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Multi-Modal Home Sleep Monitoring in Older Adults
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The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective.

Esben Ahrens1,2, Poul Jennum2,3, Jonas Duun-Henriksen4

  • 1T&W Engineering A/S, Denmark.

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|April 4, 2024
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Summary
This summary is machine-generated.

This study demonstrates that subcutaneous electroencephalography (sEEG) is a stable and user-friendly method for long-term sleep monitoring in healthy individuals. The findings support its use for understanding daily sleep variations and improving sleep quality assessments.

Keywords:
actigraphychronobiologyhome monitoringpsychomotor vigilance tasksleep monitoringsubcutaneous electroencephalogramultra‐long‐term electroencephalogram monitoring

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Area of Science:

  • Neuroscience
  • Sleep Medicine
  • Biomedical Engineering

Background:

  • Sleep deprivation and poor sleep quality are widespread issues impacting health.
  • The relationship between subjective sleep perception, objective sleep metrics, and daily performance variations is not well understood.

Purpose of the Study:

  • To assess the feasibility of year-long subcutaneous electroencephalography (sEEG) for monitoring sleep in daily life.
  • To evaluate the stability, adherence, usability, and participant experience of sEEG.

Main Methods:

  • 20 healthy participants underwent a year of minimally invasive sEEG recording.
  • Signal stability was quantified using covariance of variation.
  • Participant experience was assessed via questionnaires and interviews.

Main Results:

  • sEEG signals remained stable throughout the year with 91% average adherence.
  • Participants found the sEEG device easy to use with minimal discomfort, reporting high satisfaction and usability scores.
  • Interviews confirmed user understanding and acclimatization to the device.

Conclusions:

  • Longitudinal sleep monitoring using sEEG in everyday settings is feasible for healthy subjects.
  • The study provides robust evidence for sEEG's signal stability, adherence, and positive user experience.
  • The largest dataset of its kind generated by this study will significantly advance understanding of normal sleep variations and sleep quality.