ORIGINAL RESEARCH article
Front. Public Health
Sec. Aging and Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1666043
Analysis of Influencing Factors of Cognitive Frailty in Elderly Community Patients Based on Restricted Cubic Spline
Provisionally accepted
- 1Funing People's Hospital, Yancheng, China
- 2Shanghai Jian Qiao University Co Ltd, Shanghai, China
- 3Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Objective: To investigate the influencing factors of cognitive frailty in elderly 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. Method: A simple random sampling method was employed to select 16 community health service centers across 16 districts in Shanghai, conducting questionnaire surveys among 1,692 elderly 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. Results: The 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. Conclusion: Cognitive frailty is relatively common among community-dwelling elderly 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.
Keywords: cognitive frailty, Elderly, Restricted cubic spline, Depression, sleep quality, Community Health
Received: 06 Aug 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Chen, Chen and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shuzhi Peng, psz1994921@163.com
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