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Daniel Arvidsson, Elias Johannesson, Lars Bo Andersen, Magnus Karlsson, Per Wollmer, Ola Thorsson and Magnus Dencker

). This is a uniaxial accelerometer recording acceleration signals along the vertical axis to generate activity counts corresponding to activity intensity. 19 Recordings of physical activity in the present study were performed during the whole autumn school term. Children were instructed to wear the

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Kin-Kit Li, Lorna Ng, Sheung-Tak Cheng and Helene H. Fung

more. Activity counts were converted into minutes of moderate- to vigorous-intensity PA per day based on the cutoffs suggested by Fredman, Bertrand, Martire, Hochberg, and Harris ( 2006 ). Covariates Several demographic and health-related factors were measured and controlled for in the analyses. The

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Elroy J. Aguiar, John M. Schuna Jr., Tiago V. Barreira, Emily F. Mire, Stephanie T. Broyles, Peter T. Katzmarzyk, William D. Johnson and Catrine Tudor-Locke

monitored hours per day, with nonwear time defined as ≥60 min of consecutive zero-activity counts per minute, allowing for minimal interruptions (up to 2 min of counts between 0 and 100; Troiano et al., 2008 ). All individuals with at least one valid day of data were retained in the analysis ( Tudor

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Barbara Resnick, Elizabeth Galik, Marie Boltz, Erin Vigne, Sarah Holmes, Steven Fix and Shijun Zhu

). Prior evidence of reliability of the MotionWatch 8 was based on the consistency between recordings across 3 days of wear. Evidence of validity of the MotionWatch 8 was based on a consistent match between the activity counts and recorded activity performed and a statistically significant difference in

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Jana Slaght, Martin Sénéchal and Danielle R. Bouchard

the pedometer data we measured the total number of minutes spent in MVPA per week based on their individualized threshold at 40% VO 2 R. The raw accelerometer data was analyzed to see what activity count was associated with the same number of minutes in MVPA as the pedometer information. For accuracy

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Juliana S. Oliveira, Leanne Hassett, Catherine Sherrington, Elisabeth Ramsay, Catherine Kirkham, Shona Manning and Anne Tiedemann

use of a waist-worn accelerometer (ActiGraph GT3X+, Pensacola, FL) and was expressed as mean activity counts/min/day and mean number of steps/day. Goal Setting Participants nominated two function-related goals that met the S.M.A.R.T criteria. Goals were determined in a collaborative manner by the

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Karen C. Smith, Griffin L. Michl, Jeffrey N. Katz and Elena Losina

above 175 steps per minute. We included days with at least 10 hours of wear time and, per CDC guidelines, counted minutes of MVPA in bouts of 10 minutes, with 2 “grace minutes” during which steps could fall beneath the threshold. 32 Each minute of vigorous physical activity counted as 2 minutes of

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Kent A. Lorenz, Hans van der Mars, Pamela Hodges Kulinna, Barbara E. Ainsworth and Melbourne F. Hovell

measure the behavioral variables while simultaneously recording PA. 22 Table 2 Interobserver Correlation Coefficients and R 2 Values for Physical Activity Counts Girls Boys Intensity r R 2 r R 2 Sedentary .9973 a .99 .9932 a .99 Moderate .9080 a .82 .9053 a .82 Vigorous .8157 a .66 .8841 a .78 a

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Deirdre Dlugonski, Katrina Drowatzky DuBose and Patrick Rider

71% (n = 12) of mothers had ≥6 valid days. Mothers accumulated 188,844 (47,966) vertical axis activity counts and walked 5504 (1274) steps per day. Mothers engaged in 284.0 (73.0) minutes of light-intensity activity, 16.5 (10.0) minutes of MVPA, and 520 (62) minutes of sedentary time per day

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Sandra Silva-Santos, Amanda Santos, Michael Duncan, Susana Vale and Jorge Mota

of GMC was subsequently converted to z scores. MVPA and Sedentary Behavior MVPA and sedentary behavior were measured during seven consecutive days (Monday to Sunday using the ActiGraph GT1M accelerometer; Pensacola, FL, USA). This accelerometer provides output in activity counts, which offers