基于贝叶斯网络模型的社区老年人认知衰弱的影响因素分析

doi:10.16460/j.issn1008-9969.2024.19.012

摘要/Abstract

摘要： 目的分析社区老年人认知衰弱的影响因素及其中的网络关系,为更有效降低认知衰弱的发生提供科学依据。方法采用便利抽样法,2022年8月—2023年6月选取唐山市8个社区1 449名老年人为研究对象,采用一般资料调查问卷、衰弱量表、简易精神状态检查量表、微型营养评价精法、流调中心抑郁水平评定量表、社区老年人数字健康素养评估量表对其进行调查,分析认知衰弱的影响因素及构建贝叶斯网络模型。结果以年龄、性别、婚姻状况、居住状况、受教育程度、个人平均月收入为协变量进行匹配共439对调查对象匹配成功。倾向性评分匹配后的二元logistic回归分析模型分析结果显示,有慢性病情况（OR=4.500）、睡眠时长不良（OR=2.398）、营养不良（OR=1.440）、无体育锻炼（OR=2.398）、抑郁（OR=4.586）、数字健康素养低水平（OR=1.574）是认知衰弱的危险因素（均P<0.05）。倾向性评分匹配后贝叶斯网络模型显示慢性病情况、睡眠时长、营养状况、体育锻炼、抑郁与认知衰弱直接相关。慢性病情况通过睡眠、营养状况、数字健康素养与认知衰弱间接相关,数字健康素养通过睡眠时长、营养状况与认知衰弱间接相关,体育锻炼通过抑郁与认知衰弱间接相关。研究对象营养良好、睡眠时长不良、有慢性病、进行体育锻炼、不存在抑郁时,其患认知衰弱的风险为0.460。结论贝叶斯网络模型揭示了社区老年人认知衰弱发生的直接和间接因素以及关联强度,阐明了因素间的复杂网络关系,为社区卫生服务人员更有效降低认知衰弱的发生和提出针对性的干预措施提供科学依据。

关键词: 老年人, 社区, 认知衰弱, 倾向性评分匹配, 贝叶斯网络模型

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

中图分类号:

R473.2

郝杨, 秦艳梅, 毛美琦, 赵雅宁, 刘瑶, 韩影. 基于贝叶斯网络模型的社区老年人认知衰弱的影响因素分析[J]. 护理学报, 2024, 31(19): 12-18.

HAO Yang, QIN Yan-mei, MAO Mei-qi, ZHAO Ya-ning, LIU Yao, HAN Ying. Influencing factors of cognitive frailty in community-dwelling elderly individuals based on Bayesian network model[J]. Journal of Nursing, 2024, 31(19): 12-18.

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