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.
Original languageEnglish
Pages (from-to)1867-1873
Number of pages7
JournalJournal of Sports Sciences
Volume34
Issue number19
DOIs
Publication statusPublished - Oct 2016

Keywords

  • Accelerometry
  • validation studies
  • methodology
  • waking time
  • sleeping time
  • sedentary lifestyle
  • PHYSICAL-ACTIVITY
  • ACTIVITY MONITOR
  • SLEEP
  • ACTIVPAL(TM)

Cite this

@article{72b7468171a0454a8184fe3a593fdf3f,
title = "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, PHYSICAL-ACTIVITY, ACTIVITY MONITOR, SLEEP, ACTIVPAL(TM)",
author = "{van der Berg}, J.D. and P. Willems and {van der Velde}, J.H. and H.H. Savelberg and N.C. Schaper and M.T. Schram and Simone Sep and P.C. Dagnelie and H. Bosma and C.D. Stehouwer and A. Koster",
year = "2016",
month = "10",
doi = "10.1080/02640414.2016.1140908",
language = "English",
volume = "34",
pages = "1867--1873",
journal = "Journal of Sports Sciences",
issn = "0264-0414",
publisher = "Routledge/Taylor & Francis Group",
number = "19",

}

Identifying waking time in 24-h accelerometry data in adults using an automated algorithm. / van der Berg, J.D.; Willems, P.; van der Velde, J.H.; Savelberg, H.H.; Schaper, N.C.; Schram, M.T.; Sep, Simone; Dagnelie, P.C.; Bosma, H.; Stehouwer, C.D.; Koster, A.

In: Journal of Sports Sciences, Vol. 34, No. 19, 10.2016, p. 1867-1873.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Identifying waking time in 24-h accelerometry data in adults using an automated algorithm

AU - van der Berg, J.D.

AU - Willems, P.

AU - van der Velde, J.H.

AU - Savelberg, H.H.

AU - Schaper, N.C.

AU - Schram, M.T.

AU - Sep, Simone

AU - Dagnelie, P.C.

AU - Bosma, H.

AU - Stehouwer, C.D.

AU - Koster, A.

PY - 2016/10

Y1 - 2016/10

N2 - 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.

AB - 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.

KW - Accelerometry

KW - validation studies

KW - methodology

KW - waking time

KW - sleeping time

KW - sedentary lifestyle

KW - PHYSICAL-ACTIVITY

KW - ACTIVITY MONITOR

KW - SLEEP

KW - ACTIVPAL(TM)

U2 - 10.1080/02640414.2016.1140908

DO - 10.1080/02640414.2016.1140908

M3 - Article

VL - 34

SP - 1867

EP - 1873

JO - Journal of Sports Sciences

JF - Journal of Sports Sciences

SN - 0264-0414

IS - 19

ER -