| Yuan Guimin,Qin Haiyan,Duan Wenwen,Yu Chen,Hu Wei,Liu Shan,Longitudinal study on sleep quality trajectories and influencing factors in patients with chronic multimorbidity[J].SICHUAN MENTAL HEALTH,2026,(3):255-262 |
| Longitudinal study on sleep quality trajectories and influencing factors in patients with chronic multimorbidity |
DOI：10.11886/scjsws20251016003 |
| English keywords:Chronic multimorbidity Sleep quality Trace Influencing factors Latent class growth model |
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| Author Name | Affiliation | Postcode | | Yuan Guimin | 1The Third People's Hospital of Fuyang, Fuyang 236000, China | 236000 | | Qin Haiyan | 1The Third People's Hospital of Fuyang, Fuyang 236000, China | 236000 | | Duan Wenwen | 1The Third People's Hospital of Fuyang, Fuyang 236000, China | 236000 | | Yu Chen | 1The Third People's Hospital of Fuyang, Fuyang 236000, China | 236000 | | Hu Wei | 1The Third People's Hospital of Fuyang, Fuyang 236000, China | 236000 | | Liu Shan* | 2Chengdu Pidu District People's Hospital, Chengdu 611130, China | 611730 |
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| English abstract: |
| Background Individuals with chronic multimorbidity exhibit a high prevalence of sleep disturbances， which impair their physical and psychological well-being and impede chronic disease management. Although sleep quality is closely influencing with psychological functioning， empirical evidence regarding the longitudinal trajectories of sleep quality and their influencing factors in this population remains insufficient.Objective To investigate the longitudinal trajectories of sleep quality and associated factors among patients with chronic multimorbidity from hospitalization through 6 months post-discharge， aiming to facilitate the early identification of high-risk individuals and the development of personalized sleep interventions.Methods A total of 228 inpatients with chronic multimorbidity， primarily with cardiovascular and metabolic chronic conditions， were enrolled via random sampling at The Third People's Hospital of Fuyang from January 2023 to September 2024. Baseline assessments were conducted during hospitalization using the general socio-demographic questionnaire， the Sleep Dysfunction Rating Scale （SDRS）， the Ruminative Response Scale （RRS）， and the Hospital Anxiety and Depression Scale （HADS）. Sleep quality was further evaluated using the SDRS at 1-， 3-， and 6-month post-discharge. Latent class growth model was employed to delineate trajectories of sleep quality. Multivariate logistic regression analysis was subsequently performed to identify influencing factors of sleep quality.Results A total of 201 patients with chronic diseases were included.Based on four predefined assessment time points （baseline， 1 month， 3 months， 6 months post-discharge）， the latent class growth model identified three distinct trajectories of sleep quality： persistent high sleep disturbance （n=72， 35.82%）， moderate sleep disturbance-progression （n=73， 36.32%）， and low sleep disturbance （n=56， 27.86%）. Multivariate logistic regression analysis revealed that， compared with the low sleep disturbance group， multiple factors increased the odds of membership in the persistent high sleep disturbance group： per capita monthly household income of <3 000 yuan （β=13.131， P<0.01） or 3 000–5 000 yuan （β=5.913， P<0.05）， aged 45–65 years （β=9.536， P<0.05）， presence of 3 （β=7.792， P<0.05） or >3 comorbid chronic conditions （β=6.626， P<0.05）， as well as higher RRS （β=0.334， P<0.01） and HADS score （β=1.628， P<0.01）. Additionally， patients aged 45–65 years （β=2.777， P<0.05）， with 3 （β=3.802， P<0.01） or >3 comorbid chronic conditions （β=2.463， P<0.01）， and with higher RRS （β=0.111， P<0.01） and HDAS scores （β=0.350， P<0.05） also demonstrated significantly increased odds of belonging to the moderate sleep disturbance–progression group.Conclusion There is significant population heterogeneity in sleep quality trajectories among patients with chronic multimorbidity， with age， per capita household monthly income， number of comorbidities， rumination， and anxiety/depression symptoms being identify as the primary influencing factors. |
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