Clocks & Sleep (Feb 2024)

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

  • Ciro della Monica,
  • Kiran K. G. Ravindran,
  • Giuseppe Atzori,
  • Damion J. Lambert,
  • Thalia Rodriguez,
  • Sara Mahvash-Mohammadi,
  • Ullrich Bartsch,
  • Anne C. Skeldon,
  • Kevin Wells,
  • Adam Hampshire,
  • Ramin Nilforooshan,
  • Hana Hassanin,
  • The UK Dementia Research Institute Care Research & Technology Research Group,
  • Victoria L. Revell,
  • Derk-Jan Dijk

DOI
https://doi.org/10.3390/clockssleep6010010
Journal volume & issue
Vol. 6, no. 1
pp. 129 – 155

Abstract

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Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7–14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

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