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已发表论文
社区老年人认知衰弱亚型的关联规则分析
Authors Liang S, Lai X, Chen H, Wang Q, Huo X
Received 22 October 2025
Accepted for publication 26 January 2026
Published 6 February 2026 Volume 2026:21 574258
DOI https://doi.org/10.2147/CIA.S574258
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Zhi-Ying Wu
Shicong Liang,1 Xiaoxing Lai,2 Hongshuang Chen,3 Qingchi Wang,3 Xiaopeng Huo3
1School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 2Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 3Nursing Department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China
Correspondence: Xiaopeng Huo, Nursing Department, Peking Union Medical College Hospital, Beijing, People’s Republic of China, Email huoxp001@126.com
Purpose: In order to provide a foundation for identification and intervention strategies, this study intends to investigate the influencing factors and associative patterns of two subtypes of reversible cognitive frailty (RCF) and potential reversible cognitive frailty (PRCF) in older adults with cognitive frailty (CF) who live in the community.
Patients and Methods: From July to December 2023, we conducted a cross-sectional study in a Beijing community to recruit older adults with CF using convenience sampling. We conducted the survey using the General Information Questionnaire, the Montreal Cognitive Assessment, the Clinical Dementia Rating, the Fried Frailty Phenotype, the Geriatric Depression Scale-15, the Generalized Anxiety Disorder-7, the Athens Insomnia Scale, the Barthel index, the Tinetti Performance Oriented Mobility Assessment, and the Lubben social network scale. The participants were separated into two categories: RCF and PRCF, based on frailty and cognitive assessment. After screening variables with a random forest algorithm, we applied association rule analysis to examine the factors influencing CF and the strength of their interactions.
Results: The survey was completed by 529 older adults with CF who lived in the community. Among them, 145 participants (27.4%) were classified as PRCF and 384 (72.6%) as RCF. 24 association rules, 12 for each subtype, were developed using the Apriori algorithm and clinical practice experience. These rules identified polypharmacy, multimorbidity, low educational attainment, and high fall risk as significant factors. Furthermore, the association pattern for PRCF is more complex.
Conclusion: The influencing factors associative patterns of the two categories of CF differ. In order to better manage elderly individuals with CF in the community, improve the cognitive health of the elderly, and encourage healthy aging, medical professionals should upgrade the community evaluation system for CF.
Keywords: cognitive frailty, older adults, association rule analysis, apriori algorithm
1School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 2Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 3Nursing Department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China
Correspondence: Xiaopeng Huo, Nursing Department, Peking Union Medical College Hospital, Beijing, People’s Republic of China, Email huoxp001@126.com
Purpose: In order to provide a foundation for identification and intervention strategies, this study intends to investigate the influencing factors and associative patterns of two subtypes of reversible cognitive frailty (RCF) and potential reversible cognitive frailty (PRCF) in older adults with cognitive frailty (CF) who live in the community.
Patients and Methods: From July to December 2023, we conducted a cross-sectional study in a Beijing community to recruit older adults with CF using convenience sampling. We conducted the survey using the General Information Questionnaire, the Montreal Cognitive Assessment, the Clinical Dementia Rating, the Fried Frailty Phenotype, the Geriatric Depression Scale-15, the Generalized Anxiety Disorder-7, the Athens Insomnia Scale, the Barthel index, the Tinetti Performance Oriented Mobility Assessment, and the Lubben social network scale. The participants were separated into two categories: RCF and PRCF, based on frailty and cognitive assessment. After screening variables with a random forest algorithm, we applied association rule analysis to examine the factors influencing CF and the strength of their interactions.
Results: The survey was completed by 529 older adults with CF who lived in the community. Among them, 145 participants (27.4%) were classified as PRCF and 384 (72.6%) as RCF. 24 association rules, 12 for each subtype, were developed using the Apriori algorithm and clinical practice experience. These rules identified polypharmacy, multimorbidity, low educational attainment, and high fall risk as significant factors. Furthermore, the association pattern for PRCF is more complex.
Conclusion: The influencing factors associative patterns of the two categories of CF differ. In order to better manage elderly individuals with CF in the community, improve the cognitive health of the elderly, and encourage healthy aging, medical professionals should upgrade the community evaluation system for CF.
Keywords: cognitive frailty, older adults, association rule analysis, apriori algorithm