Identifying waking time in 24-h accelerometry data in adults using an automated algorithm

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

Acknowledgement

The researchers are indebted to the participants for their willingness to participate in the study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the European Regional Development Fund via OP-Zuid, the Province of Limburg, the Dutch Ministry of Economic Affairs [grant number 31O.041], Stichting De Weijerhorst (Maastricht, The Netherlands), the Pearl String Initiative Diabetes (Amsterdam, The Netherlands), the Cardiovascular Center (CVC, Maastricht, The Netherlands), CARIM School for Cardiovascular Diseases, Maastricht, the Netherlands, CAPHRI, School for Public Health and Primary Care (Maastricht, The Netherlands), NUTRIM, School for Nutrition and Translational Research in Metabolism (Maastricht, The Netherlands), Stichting Annadal (Maastricht, The Netherlands), Health Foundation Limburg (Maastricht, The Netherlands) and by unrestricted grants from Janssen-Cilag B.V. (Tilburg, The Netherlands), Novo Nordisk Farma B.V. (Alphen aan den Rijn, The Netherlands) and Sanofi-Aventis Netherlands B.V. (Gouda, The Netherlands). A. Koster has received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2011-CIG) under grant agreement [grant number PCIG09-GA-2011-293621].