Identification of health behaviour clusters among people at high risk of stroke: A latent class profile analysis.
attitude
health behaviour
health promotion
knowledge
latent class analysis
nursing
stroke
Journal
Journal of advanced nursing
ISSN: 1365-2648
Titre abrégé: J Adv Nurs
Pays: England
ID NLM: 7609811
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
26
12
2019
revised:
21
06
2020
accepted:
05
08
2020
pubmed:
6
9
2020
medline:
22
6
2021
entrez:
5
9
2020
Statut:
ppublish
Résumé
To identify the possible latent classes of health behaviour reported by people at high risk of stroke and to explore the predictors of these different classes of health behaviour. A cross-sectional survey study. A stratified cluster random sampling method was used to collect data from 2,500 individuals at high risk of stroke who were from Henan Province, China, from January 2018-January 2019. A latent class profile analysis was used to identify the health behaviour clusters and multinomial logistic regression was used to determine which factors predicted the emergent latent classes of health behaviour. High-risk individuals (N = 2,236) at high risk of stroke replied to the survey (89.44% response rate). Model fit indices (AIC = 257,509.610, BIC = 260,228.733, Entropy = 0.956) supported a three-class model of health behaviours. The latent classes were Class 1 (a good level of adaptive health behaviour, 31%, N = 693), Class 2 (a moderate level of adaptive health behaviour, 36%, N = 805) and Class 3 (a poor level of adaptive health behaviour, 33%, N = 738); Based on physical and belief, behaviour and clinical profiles, the three classes were further labelled self-realization deficiency subgroup, social contact anxiety subgroup and health responsibility absence subgroup respectively. Older age, male gender, no spouse, lower education and household income were risk factors associated with good health behaviour. After controlling these socio-demographic variables, high levels of health-related knowledge and attitude were the main positive predictors of health behaviour. This study has identified three different latent classes of health behaviour and their predictive factors in people at high risk of stroke in the Chinese setting. This study has significance for the promotion of adaptive health behaviour in individuals at high risk of stroke. It has allowed the identification of specific clusters of health behaviour that vary in terms of their adaptiveness and forms the basis for the development of a targeted intervention to promote health behaviour for each different subgroup. 目标: 找出中风高危人群可能报告的潜在健康行为类型,并探讨这些不同类型健康行为的预测因素。 设计: 横断面分析研究。 方法: 采用分层集群随机抽样方法,从中国河南省在2018年1月至2019年1月共2500名中风高危人群中收集数据。使用潜在类别的剖面分析识别健康行为集群,并用多项式类别变量回归分析确定哪些因素可以预测初期的健康行为潜在类别。 结果: 中风高危人群(N=2236)回答了这份调查(89.44%的回答率)。模型拟合指数(AIC=257509.610,BIC=260228.733,熵=0.956)支持健康行为的三级模型。潜在类别为1级(适应性健康行为良好,31%,N=693)、2级(适应性健康行为中等水平,36%,N=805)和3级(适应性健康行为较差,33%,N=738);基于身体情况和信仰、行为和临床特征,这三类分别被划分为自我实现缺陷组、社交焦虑亚组和健康责任缺失组。高龄、男性、无配偶、受教育程度低和家庭收入低是与良好健康行为相关的危险因素。在控制了这些社会人口变量后,拥有高度的健康相关知识和态度是健康行为的主要积极预测因素。 结论: 该研究确定了中国境内中风高危人群的三种不同的潜在健康行为类型及其预测因素。 影响: 本研究对促进中风高危人群的适应性健康行为具有重要意义。其帮助人们识别出在适应能力方面各不相同的具体健康行为集群,并构成制定有针对性的干预措施的基础,以促进每个不同小组的健康行为。.
Autres résumés
Type: Publisher
(chi)
目标: 找出中风高危人群可能报告的潜在健康行为类型,并探讨这些不同类型健康行为的预测因素。 设计: 横断面分析研究。 方法: 采用分层集群随机抽样方法,从中国河南省在2018年1月至2019年1月共2500名中风高危人群中收集数据。使用潜在类别的剖面分析识别健康行为集群,并用多项式类别变量回归分析确定哪些因素可以预测初期的健康行为潜在类别。 结果: 中风高危人群(N=2236)回答了这份调查(89.44%的回答率)。模型拟合指数(AIC=257509.610,BIC=260228.733,熵=0.956)支持健康行为的三级模型。潜在类别为1级(适应性健康行为良好,31%,N=693)、2级(适应性健康行为中等水平,36%,N=805)和3级(适应性健康行为较差,33%,N=738);基于身体情况和信仰、行为和临床特征,这三类分别被划分为自我实现缺陷组、社交焦虑亚组和健康责任缺失组。高龄、男性、无配偶、受教育程度低和家庭收入低是与良好健康行为相关的危险因素。在控制了这些社会人口变量后,拥有高度的健康相关知识和态度是健康行为的主要积极预测因素。 结论: 该研究确定了中国境内中风高危人群的三种不同的潜在健康行为类型及其预测因素。 影响: 本研究对促进中风高危人群的适应性健康行为具有重要意义。其帮助人们识别出在适应能力方面各不相同的具体健康行为集群,并构成制定有针对性的干预措施的基础,以促进每个不同小组的健康行为。.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3039-3047Subventions
Organisme : Medical Science and Technology Project of Henan Province
ID : SBGJ2018003
Informations de copyright
© 2020 John Wiley & Sons Ltd.
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