老年脑卒中患者主动健康行为的三类潜在剖面及其分层护理启示:基于Pender健康促进模型的横断面研究OA
Three latent profiles of proactive health behaviors in older adult stroke patients and implications for stratified nursing:a cross-sectional study based on Pender's health promotion model
目的 脑卒中已成为中国成人死亡和致残的首要原因,老年患者的长期康复与二级预防高度依赖主动健康行为,但现有研究多忽略其群体异质性.本研究基于Pender健康促进模型,运用潜在剖面分析探索老年脑卒中患者主动健康行为的潜在类别及其影响因素,为分层精准护理提供依据.方法 本研究采用横断面研究设计,于2025年7-12月,以便利抽样法选取合肥市第一人民医院403例老年脑卒中患者作为调查对象,采用一般资料调查表、慢性病患者主动健康行为量表(Active Health Behavior Scale for Patients with Chronic Diseases,AHBS)、慢性病管理自我效能量表(Self-Efficacy for Managing Chronic Disease 6-Item Scale,SES6C)、社会支持量表(Social Support Rating Scale,SSRS)及简易疾病感知问卷(Brief Illness Perception Questionnaire,BIPQ)进行调查.采用潜在剖面分析(Latent profile analysis,LPA)识别主动健康行为的潜在类别,以AIC、BIC、aBIC、熵值、LMR及BLRT确定最优模型;采用单因素分析及多分类Logistic回归(以低主动健康行为-被动依从型为参照)探讨影响因素.结果 403例患者主动健康行为得分(59.42±12.56)分.LPA识别出3个潜在类别:低主动健康行为-被动依从性(53.6%)、中等主动健康行为-认知行为失衡型(20.8%)、高主动健康行为-积极投入型(25.6%).模型拟合良好(AIC=9 467.672,BIC=9 555.648,aBIC=9 485.840,熵值=0.953,LMR 及BLRT:P<0.001).多分类Logistic回归分析结果显示:以低主动健康行为-被动依从型为参照,月收入1 000~2 999元(OR=0.139,95%CI:0.029~0.659)、月收入3 000~5 000元(OR=0.119,95%CI:0.023~0.605)、慢性病管理自我效能得分(OR=1.238,95%CI:1.141~1.343)、简易疾病感知问卷得分(OR=1.246,95%CI:1.181~1.314)、社会支持得分(OR=1.337,95%CI:1.250~1.431)是老年脑卒中患者归属于中等主动健康行为-认知行为失衡型的影响因素;文化程度小学及以下(OR=0.030,95%CI:0.008~0.118)、文化程度初中或高中(OR=0.305,95%CI:0.098~0.939)、月收入1 000~2 999元(OR=0.063,95%CI:0.015~0.263)、月收入 3 000~5 000 元(OR=0.219,95%CI:0.052~0.923)、与配偶同住(OR=20.693,95%CI:2.620~163.263)、与子女同住(OR=8.693,95%CI:1.523~49.625)、脑卒中家族史(OR=5.688,95%CI:1.875~17.259)、慢性病数量3~5种(OR=4.998,95%CI:1.409~17.656)、慢性病管理自我效能得分(OR=1.107,95%CI:1.046~1.173)、简易疾病感知问卷得分(OR=1.209,95%CI:1.125~1.301)、社会支持得分(OR=1.383,95%CI:1.286~1.489)是老年脑卒中患者归属于高主动健康行为-积极投入型的影响因素(P均<0.05).结论 老年脑卒中患者主动健康行为存在明显群体异质性,多种社会人口学及心理因素影响其剖面类型,临床应基于潜在剖面特征实施分层个体化干预.
Objective Stroke has become the leading cause of death and disability among Chinese adults.The long-term rehabilitation and secondary prevention of elderly patients are highly dependent on proactive health behaviors,yet existing studies largely overlook group heterogeneity.Based on Pender's Health Promotion Model,this study used latent profile analysis to explore the latent classes of proactive health behaviors among elderly stroke patients and their influencing factors,so as to provide evidence for stratified precision nursing.Methods This study adopted a cross-sectional design,from July to December 2025,403 elderly stroke patients were selected by convenience sampling from Hefei First People's Hospital.The survey instruments included a general information questionnaire,the Active Health Behavior Scale for Patients with Chronic Diseases(AHBS),the Self-Efficacy for Managing Chronic Disease 6-Item Scale(SES6C),the Social Support Rating Scale(SSRS),and the Brief Illness Perception Questionnaire(BIPQ).Latent profile analysis(LPA)was used to identify the latent classes of proactive health behaviors,with AIC,BIC,aBIC,entropy,LMR,and BLRT used to determine the optimal model.Univariate analysis and multinomial logistic regression(with the low proactive health behavior-passive compliance type as reference)were used to explore the influencing factors.Results The proactive health behavior score of the 403 patients was 59.42±12.56.LPA identified three latent classes:low proactive health behavior-passive compliance(53.6%),moderate proactive health behavior-cognitive-behavioral imbalance(20.8%),and high proactive health behavior-active engagement(25.6%).The model demonstrated a good fit(AIC=9 467.672,BIC=9 555.648,aBIC=9 485.840,entropy=0.953,LMR and BLRT:P<0.001).Multinomial logistic regression analysis revealed that,with the low proactive health behavior-passive compliance class as the reference,monthly income of 1 000 to 2 999 yuan(OR=0.139,95%CI:0.029 to 0.659),monthly income of 3 000 to 5 000 yuan(OR=0.119,95%CI:0.023 to 0.605),chronic disease management self-efficacy score(OR=1.238,95%CI:1.141 to 1.343),illness perception score(OR=1.246,95%CI:1.181 to 1.314),and social support score(OR=1.337,95%CI:1.250 to 1.431)were associated with classification into the moderate proactive health behavior-cognitive-behavioral imbalance class.Furthermore,primary school education or below(OR=0.030,95%CI:0.008 to 0.118),junior or senior high school education(OR=0.305,95%CI:0.098 to 0.939),monthly income of 1 000 to 2 999 yuan(OR=0.063,95%CI:0.015 to 0.263),monthly income of 3 000 to 5 000 yuan(OR=0.219,95%CI:0.052 to 0.923),living with spouse(OR=20.693,95%CI:2.620 to 163.263),living with children(OR=8.693,95%CI:1.523 to 49.625),family history of stroke(OR=5.688,95%CI:1.875 to 17.259),having 3 to 5 chronic diseases(OR=4.998,95%CI:1.409 to 17.656),chronic disease management self-efficacy score(OR=1.107,95%CI:1.046 to 1.173),illness perception score(OR=1.209,95%CI:1.125 to 1.301),and social support score(OR=1.383,95%CI:1.286 to 1.489)were associated with classification into the high proactive health behavior-active engagement class(all P<0.05).Conclusion Elderly stroke patients showed obvious group heterogeneity in proactive health behaviors.Multiple sociodemographic and psychological factors influenced their profile types,and clinical practice should implement stratified individualized interventions based on latent profile characteristics.
周迅;郑梦涵;吴敏;孙芷妍;张昀;潘爱红
皖南医科大学研究生学院,安徽芜湖||合肥市第一人民医院院办公室,安徽合肥皖南医科大学研究生学院,安徽芜湖皖南医科大学研究生学院,安徽芜湖安徽中医药大学护理学院,安徽合肥安徽中医药大学护理学院,安徽合肥合肥市第一人民医院院办公室,安徽合肥
医药卫生
老年人脑卒中主动健康行为潜在剖面分析影响因素Pender健康促进模型
older adultsstrokeproactive health behaviorlatent profile analysisinfluencing factorsPender's health promotion model
《陆军军医大学学报》 2026 (16)
2243-2252,10
安徽省卫生健康科研项目(AHWJ2023A20195)合肥市卫生健康科技项目(HWK2025YB004) Supported by the Scientific Research Project of Anhui Provincial Health Commission(AHWJ2023A20195)and the Health Applied Medical Research Project of Hefei(HWK2025YB004).
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