Advertisement

Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer’s Disease Dementia

  • N. Saif
  • P. Yan
  • K. Niotis
  • O. Scheyer
  • A. Rahman
  • M. Berkowitz
  • R. Krikorian
  • H. Hristov
  • G. Sadek
  • S. Bellara
  • Richard S. IsaacsonEmail author
Original Research

Abstract

Background

Alzheimer’s disease (AD) is the most common and most costly chronic neurodegenerative disease globally. AD develops over an extended period prior to cognitive symptoms, leaving a “window of opportunity” for targeted risk-reduction interventions. Further, this pre-dementia phase includes early physiological changes in sleep and autonomic regulation, for which wearable biosensor devices may offer a convenient and cost-effective method to assess AD-risk.

Methods

Patients with a family history of AD and no or minimal cognitive complaints were recruited from the Alzheimer’s Prevention Clinic at Weill Cornell Medicine & New York-Presbyterian. Of the 40 consecutive patients screened, 34 (85%) agreed to wear a wearable biosensor device (WHOOP). One subject (2.5%) lost the device prior to data collection. Of the remaining subjects, 24 were classified as normal cognition and were asymptomatic, 6 were classified as subjective cognitive decline, and 3 were amyloid-positive (one with pre-clinical AD, one with pre-clinical Lewy-Body Dementia, and one with mild cognitive impairment due to AD). Sleep-cycle, autonomic (heart rate variability [HRV]) and activity measures were collected via WHOOP. Blood biomarkers and neuropsychological testing sensitive to cognitive changes in pre-clinical AD were obtained. Participants completed surveys assessing their sleep-patterns, exercise habits, and attitudes towards WHOOP. The goal of this prospective observational study was to determine the feasibility of using a wrist-worn biosensor device in patients at-risk for AD dementia. Unsupervised machine learning was performed to first separate participants into distinct phenotypic groups using the multivariate biometric data. Additional statistical analyses were conducted to examine correlations between individual biometric measures and cognitive performance.

Results

27 (81.8%) participants completed the follow-up surveys. Twenty-four participants (88.9%) were satisfied with WHOOP after six months, and twenty-three (85.2%) wanted to continue wearing WHOOP. K-means clustering separated participants into two groups. Group 1 was older, had lower HRV, and spent more time in slow-wave sleep (SWS) than Group 2. Group 1 performed better on two cognitive tests assessing executive function: Flanker Inhibitory Attention/Control (FIAC) (p=.031), and Dimensional Change Card Sort (DCCS) (p=.061). In Group 1, DCCS was correlated with SWS (ϱ=.68, p=0.024) and HRV (ϱ=.6, p=0.019). In Group 2, DCCS was correlated with HRV (ϱ=.55, p=0.018). There were no significant differences in blood biomarkers between the two groups.

Conclusions

Wearable biosensor devices may be a feasible tool to assess AD-related physiological changes. Longitudinal collection of sleep and HRV data may potentially be a noninvasive method for monitoring cognitive changes related to pre-clinical AD. Further study is warranted in larger populations.

Key words

Alzheimer’s disease actinography unsupervised machine learning early detection biosensor devices 

Notes

Acknowledgements

This study was supported by NIH/NCATS UL1TR002384, NIH PO1AG026572; Zuckerman Family Foundation; Ace’s for Alzheimer’s; Hilarity for Charity; Women’s Alzheimer’s Movement; Memories for Mary; and philanthropic support from the patients of the Alzheimer’s Prevention Clinic.

Funding: The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Declaration of interests: Dr. Isaacson has served as a scientific advisor for Eisai. The remaining authors report no conflicts of interest or other relevant disclosures.

Ethical standards: The study procedures followed were in accordance with the ethical standards of the Institutional Review Board and the Principles of the Declaration of Helsinki (revised version of 2013).

Supplementary material

42414_2019_91_MOESM1_ESM.docx (19 kb)
Appendices

References

  1. 1.
    International AsD. Global Perspective. 2019.Google Scholar
  2. 2.
    2019 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia 2019;15(3):321–87 doi:  https://doi.org/10.1016/j.jalz.2019.01.010[published Online First: Epub Date].
  3. 3.
    Brookmeyer R, Abdalla N, Kawas CH, Corrada MM. Forecasting the prevalence of preclinical and clinical Alzheimer’s disease in the United States. Alzheimer’s & dementia: the journal of the Alzheimer’s Association 2018; 14(2): 121–9.CrossRefGoogle Scholar
  4. 4.
    Mander BA, Winer JR, Jagust WJ, Walker MP. Sleep: A Novel Mechanistic Pathway, Biomarker, and Treatment Target in the Pathology of Alzheimer’s Disease? Trends in neurosciences 2016; 39(8): 552–66.CrossRefGoogle Scholar
  5. 5.
    Aharon-Peretz J, Harel T, Revach M, Ben-Haim SA. Increased sympathetic and decreased parasympathetic cardiac innervation in patients with Alzheimer’s disease. Archives of neurology 1992; 49(9): 919–22.CrossRefGoogle Scholar
  6. 6.
    Toledo MA, Junqueira LF, Jr. Cardiac autonomic modulation and cognitive status in Alzheimer’s disease. Clinical autonomic research: official journal of the Clinical Autonomic Research Society 2010; 20(1): 11–7.CrossRefGoogle Scholar
  7. 7.
    Kim DH, Lipsitz LA, Ferrucci L, et al. Association between reduced heart rate variability and cognitive impairment in older disabled women in the community: Women’s Health and Aging Study I. Journal of the American Geriatrics Society 2006; 54(11): 1751–7.CrossRefGoogle Scholar
  8. 8.
    Collins O, Dillon S, Finucane C, Lawlor B, Kenny RA. Parasympathetic autonomic dysfunction is common in mild cognitive impairment. Neurobiology of aging 2012; 33(10): 2324–33.CrossRefGoogle Scholar
  9. 9.
    Ju Y-ES, Lucey BP, Holtzman DM. Sleep and Alzheimer disease pathology—a bidirectional relationship. Nature reviews Neurology 2014; 10(2): 115–9.CrossRefGoogle Scholar
  10. 10.
    Seifan A, Isaacson R. The Alzheimer’s Prevention Clinic at Weill Cornell Medical College / New York — Presbyterian Hospital: Risk Stratification and Personalized Early Intervention. The journal of prevention of Alzheimer’s disease 2015; 2(4): 254–66.PubMedPubMedCentralGoogle Scholar
  11. 11.
    Schelke MW, Attia P, Palenchar DJ, et al. Mechanisms of Risk Reduction in the Clinical Practice of Alzheimer’s Disease Prevention. Frontiers in aging neuroscience 2018; 10: 96.CrossRefGoogle Scholar
  12. 12.
    Guarnieri B, Adorni F, Musicco M, et al. Prevalence of sleep disturbances in mild cognitive impairment and dementing disorders: a multicenter Italian clinical cross-sectional study on 431 patients. Dementia and geriatric cognitive disorders 2012; 33(1): 50–8.CrossRefGoogle Scholar
  13. 13.
    Liguori C, Romigi A, Nuccetelli M, et al. Orexinergic system dysregulation, sleep impairment, and cognitive decline in Alzheimer disease. JAMA neurology 2014; 71(12): 1498–505.CrossRefGoogle Scholar
  14. 14.
    Lim ASP, Yu L, Kowgier M, Schneider JA, Buchman AS, Bennett DA. Modification of the Relationship of the Apolipoprotein E ε4 Allele to the Risk of Alzheimer Disease and Neurofibrillary Tangle Density by SleepSleep Effect on Apolipoprotein E ε4 AlleleSleep Effect on Apolipoprotein E ε4 Allele. JAMA neurology 2013; 70(12): 1544–51.CrossRefGoogle Scholar
  15. 15.
    Mesulam M, Shaw P, Mash D, Weintraub S. Cholinergic nucleus basalis tauopathy emerges early in the aging-MCI-AD continuum. Annals of neurology 2004; 55(6): 815–28.CrossRefGoogle Scholar
  16. 16.
    Saper CB, Chou TC, Scammell TE. The sleep switch: hypothalamic control of sleep and wakefulness. Trends Neurosci 2001; 24(12): 726–31.CrossRefGoogle Scholar
  17. 17.
    Muzur A, Pace-Schott EF, Hobson JA. The prefrontal cortex in sleep. Trends in Cognitive Sciences 2002; 6(11): 475–81.CrossRefGoogle Scholar
  18. 18.
    Baudic S, Barba GD, Thibaudet MC, Smagghe A, Remy P, Traykov L. Executive function deficits in early Alzheimer’s disease and their relations with episodic memory. Archives of clinical neuropsychology: the official journal of the National Academy of Neuropsychologists 2006; 21(1): 15–21.CrossRefGoogle Scholar
  19. 19.
    Matar G, Lina J, Carrier J, Kaddoum G. Unobtrusive Sleep Monitoring Using Cardiac, Breathing and Movements Activities: An Exhaustive Review. IEEE Access 2018; 6: 45129–52.CrossRefGoogle Scholar
  20. 20.
    Vaughan L, Redline S, Stone K, et al. Feasibility of self-administered sleep assessment in older women in the Women’s Health Initiative (WHI). Sleep and Breathing 2016; 20(3): 1079–91.CrossRefGoogle Scholar
  21. 21.
    Bianchi MT. Sleep devices: wearables and nearables, informational and interventional, consumer and clinical. Metabolism 2018; 84: 99–108.CrossRefGoogle Scholar
  22. 22.
    Marino M, Li Y, Rueschman MN, et al. Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep 2013; 36(11): 1747–55.CrossRefGoogle Scholar
  23. 23.
    Lee HA, Lee HJ, Moon JH, et al. Comparison of Wearable Activity Tracker with Actigraphy for Sleep Evaluation and Circadian Rest-Activity Rhythm Measurement in Healthy Young Adults. Psychiatry investigation 2017; 14(2): 179–85.CrossRefGoogle Scholar
  24. 24.
    Nunan D, Sandercock GR, Brodie DA. A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and clinical electrophysiology: PACE 2010; 33(11): 1407–17.CrossRefGoogle Scholar
  25. 25.
    Berntson GG, Bigger JT, Jr., Eckberg DL, et al. Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 1997; 34(6): 623–48.CrossRefGoogle Scholar
  26. 26.
    WHOOP Experience — Recovery, Strain and Sleep metrics optimize training.Google Scholar
  27. 27.
    Hackett K, Krikorian R, Giovannetti T, et al. Utility of the NIH Toolbox for assessment of prodromal Alzheimer’s disease and dementia. Alzheimer’s & dementia (Amsterdam, Netherlands) 2018; 10: 764–72.Google Scholar
  28. 28.
    Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MB. The Rapid Assessment of Physical Activity (RAPA) among older adults. Preventing chronic disease 2006; 3(4): A118–A.PubMedPubMedCentralGoogle Scholar
  29. 29.
    Gershon RC, Wagster MV, Hendrie HC, Fox NA, Cook KF, Nowinski CJ. NIH toolbox for assessment of neurological and behavioral function. Neurology 2013; 80(11 Suppl 3): S2–6.CrossRefGoogle Scholar
  30. 30.
    Isaacson RS, Ganzer CA, Hristov H, et al. The clinical practice of risk reduction for Alzheimer’s disease: A precision medicine approach. Alzheimer’s & dementia: the journal of the Alzheimer’s Association 2018; 14(12): 1663–73.CrossRefGoogle Scholar
  31. 31.
    Gareth J, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning: with applications in R. 7th ed. Springer 2017.Google Scholar
  32. 32.
    Almeida-Santos MA, Barreto-Filho JA, Oliveira JL, Reis FP, da Cunha Oliveira CC, Sousa AC. Aging, heart rate variability and patterns of autonomic regulation of the heart. Archives of gerontology and geriatrics 2016; 63: 1–8.CrossRefGoogle Scholar
  33. 33.
    Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 2004; 27(7): 1255–73.CrossRefGoogle Scholar
  34. 34.
    Hua J, Xiong Z, Lowey J, Suh E, Dougherty ER. Optimal number of features as a function of sample size for various classification rules. Bioinformatics (Oxford, England) 2005; 21(8): 1509–15.CrossRefGoogle Scholar
  35. 35.
    Donoho DL. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality. Am Math Soc Lect Challenges 21st Century 2000;:1–33.Google Scholar
  36. 36.
    Karsten J, Hagenauw LA, Kamphuis J, Lancel M. Low doses of mirtazapine or quetiapine for transient insomnia: A randomised, double-blind, cross-over, placebo-controlled trial. Journal of psychopharmacology (Oxford, England) 2017; 31(3): 327–37.CrossRefGoogle Scholar
  37. 37.
    Ware JC, Pittard JT. Increased deep sleep after trazodone use: a double-blind placebo-controlled study in healthy young adults. The Journal of clinical psychiatry 1990; 51 Suppl: 18–22.PubMedGoogle Scholar
  38. 38.
    Ezekiel F, Bosma R, Morton JB. Dimensional change card sort performance associated with age-related differences in functional connectivity of lateral prefrontal cortex. Developmental cognitive neuroscience 2013; 5: 40–50.CrossRefGoogle Scholar
  39. 39.
    Mander BA, Rao V, Lu B, et al. Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging. Nature neuroscience 2013; 16(3): 357–64.CrossRefGoogle Scholar
  40. 40.
    Kohonen T. Essentials of the self-organizing map. Neural networks: the official journal of the International Neural Network Society 2013; 37: 52–65.CrossRefGoogle Scholar
  41. 41.
    Baron KG, Duffecy J, Berendsen MA, Cheung Mason I, Lattie EG, Manalo NC. Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Medicine Reviews 2018; 40: 151–9.CrossRefGoogle Scholar
  42. 42.
    Isaacson RS, Hristov H, Saif N et al. Individualized clinical management of patients at risk for Alzheimer’s dementia. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. doi:  https://doi.org/10.1016/j.jalz.2019.08.198

Copyright information

© Serdi and Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. Saif
    • 1
  • P. Yan
    • 2
  • K. Niotis
    • 1
  • O. Scheyer
    • 3
  • A. Rahman
    • 1
  • M. Berkowitz
    • 1
  • R. Krikorian
    • 4
  • H. Hristov
    • 1
  • G. Sadek
    • 1
  • S. Bellara
    • 1
  • Richard S. Isaacson
    • 1
    Email author
  1. 1.Department of NeurologyWeill Cornell Medicine and NewYork-PresbyterianNew YorkUSA
  2. 2.Department of NeurologyBeth Israel Deaconess Medical CenterBostonUSA
  3. 3.School of LawUniversity of California Los AngelesLos AngelesUSA
  4. 4.Department of Psychiatry & Behavioral NeuroscienceUniversity of Cincinnati College of MedicineCincinnatiUSA

Personalised recommendations