A Study on the Heterogeneity of Sleep Quality and Potential Priority Symptoms for Intervention in the Elderly Population in Shanghai’s Communities Based on Latent Profile Analysis and Symptom Networks

Abstract Background: Sleep disorders in community-dwelling older adults are heterogeneous, yet interventions typically target the population as a whole. Identifying data-driven sleep subtypes and their distinct symptom-level mechanisms may support precision management. This study aimed to identify sleep quality subtypes among older adults in Shanghai communities using latent profile analysis (LPA) and to characterize subtype-specific symptom networks and potential intervention targets. Methods: A total of 11,719 community-dwelling adults aged ≥60 years were recruited from April to August 2019. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). LPA was used to identify sleep subtypes. For each subtype, an EBICglasso symptom network was estimated, and networks were compared using the Network Comparison Test (NCT). Two-step Expected Influence (EI-2) was used to rank potential intervention targets, and case-dropping bootstrap assessed stability. Results: Four sleep subtypes were identified: healthy sleep ( n =7,443, 63.51%), insufficient nighttime sleep ( n =2,355, 20.10%), difficulty falling asleep ( n =751, 6.41%), and severe sleep disorder ( n =1,170, 9.98%). NCT showed that networks differed significantly in structure across subtypes (P =0.002 to P <0.001), whereas global strength differed only between the healthy and severe groups (P =0.044). EI-2 simulations identified daytime dysfunction as the priority target in the healthy, difficulty-falling-asleep, and severe groups, and subjective sleep quality in the insufficient-sleep group. Symptoms of anxiety and depression, female gender, and histories of coronary heart disease and stroke were risk factors for insufficient nighttime sleep and severe sleep disorder, while adequate daily vegetable intake (≥300 g) was protective. Conclusions: Sleep quality in Shanghai community older adults comprises four distinct subtypes with fundamentally different symptom-interaction patterns. Subtype-specific network structures and EI-2-ranked targets support tailored rather than one-size-fits-all sleep management in this population.