Energy Availability and Sleep Quality on Training Responses and Sport Performance in Collegiate Division I Swimmers
Open Access
- Author:
- Conklin, Megan
- Area of Honors:
- Kinesiology
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Nancy Williams, Thesis Supervisor
Mark Dyreson, Thesis Honors Advisor - Keywords:
- energy availability
sleep
sport performance
athletic performance
slow-wave sleep
REM sleep
sleep quality
training response - Abstract:
- Given their frequent, intense training sessions and competitions, elite collegiate athletes often struggle to adequately fuel for their high energy needs, resulting in a high prevalence of low energy availability (EA) among elite collegiate athletes. In those with low EA, metabolic fuel repartitioning occurs to conserve energy, which, when left unaddressed, can have serious consequences on reproductive and bone health; meanwhile, low EA’s impact on training adaptations and athletic performance is less well understood, making this issue one of great importance. Also common among elite collegiate athletes is poor sleep, often attributed to the time demands of balancing academic responsibilities with rigorous training schedules. Inadequate sleep has been found to impact health and appears to impair training responses and athletic performance, although more research is needed to understand the relationship between these variables. Additionally, sleep deprivation is associated with changes in metabolic hormones that are remarkably similar to the changes that occur with metabolic fuel repartitioning in response to low EA, making the relationship between EA, sleep, training responses, and sport performance even more interesting. Therefore, the purpose of this study was to determine the interrelationships between EA, sleep quality, training responses, and sport performance. To do so, this study used WHOOP wearable technology (WHOOP Inc., Boston, MA) to collect data on sleep quality and training (strain, average HR, and maximum HR). Three-day food logs were completed through the MyFitnessPal (Under Armour Inc; Baltimore, MD) app to collect energy intake data. Dual X-ray absorptiometry (DXA) was used to quantify body composition, and a 200-yard time trial swim time (TTperf) was used to quantify athletic performance in 26 male and female NCAA Division 1 Swimmers during heavy training. Collection of EA was matched to sleep and training responses, but not matched to TTperf. Sleep measures were also recorded on the day preceding TTperf. Pearson correlations were utilized to determine relationships between variables when no sex effects persisted, whereas linear regression analyses were utilized to control for sex-differences. In all swimmers, there was a trend toward a correlation between EA and sleep duration hours (R=0.33; p=0.06). EA positively correlated with REM hours (R=0.64; p=0.001). When controlling for sex, EA was a predictor of SWS hours (R^2=0.448; p=0.001). Also in all swimmers, sleep duration hours and sleep debt hours were related to strain (R=-0.85; p=0.01, R=0.35; p=0.045, respectively), and ExHRavg (R=-0.65; p=0.001, R=0.51; p=0.01, respectively). SWS hours was inversely related to ExHRavg (R=-0.41; p=0.04). When controlling for sex, sleep duration hours and SWS% the night preceding the race predicted TTperf (R2 = 0.881; p<0.001 and R2 = 0.883; p<0.001). Meanwhile, EA was not related to any training responses, and regression analyses revealed that there was no combined effect of EA or sleep quality variables that predicted training responses. We conclude that elite swimmers with lower EA exhibited worse sleep quality. Swim training responses were related to, and performance was predicted by, sleep quantity and quality. Therefore, to avoid negative consequences of poor sleep quality and low EA, athletes should get adequate sleep and consume adequate calories to support energy needs and optimize training and performance.