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.
© Copyright 2025 Sensors. All rights reserved.