ABSTRACT
OBJECTIVE: We constructed research camps at single-effort ultramarathons (50 and 100 miles) in order to study human endurance capabilities under extreme sleep loss and stress. It takes > 24h, on average, to run 100 miles on minimal sleep, allowing us to construct 24h human performance profiles (HPP). METHODS: We collected performance data plotted across time (race splits) and distance (dropout rates; n=257), self-reported sleep and training patterns (n=83), and endpoint data on cardiovascular fitness/adaptation to total sleep deprivation and extreme exercise/stress (n=127). RESULTS: In general, we found that self-reported napping was higher for 100-miler versus 50-miler runners and that ultra-endurance racing may possibly pre-select for early morning risers. We also compared HPPs between the first 50 miles completed by all runners in order to examine amplitude and acrophase differences in performance using a cosinor model. We showed that even though all runners slowed down over time, runners who completed a 100-miler ultramarathon had an earlier acrophase shift in race pace compared to non-finishers. DISCUSSION: We were able to identify time-dependent predictions on overall performance under minimal sleep, warranting the ultramarathon athlete as a unique demographic for future study of sleep and chronobiological relationships in the real world.
ABSTRACT
Burke, TM, Lisman, PJ, Maguire, K, Skeiky, L, Choynowski, JJ, CapaldiII, VF, Wilder, JN, Brager, AJ, and Dobrosielski, DA. Examination of sleep and injury among college football athletes. J Strength Cond Res 34(3): 609-616, 2020-The purpose of this study was to characterize subjective sleep metrics in collegiate football players at the start of the season, determine the relationship between preseason subjective sleep measures and in-season objective sleep characteristics, and examine the association between subjective and objective sleep metrics and incidence of time-loss injury during the competitive season. Ninety-four Division I football players completed 5 validated sleep-related questionnaires to assess sleep quality, insomnia severity, daytime sleepiness, sleep apnea risk, and circadian preference before the start of the season. Clinical thresholds for sleep questionnaires were used to determine risk of sleep disorders. Continuous wrist actigraphy was collected throughout the season to generalize sleep behaviors. Time-loss injury incidence data were recorded and used for analysis. Results indicated that 67.4% (60 of 89) of athletes scored above clinical threshold in at least 1 questionnaire to indicate sleep disorder risk. At the start of the season, players subjectively reported an average sleep duration of 7:16 ± 1:18 hours:minutes, which was in contrast to the 6:04 ± 0:41 hours:minutes measured through actigraphy during the season. Logistic regression models adjusted for age and body mass index revealed no significant associations between injury and subjective (odds ratio [OR] = 1.00; 95% confidence interval [CI] = 0.99-1.01) and objective (OR = 1.01; 95% CI = 0.99-1.02) sleep duration or measures attained from sleep questionnaires (ORs ranged from 1.01 to 2.87). Sleep metrics (quantity and quality) were not associated with increased risk of injury in this cohort of collegiate football players.
