Wearable Technology as a Tool for Sleep-Wake Estimation in Central Disorders of Hypersomnolence
Purpose of Review
Diagnosing patients with central disorders of hypersomnolence (CDH) can be challenging. The emergence of wearable technology, such as actigraphy and consumer sleep trackers (CSTs), allows for objective characterization of habitual sleep-wake behavior, which can greatly assist the CDH diagnostic process. This review considers the current role and utility of wearable technology as a tool to estimate sleep-wake behavior in CDH.
Actigraphy is recommended by the American Academy of Sleep Medicine (AASM) as a diagnostic tool in CDH and has been widely employed in field-based investigations, yet insufficient guidelines have been provided to optimize data collection and analysis. Due to several factors, the AASM does not currently recognize CSTs as a viable diagnostic tool. However, CSTs have demonstrated promising capabilities that may lead to future clinical and research utility in CDH.
Actigraphy has an important role for sleep-wake assessment in CDH, but analytic standardization is a key barrier to their use. At present, CSTs are considered experimental, but their unique capabilities suggest they may one day be developed into a powerful tool in the assessment of these disorders.
KeywordsHypersomnolence Actigraphy Consumer sleep trackers Wearable Sleep
Compliance with Ethical Standards
Conflict of Interest
Dr. Plante has received grant support from the National Institute of Mental Health, National Institute on Aging, National Institute of Nursing Research, Brain and Behavior Research Foundation, American Sleep Medicine Foundation, University of Illinois at Chicago Occupational and Environmental Health and Safety Education and Research Center/National Institute for Occupational Safety and Health, and the Madison Education Partnership; and has served as a consultant to Teva Australia and Jazz Pharmaceuticals and a medical advisory board member for Jazz Pharmaceuticals.
Jesse Cook has served as a consultant for Bodymatter, Inc.
Human and Animal Rights and Informed Consent
This review references multiple investigations performed by the authors that utilized human participants. Each of these studies were approved by the Institutional Review Board affiliated with the University of Wisconsin-Madison. Additionally, all data utilized in these investigations were acquired from consenting participants.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 2.American Academy of Sleep Medicine. International classification of sleep disorders. 3rd ed. Darien, IL: American Academy of Sleep Medicine; 2014.Google Scholar
- 6.Ozaki A, Inoue Y, Hayashida K, Nakajima T, Honda M, Usui A, et al. Quality of life in patients with narcolepsy with cataplexy, narcolepsy without cataplexy, and idiopathic hypersomnia without long sleep time: comparison between patients on psychostimulants, drug-naïve patients and the general Japanese population. Sleep Med. 2012;13(2):200–6.CrossRefGoogle Scholar
- 9.•• Smith MT, McCrae CS, Cheung J, Martin JL, Harrod CG, Heald JL, et al. Use of actigraphy for the evaluation of sleep disorders and circadian rhythm sleep-wake disorders: an American Academy of Sleep Medicine Systematic review, meta-analysis, and GRADE assessment. J Clin Sleep Med. 2018;14(7):1209–30 This systematic review provides evidence for the American Academy of Sleep Medicine clinical practice guidelines on the use of actigraphy. CrossRefGoogle Scholar
- 10.•• Smith MT, CS MC, Cheung J, Martin JL, Harrod CG, Heald JL, et al. Use of actigraphy for the evaluation of sleep disorders and circadian rhythm sleep-wake disorders: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. 2018;14(7):1231–7 This American Academy of Sleep Medicine (AASM) clinical practice guideline describes the appropriate use of actigraphy for the assessment of a variety of sleep disorders, including central disorders of hypersomnolence. The AASM highlights the import of actigraphy to the CDH diagnostic process, yet do not provide adequate standardization recommendations to optimize actigraphic estimations. CrossRefGoogle Scholar
- 13.• de Zambotti M, Cellini N, Goldstone A, Colrain IM, Baker FC. Wearable sleep technology in clinical and research settings. Med Sci Sports Exerc. 2019; This recently conducted review comprehensively discusses the strengths and limitations of wearable sleep technology for both clinical and research purposes. Google Scholar
- 15.•• Cook JD, Prairie ML, Plante DT. Ability of the multisensory jawbone UP3 to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: a comparison against polysomnography and actigraphy. J Clin Sleep Med. 2018;14(5):841–8 This investigation evaluated the capabilities of a multi-sensory consumer sleep tracker against a clinical actigraph and polysomnography in a clinical sample of patients with suspected CDH. The design afforded direct comparisons between a consumer sleep tracker and clinical actigraph, whereby comparable estimations of sleep duration were determined. CrossRefGoogle Scholar
- 16.•• Cook JD, Eftekari SC, Dallmann E, Sippy M, Plante DT. Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: a comparison against polysomnography. J Sleep Res. 2018:e12789 This investigation evaluated the capabilities of a multi-sensory consumer sleep tracker against polysomnography in a clinical sample of patients with suspected CDH. The results suggested major limitations in this device’s classification abilities, especially relating to REM detection. Google Scholar
- 19.Goldstone A, Baker FC, de Zambotti M. Actigraphy in the digital health revolution: still asleep? Sleep. 2018;41(9).Google Scholar
- 21.•• Cook JD, Eftekari SC, Leavitt LA, et al. Optimizing actigraphic estimation of sleep duration in suspected idiopathic hypersomnia. Jour Clin Sleep Med. 2019;15(4):597–602 This investigation demonstrated wide variability in actigraphic estimations of sleep duration based on alterations in underlying setting parameters. Furthermore, these results suggest that the stock settings are suboptimal for sleep duration estimations in patients with suspected idiopathic hypersomnia. CrossRefGoogle Scholar
- 23.• Khosla S, Deak MC, Gault D, Goldstein CA, Hwang D, Kwon Y, et al. Consumer sleep technologies: how to balance the promises of new technology with evidence-based medicine and clinical guidelines. J Clin Sleep Med. 2019;15(1):163–5 This letter to the editor from the American Academy of Sleep Medicine discusses the complicating state of balancing the implementation of novel sleep technologies with established clinical practice guidelines. CrossRefGoogle Scholar
- 24.•• Khosla S, Deak MC, Gault D, Goldstein CA, Hwang D, Kwon Y, et al. Consumer sleep technology: an American Academy of Sleep Medicine position statement. J Clin Sleep Med. 2018;14(5):877–80 This American Academy of Sleep Medicine position statement explicitly prohibits the use of consumer sleep trackers as a surrogate sleep-wake diagnostic tool. CrossRefGoogle Scholar
- 25.U.S. Department of Health and Human Services food and drug administration guidelines for regulating mobile medical applications. Retrieved from https://www.fda.gov/medical-devices/digital-health/mobile-medical-applications#b
- 26.• Leger D, Gauriau C, Tonetti L, Lantin M, Filardi M, Philip P, et al. Using actigraphy to assess sleep and wake rhythms of narcolepsy type 1 patients: a comparison with primary insomniacs and healthy controls. Sleep Med. 2018;52:88–91 The field-based investigation demonstrates actigraphy’s ability to distinguish persons with narcolepsy type 1 from those with primary insomnia, as well as healthy controls. CrossRefGoogle Scholar
- 30.• Filardi M, Pizza F, Antelmi E, Ferri R, Natale V, Plazzi G. In-field assessment of sodium oxybate effect in pediatric type 1 narcolepsy: an actigraphic study. Sleep. 2018;41(6) This field-based investigation demonstrates actigraphy’s ability as a primary outcome measure capable of tracking treatment response. Google Scholar
- 31.• Filardi M, Pizza F, Antelmi E, Pillastrini P, Natale V, Plazzi G. Physical activity and sleep/wake behavior, anthropometric, and metabolic profile in pediatric narcolepsy type 1. Front Neurol. 2018;9:707 This field-based investigation demonstrated actigraphy’s utility as a primary outcome measure in a pediatric sample of narcolepsy type-1. CrossRefGoogle Scholar
- 34.Pogue D (2018, January 4). Exclusive: what Fitbit’s 6 billion nights of sleep data reveals about us. Retrieved from https://finance.yahoo.com/news/exclusive-fitbits-6-billion-nights-sleep-data-reveals-us-110058417.html
- 35.Fitbit Sleep Tracking Help. How do I track my sleep with my Fitbit device? Retrieved from https://help.fitbit.com/articles/en_US/Help_article/1314/?q=naps&l=en_US&fs=Search&pn=1#canilog