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中风高危人群健康行为集群的识别:潜在类别的剖面分析
Author(s) -
Guo Lina,
Liu Yanjin,
Zhu Yiru,
Wei Miao
Publication year - 2020
Publication title -
journal of advanced nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 155
eISSN - 1365-2648
pISSN - 0309-2402
DOI - 10.1111/jan.14523
Subject(s) - latent class model , spouse , multinomial logistic regression , social class , cluster sampling , medicine , logistic regression , demography , psychology , gerontology , environmental health , statistics , population , mathematics , sociology , anthropology , political science , law
Aims 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. Design A cross‐sectional survey study. Methods 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. Results 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. Conclusions 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. Impact 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.