Based on a collaborative symposium in 2014 hosted by the Society of Behavioral Medicine (SBM) and the American College of Sports Medicine (ACSM), this paper presents a model for physical activity counseling for primary care physicians (PCPs). Most US adults do not meet national recommendations for physical activity levels. Socioecological factors drive differences in physical activity levels by geography, sex, age, and racial/ethnic group. The recent Patient Protection and Affordable Care Act incentivizes PCPs to offer patients physical activity counseling. However, PCPs have reported socioecological barriers to physical activity counseling and also patient barriers to physical activity, spanning from the individual to the environmental (eg, lack of safe spaces for physical activity), policy (eg, reimbursement policies), and organizational (eg, electronic medical record protocols, worksite norms/policies) levels. The aims of this paper are to: 1) discuss barriers to PCP counseling for physical activity; 2) provide evidence-based strategies and techniques to help PCPs address these counseling barriers; and 3) suggest practical steps for PCPs to counsel patients on physical activity using strategies and supports from policy, the primary care team, and other support networks.

BACKGROUND: The interactive effects of different types of physical activity on cardiovascular disease (CVD) risk have not been fully considered in previous studies. We aimed to identify physical activity patterns that take into account combinations of physical activities and examine the association between derived physical activity patterns and risk of acute myocardial infarction (AMI). METHODS: We examined the relationship between physical activity patterns, identified by principal component analysis (PCA), and AMI risk in a case-control study of myocardial infarction in Costa Rica (N=4172), 1994-2004. The component scores derived from PCA and total METS were used in natural cubic spline models to assess the association between physical activity and AMI risk. RESULTS: Four physical activity patterns were retained from PCA that were characterized as the rest/sleep, agricultural job, light indoor activity, and manual labor job patterns. The light indoor activity and rest/sleep patterns showed an inverse linear relation (P for linearity=0.001) and a U-shaped association (P for non-linearity=0.03) with AMI risk, respectively. There was an inverse association between total activity-related energy expenditure and AMI risk but it reached a plateau at high levels of physical activity (P for non-linearity=0.01). CONCLUSIONS: These data suggest that a light indoor activity pattern is associated with reduced AMI risk. PCA provides a new approach to investigate the relationship between physical activity and CVD risk.