Sedentary behaviors are linked to adverse health outcomes, but the total amount of time spent in these behaviors in the United States has not been objectively quantified. The authors evaluated participants from the 2003-2004 National Health and Nutrition Examination Survey aged >/=6 years who wore an activity monitor for up to 7 days. Among 6,329 participants with at least one 10-hour day of monitor wear, the average monitor-wearing time was 13.9 hours/day (standard deviation, 1.9). Overall, participants spent 54.9% of their monitored time, or 7.7 hours/day, in sedentary behaviors. The most sedentary groups in the United States were older adolescents and adults aged >/=60 years, and they spent about 60% of their waking time in sedentary pursuits. Females were more sedentary than males before age 30 years, but this pattern was reversed after age 60 years. Mexican-American adults were significantly less sedentary than other US adults, and White and Black females were similarly sedentary after age 12 years. These data provide the first objective measure of the amount of time spent in sedentary behavior in the US population and indicate that Americans spend the majority of their time in behaviors that expend very little energy.
Highlightsd 20 inpatient adults received ultra-processed and unprocessed diets for 14 days each d Diets were matched for presented calories, sugar, fat, fiber, and macronutrients d Ad libitum intake was 500 kcal/day more on the ultraprocessed versus unprocessed diet d Body weight changes were highly correlated with diet differences in energy intake
The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data provide opportunities to improve physical activity characterization, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission, and big data computing will minimize logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (EE) estimation to activity characterization and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach toward analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.
Rediscovery of cold-activated brown adipose tissue (BAT) in humans has boosted research interest in identifying BAT activators for metabolic benefits. Of particular interest are cytokines capable of fat browning. Irisin, derived from FNDC5, is an exercise-induced myokine that drives brown-fat-like thermogenesis in murine white fat. Here we explored whether cold exposure is an afferent signal for irisin secretion in humans and compared it with FGF21, a brown adipokine in rodents. Cold exposure increased circulating irisin and FGF21. We found an induction of irisin secretion proportional to shivering intensity, in magnitude similar to exercise-stimulated secretion. FNDC5 and/or FGF21 treatment upregulated human adipocyte brown fat gene/protein expression and thermogenesis in a depot-specific manner. These results suggest exercise-induced irisin secretion could have evolved from shivering-related muscle contraction, serving to augment brown fat thermogenesis in concert with FGF21. Irisin-mediated muscle-adipose crosstalk may represent a thermogenic, cold-activated endocrine axis that is exploitable in obesity therapeutics development.
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