Identifying bedrest using waist-worn triaxial accelerometers in preschool children

Abstract

Purpose To adapt and validate a previously developed decision tree for youth to identify bedrest for use in preschool children. Methods Parents of healthy preschool (3-6-year-old) children (n = 610; 294 males) were asked to help them to wear an accelerometer for 7 to 10 days and 24 hours/day on their waist. Children with ≥3 nights of valid recordings were randomly allocated to the development (n = 200) and validation (n = 200) groups. Wear periods from accelerometer recordings were identified minute-by-minute as bedrest or wake using visual identification by two independent raters. To automate visual identification, chosen decision tree (DT) parameters (block length, threshold, bedrest-start trigger, and bedrest-end trigger) were optimized in the development group using a Nelder-Mead simplex optimization method, which maximized the accuracy of DT-identified bedrest in 1-min epochs against synchronized visually identified bedrest (n = 4,730,734). DT's performance with optimized parameters was compared with the visual identification, commonly used Sadeh’s sleep detection algorithm, DT for youth (10-18-years-old), and parental survey of sleep duration in the validation group. Results On average, children wore an accelerometer for 8.3 days and 20.8 hours/day. Comparing the DT-identified bedrest with visual identification in the validation group yielded sensitivity = 0.941, specificity = 0.974, and accuracy = 0.956. The optimal block length was 36 min, the threshold 230 counts/min, the bedrest-start trigger 305 counts/min, and the bedrest-end trigger 1,129 counts/min. In the validation group, DT identified bedrest with greater accuracy than Sadeh’s algorithm (0.956 and 0.902) and DT for youth (0.956 and 0.861) (both P<0.001). Both DT (564±77 min/day) and Sadeh’s algorithm (604±80 min/day) identified significantly less bedrest/sleep than parental survey (650±81 min/day) (both P<0.001). Conclusions The DT-based algorithm initially developed for youth was adapted for preschool children to identify time spent in bedrest with high accuracy. The DT is available as a package for the R open-source software environment (“PhysActBedRest”).

Parental Views on the Acceptability and Feasibility of Measurement Tools Used to Assess Movement Behaviour of Pre-School Children: A Qualitative Study

“…Actigraph and Actical accelerometers have most frequently been validated and worn using a hip placement [ 56 , 57 , 58 ]; however, both hip and wrist placements have shown potential to classify intensity of movement in pre-school children [ 47 ]. The findings of the present study showed variation in terms of preference between wrist and hip; with wrist being said to be feasible if the device can be worn like a watch, and the hip placement if the device is under the clothes then children are more likely to forget about it.…”

Examination of physical activity development in early childhood: protocol for a longitudinal cohort study of mother-toddler dyads

“…Participants with at least 500 min of wear per day and 3 wear-days will be included for analysis. A sleep period, defined based on mother-reported sleep onset (bedtime) and sleep offset (waking time), will be identified based on a sleep algorithm developed by Tracy et al [ 52 ]. The wear time log sheet will also be used to cross-check non-wear and sleep periods.…”

The Validity, Reliability, and Feasibility of Measurement Tools Used to Assess Sleep of Pre-school Aged Children: A Systematic Rapid Review

“…One study assessing criterion validity was quality assessed as inadequate (50) due to the type of analysis conducted. The majority of studies assessing the convergent validity of the tools were quality assessed as high risk of bias; eight inadequate (34,35,37,(51)(52)(53)(54)56) and four doubtful (36,57,59,60), with the exception of one study quality assessed as very good (61). Main reasons for poor methodological quality of the studies was due to unknown measurement properties of the comparator tools.…”

“…One study examined the ability of the Actigraph GT3X+ accelerometer to determine bedrest and wake time, using a newly developed decision tree algorithm specific to pre-school aged children (57). The developed algorithm was able to detect bedrest and wake time similarly to visual identification of the data and was better at identifying bedrest than algorithms developed in other age groups that are often applied to young children (63,64).…”

“…The algorithms used to assess sleep parameters when using accelerometers (34,35,52,53,56) were based on algorithms devised for adolescent and adult samples (63,69), despite known differences in the sleep of individuals of different ages (70). This review found that a pre-school specific algorithm was more accurate at detecting bedrest and wake time in comparison with visual identification of the data, and outcomes differed, when compared with existing algorithms frequently used (57). This highlights the importance of the data processing decisions when using accelerometers to detect sleep of pre-school aged children, and the need for device and age specific algorithms to improve accuracy.…”