ORIGINAL RESEARCH article
Front. Aging Neurosci.
Sec. Neurocognitive Aging and Behavior
Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1622804
Latent Profiles and Correlates Factors of Cognitive Function in Older Adults: A Cross-Sectional Study
Provisionally accepted
- 1Affiliated Hospital of Zunyi Medical University, Zunyi, China
- 2Guizhou Nursing Vocational and Technical College, GUIYANG, China
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Objective:This study aimed to identify the latent profiles of cognitive function among community-dwelling and institutionalized older adults, and to examine their associated influencing factors, in order to inform the development of targeted interventions. Methods:A convenience sampling method was used to select 6,708 elderly people aged 60 years and older from six communities and nine long-term care institutions across China, who were assessed using a general information questionnaire, Mini-Mental State Examination (MMSE), the Frailty Scale, the Anxiety Scale, the Depression Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis (LPA) was performed based on the MMSE scores, and multiple logistic regression was used to analyse the influencing factors of cognitive function categories. Results:A total of three cognitive function profiles were identified: High cognitive Function group (41.2%), Moderate Cognitive Function Group (48.2%) and Low cognitive Function group (10.7%). Higher Frailty (odds ratio[ORs]=1.070-1.246), higher depressive symptom scores (OR=1.059-1.191) and poorer sleep quality (higher PSQI;OR=1.088) were associated with higher odds of belonging to the Moderate/Low cognitive profiles, whereas adequate social support (Yes vs No;OR=0.530-0.696), selected middle-income categories versus ≥¥6,000 in per-capita monthly household income (OR=0.462-0.735) and male sex (OR=0.556-0.876) were associated with lower odds. Conclusion: Cognitive function among older adults can be classified into three distinct latent profiles, each associated with different influencing factors. These findings underscore the need for stratified and personalized interventions at the community level to support stratified screening and tailored community programs; given the cross-sectional design, these associations do not establish causality or intervention effects..
Keywords: older adults, Cognitive Function, latent profile analysis, InfluencingFactors, Community nursing
Received: 04 May 2025; Accepted: 12 Sep 2025.
Copyright: © 2025 XIANG, XIONG, Liang, Mao, Zhang, Li, Yuan and Jiang. 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:
Xiaoli Yuan, Affiliated Hospital of Zunyi Medical University, Zunyi, China
Zhixia Jiang, Guizhou Nursing Vocational and Technical College, GUIYANG, China
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