Jennifer L. Wheeler

Chronic stress exposure, whether cognitive or physiological in origin (psychophysiological), can contribute to long term negative health outcomes in athletes and non-athletes (e.g., depression, substance abuse, eating disorders, higher rates of sickness and injury). College student-athletes are especially susceptible to the influence of chronic stress because of the relatively unique combination of cognitive stressors (financial, social, academic), physiological stressors (training, competition), as well as limited stress management skills. PURPOSE: The purpose of this study was to pilot the development of an instrument for predicting the underlying determinants of psychophysiological stress in collegiate cross country (XC) skiers. METHODS: Fifteen collegiate XC skiers (10 women, 5 men) completed a series of measures during two separate weeks in September and October. During each week, an accelerometry-based activity monitor, worn on the wrist at night, monitored total sleep time (TST) and sleep efficiency (SE). Responses to standardized surveys were used to compute measures of daytime sleepiness (DaySLP), a sleep quality index (SQI), as well as total weekly training load (TL) from self-reported training logs. Each of these variables, along with gender (0 = women, 1 = men), were then used as potential predictors of College Student-Athlete Life Stress (CSALS), a 24-item, 8-factor, instrument validated to quantify potential stressors of college student-athletes. The CSALS prediction model was generated using standard step forward multiple linear regression (P to enter/exit = 0.05) with alpha = 0.05. RESULTS: Four independent variables (gender, SE, DaySLP, and SQI) significantly explained 21%, 25.6%, 24.6%, and 8.7% of the variance in CSALS, respectively, whereas neither TST nor TL contributed to the final model: CSALS = 504.0 -16.3xGender + 1.86xSQI + 1.48xDaySLP - 5.21xSE (R2=0.80, P<0.001). CONCLUSIONS: Measures of nighttime sleep quality (measured and perceived), daytime sleepiness, and gender were highly predictive of psychophysiological stress (80% variance explained) in collegiate XC skiers. Future studies may be able to generalize these results to other collegiate sports so that coaches and athletes can better understand the development of psychophysiological stress.

Collegiate athletes experience a unique combination of stressors: academic, athletic, physiological, and social. The relationship between physiological stress due to training and perceived cognitive stress is not yet well-understood. PURPOSE: The current study evaluated the relationship between physiological and cognitive stress as represented by training load and cumulative life stress using the College Student Athlete Life Stress Scale (LSS) for collegiate cross-country skiers, respectively. It was hypothesized that no significant relationship would be found between these two measures, indicating that physiological and perceived cognitive stress contribute independently to total stress. METHODS: Collegiate cross-country skiers (10 women: (Mean ± SD) 20 ± 2 yrs, 22.9 ± 2.3 kg/m2 BMI; 5 men: 21 ± 1 yrs, 22.5 ± 1.2 kg/m2 BMI) were recruited from Montana State University. Data was collected during four separate 1-week collection periods over the course of the fall semester, representing different parts of the skiers’ training cycle. Throughout each week, subjects recorded training within a log, noting each activity, duration, and session rating of perceived exertion (RPE; 1-10 scale). Training load (TL) was then calculated by multiplying the training volume (TV, hrs) of each activity by its session RPE. At the conclusion of each collection period, the subjects completed the LSS as part of an online survey. LSS, TV, and TL were analyzed using a 2-factor RM ANOVA with Tukey’s post-hoc analysis (0.05 alpha), while TV and TL were also correlated with LSS using Pearson Correlations. RESULTS: LSS did not vary significantly across trials, but women tended to score higher than men (P = 0.07). TV and TL were both significantly greater for men than women for all collection periods and varied as intended by the training cycle. LSS correlated weakly and negatively, but significantly with TV (R = -0.300, P = 0.02). The same correlational trends also existed for each of the 4 collection periods individually. LSS also correlated weakly and negatively, but non-significantly with TL (R = -0.251, P = 0.053). CONCLUSIONS: At higher training volumes, skiers tended to self-report lower levels of cognitive stress. The direction of this relationship was unexpected, because both TV and LSS are understood to increase total stress. It is unclear whether TV causes lower LSS or if lower LSS allows for higher TV. Further, the correlation between measures of physiological and cognitive stress indicates that they do not contribute independently to total stress. Additional investigation is necessary to assess whether the relationship between TV and LSS is observed among other collegiate athletes.

Poor sleep quality and nightly sleep duration are associated with increased stress and an increased risk for many chronic health problems. For collegiate students-athletes, who experience considerable academic and training stressors, poor sleep quality may also influence academic and performance abilities. PURPOSE: the primary purpose of this study was to evaluate the relationships between several measures of sleep quality within collegiate cross-country (XC) skiers throughout a semester. METHODS: Fifteen XC skiers (10 women, 20±2 yrs, 22.9±2.3 kg/m2 BMI, 50.8±4.2 ml/kg/min VO2MAX; 5 men, 21±1 yrs, 22.5±1.2 kg/m2 BMI, 62.4±1.7 ml/kg/min VO2MAX) from the Montana State University (MSU) Nordic Ski Team were recruited. Each month from September to December, 2014, subjects used a wrist-worn activity monitor (AW) for seven consecutive nights, while self-reported sleep and training logs and on-line surveys were completed at select times each month. Total sleep time (TST) and sleep efficiency (SE) were calculated from the AW, whereas the Pittsburgh Sleep Quality Index (PSQI) and Epsworth Sleepiness Scale questionnaire (ESS) were used to assess sleep quality and daytime sleepiness, respectively. Differences in TST, SE, PSQI, ESS and hours of training recorded in the training logs (TL, hrs) throughout the four measurement trials (T1, T2, T3, T4) were evaluated using two-factor repeated measures ANOVA and Tukey’s HSD post-hoc test (0.05 alpha). The Pearson correlation was also used to examine relationships between TST, SE, PSQI, ESS and TL. RESULTS: TST for T1 was less than that for T2 or T3 (T1 = 7.7 hrs/night; T2 and T3 = 8.1 hrs/night; P4, 7.9 hrs/night, was not significantly different from the other TST measures. ESS increased steadily (T1 < T2 < T3 < T4; P1 > T4 > T3 > T2; PP = 0.04) and ESS (R = +0.40, P = 0.002). CONCLUSIONS: Poor sleep quality, as indicated by higher PSQI values, may have led to increased nightly sleep duration and daytime sleepiness. These findings also provide preliminary evidence that training volume may serve as a moderator that tends to cause elevated TST, and thus ameliorate PSQI, ESS, and SE.