Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
School of Psychology, University of Ottawa, Ottawa, ON, Canada.
Ann Behav Med. 2023 Jul 19;57(8):662-675. doi: 10.1093/abm/kaad008.
Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviors tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behavior change. However, whether co-occurrence or co-variation-based approaches are better suited for this task remains relatively unknown.
To compare the utility of co-occurrence vs. co-variation-based approaches for understanding the interconnectedness between multiple health-impacting behaviors.
Using baseline and follow-up data (N = 40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviors. We used cluster analysis to group individuals based on their behavioral tendencies across multiple behaviors and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioral correlations and compared regression analyses of clusters and individual behaviors predicting future health outcomes.
Seven clusters were identified, with clusters differentiated by six of the seven health behaviors included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviors were generally small. In regression analyses individual behaviors accounted for more variance in health outcomes than clusters.
Co-occurrence-based approaches may be more suitable for identifying sub-groups for intervention targeting while co-variation approaches are more suitable for building an understanding of the relationships between health behaviors.
身体活动不足、不健康饮食、吸烟和饮酒等健康行为都是导致非传染性慢性病的主要危险因素。更好地了解哪些行为往往同时发生（即聚集在一起）和共同变化（即相关），可能为开发更全面的干预措施以促进多种健康行为改变提供新的机会。然而，基于共同发生或共同变化的方法是否更适合这项任务仍然相对未知。
比较共同发生和共同变化方法在理解多个健康行为之间相互关系方面的效用。
使用来自加拿大老龄化纵向研究的基线和随访数据（N=40268），我们检查了健康行为的共同发生和共同变化。我们使用聚类分析根据个体在多个行为中的行为倾向对个体进行分组，并检查这些聚类与人口统计学特征和健康指标的关联方式。我们将聚类分析的结果与行为相关性进行比较，并比较聚类和个体行为对未来健康结果的回归分析。
确定了七个聚类，这些聚类由分析中包含的七种健康行为中的六种来区分。社会人口统计学特征在多个聚类中存在差异。行为之间的相关性通常较小。在回归分析中，个体行为比聚类更能解释健康结果的变化。
基于共同发生的方法可能更适合确定干预目标的亚组，而基于共同变化的方法更适合建立对健康行为之间关系的理解。
