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Related Experiment Video

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer.

Vincent T van Hees1,2, Séverine Sabia3, Kirstie N Anderson4

  • 1MoveLab - Physical activity and exercise research, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom.

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|November 17, 2015
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Summary

Wrist-worn accelerometers can estimate sleep duration, showing moderate agreement with self-reports. This novel, generic algorithm allows for better comparison across studies assessing sleep patterns.

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Sleep Science

Background:

  • Wrist-worn accelerometers are common for physical activity monitoring.
  • Their utility for sleep assessment in population studies remains underexplored.
  • Validating novel algorithms is crucial for accurate sleep duration estimation.

Purpose of the Study:

  • To develop and validate a novel algorithm for assessing sleep duration using wrist-worn accelerometers.
  • To evaluate the agreement of the algorithm's sleep estimates with traditional measures.
  • To identify potential group differences in sleep duration estimation.

Main Methods:

  • Utilized data from 4,094 participants (aged 60-83) in the Whitehall II Study.
  • Employed a novel algorithm defining sleep as sustained inactivity (arm angle change <5° for ≥5 min).
  • Validated the algorithm against polysomnography data and compared results with sleep logs and questionnaires.

Main Results:

  • The algorithm showed moderate agreement with questionnaire-based measures for time in bed (kappa=0.32) and total sleep duration (kappa=0.39).
  • Time in bed estimates were lower for women, depressed individuals, and those with insomnia symptoms.
  • No significant group differences were observed for total sleep duration estimates.

Conclusions:

  • A novel, generic algorithm using wrist-worn accelerometers can estimate sleep duration with moderate accuracy.
  • The algorithm's ability to allow cross-study comparisons is a significant advancement.
  • Further research may refine the algorithm for specific demographic groups and sleep characteristics.