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

Julianne D van der Berg1,2, Paul J B Willems3,4, Jeroen H P M van der Velde3,4,5

  • 1a Department of Social Medicine , Maastricht University , Maastricht , The Netherlands.

Journal of Sports Sciences
|February 4, 2016
PubMed
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This summary is machine-generated.

An automated algorithm accurately determines wake and sleep times from 24-hour accelerometer data. This method shows high agreement with self-reported waking hours, crucial for activity level estimations in large studies.

Area of Science:

  • Epidemiology
  • Biomedical Engineering
  • Sleep Science

Background:

  • Accelerometers are vital for measuring daily activity over 24 hours.
  • Accurate separation of waking and sleeping times is essential for estimating daily activity levels.
  • Existing methods may require refinement for large-scale epidemiological studies.

Purpose of the Study:

  • To develop and validate an algorithm for determining wake and bed times using 24-hour accelerometry data.
  • To assess the agreement between the algorithm's output and self-reported sleep-wake patterns.
  • To establish the algorithm's utility in epidemiological research.

Main Methods:

  • An automated algorithm was developed to analyze 24-hour accelerometry data (activPAL3™).
  • One hundred seventy-seven participants (aged 40-75 years) from The Maastricht Study wore accelerometers and completed diaries.
Keywords:
Accelerometrymethodologysedentary lifestylesleeping timevalidation studieswaking time

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  • Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to compare algorithm-calculated and self-reported waking hours.

    • Main Results:

    • The algorithm-calculated mean waking hours (15.8 h/day) strongly correlated with self-reported waking hours (ICC = 0.79, P < 0.001).
    • Bland-Altman analysis showed good agreement, with a mean difference of 0.02 hours and 95% limits of agreement from -1.1 to 1.2 hours.
    • 71% of absolute differences between algorithm and self-report were less than 30 minutes.

    Conclusions:

    • The developed automated algorithm reliably determines wake and bed times from 24-hour accelerometry.
    • The algorithm demonstrates high association with self-reported data, validating its use.
    • This algorithm is suitable for identifying waking time in large-scale epidemiological studies using accelerometry.