This study examined associations between cumulative training load, travel demands and recovery days with athlete-reported outcome measures (AROMs) and countermovement jump (CMJ) performance in ...professional basketball. Retrospective analysis was performed on data collected from 23 players (mean±SD: age = 24.7±2.5 years, height = 198.3±7.6 cm, body mass = 98.1±9.0 kg, wingspan = 206.8±8.4 cm) from 2018–2020 in the National Basketball Association G-League. Linear mixed models were used to describe variation in AROMs and CMJ data in relation to cumulative training load (previous 3- and 10-days), hours travelled (previous 3- and 10-day), days away from the team’s home city, recovery days (i.e., no travel/minimal on-court activity) and individual factors (e.g., age, fatigue, soreness). Cumulative 3-day training load had negative associations with fatigue, soreness, and sleep, while increased recovery days were associated with improved soreness scores. Increases in hours travelled and days spent away from home over 10 days were associated with increased sleep quality and duration. Cumulative training load over 3 and 10 days, hours travelled and days away from home city were all associated with changes in CMJ performance during the eccentric phase. The interaction of on-court and travel related stressors combined with individual factors is complex, meaning that multiple athletes response measures are needed to understand fatigue and recovery cycles. Our findings support the utility of the response measures presented (i.e., CMJ and AROMs), but this is not an exhaustive battery and practitioners should consider what measures may best inform training periodization within the context of their environment/sport.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Hypoxic training techniques are increasingly used by athletes in an attempt to improve performance in normoxic environments. The 'live low-train high (LLTH)' model of hypoxic training may be of ...particular interest to athletes because LLTH protocols generally involve shorter hypoxic exposures (approximately two to five sessions per week of <3 h) than other traditional hypoxic training techniques (e.g., live high-train high or live high-train low). However, the methods employed in LLTH studies to date vary greatly with respect to exposure times, training intensities, training modalities, degrees of hypoxia and performance outcomes assessed. Whilst recent reviews provide some insight into how LLTH may be applied to enhance performance, little attention has been given to how training intensity/modality may specifically influence subsequent performance in normoxia. Therefore, this systematic review aims to evaluate the normoxic performance outcomes of the available LLTH literature, with a particular focus on training intensity and modality.
A systematic search was conducted to capture all LLTH studies with a matched normoxic (control) training group and the assessment of performance under normoxic conditions. Studies were excluded if no training was completed during the hypoxic exposures, or if these exposures exceeded 3 h per day. Four electronic databases were searched (PubMed, SPORTDiscus, EMBASE and Web of Science) during August 2013, and these searches were supplemented by additional manual searches until December 2013.
After the electronic and manual searches, 40 papers were deemed to meet the inclusion criteria, representing 31 separate studies. Within these 31 studies, four types of LLTH were identified: (1) continuous low-intensity training in hypoxia (CHT, n = 16), (2) interval hypoxic training (IHT, n = 4), (3) repeated sprint training in hypoxia (RSH, n = 3) and (4) resistance training in hypoxia (RTH, n = 4). Four studies also used a combination of CHT and IHT. The majority of studies reported no difference in normoxic performance between the hypoxic and normoxic training groups (n = 19), while nine reported greater improvements in the hypoxic group and three reported poorer outcomes compared with the control group. Selection of training intensity (including matching relative or absolute intensity between normoxic and hypoxic groups) was identified as a key factor in mediating the subsequent normoxic performance outcomes. Five studies included some form of normoxic training for the hypoxic group and 14 studies assessed performance outcomes not specific to the training intensity/modality completed during the training intervention.
Four modes of LLTH are identified in the current literature (CHT, IHT, RSH and RTH), with training mode and intensity appearing to be key factors in mediating subsequent performance responses in normoxia. Improvements in normoxic performance appear most likely following high-intensity, short-term and intermittent training (e.g., IHT, RSH). LLTH programmes should carefully apply the principles of training and testing specificity and include some high-intensity training in normoxia. For RTH, it is unclear whether the associated adaptations are greater than those of traditional (maximal) resistance training programmes.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The purpose of this study was to examine the changes in neuromuscular, perceptual and hormonal measures following professional rugby league matches during different length between-match microcycles.
...Twelve professional rugby league players from the same team were assessed for changes in countermovement jump (CMJ) performance (flight time and relative power), perceptual responses (fatigue, well-being and muscle soreness) and salivary hormone (testosterone T and cortisol C) levels during 5, 7 and 9 d between-match training microcycles. All training was prescribed by the club coaches and was monitored using the session-RPE method.
Lower mean daily training load was completed on the 5 d compared with the 7 and 9 d microcycles. CMJ flight time and relative power, perception of fatigue, overall well-being and muscle soreness were significantly reduced in the 48 h following the match in each microcycle (P < .05). Most CMJ variables returned to near baseline values following 4 d in each microcycle. Countermovement jump relative power was lower in the 7 d microcycle in comparison with the 9 d microcycle (P < .05). There was increased fatigue at 48 h in the 7 and 9 d microcycles (P < .05) but had returned to baseline in the 5 d microcycle. Salivary T and C did not change in response to the match.
Neuromuscular performance and perception of fatigue are reduced for at least 48 h following a rugby league match but can be recovered to baseline levels within 4 d. These findings show that with appropriate training, it is possible to recover neuromuscular and perceptual measures within 4 d after a rugby league match.
An eccentrically lengthening, energy-absorbing, brake-driven model of hamstring function during the late-swing phase of sprinting has been widely touted within the existing literature. In contrast, ...an isometrically contracting, spring-driven model of hamstring function has recently been proposed. This theory has gained substantial traction within the applied sporting world, influencing understandings of hamstring function while sprinting, as well as the development and adoption of certain types of hamstring-specific exercises. Across the animal kingdom, both spring- and motor-driven muscle–tendon unit (MTU) functioning are frequently observed, with both models of locomotive functioning commonly utilising some degree of active muscle lengthening to draw upon force enhancement mechanisms. However, a method to accurately assess hamstring muscle–tendon functioning when sprinting does not exist. Accordingly, the aims of this review article are three-fold: (1) to comprehensively explore current terminology, theories and models surrounding muscle–tendon functioning during locomotion, (2) to relate these models to potential hamstring function when sprinting by examining a variety of hamstring-specific research and (3) to highlight the importance of developing and utilising evidence-based frameworks to guide hamstring training in athletes required to sprint. Due to the intensity of movement, large musculotendinous stretches and high mechanical loads experienced in the hamstrings when sprinting, it is anticipated that the hamstring MTUs adopt a model of functioning that has some reliance upon active muscle lengthening and muscle actuators during this particular task. However, each individual hamstring MTU is expected to adopt various combinations of spring-, brake- and motor-driven functioning when sprinting, in accordance with their architectural arrangement and activation patterns. Muscle function is intricate and dependent upon complex interactions between musculoskeletal kinematics and kinetics, muscle activation patterns and the neuromechanical regulation of tensions and stiffness, and loads applied by the environment, among other important variables. Accordingly, hamstring function when sprinting is anticipated to be unique to this particular activity. It is therefore proposed that the adoption of hamstring-specific exercises should not be founded on unvalidated claims of replicating hamstring function when sprinting, as has been suggested in the literature. Adaptive benefits may potentially be derived from a range of hamstring-specific exercises that vary in the stimuli they provide. Therefore, a more rigorous approach is to select hamstring-specific exercises based on thoroughly constructed evidence-based frameworks surrounding the specific stimulus provided by the exercise, the accompanying adaptations elicited by the exercise, and the effects of these adaptations on hamstring functioning and injury risk mitigation when sprinting.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The National Basketball Association (NBA) has an extremely demanding competition schedule, requiring its athletes to compete in 82 regular-season games over a 6-mo period (∼3.4 games/wk). Despite the ...demanding schedule and high value of athletes, there is little public information on the specific game and training demands required to compete in the NBA. Although provisions in the NBA collective-bargaining agreement allow for research designed to improve player health and broaden medical knowledge, such information is sparse in the available literature. In relation to the physical demands of the NBA, the current lack of information likely results from multiple factors including limited understanding of (basketball-related) emerging technologies, impact of specific league rules, and steps taken to protect players in the age of Big Data. This article explores current limitations in describing specific game/training demands in the NBA and provides perspectives on how some of these challenges may be overcome. The authors propose that future collaborations between league entities, NBA clubs, commercial partners, and outside research institutions will enhance understanding of the physical demands in the NBA (and other health- and performance-related areas). More detailed understanding of physical demands (games, practices, and travel) and other health-related areas can augment player-centered decision making, leading to enhanced player care, increased availability, and improved physical performance.
Howarth, DJ, Cohen, DD, McLean, BD, and Coutts, AJ. Establishing the noise: interday ecological reliability of countermovement jump variables in professional rugby union players. J Strength Cond Res ...XX(X): 000-000, 2021-The purpose of this study was to examine the interday "ecological" reliability of a wide range of ground reaction force-derived countermovement jump (CMJ) variables. Thirty-six male, professional rugby union players performed 3 CMJs on 4 separate days over an 8-day period during the first week of preseason. We calculated reliability for 86 CMJ variables across 5 interday combinations using 2 criteria: mean output across 3 jump trials (Mean3) and single output from the highest jump (BestJH). Interday coefficient of variation (CV) of the 86 variables in each CMJ phase, for Mean3 and BestJH, respectively, ranged between concentric = 2-11% and 2-13%; eccentric = 1-45% and 1-107%; and landing = 4-32% and 6-45%. Mean3 interday CV was lower in all 86 variables across every interday combination, compared with BestJH. CVs were lower in our cohort than previous studies, particularly for eccentric phase variables. There was no meaningful difference between interday conditions, suggesting any 2-day combination conducted within the first 8 days of preseason, represents a measure of "noise." We did not apply arbitrary reliability "cut-offs" used in previous work (e.g., CV <10%); therefore, our analysis provides reference reliability for a wide range of CMJ variables. However, we recommend that practitioners assess reliability in their athletes, as it is likely to be environment, protocol, and cohort specific.
Kutson, CW, Russell, JL, Strack, D, Coutts, AJ, and McLean, BD. External load fluctuations across an Amateur Athletic Union basketball season. J Strength Cond Res 38(3): 592-598, 2024-Amateur ...Athletic Union (AAU) competitions are an important component of the developmental pathway for youth basketball athletes. Despite its relative importance, there is currently a paucity of research investigating the physical demands in AAU basketball. Therefore, the purpose of this study was to examine the physical demands encountered over the course of an AAU basketball season. External training load was quantified using inertial sensors (Catapult T6) from one male AAU basketball team (age: 17.5 ± 0.5 years, height: 197.3 ± 10.0 cm, and mass: 89.4 ± 11.6 kg) over the course of the 2021 AAU season and categorized post hoc into high-, medium-, and low-minute groups based on mean playing minutes. After player categorization, 2 linear mixed models were constructed, one for PlayerLoad (PL) and one for duration, to examine the differences across player category, month of the season, and activity types (practices or games). The results show that the highest training loads were encountered by high-minute players, who had total PLs of 9,766 ± 1,516 AU, 13,207 ± 2,561 AU, and 7,071 ± 2,122 AU during April, May, and June, respectively. Highly variable training loads were also evident over the course of a season, with peak PL values as high as 4,921 AU per week. Practitioners should be aware that AAU basketball players experience variable loads throughout the season, which peak around congested competition/tournament periods. In addition, players with high game minutes accumulate the most load over the course of a season. This information may be used to better inform planning and periodizing strategies during developmental phases.
: There are currently no data describing combined practice and game load demands throughout a National Basketball Association (NBA) season. The primary objective of this study was to integrate ...external load data garnered from all on-court activity throughout an NBA season, according to different activity and player characteristics.
: Data from 14 professional male basketball players (mean ± SD; age, 27.3 ± 4.8 years; height, 201.0 ± 7.2 cm; body mass, 104.9 ± 10.6 kg) playing for the same club during the 2017-2018 NBA season were retrospectively analyzed. Game and training data were integrated to create a consolidated external load measure, which was termed
. Players were categorized by years of NBA experience (1-2y, 3-5y, 6-9y, and 10 + y), position (frontcourt and backcourt), and playing rotation status (starter, rotation, and bench).
: Total weekly duration was significantly different (
< 0.001) between years of NBA playing experience, with
highest in 3-5 year players, compared with 6-9 (
= 0.46) and 10+ (
= 0.78) year players. Starters experienced the highest
compared with bench (
= 0.77) players. There were no significant differences in
or duration between positions.
: This is the first study to describe the seasonal training loads of NBA players for an entire season and shows that a most training load is accumulated in non-game activities. This study highlights the need for integrated and unobtrusive training load monitoring, with engagement of all stakeholders to develop well-informed individualized training prescription to optimize preparation of NBA players.
Howarth, DJ, McLean, BD, Cohen, DD, and Coutts, AJ. Sensitivity of countermovement jump variables in professional rugby union players within a playing season. J Strength Cond Res 37(7): 1463-1469, ...2023-The aim of this study was to explore the measurement sensitivity of a wide range of countermovement jump (CMJ) variables to a full European professional rugby union season. A secondary purpose was to compare 3 different data treatment methods for the calculation of CMJ variables. Twenty-nine professional rugby union players (mean ± SD; age 24 ± 4 years, height 183.7 ± 8.0 cm, body mass 101.6 ± 10.7 kg) completed a minimum of 12 CMJ testing sessions on Thursdays-a day preceded by a rest day and a minimum of 96 hours after a match-throughout a season. Measurement sensitivity, quantified by signal-to-noise ratio (SNR), was determined for 74 CMJ variables and was calculated by dividing the signal, (week-to-week variation expressed as a coefficient of variation CV%) by the noise (interday test/retest reliability expressed as CV%). We also identified variables which had no overlap between the 95% confidence intervals (CIs) for the signal and the noise. The 3 data treatment methods for comparison were (a) mean output across 3 jump trials (Mean3), (b) single output from the trial with the highest jump (BestJH), and (c) the trial with the highest flight time to contraction time ratio (BestFTCT). Most variables had an SNR >1.0 (Mean3 = 60/74; BestFTCT = 59/74; BestJH = 48/74). Fewer variables displayed a nonoverlap of 95% CIs (Mean3 = 23/60; BestFTCT = 22/59; BestJH = 16/48). Most CMJ variables during a professional rugby season demonstrated a signal that exceeded measured noise (SNR > 1.0) and that using the Mean3 or BestFTCT data treatment methods yields a greater number of variables considered sensitive within a season (i.e., SNR > 1.0) than when using BestJH. We also recommend the calculation of the 95% CIs for both signal and noise, with nonoverlap indicative of a greater probability that the responsiveness of the variable at team level (i.e., SNR) also applies at the individual level. As sensitivity analysis is cohort and environment specific, practitioners should conduct a sensitivity analysis using internal signal and noise data to inform their own monitoring protocols.
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