Author(s): SARTOR, F., MARINI, S., PAPINI, G.B., FONSECA, P., Institution: UNIVERSITY OF CHIETI-PESCARA, Country: ITALY, Abstract-ID: 589

INTRODUCTION:
The 2013 launch of the first wrist-worn optical heart rate monitor (OHRM) [1] has greatly impacted sports, wellness, and clinical applications. Alongside unobtrusive HR-based exercise workload monitoring [2], unobtrusive HR variability and body movement-based sleep and sleep architecture monitoring have reached outstanding levels of accuracy [3]. This study aimed to observe OHRM derived sleep architecture patterns in preparation and in response to a marathon.
METHODS:
This study was designed to observe training and sleep patterns of 20 habitual marathon runners for 8 weeks, including pre-tapering, tapering, marathon, and post-marathon periods. Because of technical difficulties, data from 17 marathon runners (15 males, aged: 38±12 y, height: 1.81±0.09 m, weight: 70±9 kg, VO2max: 64±12 ml/kg/min) were analyzed. They visited our laboratory two times for VO2max and muscle fatiguability tests. During the 8-week free-living observation, runners wore the OHRM device [2,3] 24/7. This recorded photoplethysmography and tri-axial accelerometry signals at 32 Hz. Furthermore, runners kept pre- and post-training diaries where they logged their perceived muscle fatigue, pain, and overall stress and recovery.
RESULTS:
Total Running Days (RuDs) recorded were N=206 and Resting Days (ReDs) were N = 149. Total rest time (TRT), the total time spent in bed with the intention to sleep, was longer after RuDs (466.2±90.2 min) vs. ReDs (461.8±99.1 min), t(13)=-2.28, p = 0.040. A trend towards a longer total sleep time (TST) for RuDs vs. ReD (TST: 428.8±88.4 vs. 416.7±92.3 min, t(13)=-2.13, p = 0.053) was also observed. No differences were found in either sleep efficiency (SE) nor wake after sleep onset (WASO) (SE: 91.89±6.15 vs. 89.98±8.50; t(13)=0.938, p = 0.365, WASO: 23.16± 24.89 vs. 26.46±23.09 min, t(13)=-0.328, p = 0.748).The RuD vs. ReD comparison did not show significant changes in sleep architecture either. Light non-REM sleep normalized by TST (N1/N2%: 62.55±8.04 vs. 65.88±9.34%, t(13)=-0.516, p = 0.615), slow wave sleep normalized by TST (N3%: 11.82±5.87 vs. 11.29±6.85%, t(13)=0.362, p = 0.723), and finally REM sleep normalized by TST (REM%: 25.62±5.63 vs. 22.81±5.90%, t(13)=362, p = 0.723) did not differ.
CONCLUSION:
Previous studies showed that the longer the race distance the greater the importance of optimal sleep for race performance [4], and that sleep architecture seemed to be affected by high-intensity exercise. The present study shows that marathon runners spent more time in bed trying to sleep after a training day and tended to sleep longer, whereas sleep architecture did not seem to be affected. These preliminary results need to be integrated with linear mixed effect analysis looking at sleep pattern variations during tapering, marathon, and post-marathon periods. Furthermore, relations between sleep architecture fatigue and recovery will be analyzed.
1. https://www.cnet.com/reviews/mio-alpha-preview/ 2. Sartor et al. (2018) 3. Fonseca et al. (2017) 4. Nikolaidis et al. (2023)