Resumen de Relationship Between External Load and Self-Reported Wellness Measures Across a Men's Collegiate Soccer Preseason
Jennifer B. Fields, Diane M. Lameira, Jerome L. Short, Justin J. Merrigan, Sina Gallo, Jason B. White, Margaret T. Jones
Monitoring athlete training load is important to training programming and can help balance training and recovery periods. Furthermore, psychological factors can affect athlete's performance. Therefore, the purpose was to examine the relationship between external load and self-reported wellness measures during soccer preseason. Collegiate men soccer athletes (n = 20; mean +/- SD age: 20.3 +/- 0.9 years; body mass: 77.9 +/- 6.8 kg; body height: 178.87 +/- 7.18cm; body fat: 10.0 +/- 5.0%; V[Combining Dot Above]O2max: 65.39 +/- 7.61ml[middle dot]kg-1[middle dot]min-1) participated. Likert scale self-assessments of fatigue, soreness, sleep, stress, and energy were collected daily in conjunction with the Brief Assessment of Mood (vigor, depression, anger, fatigue, and confusion). Total distance (TD), player load (PL), high-speed distance (HSD, >13 mph [5.8 m[middle dot]s-1]), high inertial movement analysis (IMA, >3.5 m[middle dot]s-2), and repeated high-intensity efforts (RHIEs) were collected in each training session using positional monitoring (global positioning system/global navigation satellite system [GPS/GNSS]) technology. Session rate of perceived exertion (sRPE) was determined from athlete's post-training rating (Borg CR-10 Scale) and time of training session. Multilevel models revealed the bidirectional prediction of load markers on fatigue, soreness, sleep, energy, and sRPE (p < 0.05). Morning ratings of soreness and fatigue were predicted by previous afternoon's practice measures of TD, PL, HSD, IMA, RHIE, and sRPE. Morning soreness and fatigue negatively predicted that day's afternoon practice TD, PL, HSD, IMA, RHIE, and sRPE. Morning ratings of negative mood were positively predicted by previous day's afternoon practice HSD. In addition, negative morning mood states inversely predicted HSD (p = 0.011), TD (p = 0.002), and PL (p < 0.001) for that day's afternoon practice. Using self-reported wellness measures with GPS/GNSS technology may enhance the understanding of training responses and inform program development.
