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Journal of Nursing ›› 2024, Vol. 31 ›› Issue (19): 12-18.doi: 10.16460/j.issn1008-9969.2024.19.012

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Influencing factors of cognitive frailty in community-dwelling elderly individuals based on Bayesian network model

HAO Yang, QIN Yan-mei, MAO Mei-qi, ZHAO Ya-ning, LIU Yao, HAN Ying   

  1. School of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan 063200, China
  • Received:2024-01-05 Online:2024-10-10 Published:2024-11-07

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Abstract: Objective To analyze the influencing factors of cognitive frailty in community-dwelling elderly individuals and their network relationships, and to provide scientific basis for reducing the occurrence of cognitive frailty more effectively. Methods Convenience sampling was used to select 1 449 elderly people from 8 communities in Tangshan City from August 2022 to June 2023, and they were investigated by using the General Information Questionnaire, FRAIL Scale, Mini-Mental State Examination, Short-Form Mini-Nutritional Assessment, Center for Epidemiologic Studies Depression Scale, and the Digital Health Literacy Assessment Scale for the Community-dwelling Elderly. The influencing factors of cognitive frailty were analyzed and a Bayesian network model was built. Results A total of 439 pairs of respondents were successfully matched with age, sex, marital status, residence status, education background and average monthly income as covariates. Binary logistic regression analysis model after matching propensity scores showed that chronic disease (OR=4.500), poor sleep duration (OR=2.398), malnutrition (OR=1.440), no physical exercise (OR=2.398), depression (OR=4.586), and low digital health literacy (OR=1.574) were risk factors for cognitive frailty (all P<0.05). Bayesian network model after propensity score matching showed that chronic disease, sleep duration, nutrition status, physical exercise, and depression were directly related to cognitive frailty. Chronic disease was indirectly related to cognitive frailty through sleep, nutritional status and digital health literacy; digital health literacy was indirectly related to cognitive frailty through sleep duration and nutritional status; physical exercise was indirectly related to cognitive frailty through depression. When participants were well-nourished, with poor sleep duration, with chronic disease(s), physically active, and without depression, the risk of cognitive frailty was 0.460. Conclusion Bayesian network model reveals the direct and indirect factors and correlation strength of cognitive frailty among the elderly in the community, clarifies the complex network relationship between the factors, and provides scientific basis for community health service personnel to reduce the incidence of cognitive frailty more effectively and propose targeted intervention measures.

Key words: elderly people, community, cognitive frailty, propensity score matching, Bayesian network model

CLC Number: 

  • R473.2
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