Skip to main content

Advertisement

SpringerLink
Log in

Connection between sleeping patterns and cognitive deterioration in women with Alzheimer’s disease

  • Neurology • Original Article
  • Published:
Sleep and Breathing Aims and scope Submit manuscript

Abstract

Background

Alzheimer’s disease (AD) causes symptoms such as dementia, memory loss, disorientation, and even aggressiveness, and is more common in women than in men. AD may also manifest itself in changes in sleep patterns. However, the relationship between AD (in all stages) and bedtime behavior has not been thoroughly investigated.

Methods

In a prospective, cross-sectional survey, we evaluated 74 women categorized in two different stages of cognitive decline associated with AD (mild and severe) along with 37 women with no cognitive decline who served as controls. We obtained demographic and medical information such as age, health status, and medication, as well as psychiatrically confirmed staging of AD. We also collected actigraphy data for several nights in a row with a medical grade wristband using a 3-axis accelerometer and solid-state on-board memory. These data served as parameters for a clustering machine learning (ML) algorithm.

Results

The ML process was able to unsupervisedly identify 85% of the participants according to their pre-assigned degree of dementia. When the clustering was carried out in a binary fashion (i.e., only taking into account healthy members vs. severely affected AD patients), it was possible to correctly classify 91% of the cases.

Conclusions

This study revealed a strong connection between the severity of the intellectual decline and the features distilled from actigraphically derived sleep parameters.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

We are in conversations with the National Sleep Research Resource (NSRR) to have our full dataset hosted in their repository. For now, data can be made available under demand by contacting the corresponding author of this paper.

References

  1. Brown BM, Rainey-Smith SR, Villemagne VL, Weinborn M, Bucks RS, Sohrabi HR, Laws SM, Taddei K, Macaulay SL, Ames D et al (2016) The relationship between sleep quality and brain amyloid burden. Sleep 39(5):1063–1068

    Article  Google Scholar 

  2. Buratti L, Viticchi G, Falsetti L, Cagnetti C, Luzzi S, Bartolini M, Provinciali L, Silvestrini M (2014) Vascular impairment in Alzheimer’s disease: the role of obstructive sleep apnea. J Alzheimers Dis 38(2):445–453

    Article  Google Scholar 

  3. Kent BA, Mistlberger RE (2017) Sleep and hippocampal neurogenesis: implications for Alzheimer’s disease. Front Neuroendocrinol 45:35–52

    Article  Google Scholar 

  4. Holth JK, Patel TK, Holtzman DM (2017) Sleep in Alzheimer’s disease–beyond amyloid. Neurobiol Sleep Circadian Rhythms 2:4–14

    Article  Google Scholar 

  5. Winer JR, Mander BA (2018) Waking up to the importance of sleep in the pathogenesis of Alzheimer disease. JAMA Neurol 75(6):654–656

    Article  Google Scholar 

  6. Irwin MR, Vitiello MV (2019) Implications of sleep disturbance and inflammation for Alzheimer’s disease dementia. Lancet Neurol 18(3):296–306

    Article  CAS  Google Scholar 

  7. Sindi S, Kåreholt I., Johansson L, Skoog J, Sjöberg L, Wang H-X, Johansson B, Fratiglioni L, Soininen H, Solomon A et al (2018) Sleep disturbances and dementia risk: a multicenter study. Alzheimers Dement 14(10):1235–1242

    Article  Google Scholar 

  8. FitzGerald JM, O’Regan N, Adamis D, Timmons S, Dunne CP, Trzepacz PT, Meagher DJ (2017) Sleep-wake cycle disturbances in elderly acute general medical inpatients: longitudinal relationship to delirium and dementia. Alzheimers Dement 7:61–68

    Google Scholar 

  9. Jung Y, Boot BP, Mielke MM, Ferman TJ, Geda YE, McDade E, Christianson TJ, Knopman DS, St Louis EK, Silber MH et al (2017) Phenoconversion from probable rapid eye movement sleep behavior disorder to mild cognitive impairment to dementia in a population-based sample. Alzheimers Dement 8(1):127–130

    Google Scholar 

  10. Benoit M, Berrut G, Doussaint J, Bakchine S, Bonin-Guillaume S, Frémont P, Gallarda T, Krolak-Salmon P, Marquet T, Mékiès C et al (2012) Apathy and depression in mild Alzheimer’s disease: a cross-sectional study using diagnostic criteria. J Alzheimers Dis 31(2):325–334

    Article  Google Scholar 

  11. André C, Tomadesso C, de Flores R, Branger P, Rehel S, Mézenge F, Landeau B, de La Sayette V, Eustache F, Chételat G et al (2019) Brain and cognitive correlates of sleep fragmentation in elderly subjects with and without cognitive deficits. Alzheimers Dement 11:142–150

    Google Scholar 

  12. Rocca WA, Hofman A, Brayne C, Breteler MM, Clarke M, Copeland JR, Dartigues J-F, Engedal K, Hagnell O, Heeren TJ et al (1991) Frequency and distribution of Alzheimer’s disease in Europe: a collaborative study of 1980–1990 prevalence findings. Ann Neurol 30(3):381–390

    Article  CAS  Google Scholar 

  13. Sierra-Sosa D, Arcila-Moreno J, Garcia-Zapirain B, Castillo-Olea C, Elmaghraby A Dementia prediction applying variational quantum classifier. arXiv:2007.08653

  14. Reisberg B, Ferris S, de Leon MJ, Crook T The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry

  15. Reisberg B, Jamil IA, Khan S, Monteiro I, Torossian C, Ferris S, Sabbagh M, Gauthier S, Auer S, Shulman MB et al (2011) Staging dementia. Principles and practice of geriatric psychiatry 3:162–169

    Google Scholar 

  16. Hebert LE, Scherr PA, Beckett LA, Albert MS, Pilgrim DM, Chown MJ, Funkenstein HH, Evans DA (1995) Age-specific incidence of Alzheimer’s disease in a community population. JAMA 273(17):1354–1359

    Article  CAS  Google Scholar 

  17. Ferre A, Ribó M, Rodríguez-Luna D, Romero O, Sampol G, Molina C, Álvarez-Sabin J (2013) Strokes and their relationship with sleep and sleep disorders. Neurologí,a (English Edition) 28(2):103–118

    Article  CAS  Google Scholar 

  18. Wei L, Wen Y-T, Thompson HJ, Liu C-Y, Su Y-K, Chen P-Y, Chen C-Y, Chuang Y-H, Lin Y-J, Chen C-T, Chen C-C, Chiu H-T, Chiu H-Y Sleep disturbances following traumatic brain injury in older adults: a comparison study. J Head Trauma Rehabil 35(4)

  19. Monti JM, Monti D (2005) Sleep disturbance in schizophrenia. Int Rev Psychiatry 17(4):247–253

    Article  Google Scholar 

  20. Vandeputte M, de Weerd A (2003) Sleep disorders and depressive feelings: a global survey with the Beck depression scale. Sleep Med 4(4):343–345

    Article  Google Scholar 

  21. Lin W-Y, Verma VK, Lee M-Y, Lai C-S (2018) Activity monitoring with a wrist-worn, accelerometer-based device. Micromachines 9(9):450

    Article  Google Scholar 

  22. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, White T, Van Hees VT, Trenell MI, Owen CG et al (2017) Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank Study. PloS One 12(2):e0169649

    Article  Google Scholar 

  23. Stewart T, Narayanan A, Hedayatrad L, Neville J, Mackay L, Duncan S (2018) A dual-accelerometer system for classifying physical activity in children and adults. Med Sci Sports Exerc 50(12):2595–2602

    Article  Google Scholar 

  24. Lima GZdS, Lopes SR, Prado TL, Lobao-Soares B, do Nascimento GC, Fontenele-Araujo J, Corso G (2017) Predictability of arousal in mouse slow wave sleep by accelerometer data. PloS One 12(5):e0176761

    Article  Google Scholar 

  25. Walch O, Huang Y, Forger D, Goldstein C (2019) Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep 42(12):zsz180

    Article  Google Scholar 

  26. Borazio M, Berlin E, Kücükyildiz N, Scholl P, Van Laerhoven K (2014) Towards benchmarked sleep detection with wrist-worn sensing units. In: International conference on healthcare informatics. IEEE, pp 125–134

  27. Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Proc Mag 13(6):47–60

    Article  Google Scholar 

  28. Sharma N, Bajpai A, Litoriya MR (2012) Comparison the various clustering algorithms of Weka tools. Int J Emerg Technol Adv Eng 4(7):78–80

    Google Scholar 

  29. Verma NK, Dwivedi S, Sevakula RK (2015) Expectation maximization algorithm made fast for large scale data. In: IEEE (ed) Workshop on computational intelligence: theories, applications and future directions (WCI), pp 1–7

  30. Fahim M, Fatima I, Lee S, Park Y-T (2013) EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer. Appl Intell 39(3):475–488

    Article  Google Scholar 

  31. El-Manzalawy Y, Buxton O, Honavar V (2017) Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. In: Conference on bioinformatics and biomedicine. IEEE, pp 718–723

  32. Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep 8 (1):1–10

    Article  CAS  Google Scholar 

  33. Ronning M (2002) A historical overview of the ATC/DDD methodology. WHO Drug Inf 16 (3):233

    Google Scholar 

  34. Crivello A, Barsocchi P, Girolami M, Palumbo F (2019) The meaning of sleep quality: a survey of available technologies, vol 7, pp 167374–167390

  35. Sharma J, Kaur S (2017) Gerontechnology—the study of alzheimer disease using cloud computing. In: 2017 International conference on energy, communication, data analytics and soft Computing (ICECDS). IEEE, 3726–3733

  36. Adrian M, Cooper J (1995) Biomechanics of human movement. Brown & Benchmark, Madison

    Google Scholar 

  37. Khalifa S, Hassan M, Seneviratne A (2013) Human activity recognition for indoor positioning using smartphone accelerometer, Tech. rep., School of Computer Science and Engineering University of South Wales

  38. Susi M, Renaudin V, Lachapelle G (2013) Motion mode recognition and step detection algorithms for mobile phone users. Sensors 13(2):1539–1562

    Article  Google Scholar 

  39. Wu C, Zhang F, Wang B, Liu KR, Au OC-L (2020) Method, apparatus, and system for wireless object tracking. US Patent App 16(/798):343

    Google Scholar 

  40. Mashita T, Shimatani K, Iwata M, Miyamoto H, Komaki D, Hara T, Kiyokawa K, Takemura H, Nishio S (2012) Human activity recognition for a content search system considering situations of smartphone users. In: 2012 IEEE virtual reality workshops (VRW). IEEE, pp 1–2

  41. Batal I, Hauskrecht M (2009) A supervised time series feature extraction technique using DCT and DWT. In: International conference on machine learning and applications. IEEE, pp 735–739

  42. Camargos EF, Louzada FM, Nóbrega OT (2013) Wrist actigraphy for measuring sleep in intervention studies with Alzheimer’s disease patients: application, usefulness, and challenges. Sleep Med Rev 17 (6):475–488

    Article  Google Scholar 

  43. Ramzan M (2016) Comparing and evaluating the performance of Weka classifiers on critical diseases. In: India international conference on information processing (IICIP). IEEE, pp 1–4

  44. Shree SB, Sheshadri H (2018) Diagnosis of Alzheimer’s disease using naive Bayesian classifier. Neural Comput and Applic 29(1):123–132

    Article  Google Scholar 

  45. Meila M, Heckerman D. An experimental comparison of several clustering and initialization methods. arXiv:1301.7401

  46. Ju Y-ES, McLeland JS, Toedebusch CD, Xiong C, Fagan AM, Duntley SP, Morris JC, Holtzman DM (2013) Sleep quality and preclinical alzheimer disease. JAMA Neurol 70(5):587–593

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to sincerely thank the following organizations and people for their commitment to this research work and for their kind, firm and faithful support from day one: C. Gombao and AFA Elda-Petrer (including its profoundly hard-working board of directors and dedicated staff); V. Gossage and D. Jackson from Axivity Ltd.; F. Portillo, L. Alted, Q. Maestre and Petrer’s council corporation; Santa Cruz church community (Petrer); M. Erades and CaixaPetrer; J. Tatay and J. Cortell from Kanteron Systems; B. Martínez, R. Miralles, A. Mairena, P. Marín, M. Manrique, C. Belando, S. Tarí and R. González from DomusVi Nursing Home; P. Camacho and J. Rico from La Molineta Retirement Home (part of the Lares association); E. Guerrero, V. Baún, M. Montoliu, L. Escalza and O. Peña from La Saleta Welfare Services for Elderly People; G. Ramiro; V. Medrano M.D; I. Montero; R. Martínez and A. Barceló. Finally, the authors would love to express their sincerest and most respectful gratitude towards all the participants in the present study, their corresponding families and caretakers.

Funding

This research is partially funded by Universidad Internacional de la Rioja (UNIR) through the Research Institute for Innovation & Technology in Education (UNIR iTED).

Author information

Authors and Affiliations

  1. Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, Spain

    Alberto Corbi & Daniel Burgos

Authors
  1. Alberto Corbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Burgos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Corbi.

Ethics declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the all the involved institutions (both national and local), including retirement homes and cognitive stimulation centres, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Corbi, A., Burgos, D. Connection between sleeping patterns and cognitive deterioration in women with Alzheimer’s disease. Sleep Breath 26, 361–371 (2022). https://doi.org/10.1007/s11325-021-02327-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11325-021-02327-x

Keywords

Search

Navigation