Effects of buffer size and shape on associations between the built environment and energy balance

TL;DR: Orstad et al. as discussed by the authors examined the role of demographic factors and depressive symptoms in direct and indirect associations between the objective and perceived neighborhood environment and physical activity in three U.S. populations.

TL;DR: The Automatic Context Measurement Tool is developed, which measures neighborhood environment characteristics from public data for any point location in the United States, and found that of 146 neighborhood characteristics, 92 were associated with walking bout counts after adjustment for individual characteristics and correction for false discovery.

TL;DR: The association between residential fast-food outlets and obesity was inconsistent and effects remained substantively the same when considering methodological diversity, and a more comprehensive conceptual model was proposed adjusting for wider markers of the food environment.

TL;DR: Gaps in current practice and understanding are described and solutions are suggested to move this promising area of research forward on how individuals and populations interact with their environment.

TL;DR: In this article, the authors quantify the effect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level.

TL;DR: In this article, the authors quantify the effect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level.

TL;DR: In this article, a coding scheme for classifying physical activity by rate of energy expenditure, i.e., by intensity, was presented for classification of physical activity using five digits that classify activity by purpose (i.e. sports, occupation, sel

TL;DR: This analysis presents updated estimates of the costs of obesity for the United States across payers (Medicare, Medicaid, and private insurers), in separate categories for inpatient, non-inpatient, and prescription drug spending.