u003e. \u003cem\u003ePSYCHOGERIATRICS\u003c/em\u003e 2016, \u003cstrong\u003e16\u003c/strong\u003e(3):185-190.\u003c/li\u003e\n\u003cli\u003eSuzuki A, Mutai H, Furukawa T, Wakabayashi A, Hanihara T: \u003cstrong\u003eThe prevalence and course of neuropsychiatric symptoms in stroke patients impact functional recovery during in-hospital rehabilitation\u003c/strong\u003e. \u003cem\u003eTOP STROKE REHABIL\u003c/em\u003e 2021, \u003cstrong\u003e29\u003c/strong\u003e:1-8.\u003c/li\u003e\n\u003cli\u003eChao X, Wang J, Dong Y, Fang Y, Yin D, Wen J, Wang P, Sun W: \u003cstrong\u003eNeuroimaging of neuropsychological disturbances following ischaemic stroke (CONNECT): a prospective cohort study protocol\u003c/strong\u003e. \u003cem\u003eBMJ OPEN\u003c/em\u003e 2024, \u003cstrong\u003e14\u003c/strong\u003e(1):e077799.\u003c/li\u003e\n\u003cli\u003eEl HN, Katzan IL, Rost NS, Blake ML, Byun E, Pendlebury ST, Aparicio HJ, Marquine MJ, Gottesman RF, Smith EE: \u003cstrong\u003eCognitive Impairment After Ischemic and Hemorrhagic Stroke: A Scientific Statement From the American Heart Association/American Stroke Association\u003c/strong\u003e. \u003cem\u003eSTROKE\u003c/em\u003e 2023, \u003cstrong\u003e54\u003c/strong\u003e(6):e272-e291.\u003c/li\u003e\n\u003cli\u003eChan LG: \u003cstrong\u003eThe Comorbidity and Associations between Depression, Cognitive Impairment, and Sleep after Stroke and How They Affect Outcomes: A Scoping Review of the Literature\u003c/strong\u003e. 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However, the patterns of co-occurring symptoms and their relationship with functional outcomes remain unclear. This study aimed to identify distinct neuropsychiatric profiles in acute stroke patients and examine their association with functional independence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 417 stroke patients hospitalized in Guangzhou and Changchun from October 2024 to September 2025 were recruited. Data were collected using a general information questionnaire, the Patient Health Questionnaire-9, the Athens Insomnia Scale, the Eight-item Informant Interview to Differentiate Aging and Dementia, and the Barthel Index. Latent profile analysis identified types of neuropsychiatric disturbance, and multiple linear regression examined their associations with functional independence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEighty-four percent of participants had two or more types of neuropsychiatric disturbances. Four profiles were identified: 16% asymptomatic (Profile 1), 10% mild depression\u0026ndash;sleep\u0026ndash;cognitive impairment (Profile 2), 66% mild depression\u0026ndash;sleep disorder (Profile 3), and 8% major depression\u0026ndash;sleep\u0026ndash;cognitive disorder (Profile 4). Barthel Index scores were significantly lower in Profiles 2, 3, and 4 compared with Profile 1 (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients in Profile 4 had the highest odds of functional dependence. Mean scores for bathing, stair climbing, and grooming in Profile 4 were 1.71, 2.71, and 3.00, respectively, the lowest among all groups. Sensitivity analysis confirmed poorer functional independence in patients with neuropsychiatric disturbances (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identified distinct post-stroke neuropsychiatric profiles and their graded association with functional independence. Patients with moderate-to-severe depression, sleep disturbance, and cognitive impairment had the poorest performance in complex daily activities such as bathing, stair climbing, and grooming. Tailoring strategies to neuropsychiatric profiles may enhance functional recovery.\u003c/p\u003e","manuscriptTitle":"Post-stroke neuropsychiatric profiles and their association with functional independence: A latent profile analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 11:09:31","doi":"10.21203/rs.3.rs-8726224/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-11T11:45:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307326716333129347602804260150891088038","date":"2026-03-11T11:13:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T06:47:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T09:41:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T09:24:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T16:54:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-02-04T16:43:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8ba9b34-d264-4780-b7ea-847afb16167e","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T11:09:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 11:09:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8726224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8726224","identity":"rs-8726224","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}