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Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry and Karin A. Pfeiffer

(EE) using accelerometers is common for determining the volume and intensity of PA, and accurate EE measurement is critical for identification of, and intervention in, youth with low PA. Due to memory capacity and battery life limitations, early accelerometers summarized raw data into ‘activity counts

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Jaehun Jung, Willie Leung, Bridgette Marie Schram and Joonkoo Yun

sample and age), publication year, number of days for data collection, and physical activity levels data. Physical activity levels data were included if it was provided as either (a) average minutes spent in physical activity or (b) physical activity counts measured by accelerometers, so that the effect

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Emma L. J. Eyre, Jason Tallis, Susie Wilson, Lee Wilde, Liam Akhurst, Rildo Wanderleys and Michael J. Duncan

associated health outcomes. Accelerometers are a widely used tool to determine physical activity levels in public health research. Many accelerometer-based tools exist, providing an activitycount’ as their output. These counts are applied to thresholds which determine durations and frequencies of

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Alex V. Rowlands

metrics do lend themselves to the creation of population-specific physical activity percentiles that would facilitate interpretation in relation to norms, as Wolff-Hughes et al ( 47 ) have done with US children’s age- and sex-specific percentile curves for total activity counts per day for ActiGraph. To

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Robert W. Motl and Rachel Bollaert

under free-living conditions, and specific algorithms can be applied for quantifying the intensity of activity based on the classification of arbitrary units (i.e., accelerometer/activity counts) over a specified time period or epoch (i.e., 1 min) into “buckets” or categories. The classification

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Jodie Andruschko, Anthony D. Okely and Phil Pearson

. Activity counts per 30-sec were uploaded to determine the amount of time spent sedentary, and in light, moderate, and vigorous intensity activity during the monitoring period based on the classifications of Freedson, Melanson, and Sirard ( 1998 ). Analyses were conducted separately for weekdays and weekend

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Jocelyn F. Hafer, Mark S. Miller, Jane A. Kent and Katherine A. Boyer

. Participants wore accelerometers (GT3X; ActiGraph, Pensacola, FL) at the hip for at least 5 days (including at least 1 weekend day). Weekly time spent in MVPA 43 and weekly activity counts were determined for all participants. Gait analyses were performed from data collected during walking overground at 1.4 m

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Jocelyn Kernot, Lucy Lewis, Tim Olds and Carol Maher

.56 to 0.64. 34 Secondary Physical Activity Outcomes In addition to the primary outcomes, accelerometer-derived total activity counts (ActiGraph GT3X+) and self-reported MVPA (AAS) were secondary outcomes for this study. Other Secondary Outcomes A secondary aim of this study was to determine whether the

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Benita J. Lalor, Shona L. Halson, Jacqueline Tran, Justin G. Kemp and Stuart J. Cormack

the participant was lying down attempting to sleep or activity counts from the monitor were sufficiently low to indicate that the participant was immobile (ie, where the weighted activity count for an epoch fell below the defined threshold). When these 2 conditions were satisfied simultaneously, time

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Alex V. Rowlands

’s Accelerometry Database (ICAD). Daily accelerometer-measured physical activity [counts per minute (cpm)] was matched to local weather conditions and the relationships assessed using multilevel regression models. Multilevel models accounted for clustering of days within occasions within children within study