Meeting recommendations for multiple healthy lifestyle factors. Prevalence, clustering, and predictors among adolescent, adult, and senior health plan members.
BACKGROUND
Whereas much is known about single lifestyle-related health risk factor prevalence and covariates, more research is needed to elucidate the interactions among multiple healthy lifestyle factors and variables that may predict adherence to these factors. Such data may guide both clinical and health policy decision making and person-centered approaches to population health improvement.


METHODS
We document the prevalence and cluster patterns of multiple healthy lifestyle factors among a random sample of adolescents (n =616), adults (n =585), and seniors (n =685) from a large Midwestern health plan. Modifiable, lifestyle-related health factors assessed included physical activity, nonsmoking, high-quality diet, and healthy weight for all subjects; adults and seniors were also asked about their alcohol consumption. Second, we sought to identify characteristics associated with the likelihood of meeting recommendations for healthy lifestyle factors. The healthy lifestyle factors sum score was categorized into three levels, that is, 0 to 2, 3, or 4 to 5 healthy lifestyle factors (4 for adolescents), and we used ordinal logistic regression to estimate the odds of meeting each of these criteria from several demographic characteristics and disease states.


RESULTS
Overall, only 14.5% of adolescent, adult, and senior health plan members meet recommended guidelines for four common healthy lifestyle factors. Only 10.8% of adults and 12.8% of seniors met all five behavior-related factors. For adolescents, only being nondepressed was associated with an increased likelihood to be in adherence to multiple healthy lifestyle factors (odds ratio [OR]=2.15; p <0.05). For adults, being in the 50- to 64-year-old cohort (OR=1.46, p<0.05), having a college degree (OR=1.65; p <0.05), and having no chronic disease (OR=1.92; p <0.05) were all associated with an increased likelihood to be in adherence to multiple healthy lifestyle factors. For seniors, having a college degree (OR=1.61; p <0.05), was the only variable associated with an increased likelihood to be in adherence to multiple healthy lifestyle factors.


CONCLUSIONS
A small proportion of health plan members meet multiple recommended healthy lifestyle guidelines at once. This analysis identifies population subgroups of specific interest and importance based on adherence to multiple healthy lifestyle factors, and predictors for increased likelihood to be in adherence to multiple healthy lifestyle factors. It presents a potentially useful summary measure based on person-centered measures of healthy lifestyle factors. Clinicians may derive meaningful information from analyses that address adherence to multiple healthy lifestyle factors. Health systems administrators may use this information to influence health policy and resource allocation decisions. Further studies are needed to assess the usefulness of this comprehensive lifestyle-related health measure as a metric of progress toward public health goals, or as a clinical metric that conveys information on future health status and directs interventions at the individual level.
Center for Health Promotion, HealthPartners Research Foundation, HealthPartners, Minneapolis, Minnesota, USA. nico.p.pronk@healthpartners.com
Nicolaas P
NP
Pronk
Louise H
LH
Anderson
A Lauren
AL
Crain
Brian C
BC
Martinson
Patrick J
PJ
O'Connor
Nancy E
NE
Sherwood
Robin R
RR
Whitebird
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000428:Alcohol Drinking / Q000453:epidemiology; D016000:Cluster Analysis; D003625:Data Collection; D004522:Educational Status; D005260:Female; D015438:Health Behavior; D006304:Health Status; D006801:Humans; D008019:Life Style; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015146:Midwestern United States / Q000453:epidemiology; D015995:Prevalence; D012907:Smoking / Q000453:epidemiology