TitleCan an automated sleep detection algorithm for waist worn accelerometry replace sleep logs?
Publication TypeJournal Article
Year of Publication2018
AuthorsBarreira, TV, Redmond, JG, Brutsaert, TD, Schuna, Jr, JM, Mire, EF, Katzmarzyk, PT, Tudor-Locke, C
JournalAppl Physiol Nutr Metab
Date Published04/2018


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.

Can an automated sleep detection algorithm for waist worn accelerometry replace sleep logs?

Alternate JournalAppl Physiol Nutr Metab
PubMed ID29701486