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Multi-Modal Home Sleep Monitoring in Older Adults
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Sleep-wake parameters can be detected in patients with chronic stroke using a multisensor accelerometer: a validation

Elie Gottlieb1,2, Leonid Churilov2, Emilio Werden1,2

  • 1The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.

Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine
|September 25, 2020
PubMed
Summary

The SenseWear Armband (SWA) shows promise for measuring overall sleep in stroke patients, but its accuracy for detailed sleep stages is limited. Caution is advised for diagnostic use due to moderate-to-fair epoch-level agreement.

Keywords:
accelerometeragingbehavioral sleep medicineinstrumentationscoringsleep/wake physiologystrokevalidation

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Area of Science:

  • Neurology
  • Sleep Medicine
  • Biomedical Engineering

Background:

  • Sleep-wake disturbances are linked to stroke development and progression.
  • Objective, ambulatory sleep measurement in neurological populations is challenging due to a lack of validated tools.
  • Understanding post-stroke sleep is crucial for patient recovery and management.

Purpose of the Study:

  • To validate the SenseWear Armband (SWA), a multisensor sleep monitor, for assessing sleep in ischemic stroke patients.
  • To compare SWA-derived sleep parameters with at-home polysomnography (PSG) in stroke survivors and controls.
  • To evaluate the SWA's reliability for both macro sleep parameters and epoch-by-epoch sleep staging.

Main Methods:

  • Twenty-eight ischemic stroke patients and 16 control participants underwent simultaneous at-home polysomnography and SWA monitoring.
  • Sleep parameters including total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset were analyzed.
  • Statistical methods included Lin's concordance correlation coefficient, reduced major axis regressions, and epoch-by-epoch agreement metrics (Cohen's kappa, sensitivity, specificity).

Main Results:

  • The SWA demonstrated robust quantification for total sleep time (concordance correlation coefficient = 0.49).
  • Performance was weaker for sleep onset latency (concordance correlation coefficient = 0.16), indicating challenges in discriminating wakefulness.
  • High sensitivity (95.70–95.90%) was observed, but specificity was only fair-to-moderate (40.45–45.60%). Epoch-by-epoch agreement was fair (74–78%).

Conclusions:

  • The SWA is a promising ambulatory tool for estimating macro sleep parameters in stroke populations.
  • However, its moderate-to-fair epoch-level agreement necessitates caution when used for diagnostic purposes or in individuals with fragmented sleep.
  • Further validation is needed for precise sleep staging and clinical decision-making.