Validation of a Neurophysiological-Based Wearable Device (Somfit) for the Assessment of Sleep in Athletes.
| Title: | Validation of a Neurophysiological-Based Wearable Device (Somfit) for the Assessment of Sleep in Athletes. |
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| Authors: | Roach GD; Appleton Institute for Behavioural Science, Central Queensland University, Adelaide 4701, Australia.; Miller DJ; Appleton Institute for Behavioural Science, Central Queensland University, Adelaide 4701, Australia.; Shell SJ; Australian Institute of Sport, Australian Sports Commission, Canberra 2617, Australia.; Miles KH; School of Medicine and Psychology, The Australian National University, Canberra 2601, Australia.; Sargent C; Appleton Institute for Behavioural Science, Central Queensland University, Adelaide 4701, Australia. |
| Source: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2025 Mar 27; Vol. 25 (7). Date of Electronic Publication: 2025 Mar 27. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI, c2000- |
| MeSH Terms: | Sleep*/physiology ; Wearable Electronic Devices* ; Athletes*; Polysomnography/methods ; Polysomnography/instrumentation ; Sleep Stages/physiology ; Humans ; Male ; Female ; Adult ; Young Adult ; Electroencephalography |
| Abstract: | The aim of the study was to examine the validity of a neurophysiological-based wearable device, i.e., Somfit (Compumedics Ltd.), for the assessment of sleep in athletes. Twenty-seven athletes (14 F, 13 M, aged 22.3 ± 5.1 years) spent a single night in a sleep laboratory. The participants had 9 h in bed (23:00-08:00) while fitted simultaneously with Somfit and polysomnography (PSG), i.e., the gold standard for the assessment of sleep. Somfit and PSG were used to independently categorise each 30-s epoch of time in bed into one of five states, i.e., wake, stage 1 non-REM sleep (N1), stage 2 non-REM sleep (N2), stage 3 non-REM sleep (N3), or REM sleep. There were large differences between participants in terms of the amount of Somfit data that were successfully captured/scored, so three subsets were considered in the subsequent analyses: unfiltered subset (n = 26)-all participants, except one for whom no Somfit data were captured/scored; good-capture subset (n = 15)-participants for whom > 80% of Somfit data were captured/scored; excellent-capture subset (n = 7)-participants for whom > 99.9% of Somfit data were captured/scored. Agreement for the five-state categorisation of time in bed was calculated as the percentage of PSG epochs correctly scored by Somfit as N1, N2, N3, REM, or wake. Agreement (and Cohen's kappa) was 63% (0.47) for the unfiltered subset, 66% (0.52) for the good-capture subset, and 79% (0.70) for the excellent-capture subset. These data indicate a moderate-substantial level of agreement between Somfit and PSG for the assessment of sleep in athletes. Wearable devices that can capture valid sleep data may also be used to derive important measures related to the circadian system, such as sleep consistency and social jet lag. |
| References: | Nat Sci Sleep. 2022 Apr 26;14:819-827. (PMID: 35502231); J Sleep Res. 2019 Aug;28(4):e12789. (PMID: 30407680); J Sleep Res. 2018 Feb;27(1):113-119. (PMID: 28726307); J Sci Med Sport. 2017 Aug;20(8):756-760. (PMID: 28189461); Nat Sci Sleep. 2024 Jul 22;16:1027-1043. (PMID: 39071546); Chronobiol Int. 2018 Jun;35(6):754-758. (PMID: 29750580); Biometrics. 1977 Mar;33(1):159-74. (PMID: 843571); Curr Biol. 2012 May 22;22(10):939-43. (PMID: 22578422); Sensors (Basel). 2022 Aug 22;22(16):. (PMID: 36016077); Lancet. 1986 Feb 8;1(8476):307-10. (PMID: 2868172); J Sci Med Sport. 2016 Oct;19(10):848-53. (PMID: 26794719); Med Sci Sports Exerc. 2019 Jul;51(7):1538-1557. (PMID: 30789439); Chronobiol Int. 2014 Jun;31(5):603-12. (PMID: 24491157); J Sports Sci. 2012;30(6):541-5. (PMID: 22329779); Sensors (Basel). 2022 Dec 06;22(23):. (PMID: 36502241); Sleep. 2024 Jan 11;47(1):. (PMID: 37738616); J Sleep Res. 2020 Feb;29(1):e12931. (PMID: 31626361); Chronobiol Int. 2011 Nov;28(9):771-8. (PMID: 21895489); Sleep. 2021 Feb 12;44(2):. (PMID: 32882005); J Clin Psychiatry. 2017 Jun;78(6):e678-e687. (PMID: 28682534); J Clin Sleep Med. 2019 Nov 15;15(11):1645-1653. (PMID: 31739855); Behav Brain Sci. 2000 Dec;23(6):851-66; discussion 904-1121. (PMID: 11515145); J Sci Med Sport. 2000 Dec;3(4):414-33. (PMID: 11235007); J Sports Sci. 2020 Nov;38(22):2631-2636. (PMID: 32713257); Sensors (Basel). 2024 Oct 10;24(20):. (PMID: 39460013) |
| Grant Information: | NA Australian Sports Commission |
| Contributed Indexing: | Keywords: Bland–Altman; agreement; athlete; error matrix; polysomnography; sensitivity; sleep; specificity; validation; wearable |
| Entry Date(s): | Date Created: 20250412 Date Completed: 20250412 Latest Revision: 20250414 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11991079 |
| DOI: | 10.3390/s25072123 |
| PMID: | 40218633 |
| Database: | MEDLINE |
Journal Article