| 袁桂敏,秦海燕,段雯雯,喻晨,胡伟,刘珊.慢性病共病患者睡眠质量变化轨迹及影响因素的纵向研究[J].四川精神卫生杂志,2026,(3):255-262.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 |
| 慢性病共病患者睡眠质量变化轨迹及影响因素的纵向研究 |
| Longitudinal study on sleep quality trajectories and influencing factors in patients with chronic multimorbidity |
| 投稿时间：2025-10-16 |
| DOI：10.11886/scjsws20251016003 |
| 中文关键词: 慢性病共病 睡眠质量 轨迹 影响因素 潜类别增长模型 |
| 英文关键词:Chronic multimorbidity Sleep quality Trace Influencing factors Latent class growth model |
| 基金项目: |
|
| 摘要点击次数: |
| 全文下载次数: |
| 中文摘要: |
| 背景 慢病共病患者睡眠障碍发生率较高，且显著影响其身心健康与慢性病管理。睡眠质量与心理功能密切相关，但关于慢性病共病患者睡眠质量动态变化轨迹及其影响因素的证据仍然不足。目的 探讨慢性病共病患者从住院至出院后6个月的睡眠质量变化轨迹，并分析其影响因素，为早期识别高风险人群及制定个性化睡眠干预策略提供参考。方法 采用随机抽样法选取阜阳市第三人民医院2023年1月—2024年9月以心血管及代谢性慢性病为主的住院慢性病共病患者228例。使用一般资料调查表、睡眠功能障碍评定量表（SDRS）、反刍思维量表（RRS）和医院焦虑抑郁量表（HADS）于住院期间进行评定（基线期），并于出院后1、3、6个月采用SDRS评定睡眠质量。采用潜类别增长模型识别睡眠质量变化轨迹的潜在类别；采用多元Logistic回归分析睡眠质量的影响因素。结果 共纳入201例慢性病共病患者。潜类别增长模型结果显示，基于4个时间点（基线期、出院后1个月、出院后3个月以及出院后6个月）的睡眠质量数据，共识别出3种睡眠质量变化轨迹类型：持续高睡眠障碍组72例（35.82%）、中睡眠障碍-进展组73例（36.32%）和低睡眠障碍组56例（27.86%）。多元Logistic回归分析显示，与低睡眠障碍组相比，家庭人均月收入<3 000元（β=13.131，P<0.01）及3 000~5 000元（β=5.913，P<0.05）、年龄45~65岁（β=9.536，P<0.05）、共病数量3种（β=7.792，P<0.05）或>3种（β=6.626，P<0.05）、RRS评分较高（β=0.334，P<0.01）以及HADS评分较高（β=1.628，P<0.05）更易归入持续高睡眠障碍组；年龄45~65岁（β=2.777，P<0.01）、共病数量3种（β=3.802，P<0.05）或>3种（β=2.463，P<0.05）、RRS评分较高（β=0.111，P<0.01）以及HADS评分较高（β=0.350，P<0.05）更易归入中睡眠障碍-进展组。结论 慢性病共病患者睡眠质量变化轨迹存在群体异质性，年龄、家庭人均月收入、共病数量、反刍思维和焦虑抑郁症状是慢性病共病患者睡眠质量变化轨迹的主要影响因素。 |
| 英文摘要: |
| 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. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |