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Introduction: This study examined the equivalency of sleep estimates from Axivity, GENEActiv, and ActiGraph accelerometers worn on the nondominant and dominant wrists and with and without using a sleep log to guide the algorithm. Methods: 47 young adults wore an Axivity, GENEActiv, and ActiGraph accelerometer continuously on both wrists for 4–7 days. Sleep time, sleep window, sleep efficiency, sleep onset, and wake time were produced using the open-source software (GGIR). For each outcome, agreement between accelerometer brands, dominant and nondominant wrists, and with and without use of a sleep log, was examined using pairwise 95% equivalence tests (±10% equivalence zone) and intraclass correlation coefficients (ICCs), with 95% confidence intervals and limits of agreement. Results: All sleep outcomes were within a 10% equivalence zone irrespective of brand, wrist, or use of a sleep log. ICCs were poor to good for sleep time (ICCs ≥ .66) and sleep window (ICCs ≥ .56). Most ICCs were good to excellent for sleep efficiency (ICCs ≥ .73), sleep onset (ICCs ≥ .88), and wake time (ICCs ≥ .87). There were low levels of mean bias; however, there were wide 95% limits of agreement for sleep time, sleep window, sleep onset, and wake time outcomes. Sleep time (up to 25 min) and sleep window (up to 29 min) outcomes were higher when use of the sleep log was not used. Conclusion: The present findings suggest that sleep outcomes from the Axivity, GENEActiv, and ActiGraph, when analyzed identically, are comparable across studies with different accelerometer brands and wear protocols at a group level. However, caution is advised when comparing studies that differ on sleep log availability.

Background: The accuracy of wrist-worn accelerometers in identifying sedentary time has been scarcely studied in free-living conditions. The aim of this study was to compare daily sedentary time estimates between a thigh-worn accelerometer, which measured sitting and lying postures, and a wrist-worn accelerometer, which measured low levels of movement. Methods: The study population consisted of 259 participants (M _age = 62.8 years, SD = 0.9) from the Finnish Retirement and Aging Study (FIREA). Participants wore an Axivity AX3 accelerometer on their mid-thigh and an Actigraph wActiSleep-BT accelerometer on their non-dominant wrist simultaneously for a minimum of 4 days in free-living conditions. Two definitions to estimate daily sedentary time were used for data from the wrist-worn accelerometer: 1) the count cutpoint, ≤1853 counts per minute; and 2) the Euclidean Norm Minus One (ENMO) cutpoint, <30 mg. Results: Compared to the thigh-worn accelerometer, daily sedentary time estimate was 63 min (95% confidence interval [CI] = −53 to −73) lower by the count cutpoint and 50 min (95% CI = 34 to 67) lower by the ENMO cutpoint. The limits of agreement in daily sedentary time estimates between the thigh- and cutpoint methods for wrist-worn accelerometers were wide (the count cutpoint: −117 to 243, the ENMO cutpoint: −212 to 313 min). Conclusions: Currently established cutpoint-based methods to estimate sedentary time from wrist-worn accelerometers result in underestimation of daily sedentary time compared to posture-based estimates of thigh-worn accelerometers. Thus, sedentary time estimates obtained from wrist-worn accelerometers using currently available cutpoint-based methods should be interpreted with caution and future work is needed to improve their accuracy.

Although 24-hour time-use data are increasingly being examined in relation to indices of health, consensus has yet to be reached about the best way to present estimates from compositional analyses. This analysis explored the impact of different presentations of results when assessing the relationship between 24-hour time-use and body mass index (BMI) z-score using compositional analysis of 5-day actigraphy data in 742 children. First it was found that reallocating non-wear time to day-time components only (sedentary behavior, light physical activity, and moderate-to-vigorous physical activity [MVPA]) before normalization to 24 hours provided stronger estimates with BMI z-score than simply removing non-wear time before normalization. Estimates for sleep time were substantially affected, where associations with BMI z-score nearly doubled (mean difference [95% CI] in BMI z-score for 10% longer sleep were −0.20 [−0.32, −0.08] compared to −0.11 [−0.23, 0.002]). Presenting estimates in terms of a greater number of minutes in a component, relative to all others, showed MVPA to be the strongest predictor of BMI z-score, while estimates in terms of the proportion of minutes showed sleep to be the strongest predictor. Both presentations have value. However, presentations in terms of one-to-one “substitutions” of time may need careful interpretation due to the uneven distribution of time in each component. In conclusion, when analyzing relationships between 24-hour time-use and health outcomes, non-wear time and presentation of estimates can impact final conclusions. As a result, the current understanding of the importance of sleep for child health may be underestimated.