ArticleBMC geriatrics2026
Factors associated with sleep quality in community-dwelling older adults: a latent class analysis.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundChina 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.
methodsFrom 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.
resultsSleep 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.
conclusionsResults 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. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.