Individual Patterns in Blood-Borne Indicators of Fatigue - Trait or Chance

© 2016 National Strength and Conditioning Association. Julian, R, Meyer, T, Fullagar, HHK, Skorski, S, Pfeiffer, M, Kellmann, M, Ferrauti, A, and Hecksteden, A. Individual patterns in blood-borne indicators of fatigue - trait or chance. J Strength Cond Res 31(3): 608-619, 2017 - Blood-borne markers of fatigue such as creatine kinase (CK) and urea (U) are widely used to fine-tune training recommendations. However, predictive accuracy is low. A possible explanation for this dissatisfactory characteristic is the propensity of athletes to react to different patterns of fatigue indicators (e.g., predominantly muscular [CK] or metabolic [U]). The aim of the present trial was to explore this hypothesis by using repetitive fatigue-recovery cycles. A total of 22 elite junior swimmers and triathletes (18 ± 3 years) were monitored for 9 weeks throughout 2 training phases (low-intensity, high-volume [LIHV] and high-intensity, low-volume [HILV] phases). Blood samples were collected each Monday (recovered) and Friday (fatigued) morning. From measured values of CK, U, free-testosterone (FT), and cortisol (C) as determined in the rested and fatigued state, respectively, Monday-Friday differences (Δ) were calculated and classified by magnitude before calculation of ratios (ΔCK/ΔU and ΔFT/ΔC). Coefficient of variation (CV) was calculated as group-based estimates of reproducibility. Linear mixed modeling was used to differentiate inter- and intraindividual variability. Consistency of patterns was analyzed by comparing with threshold values (1.1 for all weeks). Reproducibility was very low for fatigue-induced changes (CV ≥ 100%) with interindividual variation accounting for 45-60% of overall variability. Case-wise analysis indicated consistent ΔCK/ΔU patterns for 7 individuals in LIHV and 7 in HILV; 5 responded consistently throughout. For ΔFT/ΔC the number of consistent patterns was 2 in LIHV and 3 in HILV. These findings highlight the potential value of an individualized and multivariate approach in the assessment of fatigue

Tennis play intensity distribution and relation with aerobic fitness in competitive players

15 p.Los objetivos de este estudio fueron (1) describir la intensidad relativa del juego de tenis simulado en función del tiempo acumulado en tres zonas de intensidad metabólica y (2) determinar las relaciones entre esta distribución de intensidad de juego y la aptitud aeróbica de un grupo de jugadores competitivos. 20 jugadores masculinos de nivel avanzado a élite (ITN) realizaron una prueba de tenis de resistencia específica en el campo incremental hasta el agotamiento para determinar el consumo máximo de oxígeno (VO2max) y los umbrales de ventilación primero y segundo (VT1, VT2). Los parámetros de ventilación y de intercambio de gases se monitorizaron utilizando un analizador de gas portátil telemétrico (K4 b2, Cosmed, Roma, Italia). Dos semanas después, los participantes jugaron un juego de tenis simulado contra un oponente de nivel similar. Las zonas de intensidad (1: baja, 2: moderada y 3: alta) fueron delimitadas por los valores individuales de VO2 correspondientes a VT1 y VT2, y se expresaron como porcentaje del VO2 máximo y la frecuencia cardíaca. Cuando se expresó en relación con el VO 2 máx. El porcentaje de tiempo de juego en la zona 1 (77 ± 25%) fue significativamente mayor (p <0,001) que en la zona 2 (20 ± 21%) y la zona 3 (3 ± 5%). Se encontraron correlaciones positivas de moderadas a altas entre VT1, VT2 y VO2max, y el porcentaje del tiempo de juego transcurrido en la zona 1 (r = 0,68-0,75), así como las correlaciones inversas de bajas a altas entre las variables metabólicas y el porcentaje de tiempo empleado en las zonas 2 y 3 (r = -0.49–0.75). Los jugadores con mejor aptitud aeróbica juegan a intensidades relativamente más bajas. Concluimos que los jugadores pasaron más del 75% del tiempo en su zona de baja intensidad, con menos del 25% del tiempo dedicado a intensidades moderadas a altas. La aptitud aeróbica parece determinar la intensidad metabólica que los jugadores pueden mantener durante todo el juegoS

Sleep in sports: A short summary of alterations in sleep/wake patterns and the effects of sleep loss and jet-lag

This review article aims to summarise general aspects regarding sleep and sports. It is generally assumed that sleep is a basic requirement for physiological and psychological recovery, whereas it appears that sleep of elite athletes is highly influenced by training stimuli and external as well as internal factors. Studies about sleep deprivation and restricted sleep indicate the importance for cognitive functions as well as mood and behavioural aspects. Effects on performance parameters are not reported as consistently. In contrast to sleep restriction, extending sleep for several nights might lead to improvements in performance, mood, and alertness. In terms of intercontinental travel, jet-lag might be an issue for elite athletes. Although a few studies indicate that effects on athletic performance might not be present, sleep, mood and some physiological measures are affected due to shift in time zones. Simple strategies can be applied to enhance adaptation to the destination and diminish jet-lag symptoms

Acute effects of psychological relaxation techniques between two physical tasks

The concept of recovery strategies includes various ways to achieve a state of well-being, prevent underrecovery syndromes from occurring and re-establish pre-performance states. A systematic application of individualised relaxation techniques is one of those. Following a counterbalanced cross-over design, 27 sport science students (age 25.22 +/- 1.08years; sports participation 8.08 +/- 3.92 h/week) were randomly assigned to series of progressive muscle relaxation, systematic breathing, power nap, yoga, and a control condition. Once a week, over the course of five weeks, their repeated sprint ability was tested. Tests (6 sprints of 4s each with 20s breaks between them) were executed on a non-motorised treadmill twice during that day intermitted by 25min breaks. RM-ANOVA revealed significant interaction effects between the relaxation conditions and the two sprint sessions with regard to average maximum speed over all six sprints, F(4,96)=4.06, P=0.004, eta(2)(p) = 0.15. Post-hoc tests indicated that after systematic breathing interventions, F(1,24)=5.02, P=0.033, eta(2)(p) = 0.18, participants performed significantly better compared to control sessions. As the focus of this study lied on basic mechanisms of relaxation techniques in sports, this randomised controlled trial provides us with distinct knowledge on their effects, i.e., systematic breathing led to better performances, and therefore, seems to be a suited relaxation method during high-intensity training