|Title||Can an automated sleep detection algorithm for waist worn accelerometry replace sleep logs?|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Barreira, TV, Redmond, JG, Brutsaert, TD, Schuna, Jr, JM, Mire, EF, Katzmarzyk, PT, Tudor-Locke, C|
|Journal||Appl Physiol Nutr Metab|
The purpose of this study was to test whether estimates of bedtime, wake time, and sleep period time (SPT) were comparable between an automated algorithm (ALG) applied to waist-worn accelerometry data and a sleep log (LOG), in an adult sample. A total of 104 participants were asked to log evening bedtime and morning wake time and wear an ActiGraph GT3X+ accelerometer at their waist 24 h/days for 7 consecutive days. Mean difference (MD) and mean absolute difference (MAD) were computed. Pearson correlations and dependent sample t-tests were used to compare LOG-based and ALG-based sleep variables. Effect sizes were calculated for variables with significant mean differences. A total of 84 participants provided 2+ days of valid accelerometer and LOG data for a total of 368 days. There was no mean difference (p=.47) between LOG 472±59 min and ALG SPT 475±66 min (MAD=31±23 min, r=.81). There was no significant mean difference between bedtime (11:48 pm and 11:53 pm for LOG and ALG, respectively, p=.14, MAD=25±21 min, r=.92). However, there was a significant mean difference between LOG (7:41 am) and ALG (7:49 am) wake times (p=.01, d=0.11, MAD=24±21 min, r=.92). The LOG and ALG data were highly correlated and relatively small differences were present. The significant mean difference in wake time might not be practically meaningful (Cohen's d=0.11) making the ALG useful for sample estimates. MAD, which gives a better estimate of the expected differences at the individual level, also demonstrated good evidence supporting ALG individual estimates.
|Alternate Journal||Appl Physiol Nutr Metab|