Young children’s activity and sedentary time were simultaneously measured via the Actical method (i.e., Actical accelerometer and specific cut-points) and the ActiGraph method (i.e., ActiGraph accelerometer and specific cut-points) at both 15-s and 60-s epochs to explore possible differences between these 2 measurement approaches. For 7 consecutive days, participants (n = 23) wore both the Actical and ActiGraph side-by-side on an elastic neoprene belt. Device-specific cut-points were applied. Paired sample t tests were conducted to determine the differences in participants’ daily average activity levels and sedentary time (min/h) measured by the 2 devices at 15-s and 60-s time sampling intervals. Bland-Altman plots were used to examine agreement between Actical and ActiGraph accelerometers. Regardless of epoch length, Actical accelerometers reported significantly higher rates of sedentary time (15 s: 42.7 min/h vs 33.5 min/h; 60 s: 39.4 min/h vs 27.1 min/h). ActiGraph accelerometers captured significantly higher rates of moderate-to-vigorous physical activity (15 s: 9.2 min/h vs 2.6 min/h; 60 s: 8.0 min/h vs 1.27 min/h) and total physical activity (15 s: 31.7 min/h vs 22.3 min/h; 60 s: = 39.4 min/h vs 25.2 min/h) in comparison with Actical accelerometers. These results highlight the present accelerometry-related issues with interpretation of datasets derived from different monitors.
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Leigh M. Vanderloo, Natascja A. Di Cristofaro, Nicole A. Proudfoot, Patricia Tucker, and Brian W. Timmons
Kyle R. Leister, Jessica Garay, and Tiago V. Barreira
waist-worn accelerometer (ActiGraph wGT3X-BT), and with the Winkler algorithm in an adult sample. Methods Data collected from a larger study were used to compare LOG and accelerometry estimated bedtime, wake time, and sleep time. A previous publication using a portion of the data compared sleep
Jessica Gorzelitz, Chloe Farber, Ronald Gangnon, and Lisa Cadmus-Bertram
Accurate assessment of physical activity remains challenging. Wearable fitness trackers are ubiquitous among consumers and represent new opportunities for measurement ( Kaewkannate & Kim, 2016 ; Lunney, Cunningham, & Eastin, 2016 ). Compared with research-grade devices like the ActiGraph, consumer
Kelli L. Cain, Edith Bonilla, Terry L. Conway, Jasper Schipperijn, Carrie M. Geremia, Alexandra Mignano, Jacqueline Kerr, and James F. Sallis
difficult to establish a nonwear criterion that correctly differentiates true sedentary behavior from nonwear time. Further complicating the issue, ActiGraph (Pensacola, FL) introduced a new line of accelerometers in 2005 (referred to as GT models) that utilized a microelectromechanical system accelerometer
Marcin Straczkiewicz, Jacek Urbanek, and Jaroslaw Harezlak
general-purpose data processing software (e.g., Matlab or R) or dedicated software provided by device manufacturer (e.g., ActiLife by ActiGraph) ( Brønd & Arvidsson, 2016 ; Skotte, Korshøj, Kristiansen, Hanisch, & Holtermann, 2014 ; Zhou et al., 2015 ). The downside of using software like Matlab or R
Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, Karin A. Pfeiffer, Alexander H.K. Montoye, and Jan Christian Brønd
ActiGraph accelerometers are often used to characterize habitual physical activity ( Migueles et al., 2017 ; Montoye et al., 2016 ). Historically, ActiGraph devices measured, filtered, and rectified acceleration (in gravitational units) to generate “activity counts” as a measure of physical
Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan
these 34 used an ActiGraph device ( Powell, Herring, Dowd, Donnelly, & Carson, 2018 ). Objective measurement of an adult’s sedentary time from hip-worn accelerometers is most often quantified using a cut-point-based threshold of <100 counts/min that is applied to the vertical axis ( Migueles et
Tatiana Plekhanova, Alex V. Rowlands, Tom Yates, Andrew Hall, Emer M. Brady, Melanie Davies, Kamlesh Khunti, and Charlotte L. Edwardson
; however, the three most widely used research-grade raw data accelerometer brands deployed in epidemiological studies are the Axivity (Axivity Ltd., Newcastle, United Kingdom), ActiGraph (ActiGraph LLC, Pensacola, FL), and GENEActiv (ActivInsights Ltd., Cambridgeshire, United Kingdom). Various
Hilary Hicks, Alexandra Laffer, Kayla Meyer, and Amber Watts
measure activity in a free-living environment, which is especially important in an older adult population known to spend less time in higher intensity activity and more time being sedentary (e.g., Harvey, Chastin, & Skelton, 2013 ). ActiGraph introduced the low-frequency extension (LFE) filter in 2009
Kimberly A. Clevenger, Jan Christian Brønd, Daniel Arvidsson, Alexander H.K. Montoye, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer
Since the 1980s, accelerometers have been used to estimate free-living energy expenditure and physical activity levels ( Wong et al., 1981 ). ActiGraph accelerometers are the most widely used brand of research-grade monitors ( Migueles et al., 2017 ; Montoye et al., 2016 ) and have been used in
