Chen Furong, Li Jiaying, Guo Junchen, Xiong Ying, Ye Zengjie
School of Nursing, Guangzhou Medical University, No.1 Xinzao Road, Panyu District, Guangzhou, Guangdong Province, 511436, China, 86 02037103000.
The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
JMIR Aging. 2025 Sep 16;8:e76210. doi: 10.2196/76210.
While bidirectional associations among sleep duration, cognitive function, and depression are established, the symptom-level temporal interactions among these factors in China's aging population, which is experiencing unprecedented growth, remain poorly characterized.
We aim to use a novel temporal network analysis to clarify these dynamics and guide targeted interventions, with a focus on sex-specific dynamic pathways.
We conducted a longitudinal temporal network analysis on 3136 Chinese adults aged ≥45 years from the China Health and Retirement Longitudinal Study (CHARLS) across 5 waves (2011, 2013, 2015, 2018, and 2020). A graphical vector autoregressive (GVAR) model delineated the interdependencies among sleep duration, cognitive performance (assessed via the Mini-Mental State Examination [MMSE]), and depressive symptoms (evaluated with the 10-item Center for Epidemiologic Studies Depression Scale [CESD-10]). We also examined sex-specific differences in network structures.
The symptom "bothered" was found to predict all other CESD-10 symptoms. There were significant predictive links between sleep and the CESD-10 node (ie, bothered, drained, and depressed), along with sleep and the MMSE functions (ie, numerical ability). Furthermore, sleep duration served as a bridge between depression symptoms and cognitive functions. There were significant differences in longitudinal network structure between sexes. Sex-specific analyses revealed distinct network patterns. Among female participants, the "bothered" node significantly predicted several outcomes over time. In contrast, the temporal network for male participants was sparser, with the "stuck" node in the depression domain being predominantly influenced by other nodes.
Our study revealed that emotional distress, especially the "bothered" symptom, plays a central role in depressive symptoms and cognitive decline. The bridging effect of short sleep duration underscores the potential of interventions targeting both sleep and emotional distress for alleviating depressive symptoms and delaying cognitive deterioration in older adults.
虽然睡眠时间、认知功能和抑郁之间的双向关联已得到证实,但在中国人口前所未有的老龄化过程中,这些因素在症状层面的时间交互作用仍未得到充分描述。
我们旨在使用一种新颖的时间网络分析来阐明这些动态变化并指导针对性干预措施,重点关注性别特异性动态途径。
我们对来自中国健康与养老追踪调查(CHARLS)的3136名年龄≥45岁的中国成年人进行了纵向时间网络分析,共涉及5轮调查(2011年、2013年、2015年、2018年和2020年)。图形向量自回归(GVAR)模型描绘了睡眠时间、认知表现(通过简易精神状态检查表[MMSE]评估)和抑郁症状(用10项流行病学研究中心抑郁量表[CESD-10]评估)之间的相互依存关系。我们还研究了网络结构中的性别差异。
发现“困扰”症状可预测所有其他CESD-10症状。睡眠与CESD-10节点(即困扰、疲惫和抑郁)以及睡眠与MMSE功能(即数字能力)之间存在显著的预测联系。此外,睡眠时间充当了抑郁症状和认知功能之间的桥梁。两性之间的纵向网络结构存在显著差异。性别特异性分析揭示了不同的网络模式。在女性参与者中,“困扰”节点随时间推移显著预测了多个结果。相比之下,男性参与者的时间网络较为稀疏,抑郁领域的“卡顿”节点主要受其他节点影响。
我们的研究表明,情绪困扰,尤其是“困扰”症状,在抑郁症状和认知衰退中起核心作用。短睡眠时间的桥梁作用突出了针对睡眠和情绪困扰的干预措施对于减轻老年人抑郁症状和延缓认知衰退的潜力。