ArticleMeeting recommendations for multiple healthy lifestyle factors: Prevalence, clustering, and predictors among adolescent, adult, and senior health plan members
Introduction
General consensus exists among health researchers, health promotion and medical care practitioners, health systems administrators, and public health policy officials that chronic disease morbidity and mortality in the United States today is strongly associated with behaviors, or factors influenced by behavior, that may be characterized as modifiable, lifestyle-related health risk factors. Such individual-level health risk factors include the use of tobacco products, sedentary behavior and low levels of physical activity, less than optimal body weight for health, low multifactorial diet-quality practices (considering fat, fiber, fruits, and vegetables consumption), and excess consumption of alcohol.1, 2
Meeting public health recommendations for controlling single health risk factors significantly reduces the likelihood of multiple chronic diseases. For example, not using tobacco products reduces the risk for cardiovascular disease,3 but will also reduce the risk for cancer, chronic lung disease, and musculoskeletal diseases.2, 4 Similarly, the risk for chronic conditions is reduced when individuals meet public health recommendations for diet, physical activity, alcohol use, and obesity.2 These modifiable lifestyle-related health risk factors also tend to cluster among themselves,5, 6, 7, 8, 9 increasing the likelihood that individuals are dealing with multiple health risk factors at a given time.
Whereas much is known about the prevalence of single health risk factors and their associations with demographic characteristics including pairwise associations between behaviors and other lifestyle-related health factors, only a modest literature addresses the relationships among multiple lifestyle-related health factors or the clusters of such factors and their demographic correlates. It is important to explore such relationships for a variety of reasons. First, understanding the prevalence, distribution, and frequencies at which these behavioral clusters occur among various populations may inform health improvement planning efforts across multiple settings, such as primary care clinics, work sites, health systems, and public health agencies.4, 6, 10, 11, 12, 13 Second, there is a potential for synergistic effects of multiple healthy lifestyle factors on the risk of chronic conditions and health outcomes.1, 2, 4, 14, 15 Therefore, an increased understanding of the prevalence and clustering patterns of multiple lifestyle-related health factors may support efforts to reduce incidence of disease, management of existing chronic disease, and improve overall health outcomes.
In addition, demographic characteristics, including age and disease status, are associated with lifestyle-related health behaviors.16, 17 Population-based analyses are needed to further enhance our understanding of the relationships between multiple health behaviors and health outcomes. Consequently, conducting analyses across various age groups and considering chronic conditions specifically, may provide additional insights into the challenges and opportunities that exist to improve upon the proportion of the population that meets not merely a single, but multiple health-related recommendations for healthy lifestyle factors.
Development of a meaningful summary score for lifestyle-related health factors could provide a useful clinical metric that quantifies the “state of health” of a defined population by enumerating the proportion that meets recommended guidelines for multiple behaviors or behavior-related factors. Such a summary measure may also prove to be a meaningful health policy metric, as it would represent in one comprehensive health measure several objectives typically stated in the context of multiple single behaviors, for example as in Healthy People 2010.18 On the other hand, the inverse of such a comprehensive health measure would quantify risk, indicate the magnitude of potential benefits related to change, and draw clinician and patient attention to those who are most in need of change. Such a measure could be conceptualized as analogous to a cardiovascular risk index that scales in a single metric the risk associated with multiple components. Finally, since such a comprehensive lifestyle-related health measure would represent the number of individuals who meet all healthy lifestyle factors specified, it would also be an inherently person-centered metric. The overall objective would be to have all or most members of the population meet recommended guidelines for a specified list of healthy lifestyle factors. The comprehensive lifestyle-related health measure would describe the gap between current and optimal state.
It is the purpose of this study to document the prevalence of meeting recommended guidelines for healthy lifestyle factors, the clustering patterns among these healthy lifestyle factors, and the relative influence of demographic characteristics and chronic conditions on healthy lifestyle factors among adolescent, adult and senior health plan members. An additional objective of this paper is to provide support for the feasibility of a comprehensive lifestyle-related health measure that would be computed as the proportion of the population that meets multiple healthy lifestyle factors as quantified by the sum of the number of healthy lifestyle factors for which they meet recommended guidelines.
Section snippets
Sample and procedures
The subject population for this study was derived from a stratified random sample of the HealthPartners membership, a large Midwestern health plan in the United States. Random samples of 1000 members were selected from among three subgroups of the entire health plan population: adolescents (aged 13 to 17), adults (aged 18 to 64), and seniors (aged ≥65). Subsequently, a survey, created specifically to monitor the impact of a systemwide health improvement program, was mailed to the subjects.
Prevalence of meeting recommended healthy lifestyle guidelines
Table 2, Table 3 show the prevalence of meeting healthy lifestyle guidelines. Table 2 shows the prevalence of meeting recommendations for each factor by subgroup. A large percentage of adolescents met recommendations for nonsmoking (90.9%) and healthy weight (78.9%). However, only 64.0% of adolescents met the recommendation for high-quality diet, and 59.1% met the physical activity recommendation. Most adults met recommendations for alcohol use (89.4%), nonsmoking (85.1%), and high-quality
Discussion
The main findings of this study include the observation that only 14.5% of members aged ≥13 years of a large Midwestern health plan meet recommended guidelines for a comprehensive set of four healthy lifestyle factors, including not smoking, being physically active, consuming high-quality diet foods, and being at healthy weight. Among adults and seniors, when the addition of no or moderate levels of alcohol use is considered, only 10.8% and 12.8%, respectively, meet recommended guidelines for a
Acknowledgements
The research reported here was supported in part by the Robert Wood Johnson Foundation (grant 046929) and the HealthPartners Center for Health Promotion.
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