不健康行为的聚类：来自加拿大老龄化纵向研究数据的多行为分析方案
Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging.
作者信息
van Allen Zack, Bacon Simon L, Bernard Paquito, Brown Heather, Desroches Sophie, Kastner Monika, Lavoie Kim, Marques Marta, McCleary Nicola, Straus Sharon, Taljaard Monica, Thavorn Kednapa, Tomasone Jennifer R, Presseau Justin
机构信息
School of Psychology, University of Ottawa, Ottawa, ON, Canada.
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
出版信息
JMIR Res Protoc. 2021 Jun 11;10(6):e24887. doi: 10.2196/24887.
BACKGROUND
Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life.
OBJECTIVE
The aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes.
METHODS
Using cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables.
RESULTS
Baseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H).
CONCLUSIONS
This study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24887.
背景
缺乏体育活动、不健康饮食、吸烟和饮酒等健康行为是非传染性慢性病的主要危险因素，在限制健康和生活满意度方面起着核心作用。然而，迄今为止，健康行为往往被彼此分开考虑，导致健康老龄化的指南和干预措施按特定行为分类，并且通常仅关注某一特定健康行为，而不考虑家庭、社会、工作及日常生活中的其他行为是否同时存在。
目的
本研究旨在了解行为如何聚类，以及这些聚类如何与身心健康、生活满意度和医疗保健利用相关联，这可能为利用这种行为共存情况来制定和评估促进多种健康行为改变的干预措施提供机会。
方法
利用加拿大老龄化纵向研究的横断面基线数据，我们将进行一组预定义的探索性和假设生成分析，以研究健康行为与日常生活行为的共存情况。我们将使用凝聚层次聚类分析，根据个体的行为倾向对其进行聚类。然后，将使用多项逻辑回归对聚类与人口统计学指标、医疗保健利用、总体健康和生活满意度之间的关系进行建模，并评估性别和年龄是否会缓和这些关系。此外，我们将使用派系渗流算法进行网络社区检测分析，以根据变量之间关系的强度检测行为的重叠社区。
结果
加拿大老龄化纵向研究的基线数据收集自51338名年龄在45至85岁之间的参与者。数据收集时间为2010年至2015年。本项目的二次数据分析已获得渥太华健康科学网络研究伦理委员会批准（协议编号#20190506 - 01H）。
结论
本研究将有助于为针对由描述其日常生活经历的多种行为所定义的成年亚人群（例如缺乏体育活动的吸烟者）量身定制干预措施提供信息。
国际注册报告识别码（IRRID）：DERR1 - 10.2196/24887
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