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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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Detecting Sleep Disruptions in Adolescents Using Context-Sensitive Ecological Momentary Assessment: A Feasibility

Rithika Lakshminarayanan1, Arushi Uppal1, Ha Le1

  • 1Northeastern University, Boston, MA 02115, USA.

International Conference on Pervasive Computing Technologies for Healthcare : [Proceedings]. International Conference on Pervasive Computing Technologies for Healthcare
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Summary

Adolescents need 8-10 hours of sleep. A new app uses passive sensing and ecological momentary assessments (EMAs) to help teens identify sleep disruptions, improving sleep quality.

Keywords:
EMAadolescentsexperience samplingmobile healthsleep disruptionssmartphonesmartwatch

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Area of Science:

  • Sleep Science
  • Human-Computer Interaction
  • Behavioral Science

Background:

  • Adolescents require 8-10 hours of sleep nightly for optimal wellbeing and academic success.
  • Inadequate sleep negatively impacts adolescent health and performance.
  • Understanding sleep environment disruptions is crucial for improving sleep quality.

Purpose of the Study:

  • To test the feasibility of an in-situ sleep monitoring application.
  • To utilize passive sensing for context-sensitive ecological momentary assessments (EMAs).
  • To aid adolescents in recalling sleep disruptions upon waking.

Main Methods:

  • Developed and tested an in-situ sleep monitoring application.
  • Employed passive sensing technology to trigger EMAs.
  • Delivered context-sensitive EMAs in the morning for recall of sleep events.

Main Results:

  • Participants completed over 80% of delivered EMAs.
  • Over 40% of noise and motion events were recalled with meaningful reasons.
  • Feasibility of the passive sensing and EMA approach was demonstrated.

Conclusions:

  • The developed application shows promise for in-situ sleep monitoring in adolescents.
  • Passive sensing combined with EMAs can effectively help recall sleep disruptions.
  • Further research is needed to address challenges and enhance sleep disruption detection.