Monitoring Training Loads and Perceived Stress in Young Elite University Athletes

Abstract

With increased professionalism in sport there has been a greater interest in the scientific approach to training and recovery of athletes. Applying appropriate training loads along with adequate recovery, is essential in gaining maximal adaptation in athletes, while minimizing harm such as overreaching, overtraining, injury and illness. Although appropriate physical stress is essential, stress for many athletes may come from areas other than training. Stress from may arise from social or environmental pressure, and for many athletes who combine elite athletic training with university study, academic workloads create significant stress which adds to the constant pressure to perform athletically. This research aimed to determine if subjective stressors were associated with counterproductive training adaptations in university athletes. Moreover, it aimed to elucidate if, and when, such stressors are most harmful (i.e., certain times of the academic year or sports training season). We monitored subjective (mood state, energy levels, academic stress, sleep quality/quantity, muscle soreness, training load) and objective (injury and illness) markers in 182 young (18–22 years) elite athletes over a 4-year period using a commercially available software package. Athletes combined full-time university study with elite sport and training obligations. Results suggest athletes were relatively un-stressed with high levels of energy at the beginning of each university semester, however, energy levels deteriorated along with sleep parameters toward the examination periods of the year. A logistical regression indicated decreased levels of perceived mood (0.89, 0.85–0.94, Odds Ratio and 95% confidence limits), sleep duration (0.94, 0.91–0.97) and increased academic stress (0.91, 0.88–0.94) and energy levels (1.07, 1.01–1.14) were able to predict injury in these athletes. Examination periods coincided with the highest stress levels and increased likelihood of illness. Additionally, a sudden and high increase in training workload during the preseason was associated with an elevated incidence of injury and illness (r = 0.63). In conclusion, young elite athletes undertaking full-time university study alongside their training and competition loads were vulnerable to increased levels of stress at certain periods of the year (pre-season and examination time). Monitoring and understanding these stressors may assist coaches and support staff in managing overall stress in these athletes.

<p>The Relationship Between Acute: Chronic Workload Ratios and Injury Risk in Sports: A Systematic Review</p>

“…The most common timeframe used was a 1-week acute training load and 4-week chronic training load. [9][10][11]17,18,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] Other timeframes included a 3-day acute load and a 21 daychronic load, 19 or varying timeframes. [37][38][39][40] One study examined various timeframes for calculating ACWR, 37 utilizing 2-9 days for acute loads, and chronic loads varying from 14 to 35 days, while the other study ranged from 1 to 2 weeks for acute loads, and 3-8 weeks for chronic loads, but only utilized the 1:4 week ratio for relationship to injury.…”

“…38 Colby et al 30 reported, compared to ACWR range of 0.99 to 1.08, ranges of <0.88 (IRR=1.17), 1.08-1.21 (IRR=1.37), and >1.21 (IRR=1.53) resulted in higher incidence of injury, while 0.88-0.99 (IRR=0.60) resulted in a lower incidence when using TD. This same study found that when compared to an above average chronic TD load (81694m over 4 weeks) and moderate 27,30,34,[36][37][38][39] Total [9,10,17,20,21,[23][24][25][26][28][29][30][31][32][33][35][36][37][38]40] ACWR [11,[17][18][19][20][21][22][23][25][26][27][28][29][30][31][32][33][34][35][36]…”

“…The normal and high ACWR were calculated by utilizing within-individual z-scores which were parsed into tertiles resulting in z-scores of 0.31 to 0.41 for normal ACWR and >0.41 for high ACWR. 29 Hamlin et al 31 compared various IL measures and found that mood (OR=0.89), energy (OR=1.07), sleep duration (OR=0.94) and academic stress (OR=0.91) were significantly related to odds of injury in elite university athletes. Please refer to Table 4 for an overview of the statistics from each study including confidence intervals and p-values where appropriate.…”

“…In addition, it was also found that utilizing ACWR and measures of stress, sleep, mood, and energy were significantly related to injury risk. 31 Limitations Several limitations exist in this paper. As noted in the Materials and Methods section, there is potential for search bias, duplication bias, inclusion criteria bias and selector bias.…”

Masticatory Muscles Activity in Sport Climbers

“…In addition, regular training affects the formation of structural and functional changes in all systems of the human body. However, too much training load and lack of proper regeneration lead to the crossing of the body's adaptive barrier and, as a result, to injury [5]. Extreme sport is also associated with a high psychological burden for athletes [4].…”