Skip to main content

Advertisement

SpringerLink
Log in

Wearable Technology as a Tool for Sleep-Wake Estimation in Central Disorders of Hypersomnolence

  • Hypersomnia Disorders (D Plante, Section Editor)
  • Published:
Current Sleep Medicine Reports Aims and scope Submit manuscript

Abstract

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.

Recent Findings

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.

Summary

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Khan Z, Trotti LM. Central disorders of hypersomnolence: focus on the narcolepsies and idiopathic hypersomnia. Chest. 2015;148(1):262–73.

    Article  Google Scholar 

  2. American Academy of Sleep Medicine. International classification of sleep disorders. 3rd ed. Darien, IL: American Academy of Sleep Medicine; 2014.

    Google Scholar 

  3. Bayon V, Léger D, Philip P. Socio-professional handicap and accidental risk in patients with hypersomnias of central origin. Sleep Med Rev. 2009;13(6):421–6.

    Article  Google Scholar 

  4. Billiard M, Dauvilliers Y. Idiopathic hypersomnia. Sleep Med Rev. 2001;5(5):349–58.

    Article  CAS  Google Scholar 

  5. Ingravallo F, Gnucci V, Pizza F, Vignatelli L, Govi A, Dormi A, et al. The burden of narcolepsy with cataplexy: how disease history and clinical features influence socio-economic outcomes. Sleep Med. 2012;13(10):1293–300.

    Article  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.

    Article  Google Scholar 

  7. Sowa NA. Idiopathic Hypersomnia and hypersomnolence disorder: a systematic review of the literature. Psychosomatics. 2016;57(2):152–64.

    Article  Google Scholar 

  8. Kretzschmar U, Werth E, Sturzenegger C, Khatami R, Bassetti CL, Baumann CR. Which diagnostic findings in disorders with excessive daytime sleepiness are really helpful? A retrospective study. J Sleep Res. 2016;25(3):307–13.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  11. Lawrence G, Muza R. Assessing the sleeping habits of patients in a sleep disorder centre: a review of sleep diary accuracy. J Thorac Dis. 2018;10(Suppl 1):S177–S83.

    Article  Google Scholar 

  12. Sadeh A. The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev. 2011;15(4):259–67.

    Article  Google 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.

  14. Cook JD, Prairie ML, Plante DT. Utility of the Fitbit Flex to evaluate sleep in major depressive disorder: a comparison against polysomnography and wrist-worn actigraphy. J Affect Disord. 2017;217:299–305.

    Article  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.

    Article  Google 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.

  17. Beattie Y, Oyang Y, Statan A, Ghoreyshi A, Pantelopoulos A, Russell A, et al. Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol Meas. 2017;38:1968–79.

    Article  CAS  Google Scholar 

  18. Muzet A, Werner S, Fuchs G, Roth T, Saoud JB, AU V, et al. Assessing sleep architecture and continuity measures through analysis of heart rate and wrist movement recordings in healthy subjects: comparison with results based on polysmongraphy. Sleep Med. 2016;21:47–56.

    Article  Google Scholar 

  19. Goldstone A, Baker FC, de Zambotti M. Actigraphy in the digital health revolution: still asleep? Sleep. 2018;41(9).

  20. Ancoli-Israel S, Martin JL, Blackwell T, Buenaver L, Liu L, Meltzer LJ, et al. The SBSM guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med. 2015;13(sup1):S4-S38. https://doi.org/10.1080/15402002.2015.1046356.

    Article  PubMed  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.

    Article  Google Scholar 

  22. de Zambotti M, Godino JG, Baker FC, Cheung J, Patrick K, Colrain IM. The boom in wearable technology: cause for alarm or just what is needed to better understand sleep? Sleep. 2016;39(9):1761–2.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  27. Filardi M, Pizza F, Martoni M, Vandi S, Plazzi G, Natale V. Actigraphic assessment of sleep/wake behavior in central disorders of hypersomnolence. Sleep Med. 2015;16(1):126–30.

    Article  Google Scholar 

  28. Plante DT, Cook JD, Goldstein MR. Objective measures of sleep duration and continuity in major depressive disorder with comorbid hypersomnolence: a primary investigation with contiguous systematic review and meta-analysis. J Sleep Res. 2017;26(3):255–65.

    Article  Google Scholar 

  29. Filardi M, Pizza F, Bruni O, Natale V, Plazzi G. Circadian Rest-Activity Rhythm in Pediatric Type 1 Narcolepsy. Sleep. 2016;39(6):1241–7.

    Article  Google 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.

  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.

    Article  Google Scholar 

  32. Alakuijala A, Sarkanen T, Partinen M. Hypocretin-1 levels associate with fragmented sleep in patients with narcolepsy type 1. Sleep. 2016;39(5):1047–50.

    Article  Google Scholar 

  33. Maher C, Ryan J, Ambrosi C, Edney S. Users’ experiences of wearable activity trackers: a cross-sectional study. BMC Public Health. 2017;17(1):880.

    Article  Google 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

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Jesse D. Cook & David T. Plante

  2. Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin-Madison, 6001 Research Park Blvd, Madison, WI, 53703, USA

    Jesse D. Cook & David T. Plante

Authors
  1. Jesse D. Cook
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David T. Plante
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David T. Plante.

Ethics declarations

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.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Hypersomnia Disorders

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cook, J.D., Plante, D.T. Wearable Technology as a Tool for Sleep-Wake Estimation in Central Disorders of Hypersomnolence. Curr Sleep Medicine Rep 5, 193–200 (2019). https://doi.org/10.1007/s40675-019-00156-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40675-019-00156-9

Keywords

Search

Navigation