Email this article Comparison of somnological parameters obtained using sports watches POLAR VANTAGE V and polysomnography method
- Authors: Vjotosh A.N.1,2, Petrov A.B.2, Djubenkov S.A.2, Tikhomirova O.V.3
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Affiliations:
- Sechenov Institute of Evolutionary Physiology and Biochemistry of the RAS
- Lesgaft National State University of Physical Education, Sport and Health
- Nikiforov Russian Center of Emergency and Radiation Medicine
- Issue: Vol 51, No 2 (2025)
- Pages: 131-136
- Section: Articles
- URL: https://journal-vniispk.ru/0131-1646/article/view/304848
- DOI: https://doi.org/10.31857/S0131164625020098
- EDN: https://elibrary.ru/UDJHFV
- ID: 304848
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Abstract
Synchronous registration of sleep parameters in practically healthy young womens was carried out using polysomnography and by recording the rhythmocardiographic activity of the sleeping body using a Polar Vantage V sports watch. The data obtained were compared in pairs, using the Spearman rank correlation method and by epoch-by-epoch comparison of sleep phase coincidence indicators. A high degree of correspondence was revealed with the total duration of sleep, time spent in bed, sleep efficiency values and the total duration of short-term awakenings recorded by the two above-mentioned methods. The mode of epoch-by-epoch comparison of the periods of coincidence of the Rem phases, as well as the N2 and N3 phases during polysomnographic and rhythmocardiographic registration, brought results not exceeding 60%.
About the authors
A. N. Vjotosh
Sechenov Institute of Evolutionary Physiology and Biochemistry of the RAS; Lesgaft National State University of Physical Education, Sport and Health
Email: vjotnn@yahoo.com
St. Peterburg, Russia; St. Peterburg, Russia
A. B. Petrov
Lesgaft National State University of Physical Education, Sport and Health
Email: vjotnn@yahoo.com
St. Peterburg, Russia
S. A. Djubenkov
Lesgaft National State University of Physical Education, Sport and Health
Email: vjotnn@yahoo.com
St. Peterburg, Russia
O. V. Tikhomirova
Nikiforov Russian Center of Emergency and Radiation Medicine
Author for correspondence.
Email: vjotnn@yahoo.com
St. Peterburg, Russia
References
- Vein A.M. [Waking ang sleep]. M.: Nauka, 1970. 125 p.
- Stone J.D., Rentz L.E., Forsley J. et al. Evaluations of commercial sleep tehnologies for objective monitoring during routine sleeping conditions // Nat. Sci. Sleep. 2020. V. 12. P. 821.
- Karmanova I.G., Oganesyan G.A. [Physiology and Pathology of the Sleep-Wake Cycle. Evolutionary Aspects]. М.: Nauka, 1994. 200 p.
- Kovalzon V.M. [Basics of Somnology]. M.: “Laboratoriya znanii”, 2012. 239 p.
- Vjotosh A.N., Petrov A.B., Kotova A.S., Tikhomiro-va O.V. Possibilities and Limitations on Using Sleep Cycle Smartphone Apps for Sleep Analysis in Sports Practice // Human Physiology. 2023. V. 49. № 6. P. 663.
- Ameen M.S., Cheung L.M., Hauser T. et al. About the accuracy and problems of consumer devices in the assessment of sleep // Sensors. 2019. V. 19. № 19. P. 4160.
- Chinoy E.D., Cuellar J.A., Jameson J.T., Mark- wald R.R. Performance of four commercial wearable sleep-tracking devices tested under unrestricted conditions at home in healthy young adults // Nat. Sci. Sleep. 2022. V. 14. P. 493.
- Miller D.J., Sargent C., Roach G.D. A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults // Sensors. 2022. V. 22. № 16. P. 6317.
- Kushida C.F., Littner M.R., Morgenthaler T. et al. Practice parameters for the indications for polysomnography and related procedures: An update for 2005 // Sleep. 2005. V. 28. № 4. P. 499.
- Lee J.-M., Byun W., Keill A. et al. Comparison of wearable trackers’ ability to estimate sleep // Int. J. Envir. Res. Public Health. 2018. V. 15. № 6. P. 1265.
- Chinoy E.D., Cuellar J.A., Jameson J.T., Mark- wald R.R. Daytime sleep-tracking performance of four commercial wearable devices during unrestricted home sleep // Nat. Sci. Sleep. 2023. V. 15. P. 151.
- Radha M., Fonseca P., Moreau A. et al. Sleep stage classification from heart-rate variability using long short-term memory neural networks // Sci. Rep. 2019. V. 9. № 1. P. 14149.
- Li D., Ruan Y., Zheng F. et al. Fast sleep stage classification using cascaded support vector machines with single-channel EEG signals // Sensors. 2022. V. 22. № 24. P. 9914.
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