ArticleFrontiers in psychiatry2026
A network analysis of sleep quality, cognitive function, and depressive symptoms among community-dwelling older adults.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Many countries, including China, are facing rapid population aging issues. Among middle-aged and older adults, depressive symptoms and sleep disturbances are associated with cognitive impairment. Therefore, this study aimed to examine the relationships between sleep, depression, and cognitive function in older adults. Method: We recruited 457 elderly people aged over 60 years from the Matang community of Wuhu to participate in this cross-sectional study. We utilized the Mini-Mental State Examination (MMSE), Pittsburgh Sleep Quality Index (PSQI), and Geriatric Depression Scale (GDS) to measure our participants, with 126 individuals completing all of them. We used the collected data for network analysis to identify the bridge symptoms in the MMSE-PSQI-GDS network by calculating bridge centrality. Results: The core symptom of the MMSE, GDS, and PSQI networks was subjective sleep quality (SSQ). The bridge symptoms in MMSE, GDS, and PSQI were "GDS" and "OR" (orientation). Among the symptoms, "GDS" had the highest bridge centrality. The edge connecting nodes habitual sleep efficiency (HSE) and used sleep medication (USM) had the strongest weight (0.67) around all the symptoms of PSQI. Nodes attention and calculation (AC) and registration (RG) had the strongest weight (0.48), connecting all MMSE symptoms. Conclusions: This study highlights the central role of subjective sleep quality and the bridging role of depression in the interconnected network of cognition, sleep, and mood among older adults. These findings suggest that interventions targeting sleep quality and depressive symptoms may have broad benefits for cognitive function and mental health. By identifying key symptom interactions, this network-based approach provides guidance for prioritizing assessment and intervention strategies in the geriatric population.
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