HALO alumnus Mike Borghese is lead author on a paper, “Estimating sleep efficiency in 10- to- 13-year-olds using a waist-worn accelerometer,” that was recently published in Sleep Health. Citation details and a summary of the paper are below.

Borghese MM, Lin Y, Chaput JP, Janssen I. Estimating sleep efficiency in 10- to- 13-year-olds using a waist-worn accelerometer. Sleep Health. 2018;4(1):110-15.


Objective. In field settings, wrist- and waist-worn accelerometers are typically used to assess sleep characteristics and movement behaviors, respectively. There has been a shift in movement behavior studies to wear accelerometers 24 h/d. Sleep characteristics could be assessed in these studies if sleep algorithms were available for waist-worn accelerometers. The objective of this study was to develop and provide validity data for an algorithm/sleep likelihood score cut-off to estimate sleep efficiency in children using the waist-worn Actical accelerometer. Design. Cross-sectional study. Participants. Fifty healthy children aged 10-13 years. Measurements. Children wore an Actical on their waist and an Actiwatch 2 on their nondominant wrist for 8 nights at home in their normal sleep environment. Participants were randomized into algorithm/sleep likelihood score “development” and “test” groups (n = 25 per group). Within the development group, we assessed sleep efficiency with the Actical using the same algorithm that the Actiwatch 2 uses and selected the sleep likelihood score cut-off value that was the most accurate at predicting sleep efficiency at the nightly level compared with the Actiwatch 2. We applied this algorithm and cut-off value to the test group. Results. Mean (SD) sleep efficiency estimates for the test group from the Actical and Actiwatch 2 were 89.0% (3.9%) and 88.7% (3.1%), respectively. Bland-Altman plots and absolute difference scores revealed considerable agreement between devices for both nightly and weekly estimates of sleep efficiency. Conclusion. A waist-worn Actical accelerometer can accurately predict sleep efficiency in field settings among healthy 10- to 13-year-olds.

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