慢病共病患者睡眠质量变化轨迹及影响因素的纵向研究

A longitudinal study on the trajectory of sleep quality changes and influencing factors in patients with chronic diseases

中文关键词:  【】慢病共病  睡眠质量  轨迹  影响因素  潜类别增长模型

英文关键词:Multiple chronic conditions  Sleep quality  Trajectory analysis  Influencing factors  Latent Class Growth Model

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。尽管睡眠质量与心理功能密切相关，但关于慢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　探讨慢病共病患者自住院期至出院后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订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病为主的住院慢病共病患者201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评估患者的睡眠质量?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　潜类别增长模型结果显示，基于T0（基线期）、T1（出院后1个月）、T2（出院后3个月）和T3（出院后6个月）4个时间点的睡眠质量数据，共识别出3种睡眠质量变化轨迹；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<0.05）及3 000~5 000元（β=5.913，P<0.05）、年龄45~65岁（β=9.536，P<0.05）、合并症3种（β=7.792，P<0.05）及3种以上（β=6.626，P<0.05）、RRS评分较高（β=0.334，P<0.05）和HADS评分较高（β=1.628，P<0.05）更易归入高睡眠障碍组；年龄45~65岁（β=2.777，P <0.05）、合并症3种（β=3.802，P<0.05）及3种以上（β=2.463，P<0.05）、反刍思维评分较高（β=0.111，P<0.05）和焦虑评分较高（β=0.35，P<0.05）更易归入中睡眠障碍-进展组。结论　慢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水平是慢病共病患者睡眠质量轨迹的主要影响因素。

英文摘要:

      Background　Patients with multiple chronic conditions have a high prevalence of sleep disorders, which significantly impact their physical and mental health as well as the management of their chronic conditions. Although sleep quality is closely associated with psychological functioning, evidence regarding the dynamic trajectories of sleep quality and its influencing factors in patients with comorbid chronic diseases remains insufficient. Objective To investigate the trajectories of sleep quality in patients with comorbid chronic diseases from hospitalization through 6 months post-discharge and to analyze the influencing factors, thereby providing a reference for the early identification of high-risk populations and the development of personalized sleep intervention strategies. Methods A random sample of 201 inpatients with comorbid chronic diseases, primarily cardiovascular and metabolic conditions, was selected from Fuyang Third People’s Hospital between January 2023 and September 2024. Assessments were conducted during hospitalization (baseline) using a demographic questionnaire, Sleep Dysfunction Rating Scale (SDRS), Ruminative Thoughts Scale (RRS), and Hospital Anxiety and Depression Scale (HADS). Sleep quality was evaluated using the SDRS at 1, 3, and 6 months post-discharge. A latent class growth model was employed to identify latent categories of sleep quality trajectories; multivariate logistic regression was used to analyze the factors influencing sleep quality. Results The latent class growth model identified three distinct sleep quality trajectories based on data collected at four time points: T0 (baseline), T1 (1 month post-discharge), T2 (3 months post-discharge), and T3 (6 months post-discharge). Among the participants, 72 (35.82%) were classified into the high sleep disturbance group, 73 cases (36.32%) in the moderate sleep disturbance-progression group, and 56 cases (27.86%) in the low sleep disturbance group. Multivariate logistic regression analysis revealed that, compared with the low sleep disturbance group, a household monthly per capita income of <3,000 yuan (β = 13.131, P < 0.05) and 3,000–5,000 yuan (β = 5.913, P < 0.05), age 45–65 years (β = 9.536, P < 0.05), having 3 comorbidities (β = 7.792, P < 0.05) or 3 or more (β = 6.626, P < 0.05), higher RRS scores (β = 0.334, P < 0.05), and higher HADS scores (β = 1.628, P < 0.05) were more likely to be classified into the high sleep disturbance group; age 45–65 years (β = 2.777, P < 0.05), having three comorbidities (β = 3.802, P < 0.05) or three or more (β = 2.463, P < 0.05), higher rumination scores (β = 0.111, P < 0.05), and higher anxiety scores (β = 0.35, P < 0.05) were more likely to be classified into the moderate sleep disturbance–progression group. Conclusion The trajectories of sleep quality changes in patients with chronic disease comorbidity exhibit population heterogeneity. Age, monthly per capita household income, number of comorbidities, rumination, and levels of anxiety and depression are the primary factors influencing the sleep quality trajectories of patients with chronic disease comorbidity.