The Integration of Internal and External Training Load Metrics in Hurling

The current study aimed to assess the relationship between the hurling player's fitness profile and integrated training load (TL) metrics. Twenty-five hurling players performed treadmill testing for VO2max, the speed at blood lactate concentrations of 2 mmol•L-1 (vLT) and 4 mmol•L-1 (vOBLA) and the heart rate-blood lactate profile for calculation of individual training impulse (iTRIMP). The total distance (TD; m), high speed distance (HSD; m) and sprint distance (SD; m) covered were measured using GPS technology (4-Hz, VX Sport, Lower Hutt, New Zealand) which allowed for the measurement of the external TL. The external TL was divided by the internal TL to form integration ratios. Pearson correlation analyses allowed for the assessment of the relationships between fitness measures and the ratios to performance during simulated match play. External measures of the TL alone showed limited correlations with fitness measures. Integrated TL ratios showed significant relationships with fitness measures in players. TD:iTRIMP was correlated with aerobic fitness measures VO2max (r = 0.524; p = 0.006; 95% CI: 0.224 to 0.754; large) and vOBLA (r = 0.559; p = 0.003; 95% CI: 0.254 to 0.854; large). HSD:iTRIMP also correlated with aerobic markers for fitness vLT (r = 0.502; p = 0.009; 95% CI: 0.204 to 0.801; large); vOBLA (r = 0.407; p = 0.039; 95% CI: 0.024 to 0.644; moderate). Interestingly SD:iTRIMP also showed significant correlations with vLT (r = 0.611; p = 0.001; 95% CI: 0.324 to 0.754; large). The current study showed that TL ratios can provide practitioners with a measure of fitness as external performance alone showed limited relationships with aerobic fitness measures. © Editorial Committee of Journal of Human Kinetics 2016

The dose–response relationship between training-load measures and aerobic fitness in elite academy soccer players

The aim of the current study is to examine the dose-response relationships between training load (TL) measures and the consequent changes in aerobic fitness. Data were collected over the 6-week pre-season period in elite youth soccer players. Participants completed a lactate threshold test to identify changes in treadmill speed at 2 mmol · l (S2) and 4 mmol · l (S4). Internal TL was quantified with the following training impulse (TRIMP) methods: Banister TRIMP, Edwards TRIMP, Lucia TRIMP, individual TRIMP (iTRIMP) and rate of perceived exertion was also collected. External TL measures were total distance, PlayerLoad, high speed running (14.4-19.8 km · h ), very high-speed running (19.8-25.2 km · h ) and maximal sprint distance (>25.2 km · h ). Individual high-speed distance was derived from each participants treadmill speed at S4. Different Bayesian regression models were run with different likelihood functions. The best-fitting models with both the lowest out-of-sample prediction error and the highest variance explained ( ) were used. iTRIMP had the strongest relationships with changes in S2 (r = 0.93, = 0.90) and S4 (r = 0.88, = 0.82). Explained variance ranged from 10%-69% and 11%-38% for all other internal TL measures and external measures, respectively. In summary, the iTRIMP method demonstrates a dose-response relationship with changes in aerobic fitness in elite youth soccer players

Variations of training load, monotony, and strain and dose-response relationships with maximal aerobic speed, maximal oxygen uptake, and isokinetic strength in professional soccer players

This study aimed to identify variations in weekly training load, training monotony, and training strain across a 10-week period (during both, pre- and in-season phases); and to analyze the dose-response relationships between training markers and maximal aerobic speed (MAS), maximal oxygen uptake, and isokinetic strength. Twenty-seven professional soccer players (24.9±3.5 years old) were monitored across the 10-week period using global positioning system units. Players were also tested for maximal aerobic speed, maximal oxygen uptake, and isokinetic strength before and after 10 weeks of training. Large positive correlations were found between sum of training load and extension peak torque in the right lower limb (r = 0.57, 90%CI[0.15;0.82]) and the ratio agonist/antagonist in the right lower limb (r = 0.51, [0.06;0.78]). It was observed that loading measures fluctuated across the period of the study and that the load was meaningfully associated with changes in the fitness status of players. However, those magnitudes of correlations were small-to-large, suggesting that variations in fitness level cannot be exclusively explained by the accumulated load and loading profile

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

Undergraduate and Postgraduate Strength and Conditioning Courses in the United Kingdom: A Report Study.

In the United Kingdom (UK), a degree in strength and conditioning (S&C) or an associated discipline is a common requirement for obtaining a professional S&C certification and employment as a S&C coach. However, limited research has comprehensively reviewed undergraduate and postgraduate S&C degrees in the UK, which this study aimed to do. A search for S&C degrees was conducted via two directories. In total, 20 undergraduate and 29 postgraduate courses were identified. All course information, including module titles, was extracted. Course information was assessed using frequency analysis and module titles via open coding. Entry requirements for undergraduate degrees ranged from 80–120 UCAS points, and a 2:1–2:2 degree classification for postgraduate degrees. Almost half of undergraduate S&C degrees were considered ‘multidisciplinary’ and included other topics (e.g., BSc S&C and rehabilitation). Over half of the undergraduate degrees offered a foundation year, and 59% of postgraduate degrees offered a non-academic entry option. Overall, 50% of undergraduate degrees could be completed as full-time or part-time, which increased to 79% at postgraduate level. Placement modules were compulsory across undergraduate degrees (except for one) and featured to a lesser extent at the postgraduate level. The most common modules at the undergraduate level focused on anatomy and physiology, S&C, biomechanics and movement analysis, research, and academic and professional skills. The least common modules were motor learning and control, business, and sociology. Differences were observed with postgraduate degrees, given an increased focus on modules associated with research, S&C, and academic and professional skills. This information may help higher education providers to evaluate, revise, and develop S&C courses; awarding associations further enhance recognition and accreditation pathways for S&C degrees; potential employers tailor job descriptions and specifications to align with graduate capabilities; and prospective students gain insight into each course, potentially informing their course choice(s)

Training Mode’s Influence on the Relationships between Training-Load Models During Basketball Conditioning

Purpose: To compare perceptual and physiological training load responses during various basketball training modes. Methods: Eight semi-professional male basketball players (age: 26.3 ± 6.7 years; height: 188.1 ± 6.2 cm; body mass: 92.0 ± 13.8 kg) were monitored across a 10-week period in the preparatory phase of the training plan. Player session ratings of perceived exertion (sRPE) and heart rate (HR) responses were gathered across base, specific, and tactical/game-play training modes. Pearson correlations were used to determine the relationships between the sRPE model and two HR-based models, the training impulse (TRIMP) and summated-heart-rate-zones (SHRZ). One-way ANOVAs were used to compare training loads between training modes for each model. Results: Stronger relationships between perceptual and physiological models were evident during base (sRPE-TRIMP: r = 0.53, P < 0.05; sRPE-SHRZ: r = 0.75, P < 0.05) and tactical/game-play conditioning (sRPE-TRIMP: r = 0.60, P < 0.05; sRPE-SHRZ: r = 0.63; P < 0.05) than during specific conditioning (sRPE-TRIMP: r = 0.38, P < 0.05; sRPE-SHRZ: r = 0.52; P < 0.05). Further, the sRPE model detected greater increases (126-429 AU) in training load than the TRIMP (15-65 AU) and SHRZ models (27-170 AU) transitioning between training modes. Conclusions: While the training load models were significantly correlated during each training mode, weaker relationships were observed during specific conditioning. Comparisons suggest the HR-based models were less effective in detecting periodized increases in training load, particularly during court-based, intermittent, multidirectional drills. The practical benefits and sensitivity of the sRPE model support its use across different basketball training modes