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.

OBJECTIVEThis study examined the association between objectively measured sedentary activity and metabolic syndrome among older adults.RESEARCH DESIGN AND METHODSData were from 1,367 men and women, aged ≥60 years who participated in the 2003–2006 National Health and Nutrition Examination Survey (NHANES). Sedentary time during waking hours was measured by an accelerometer (<100 counts per minute). A sedentary bout was defined as a period of time >5 min. A sedentary break was defined as an interruption in sedentary time (≥100 counts per minute). Metabolic syndrome was defined according to the Adult Treatment Panel (ATP) III criteria.RESULTSOn average, people spent 9.5 h (65% of wear time) as sedentary. Compared with people without metabolic syndrome, people with metabolic syndrome spent a greater percentage of time as sedentary (67.3 vs. 62.2%), had longer average sedentary bouts (17.7 vs. 16.7 min), had lower intensity during sedentary time (14.8 vs. 15.8 average counts per minute), and had fewer sedentary breaks (82.3 vs. 86.7), adjusted for age and sex (all P < 0.01). A higher percentage of time sedentary and fewer sedentary breaks were associated with a significantly greater likelihood of metabolic syndrome after adjustment for age, sex, ethnicity, education, alcohol consumption, smoking, BMI, diabetes, heart disease, and physical activity. The association between intensity during sedentary time and metabolic syndrome was borderline significant.CONCLUSIONSThe proportion of sedentary time was strongly related to metabolic risk, independent of physical activity. Current results suggest older people may benefit from reducing total sedentary time and avoiding prolonged periods of sedentary time by increasing the number of breaks during sedentary time.

WELK, GREGORY J., JAMES J. MCCLAIN, JOEY C. EISENMANN, AND ERIC E. WICKEL. Field validation of the MTI Actigraph and BodyMedia armband monitor using the IDEEA monitor. Obesity. 2007;15:918 -928. Objective: Accelerometers offer considerable promise for improving estimates of physical activity (PA) and energy expenditure (EE) in free-living subjects. Differences in calibration equations and cut-off points have made it difficult to determine the most accurate way to process these data. The objective of this study was to compare the accuracy of various calibration equations and algorithms that are currently used with the MTI Actigraph (MTI) and the Sensewear Pro II (SP2) armband monitor. Research Methods and Procedures: College-age participants (n ϭ 30) wore an MTI and an SP2 while participating in normal activities of daily living. Activity patterns were simultaneously monitored with the Intelligent Device for Estimating Energy Expenditure and Activity (IDEEA) monitor to provide an accurate estimate (criterion measure) of EE and PA for this field-based method comparison study. Results: The EE estimates from various MTI equations varied considerably, with mean differences ranging from Ϫ1.10 to 0.46 METS. The EE estimates from the two SP2 equations were within 0.10 METS of the value from the IDEEA. Estimates of time spent in PA from the MTI and SP2 ranged from 34.3 to 107.1 minutes per day, while the IDEEA yielded estimates of 52 minutes per day.

All AG_F epoch lengths provide comparable mean estimates to DO-detected MVPA time in fifth-grade children during PE. To minimize error among individual estimates, shorter epoch lengths should be used, with 5-s epochs yielding the lowest RMSE in the current study. Considerations of both epoch length and activity count cutpoint are important to improved detection of intermittent bouts of MVPA among fifth-grade children.