Identifying waking time in 24-h accelerometry data in adults using an automated algorithm |
| |
| Authors: | Julianne D van der Berg Paul J B Willems Jeroen H P M van der Velde Hans H C M Savelberg Nicolaas C Schaper Miranda T Schram |
| |
| Institution: | 1. Department of Social Medicine, Maastricht University, Maastricht, The Netherlands;2. CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands;3. Department of Human Movement Sciences, Maastricht University, Maastricht, The Netherlands;4. NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands;5. Department of Internal Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands;6. CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands;7. CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands;8. Department of Internal Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands |
| |
| Abstract: | As accelerometers are commonly used for 24-h measurements of daily activity, methods for separating waking from sleeping time are necessary for correct estimations of total daily activity levels accumulated during the waking period. Therefore, an algorithm to determine wake and bed times in 24-h accelerometry data was developed and the agreement of this algorithm with self-report was examined. One hundred seventy-seven participants (aged 40–75 years) of The Maastricht Study who completed a diary and who wore the activPAL3? 24 h/day, on average 6 consecutive days were included. Intraclass correlation coefficient (ICC) was calculated and the Bland–Altman method was used to examine associations between the self-reported and algorithm-calculated waking hours. Mean self-reported waking hours was 15.8 h/day, which was significantly correlated with the algorithm-calculated waking hours (15.8 h/day, ICC = 0.79, P = < 0.001). The Bland–Altman plot indicated good agreement in waking hours as the mean difference was 0.02 h (95% limits of agreement (LoA) = ?1.1 to 1.2 h). The median of the absolute difference was 15.6 min (Q1–Q3 = 7.6–33.2 min), and 71% of absolute differences was less than 30 min. The newly developed automated algorithm to determine wake and bed times was highly associated with self-reported times, and can therefore be used to identify waking time in 24-h accelerometry data in large-scale epidemiological studies. |
| |
| Keywords: | Accelerometry validation studies methodology waking time sleeping time sedentary lifestyle |
|
|
