AUTHOR=Chen Shuai , Chen Jiahe , Peng Shuzhi 

TITLE=Analysis of influencing factors of cognitive frailty in older adults community patients based on restricted cubic spline

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1666043

DOI=10.3389/fpubh.2025.1666043

ISSN=2296-2565

ABSTRACT=ObjectiveTo investigate the influencing factors of cognitive frailty in older adults community-dwelling patients and analyze the nonlinear relationships between key variables such as age, depression scores, sleep quality, and cognitive frailty, providing a basis for accurately identifying high-risk populations and developing individualized intervention strategies.MethodA simple random sampling method was employed to select 16 community health service centers across 16 districts in Shanghai, conducting questionnaire surveys among 1,692 older adults patients with multiple coexisting chronic conditions. The restricted cubic spline (RCS) model was used to analyze the dose–response relationship between age, depression score (CES-D), sleep quality (PSQI), and cognitive frailty, while controlling for confounding factors such as gender, types of chronic diseases, and social engagement.ResultsThe detection rate of cognitive frailty was 44.56%. RCS analysis revealed significant nonlinear associations between age, depression score, sleep quality, and cognitive frailty. Key inflection points where the risk of cognitive frailty significantly increased were age ≥75 years, depression score ≥20 points, and sleep quality score ≤5 points. After adjusting for confounding factors, the nonlinear relationship between depression score and cognitive frailty remained significant (p = 0.043), while the associations with age and sleep quality tended to be linear.ConclusionCognitive frailty is relatively common among community-dwelling older adults individuals, with age, depression, and sleep quality being its significant influencing factors. The restricted cubic spline model effectively reveals the nonlinear interaction characteristics of these factors, providing a scientific basis for implementing stratified early warning and precise interventions at the community level.