Shi Honglan, Sun Jing, Wang Yanrong
Mental Health Center, General Hospital of Ningxia Medical University, No.804 South Shengli Street, Yinchuan, 750004 Ningxia China.
Ning An Hospital of Ningxia, No.236 South Jinbo Street, Yinchuan, 750004 Ningxia China.
Sleep Biol Rhythms. 2024 Aug 26;23(1):47-54. doi: 10.1007/s41105-024-00550-z. eCollection 2025 Jan.
In this paper, we investigated the relationship between different levels of sleep and the risk of suicide among depressive patients. The sample consisted of 301 adults with depression who were recruited from a hospital in Ningxia, China. The Pittsburgh Sleep Quality Index (PSQI) and the Self-Rating Depression Scale (SDS) were applied to evaluate the quality of sleep and the degree of depression. The Suicidal Risk Factor Assessment Form evaluated suicide risk. A Latent Class Analysis (LCA) has been performed with MPLUS 7.0 to investigate the most probable category of the PSQI sub-scales. Multivariate Logistic Regression was applied to analyse the relation between Sleep Quality and Suicide Hazard in Adult Depressive Patients. Classes identified were "Global sleep impairment", "Poor sleep quality", "Short sleep duration" and "Good sleep quality." Patients with poor overall sleep quality and clear daytime dysfunction had a higher risk of suicide than those with good sleep quality. The results are helpful in understanding the relationship between the variability of sleep patterns and the risk of suicide among depressed people, and it is suggested that some sleep variables may have a higher predictive value than others. The results will provide guidance on how to improve and implement therapy for depressive disorders in adults, and to lower suicidal rates.
在本文中,我们研究了不同睡眠水平与抑郁症患者自杀风险之间的关系。样本包括301名患有抑郁症的成年人,他们是从中国宁夏的一家医院招募的。采用匹兹堡睡眠质量指数(PSQI)和自评抑郁量表(SDS)来评估睡眠质量和抑郁程度。自杀风险因素评估表用于评估自杀风险。使用MPLUS 7.0进行潜在类别分析(LCA),以探究PSQI子量表最可能的类别。应用多因素逻辑回归分析成年抑郁症患者睡眠质量与自杀风险之间的关系。确定的类别为“整体睡眠障碍”、“睡眠质量差”、“睡眠时间短”和“睡眠质量好”。整体睡眠质量差且白天功能明显障碍的患者比睡眠质量好的患者自杀风险更高。这些结果有助于理解睡眠模式变异性与抑郁症患者自杀风险之间的关系,并且表明某些睡眠变量可能比其他变量具有更高的预测价值。研究结果将为如何改善和实施成人抑郁症的治疗以及降低自杀率提供指导。