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-worn accelerometers, unless they were uncomfortable. Since there was no planned after-school activity, only school-time data were used. The accelerometer data were collected with a sampling rate of 30 Hz, and activity counts were integrated for every 10 s (epoch). The software identified time without the

, “a wearable device that objectively measures lifestyle PA and can provide feedback, beyond the display of basic activity count information, via the monitor display or through a partnering application to elicit continual self-monitoring of activity behaviour.” 4 These devices continue to be developed

determine the average activity counts and steps during the activities. The metabolic and accelerometry data are provided in a Supplemental Table (available online). Perceived State PA Enjoyment During the rest periods immediately following each activity, the children used the 4-item HHS to score their

at the exam visit. Data from the accelerometer, expressed as 60-second epochs, were downloaded and screened for wear-time using methods described by Troiano et al. 16 Activity count-based measures (counts·d −1 and counts·min −1 ·d −1 ) were calculated using summed or averaged daily counts detected

activity counts recorded by the AG to Calorie data ( Freedson, Melanson, & Sirard, 1998 ; Sasaki, John, & Freedson, 2011 ); these are henceforth abbreviated as AG 1998 and AG 2011. Since metabolism lags behind the true metabolic cost of an activity when changing intensities ( McArdle, Katch, & Katch, 2015

); latency (time in min attempting to fall asleep); and sleep efficiency (percentage of time in bed that was spent asleep). The sampling frequency was 50 Hz and the epoch of activity counts was 60 seconds. All movements greater than 0.05  g were measured, and values outside the range of 3 to 11 Hz were

. Studies with older adults consistently used 60-s epochs to summarize activity counts (see Gorman et al., 2014 ). Individual participant data were first uploaded to Microsoft Office Excel files. The accelerometers did not capture any counts greater than 15,000/min, which are regarded to be physiologically

between neighborhood tree density with either physical activity or sedentary behavior. This contrasts the findings by Lovasi et al ( 34 ), where a greater number of trees embedded within sidewalks per neighborhood was associated with higher physical activity counts per minute in preschoolers living in New

activities in the previous 2 weeks ( Dirajlal-Fargo et al., 2016 ). In the second trial, participants were instructed to wear an accelerometer for 96 consecutive hours, including 2 weekdays and 2 weekend days. Accelerometers recorded several parameters of participants’ physical activity including activity