Using actigraphy to assess sleep characteristics
G. A. Trusov,
A. V. Korobeinikova,
L. V. Getmantseva,
S. Yu. Bakoev,
A. N. Lomov,
A. A. Keskinov,
V. S. Yudin https://doi.org/10.29413/ABS.2024-9.6.10
Abstract
Research points to the importance of circadian rhythms for health; their disruptions are associated with various diseases. This has led to the development of circadian medicine, which focuses on using knowledge of physiological rhythms to optimize treatment and diagnostic methods. Our article highlights the role of actigraphy, a non-invasive method for assessing sleep-wake cycles, in the study and diagnosis of sleep. Actigraphs, wearable devices shaped like watches, use motion sensors to monitor activity, providing important data on sleep quality. Particular attention is given to the methodology for obtaining actigraphy data and the analysis of sleep parameters, which includes the assessment of total sleep time, sleep efficiency, and frequency of awakenings. The importance of accurate methodology and validated algorithms for actigraphy data analysis is emphasized through a review of various sleep assessment algorithms and their application in clinical and research settings. Additionally, the paper explores the potential use of artificial intelligence, including machine and deep learning, to improve sleep data analysis. The conclusion emphasizes that despite the reliability of actigraphy for determining sleep phases, additional studies are needed to validate it in clinical use. This highlights the potential of actigraphy as an important tool in circadian medicine and sleep studies, which requires its further development and integration with new technological advances.
About the Authors
G. A. Trusov
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Georgii A. Trusov – 1st category analyst,
Pogodinskaya str. 10 build. 1, Moscow 119121
A. V. Korobeinikova
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Anna V. Korobeinikova – Analyst,
Pogodinskaya str. 10 build. 1, Moscow 119121
L. V. Getmantseva
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Lyubov V. Getmantseva – Dr. Sc. (Biol.), Leading Analyst,
Pogodinskaya str. 10 build. 1, Moscow 119121
S. Yu. Bakoev
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Sirozhdin Y. Bakoev – Cand. Sc. (Biol.), 1st category analyst,
Pogodinskaya str. 10 build. 1, Moscow 119121
A. N. Lomov
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Alexey N. Lomov – Head of the Department,
Pogodinskaya str. 10 build. 1, Moscow 119121
A. A. Keskinov
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Anton A. Keskinov – Cand. Sc. (Med.), Cand. Sc. (Econ.), Deputy General Director,
Pogodinskaya str. 10 build. 1, Moscow 119121
V. S. Yudin
Centre for Strategic Planning and Management of Biomedical Health Risks
Russian Federation
Russian Federation
Vladimir S. Yudin – Cand. Sc. (Biol.), Director,
Pogodinskaya str. 10 build. 1, Moscow 119121
References
1. Patterson MR, Nunes AAS, Gerstel D, Pilkar R, Guthrie T, Neishabouri A, et al. 40 years of actigraphy in sleep medicine and current state of the art algorithms. NPJ Digit Med. 2023; 6(1): 51. doi: 10.1038/s41746-023-00802-1
2. Patke A, Young MW, Axelrod S. Molecular mechanisms and physiological importance of circadian rhythms. Nat Rev Mol Cell Biol. 2020; 21(2): 67-84. doi: 10.1038/s41580-019-0179-2
3. Gamble KL, Silver R. Circadian rhythmicity and the community of clockworkers. Eur J Neurosci. 2020; 51(12): 2314-2328. doi: 10.1111/ejn.14626
4. Logan RW, McClung CA. Rhythms of life: Circadian disruption and brain disorders across the lifespan. Nat Rev Neurosci. 2019; 20, 49-65. doi: 10.1038/s41583-018-0088-y
5. Yu X, Franks NP, Wisden W. Brain clocks, sleep, and mood. Adv Exp Med Biol. 2021; 1344: 71-86. doi: 10.1007/978-3-030- 81147-1_5
6. Lambert I, Peter-Derex L. Spotlight on sleep stage classification based on EEG. Nat Sci Sleep. 2023; 15: 479-490. doi: 10.2147/NSS.S401270
7. Van Laake LW, Lüscher TF, Young ME. The circadian clock in cardiovascular regulation and disease: Lessons from the Nobel Prize in physiology or medicine 2017. Eur Heart J. 2018; 39(24): 2326-2329. doi: 10.1093/eurheartj/ehx775
8. Elder GJ, Lazar AS, Alfonso-Miller P, Taylor JP. Sleep disturbances in Lewy body dementia: A systematic review. Int J Geriatr Psychiatry. 2022; 37(10): 10.1002/gps.5814. doi: 10.1002/gps.5814
9. Medic G, Wille M, Hemels ME. Short- and long-term health consequences of sleep disruption. Nat Sci Sleep. 2017; 9: 151-161. doi: 10.2147/NSS.S134864
10. Ibáñez V, Silva J, Cauli O. A survey on sleep assessment methods. Peer J. 2018; 6: e4849. doi: 10.7717/peerj.4849
11. Patel P, Kim JY, Brooks LJ. Accuracy of a smartphone application in estimating sleep in children. Sleep Breath. 2017; 21(2): 505-511. doi: 10.1007/s11325-016-1425-x
12. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015; 12: 159. doi: 10.1186/s12966-015-0314-1
13. Rundo JV, Downey R 3rd. Polysomnography. Handb Clin Neurol. 2019; 160: 381-392. doi: 10.1016/B978-0-444-64032-1.00025-4
14. Bani Younis M, Hayajneh F, Batiha AM. Measurement and nonpharmacologic management of sleep disturbance in the intensive care units: A literature review. Crit Care NursQ. 2019; 42(1): 75-80. doi: 10.1097/CNQ.0000000000000240
15. Dorsch JJ, Martin JL, Malhotra A, Owens RL, Kamdar BB. Sleep in the intensive care unit: Strategies for improvement. Semin Respir Crit Care Med. 2019; 40(5): 614-628. doi: 10.1055/s-0039-1698378
16. Ko PR, Kientz JA, Choe EK, Kay M, Landis CA, Watson NF. Consumer sleep technologies: A review of the landscape. J Clin Sleep Med. 2015; 11(12): 1455-1461. doi: 10.5664/jcsm.5288
17. 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 Med Rev. 2018; 40: 151-159. doi: 10.1016/j.smrv.2017.12.002
18. 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): 1209-1230. doi: 10.5664/jcsm.7228
19. Biegański P, Stróż A, Dovgialo M, Duszyk-Bogorodzka A, Durka P. On the Unification of common actigraphic data scoring algorithms. Sensors (Basel). 2021; 21(18): 6313. doi: 10.3390/ s21186313
20. Goldstone A, Baker FC, de Zambotti M. Actigraphy in the digital health revolution: Still asleep? J Sleep Res. 2018, 9: zsy120. doi: 10.1093/sleep/zsy120
21. Comer D. Advances in clinical actigraphy. J Lung Pulm Respir Res. 2015; 2(4): 77-79. doi: 10.15406/jlprr.2015.02.00051
22. Rösler L, van der Lande G, Leerssen J, Cox R, Ramautar JR, van Someren EJW. Actigraphy in studies on insomnia: Worth the effort? J Sleep Res. 2023; 32(1): 13750. doi: 10.1111/jsr.13750
23. Acker JG, Becker-Carus C, Büttner-Teleaga A, Cassel W, Danker-Hopfe H, Dück A, et al. The role of actigraphy in sleep medicine. Somnologie. 2021; 25: 89-98. doi: 10.1007/s11818-021-00306-8
24. Conley S, Knies A, Batten J, Ash G, Miner B, Hwang Y, et al. Agreement between actigraphic and polysomnographic measures of sleep in adults with and without chronic conditions: A systematic review and meta-analysis. Sleep Med Rev. 2019; 46: 151-160. doi: 10.1016/j.smrv.2019.05.001
25. Natale V, Léger D, Martoni M, Bayon V, Erbacci A. The role of actigraphy in the assessment of primary insomnia: A retrospective study. Sleep Med. 2014; 15(1): 111-115. doi: 10.1016/j.sleep.2013.08.792
26. Fawkes DB, Malow BA, Weiss SK, Reynolds AM, Loh A, Adkins KW, et al. Conducting actigraphy research in children with neurodevelopmental disorders – A practical approach. Behav Sleep Med. 2015; 13(3): 181-196. doi: 10.1080/15402002.2013.854245
27. Fekedulegn D, Andrew ME, Shi M, Violanti JM, Knox S, Innes KE. Actigraphy-based assessment of sleep parameters. Ann Work Expo Health. 2020; 64(4): 350-367. doi: 10.1093/annweh/wxaa007
28. Khademi A, Yasser EM, Master L, Buxton OM, Honavar VG. Personalized sleep parameters estimation from actigraphy: A machine learning approach. Nat Sci Sleep. 2019; 11: 387. doi: 10.2147/NSS.S220716
29. Jokar F, Azzopardi G, Palotti J. Towards accurate and efficient sleep period detection using wearable devices. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science. 2023. doi: 10.1007/978-3-031-44240-7_5
30. Roberts DM, Schade MM, Master L, Honavar VG, Nahmod NG, Chang AM, et al. Performance of an open machine learning model to classify sleep/wake from actigraphy across ~24- hour intervals without knowledge of rest timing. Sleep Health. 2023; 9(5): 596-610. doi: 10.1016/j.sleh.2023.07.001
31. Webster JB, Kripke DF, Messin S, Mullaney DJ, Wyborney G. An activity-based sleep monitor system for ambulatory use. Sleep. 1982; 5: 389-399. doi: 10.1093/sleep/5.4.389
32. Sadeh A, Sharkey M, Carskadon MA. Activity-based sleepwake identification: An empirical test of methodological issues. Sleep. 1994; 17: 201-207. doi: 10.1093/sleep/17.3.201
33. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992; 15(5): 461-469. doi: 10.1093/sleep/15.5.461
34. Sazonov E, Sazonova N, Schuckers S, Neuman M. Chime Study Group activity-based sleep-wake identification in infants. Physiol Meas. 2004; 25: 1291-1304. doi: 10.1088/0967-3334/25/5/018
35. Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data. Chronobiol Int. 2019; 36(12): 1752-1760. doi: 10.1080/07420528.2019.1679826
36. Jean-Louis G, Kripke DF, Mason WJ, Elliott JA, Youngstedt SD. Sleep estimation from wrist movement quantified by different actigraphic modalities. JNeurosci Methods. 2001; 105: 185-191. doi: 10.1016/S0165-0270(00)00364-2
37. Kripke DF, Hahn EK, Grizas AP, Wadiak KH, Loving RT, Poceta JS, et al. Wrist actigraphic scoring for sleep laboratory patients: Algorithm development. J Sleep Res. 2010; 19: 612-619. doi: 10.1111/j.1365-2869.2010.00835.x
38. Djanian S, Bruun A, Nielsen TD. Sleep classification using consumer sleep technologies and AI: A review of the current landscape. Sleep Med. 2022; 100: 390-403. doi: 10.1016/j.sleep.2022.09.004
39. van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, et al. A novel, open access method to assess sleep duration using a wrist-worn accelerometer. PLoS One. 2015; 10(11): e0142533. doi: 10.1371/journal.pone.0142533
40. Sundararajan K, Georgievska S, Te Lindert BHW, Gehrman PR, Ramautar J, Mazzotti DR, et al. Sleep classification from wrist-worn accelerometer data using random forests. Sci Rep. 2021; 11(1): 24. doi: 10.1038/s41598-020-79217-x
41. Palotti J, Mall R, Aupetit M, Rueschman M, Singh M, Sathyanarayana A, et al. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. NPJ Digit Med. 2019; 2: 50. doi: 10.1038/s41746-019-0126-9
42. Oakley NR. Technical report to Mini Mitter Co., Inc.; Validation with polysomnography of the sleep-watch sleep/wake scoring algorithm used by the actiwatch activity monitoring system. 1997.
43. Patel AK, Reddy V, Shumway KR, Araujo JF. Physiology, sleep stages. Treasure Island (FL): StatPearls Publishing; 2024. URL: https://www.ncbi.nlm.nih.gov/books/NBK526132 [date of access: 01.04.2024].
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For citations:
Trusov G.A., Korobeinikova A.V., Getmantseva L.V., Bakoev S.Yu., Lomov A.N., Keskinov A.A., Yudin V.S. Using actigraphy to assess sleep characteristics. Acta Biomedica Scientifica. 2024;9(6):100-110. (In Russ.) https://doi.org/10.29413/ABS.2024-9.6.10
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