Identifying bedrest using 24-h waist or wrist accelerometry in adults

Abstract

ObjectivesTo adapt and refine a previously-developed youth-specific algorithm to identify bedrest for use in adults. The algorithm is based on using an automated decision tree (DT) analysis of accelerometry data.DesignHealthy adults (n = 141, 85 females, 19–69 years-old) wore accelerometers on the waist, with a subset also wearing accelerometers on the dominant wrist (n = 45). Participants spent ≈24-h in a whole-room indirect calorimeter equipped with a force-platform floor to detect movement.MethodsMinute-by-minute data from recordings of waist-worn or wrist-worn accelerometers were used to identify bedrest and wake periods. Participants were randomly allocated to development (n = 69 and 23) and validation (n = 72 and 22) groups for waist-worn and wrist-worn accelerometers, respectively. The optimized DT algorithm parameters were block length, threshold, bedrest-start trigger, and bedrest-end trigger. Differences between DT classification and synchronized objective classification by the room calorimeter to bedrest or wake were assessed for sensitivity, specificity, and accuracy using a Receiver Operating Characteristic (ROC) procedure applied to 1-min epochs (n = 92,543 waist; n = 30,653 wrist).ResultsThe optimal algorithm parameter values for block length were 60 and 45 min, thresholds 12.5 and 400 counts/min, bedrest-start trigger 120 and 400 counts/min, and bedrest-end trigger 1,200 and 1,500 counts/min, for the waist and wrist-worn accelerometers, respectively. Bedrest was identified correctly in the validation group with sensitivities of 0.819 and 0.912, specificities of 0.966 and 0.923, and accuracies of 0.755 and 0.859 by the waist and wrist-worn accelerometer, respectively. The DT algorithm identified bedrest/sleep with greater accuracy than a commonly used automated algorithm (Cole-Kripke) for wrist-worn accelerometers (p<0.001).ConclusionsThe adapted DT accurately identifies bedrest in data from accelerometers worn by adults on either the wrist or waist. The automated bedrest/sleep detection DT algorithm for both youth and adults is openly accessible as a package “PhysActBedRest” for the R-computer language.

Sleep/Wake Regularity Associated with Default Mode Network Structure among Healthy Adolescents and Young Adults

“…Participants also completed a daily paper sleep/ wake diary, which recorded bed-, wake-, and naptimes as well as nighttime awakenings and times the actigraph was removed. Data were collected in 60-second epochs and each epoch was labelled as sleep or wake using a clinically-validated decision tree algorithm 41,42 . Data were visually inspected for accuracy using the sleep/wake diary.…”

Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents

“…If accelerometry is to be used to provide an indirect assessment of time in bed and potentially sleep, it is of utmost importance to distinguish between these behaviors. The accurate identification of lying and time in bed/asleep has to be performed using measurements conducted with subjects during their free-living behavior similar to in previous studies [ 29 , 30 , 31 , 32 , 33 ], and thus including the important temporal information regarding circadian rhythm and essentially sleep behaviors. Currently, there is only one study investigating the assessment of sleep using free-living recordings and a thigh-worn device.…”

Parameterizing and validating existing algorithms for identifying out-of-bed time using hip-worn accelerometer data from older women

“…The algorithms were originally designed then newly optimized and validated using data from ActiGraph accelerometers; caution should be taken when applying them to accelerometer data from other devices. New algorithms were published after the design and implementation of our study protocol and were therefore not evaluated [eg, (Tracy et al 2018)]. Our study was conducted among older community-living, ambulatory women and we are not sure whether the results can be generalized to the entire older adult population.…”