Factors associated with sleep quality in community-dwelling older adults: a latent class analysis

China is home to 264 million adults aged ≥ 60 years, with 46% of community-dwelling older adults reporting poor sleep quality. Sleep quality is closely associated with biological, psychological, and social factors; however, few studies have explored its heterogeneity, leading to poorly targeted interventions. The aim of this study is to explore latent classes of sleep quality among community-dwelling older adults and to analyze their biopsychosocial correlates, providing evidence for tailored intervention approaches. From April to November 2025, convenience sampling was used to recruit community-dwelling individuals aged ≥ 60 years at the Pujin Community Health Service Center of Minhang District, Shanghai. Data were collected using a general information questionnaire and the Social Support Rating Scale, Geriatric Depression Scale-15, and Pittsburgh Sleep Quality Index. Latent class analysis was conducted to identify distinct sleep quality groups. Univariate and multivariate analyses were performed to explore relationships between latent classes of sleep quality and sociodemographic characteristics, physical health, lifestyle behaviors, depression, and social support. Sleep quality was categorized into Class 1 (high sleep quality, non-pharmacological dependence), Class 2 (high sleep efficiency, high functional impairment), and Class 3 (long sleep latency, low sleep efficiency), observed in 19.37%, 42.03%, and 38.60% of the sample, respectively. Multinomial logistic regression analysis revealed that gender, body mass index, educational attainment, living arrangements, Number of Chronic Diseases, number of medications, number of missing teeth, physical activity, visual impairment, depression, and social support levels significantly influenced sleep quality classes. Results demonstrated that 62.6% of participants experienced impaired sleep quality. Significant associations were identified between latent classes of sleep quality, and biological, psychological, and social factors. Healthcare professionals should conduct early screenings and implement biopsychosocial model–based interventions to improve sleep quality, quality of life, and life expectancy among community-dwelling older adults. Future research should focus on conducting multicenter longitudinal studies, optimizing assessment tools, and exploring other factors influencing sleep. These efforts will provide scientific evidence to support the development and implementation of personalized sleep intervention strategies for community-dwelling older adults. Not applicable.