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Various terms used in sport and exercise science, and medicine, are derived from other fields such as epidemiology, pharmacology and causal inference. Conceptual and nomological frameworks have described training load as a multidimensional construct manifested by two causally related subdimensions: external and internal training load. In this article, we explain how the concepts of training load and its subdimensions can be aligned to classifications used in occupational medicine and epidemiology, where exposure can also be differentiated into external and internal dose. The meanings of terms used in epidemiology such as exposure, external dose, internal dose and dose-response are therefore explored from a causal perspective and their underlying concepts are contextualised to the physical training process. We also explain how these concepts can assist in the validation process of training load measures. Specifically, to optimise training (i.e. within a causal context), a measure of exposure should be reflective of the mediating mechanisms of the primary outcome. Additionally, understanding the difference between intermediate and surrogate outcomes allows for the correct investigation of the effects of exposure measures and their interpretation in research and applied settings. Finally, whilst the dose-response relationship can provide evidence of the validity of a measure, conceptual and computational differentiation between causal (explanatory) and non-causal (descriptive and predictive) dose-response relationships is needed. Regardless of how sophisticated or "advanced" a training load measure (and metric) appears, in a causal context, if it cannot be connected to a plausible mediator of a relevant response (outcome), it is likely of little use in practice to support and optimise the training process.

BACKGROUND: Hyperthermia (and associated health and performance implications) can be a significant problem for athletes and teams involved in intermittent sports. Quantifying the highest thermal strain (i.e. peak core body temperature [peak Tc]) from a range of intermittent sports would enhance our understanding of the thermal requirements of sport and assist in making informed decisions about training or match-day interventions to reduce thermally induced harm and/or performance decline. OBJECTIVE: The objective of this systematic review was to synthesise and characterise the available thermal strain data collected in competition from intermittent sport athletes. METHODS: A systematic literature search was performed on Web of Science, MEDLINE, and SPORTDiscus to identify studies up to 17 April 2023. Electronic databases were searched using a text mining method to provide a partially automated and systematic search strategy retrieving terms related to core body temperature measurement and intermittent sport. Records were eligible if they included core body temperature measurement during competition, without experimental intervention that may influence thermal strain (e.g. cooling), in healthy, adult, intermittent sport athletes at any level. Due to the lack of an available tool that specifically includes potential sources of bias for physiological responses in descriptive studies, a methodological evaluation checklist was developed and used to document important methodological considerations. Data were not meta-analysed given the methodological heterogeneity between studies and therefore were presented descriptively in tabular and graphical format. RESULTS: A total of 34 studies were selected for review; 27 were observational, 5 were experimental (2 parallel group and 3 repeated measures randomised controlled trials), and 2 were quasi-experimental (1 parallel group and 1 repeated measures non-randomised controlled trial). Across all included studies, 386 participant

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 greate

OBJECTIVES: The purpose of this study was to investigate exercise enjoyment in high-intensity multimodal training (HIMT) in current and previous HIMT participants and identify factors associated with HIMT that mediate exercise enjoyment and motivation. DESIGN AND SETTING: A 124-item web-based survey was distributed to a cross-sectional voluntary convenience sample from August to the end of September 2021. PARTICIPANTS: Global current and previous HIMT participants. RESULTS: The final sample included 469 responses (completion rate: 61.6%). Among eligible respondents (n=434), 379 were current HIMT participants, 55 were previous HIMT participants. Current participants demonstrated high enjoyment (Physical Activity Enjoyment Scale). The most frequently reported reasons for increased enjoyment and motivation to continue HIMT in current participants included (1) it keeps me fit, (2) training in a group and (3) variety in a session. The most frequently reported reasons for reduced motivation to continue HIMT among previous HIMT participants included (1) other (injury, COVID-19 restrictions, low motivation, personal preferences), (2) work commitments and (3) I started another type of sport, exercise or training. CONCLUSIONS: These findings indicate that HIMT is an enjoyable training method among current participants. The most commonly reported reasons for increased enjoyment and motivation were associated with the combined training method and the group environment. Reasons for reduced motivation to continue HIMT among previous HIMT participants may be related to commonly reported barriers to exercise and personal factors.

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 XX(X): 000-000, 2023-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.

PURPOSE: The aim of this investigation was to examine the impact of mental fatigue on basketball specific shooting performance, utilising the newly developed basketball Standardized Shooting Task (SST). METHODS: Fifteen male elite NCAA Division 1 collegiate basketball players (Age 20.2 ± 1.2 y, height 199.3 ± 7.1 cm, body mass 93.1 ± 8.6 kg) volunteered to participate in a randomised, counterbalanced crossover design undergoing three conditions (Control, Stroop, and Film). The task, performed on three consecutive days, was comprised of 60 free throw attempts followed by a 4-minute spot-to-spot shooting. RESULTS: Visual Analog Scales revealed significantly higher levels of mental fatigue following the Stroop (54.2 ± 24.5) condition compared to the Control (24.5 ± 16.2) and higher levels of mental effort in the Stroop (61.0 ± 31.3) and Film (49.9 ± 27.7) compared to the Control (14.0 ± 18.5). No significant differences were observed for Motivation among groups (p > 0.05). There was a significant decrease (p = 0.006) in number of shots made in 4-minutes (MAKE4MIN; control = 49.5 ± 10.2, Stroop = 44.0 ± 10.6, and Film = 45.1 ± 11.7) and shots missed in 4-minutes (MISS4MIN; control = 27.3 ± 7.0, Stroop = 30.9 ± 7.1, and Film = 30.9 ± 7.6). No significant differences were detected for any other performance variables. CONCLUSION: These data demonstrate that mental fatigue negatively impacts basketball shooting performance in elite collegiate basketball players. We suggest that practitioners and coaches encourage athletes to abstain from cognitively demanding tasks prior to basketball competition.

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.

This study aimed to investigate the test-retest reliability and discriminant validity of the Mobalytics Proving Ground™ assessment for League of Legends. Forty participants (age: 24.15 ± 3.68 y, sex: male = 31, female = 9) were a priori classified into two expertise groups: (1) esports players (age: 22.98 ± 3.64 y, sex: male = 18, female = 2), and (2) controls (age: 25.31 ± 3.42 y, sex: male = 13, female = 7). Participants completed three separate trials (60 s each) online. To assess test-retest reliability, variables displaying normal distributions were analysed using intraclass correlation coefficient (ICC) estimates for two-way mixed-effects models with 95% confidence intervals. The average ICC for all the independent variables in the esports group and control group were moderate (ICC esports = 0.53 and ICC control = 0.72). The average 95% confidence intervals for the independent variables in the esports and control group were ICC = 0.30–0.75 and ICC = 0.55–0.86, respectively. A Friedman test revealed an effect size of 0.11 in the esports group and 0.07 in the control group. In terms of discriminant validity, there were significant differences for 17 variables when comparing the best scores of each group. Overall, the Mobalytics Proving Ground™ assessment used in the current study can, to some extent, distinguish esports players from controls.

BACKGROUND: Elite Australian Football (AF) match-play requires proficiency in physical, technical, and tactical elements. However, when analysing player movement practitioners commonly exclude technical and tactical considerations, failing to recognise the multifactorial nature of AF match-play and providing little context into the movement requirements of the players. OBJECTIVES: This systematic review aimed to identify the physical, technical, and tactical requirements of the Australian Football League (AFL) and to highlight the importance of integrating data from multiple sources when analysing player output. METHODS: A systematic search of electronic databases (CINAHL, PubMed, Scopus, SPORTDiscus, and Web of Science) was conducted from January 2009 to June 2022. Keywords relating to physical, technical, and tactical match requirements were used. RESULTS: Forty-eight studies met the inclusion criteria. In isolation, physical requirements were the most analysed construct within the AFL (n = 17), followed by technical (n = 9) and then tactical (n = 6). Thirteen studies integrated physical and technical elements, one study integrated technical and tactical elements, one study integrated physical and tactical elements, and one study integrated all three elements. Movement analysis centred around average 'whole' match requirements, whereas technical and tactical match analyses focused on key performance indicators of match performance. CONCLUSION: While the physical requirements of the AFL have been well documented, there is little understanding of how player technical output and various team tactics influence player movement requirements. Knowledge of how the elements of AF match-play interact with one another could enhance our understanding of match performance and provide a greater resource for training prescription.

Purpose: Previous research investigating the impact of induced mental fatigue in football (soccer) has demonstrated associated performance decrements in physical, technical, tactical and decision-making performance. A common limitation amongst this research is the protocols used to induce mental fatigue which provides low ecological validity, and the inclusion of recreational or sub-elite players. Therefore, understanding the presence of mental fatigue in elite football can provide insight into protocols with greater ecological validity. Methods: The current study used focus groups with 10 elite female football players, focusing on five topics (travel, fixture congestion, receiving tactical information, pre-match routine and pressure to win) related to the perceived causes of mental fatigue in elite football (directed by anecdotal quotes in elite football and research-based theories). Results: Several themes emerged from the data; travel fatigue, inability to switch off from football, fatigue experienced following team meetings, use of pre-match music and internal pressure to succeed. Conclusion: These findings present practical recommendations to reduce mental fatigue in elite football settings, such as considering the timing, content and duration of team meetings, providing players with free time/rest where possible, and considering the modality of coaching instructions during matches.

The purpose of this investigation was to develop a basketball shooting performance test and subsequently assess the tests measurement characteristics and construct validity. The novel standardized shooting task (SST) was comprised of 60 free throw attempts followed by a 4-min spot-to-spot shooting segment (including seven sequential locations either outside the 3-point line or at a 15-foot mark, depending upon player role). After development of the SST, 28 (male = 16, female = 12) NCAA Division I basketball players completed the task on separate days (Part 1; reliability) and then following a standard basketball practice (Part 2; sensitivity). SST performance collected from 13 males was then compared with shots made during live practices and expert ranking's (Part 3; construct validity). Interday reliability (Part 1) measures were: intraclass correlations coefficient = 0.77–0.86; coefficient of variation = 1.9–12.0%. There were no significant differences (p>0.05) between days for any performance variables. From pre- to post-practice (Part 2) repeated measures analyses of variance showed a significant difference (p = 0.03) for shots made in 4-min (MAKE4MIN; pre = 51.6 ± 8.8, post practice = 48.7 ± 9.3), while no significant differences were detected for any other variables. Results from Part 3 indicated large correlations for Shooter Rank versus MAKE4MIN (r = 0.814, p = 0.001) and Shooter Rank versus shooting percentage in live play (r = 0.815, p = 0.001). These data demonstrate the SST poses sufficient reliability and sensitivity to detect meaningful changes in performance, as well as adequate construct validity. Therefore, offering an ecologically valid measurement which can be incorporated to athlete monitoring strategies in elite basketballers.

PURPOSE: To describe the physiological (resting core temperature, exercising heart rate, and sweat rate) and psychophysical (rating of perceived exertion, thermal sensation, and thermal comfort) responses to a short-term heat acclimatization (HA) training camp in elite female rugby sevens athletes. METHODS: Nineteen professional female rugby sevens athletes participated in a 5-day HA camp in Darwin, Australia (training average: 32.2°C and 58% relative humidity). Training involved normal team practice prescribed by appropriate staff. Markers of physiological and psychophysical adaptations to HA were collected at various stages during the camp. Partial eta-squared effect sizes (from linear mixed-effects models), rank-biserial correlations (from Freidman tests), and P values were used to assess changes across the protocol. RESULTS: Resting core temperature did not significantly change. Exercising heart rate showed a large and significant reduction from day 1 to day 5 (175 [13] vs 171 [12] beats·min-1), as did sweat rate (1.1 [0.3] vs 1.0 [0.2] L·h-1). Thermal sensation showed a large and significant reduction between day 1 and day 5 (median [interquartile range] = 5 [5-5.5] vs 4.5 [4-5]). Changes in rating of perceived exertion and thermal comfort were unclear. CONCLUSIONS: Beneficial cardiovascular adaptations were observed simultaneously across a full squad of elite female rugby sevens players (without expensive facilities/equipment or modifying training content). However, beneficial changes in resting core temperature, sweat rate, and thermal/effort perceptions likely require a greater thermal impulse. These data contribute to the development of evidence-informed practice for minimal effective HA doses in female team-sport athletes, who are underrepresented in the current research.

BACKGROUND: High-intensity multimodal training (HIMT) is emerging as a popular training method that combines aerobic and resistance training throughout a single exercise session. The current literature is limited by a lack of terminology that broadly encompasses all styles of combined aerobic and resistance training. The magnitude of chronic or long-term (i.e. ≥ 4 weeks) effects of HIMT participation on aerobic and muscular fitness also remains unclear. Additionally, one of many complex reasons for the growing popularity of HIMT may be attributed to the affective response to exercise, namely levels of enjoyment. However, this concept is not yet well understood across all styles of HIMT. A comprehensive systematic review is required to synthesise the available literature and attempt to provide an operational definition of HIMT to capture the breadth of combined training styles that exist. OBJECTIVE: The objective of this systematic review was to determine the chronic effects of HIMT participation on aerobic and muscular fitness and to compare HIMT to established concurrent training methods. Enjoyability and other adherence-related subjective responses were also examined in HIMT participants. This review critically assessed the level of evidence and feasibility of current HIMT guidelines. METHODS: A systematic literature search was conducted on PubMed, Web of Science and SPORTDiscus to identify studies up until March 2021. RESULTS: A total of 20 studies were included for review. Studies generally reported moderate to large effects on aerobic fitness and subjective responses in favour of HIMT interventions. Mixed outcomes were demonstrated in muscular fitness. These results should be treated with caution due to high risk of bias among included studies. CONCLUSIONS: Few studies have assessed the chronic effects of HIMT participation on aerobic, and musculoskeletal adaptations and subjective responses, in particular exercise enjoyment. Research conclusions are limited b

A recent paper called for the abandonment of the term load (and training load) when used outside its mechanical meaning, claiming it is "unscientific" and "breaches scientific principles." In this article, we explain why its use does not breach any scientific principles and we clarify the process of labelling, conceptualising and operationalising a construct. Training load is simply a label attributed to a higher-order construct overarching other interrelated sub-dimensions. This multi-level structure provides a framework (nomological network) to support the research process and also practical applications. Load is a word, and therefore cannot be "unscientific". The "use" or "misuse" of words and terms entirely depends upon definitions that should be based on current understanding. Misuse occurs when a term is decontextualised or interpreted according to a unilateral perspective. The field of mechanics does not have a monopoly on the term load (or other common terms such as work, stress and fatigue), which are legitimately used in many scientific areas and with various meanings. The 'obligation' to rely on terms abiding by the Système International d'Unités (SI) when describing a construct is inappropriate. The SI relates to how we can measure, not describe training load; i.e., SI is relevant to its operational and not its constitutive (descriptive) definition. Discussions regarding shared and standardised descriptions and definitions are more relevant than discussions about discarding terms in sport and exercise science. Researchers (and practitioners) can continue to use the term training load as it does not breach any scientific principles.

This study examined the influence of birth date, salivary testosterone [sT] concentration, sexual maturity status, and general strength on the selection process of an elite Brazilian soccer club during a 12-month period, which was a 2nd phase of a 24-month selection process. The stature, body mass, sT, sexual maturity status [Tanner scale], and hand grip strength were assessed for 143 players during 2 weeks. From these 143 players, 100 players were dismissed [DIS] and 43 players were selected to integrate the club's under-14 squad. Following 1-year training period, the under-14 team was assembled with 9 players designated as starters [STA], and then, comparisons were conducted taking into account a group of non-starters (selected; SEL = 34 players) and STA (n = 09). The DIS, SEL, STA players, and reference population, were compared for birth distribution. A greater proportion of players was born in the first trimester in the STA [75.0%], SEL [57.1%] and DIS [50.0%] groups compared to the reference population [25.8%]. One-way ANOVA showed a higher sT for STA group [516.0 ± 129.9 pmol·L-1], compared to SEL [415.5 ± 117.9 pmol·L-1] and DIS groups [390.9 ± 84.9 pmol·L-1; p = 0.003), and Kruskall-Wallis test showed a higher gonadal development for STA compared to DIS [p = 0.001]. The current findings suggest a strong influence of birth date and biological maturation on young soccer players selection process. Soccer coaches should consider these influences when making decisions about player selection of elite youth players.

Purpose: To investigate the relationships between internal and external training load (TL) metrics with elite international women’s basketball performance. Methods: Sessional ratings of perceived exertion, PlayerLoad™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance. Results: For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47–.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305–0.431). Conclusions: Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women’s basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players’ performance, especially in internal TL.

Purpose: 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. Methods: 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 integrated load. 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). Results: Total weekly duration was significantly different (p < 0.001) between years of NBA playing experience, with duration highest in 3-5 year players, compared with 6-9 (d = 0.46) and 10+ (d = 0.78) year players. Starters experienced the highest integrated load, compared with bench (d = 0.77) players. There were no significant differences in integrated load or duration between positions. Conclusion: 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.

A conceptual framework has a central role in the scientific process. Its purpose is to synthesize evidence, assist in understanding phenomena, inform future research and act as a reference operational guide in practical settings. We propose an updated conceptual framework intended to facilitate the validation and interpretation of physical training measures. This revised conceptual framework was constructed through a process of qualitative analysis involving a synthesis of the literature, analysis and integration with existing frameworks (Banister and PerPot models). We identified, expanded, and integrated four constructs that are important in the conceptualization of the process and outcomes of physical training. These are: (1) formal introduction of a new measurable component 'training effects', a higher-order construct resulting from the combined effect of four possible responses (acute and chronic, positive and negative); (2) explanation, clarification and examples of training effect measures such as performance, physiological, subjective and other measures (cognitive, biomechanical, etc.); (3) integration of the sport performance outcome continuum (from performance improvements to overtraining); (4) extension and definition of the network of linkages (uni and bidirectional) between individual and contextual factors and other constructs. Additionally, we provided constitutive and operational definitions, and examples of theoretical and practical applications of the framework. These include validation and conceptualization of constructs (e.g., performance readiness), and understanding of higher-order constructs, such as training tolerance, when monitoring training to adapt it to individual responses and effects. This proposed conceptual framework provides an overarching model that may help understand and guide the development, validation, implementation and interpretation of measures used for athlete monitoring.

This study investigated the variability in the worst-case scenario (WCS) and suggested a framework to improve the definition and guide further investigation. Optical tracking data from 26 male players across 38 matches were analysed to determine the WCS for total distance, high-speed running (>5.5 m.s-1) and sprinting (>7.0 m.s-1) using a 3-minute rolling window. Position, total output, previous epoch, match half, time of occurrence, classification of starter vs substitute, and minutes played were modelled as selected contextual factors hypothesized to have associations with the WCS. Linear mixed effects models were used to account for cross-sectional observations and repeated measures. Unexplained variance remained high (total distance R2 = 0.53, high-speed running R2 = 0.53 and sprinting R2 = 0.40). Intra-individual variability was also high (total distance CV = 4.6-8.2%; high-speed CV = 15.6-37.8% and Sprinting CV = 21.1-76.4%). The WCS defined as the maximal physical load in a given time-window, produces unstable metrics lacking context, with high variability. Furthermore, training drills targetting this metric concurrently across players may not have representative designs and may underprepare athletes for complete match demands and multifaceted WCS scenarios. Using WCS as benchmarks (reproducing similar physical activity for training purposes) is conceptually questionable.

Coyne, JOC, Coutts, AJ, Newton, RU, and Haff, GG. The influence of mental fatigue on sessional ratings of perceived exertion in elite open and closed skill sports athletes. J Strength Cond Res 35(4): 963–969, 2021—The main purpose of this investigation was to examine influence of mental fatigue on sessional ratings of perceived exertion (sRPE) over a training week in elite athletes in open skill (OS, i.e., more unpredictable and externally paced sports) and closed skill (CS, i.e., more predictable and internally paced) sports. Visual analogue scales for mental fatigue, sRPE (CR-10 scale), and training duration were collected from an OS group (n = 27) of basketball and volleyball athletes and a CS group (n = 28) of weightlifting and track and field athletes during a typical training week 5 months before the 2016 Olympic Games. These variables were then examined using repeated measure correlations and linear mixed models with the level of significance set for the study at p < 0.05. There was a small significant correlation between mental fatigue and sRPE in the OS group (r = 0.23, p < 0.01), but not in the CS group (r = −0.07, p = 0.38). Mental fatigue had trivial influence on sRPE during individual sessions, but had a moderate effect on total sRPE over a week (p < 0.001, f2 = 0.265) when accounting for type of sport, training duration, and injury/illness burden. It seems mental fatigue may not significantly influence sRPE in individual training sessions, but may potentially have a cumulative effect that may affect the sRPE over a training week. This suggests monitoring mental fatigue independently of other training load (TL) measures may be worthwhile for strength and conditioning specialists and sports coaches to manage their athletes and researchers conducting studies into TL and performance.

PURPOSE: This study aimed to independently validate a wearable inertial sensor designed to monitor training and performance metrics in swimmers. METHODS: A total of 4 male (21 [4] y, 1 national and 3 international) and 6 female (22 [3] y, 1 national and 5 international) swimmers completed 15 training sessions in an outdoor 50-m pool. Swimmers were fitted with a wearable device (TritonWear, 9-axis inertial measurement unit with triaxial accelerometer, gyroscope, and magnetometer), placed under the swim cap on top of the occipital protuberance. Video footage was captured for each session to establish criterion values. Absolute error, standardized effect, and Pearson correlation coefficient were used to determine the validity of the wearable device against video footage for total swim distance, total stroke count, mean stroke count, and mean velocity. A Fisher exact test was used to analyze the accuracy of stroke-type identification. RESULTS: Total swim distance was underestimated by the device relative to video analysis. Absolute error was consistently higher for total and mean stroke count, and mean velocity, relative to video analysis. Across all sessions, the device incorrectly detected total time spent in backstroke, breaststroke, butterfly, and freestyle by 51% (15%). The device did not detect time spent in drill. Intraclass correlation coefficient results demonstrated excellent intrarater reliability between repeated measures across all swimming metrics. CONCLUSIONS: The wearable device investigated in this study does not accurately measure distance, stroke count, and velocity swimming metrics or detect stroke type. Its use as a training monitoring tool in swimming is limited.

Causal pathways between training loads and the mechanisms of tissue damage and athletic injury are poorly understood. Here, the relation between specific training load measures and metrics, and causal pathways of gradual onset and traumatic injury are examined. Currently, a wide variety of internal and external training load measures and metrics exist, with many of these being commonly utilized to evaluate injury risk. These measures and metrics can conceptually be related to athletic injury through the mechanical load-response pathway, the psycho-physiological load-response pathway, or both. However, the contributions of these pathways to injury vary. Importantly, tissue fatigue damage and trauma through the mechanical load-response pathway is poorly understood. Furthermore, considerable challenges in quantifying this pathway exist within applied settings, evidenced by a notable absence of validation between current training load measures and tissue-level mechanical loads. Within this context, the accurate quantification of mechanical loads holds considerable importance for the estimation of tissue damage and the development of more thorough understandings of injury risk. Despite internal load measures of psycho-physiological load speculatively being conceptually linked to athletic injury through training intensity and the effects of psycho-physiological fatigue, these measures are likely too far removed from injury causation to provide meaningful, reliable relationships with injury. Finally, we used a common training load metric as a case study to show how the absence of a sound conceptual rationale and spurious links to causal mechanisms can disclose the weaknesses of candidate measures as tools for altering the likelihood of injuries, aiding the future development of more refined injury risk assessment methods.

AIM: The COVID-19 pandemic forced the 2019–20 Italian Serie A competition to stop and players went into lockdown. During lockdown, players only trained at home, likely having a detrimental effect on players’ physical fitness and capacity. This study investigated the effect of the COVID-19 lockdown on professional soccer players’ match physical activities. METHODS: Match activities of 265 male professional soccer players were assessed in two periods prior to (PRE1 and PRE2) and one period following the lockdown (POST) using a video tracking system. Linear mixed models were used to examine differences between-periods in total (TD), very high-speed (VHS), sprint (SPR), high-acceleration (ACC) and high-deceleration (DEC) distances, considering full match data and data from six 15-min intervals. RESULTS: TD and VHS during POST were lower than the two other competitive periods (p < 0.001, d small-moderate). SPR did not show differences between periods (p > 0.636). ACC and DEC during POST were lower than PRE2 (p < 0.015, d small). Declines in most 15-min intervals after lockdown were observed in TD and VHS. CONCLUSIONS: There were small differences in the temporal distribution of SPR, ACC and DEC at POST. After the COVID-19 lockdown, soccer players’ higher-intensity running activities were similar to those of games played before the lockdown, but TD and VHS decreased, both considering the entire match and 15-min intervals. The temporal distribution of running activities was mostly stable throughout the season.

Abstract

Information contained in our article published in the Journal of Athletic Training was intended to be complementary to, and to address the integration of, concepts introduced in our clinical commentary published in the Journal of Orthopaedic & Sports Physical Therapy. The contents of the Journal of Athletic Training article were substantively built upon some concepts initially presented in the Journal of Orthopaedic & Sports Physical Therapy clinical commentary that, regrettably, could not be explained in detail in the original publication because of word-count limitations. As the review process of the 2 manuscripts overlapped, the authors missed the opportunity to cite the first clinical commentary but acknowledge that we could have done this during the final stage of the Journal of Athletic Training review process. We acknowledge that the clinical commentary first accepted in the Journal of Orthopaedic & Sports Physical Therapy could have been cited in the Journal of Athletic Training article.

Purpose: Previous research investigating the impact of induced mental fatigue in football (soccer) has demonstrated associated performance decrements in physical, technical, tactical and decision-making performance. A common limitation amongst this research is the protocols used to induce mental fatigue which provides low ecological validity, and the inclusion of recreational or sub-elite players. Therefore, understanding the presence of mental fatigue in elite football can provide insight into protocols with greater ecological validity. Methods: The current study used focus groups with 10 elite female football players, focusing on five topics (travel, fixture congestion, receiving tactical information, pre-match routine and pressure to win) related to the perceived causes of mental fatigue in elite football (directed by anecdotal quotes in elite football and research-based theories). Results: Several themes emerged from the data; travel fatigue, inability to switch off from football, fatigue experienced following team meetings, use of pre-match music and internal pressure to succeed. Conclusion: These findings present practical recommendations to reduce mental fatigue in elite football settings, such as considering the timing, content and duration of team meetings, providing players with free time/rest where possible, and considering the modality of coaching instructions during matches.

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BACKGROUND:Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport. OBJECTIVES:The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term 'training load' to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature. METHODS:A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy. RESULTS:Electronic and manual searches identified 122 papers that met the

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The purpose of this 2-part commentary series is† to explain why we believe our ability to control injury risk by manipulating training load (TL) in its current state is an illusion and why the foundations of this illusion are weak and unreliable. In part 1, we introduce the training process framework and contextualize the role of TL monitoring in the injury-prevention paradigm. In part 2, we describe the conceptual and methodologic pitfalls of previous authors who associated TL and injury in ways that limited their suitability for the derivation of practical recommendations. The first important step in the training process is developing the training program: the practitioner develops a strategy based on available evidence, professional knowledge, and experience. For decades, exercise strategies have been based on the fundamental training principles of overload and progression. Training-load monitoring allows the practitioner to determine whether athletes have completed training as planned and how they have coped with the physical stress. Training load and its associated metrics cannot provide a quantitative indication of whether particular load progressions will increase or decrease the injury risk, given the nature of previous studies (descriptive and at best predictive) and their methodologic weaknesses. The overreliance on TL has moved the attention away from the multifactorial nature of injury and the roles of other important contextual factors. We argue that no evidence supports the quantitative use of TL data to manipulate future training with the purpose of preventing injury. Therefore, determining "how much is too much" and how to properly manipulate and progress TL are currently subjective decisions based on generic training principles and our experience of adjusting training according to an individual athlete's response. Our message to practitioners is to stop seeking overly simplistic solutions to complex problems and instead embrace the risks and uncerta

© 2020 Informa UK Limited, trading as Taylor & Francis Group. The purpose of this study was to assess relationships between measures of training load, training response and neuromuscular performance and changes in individual match performance in professional Australian football. Data were collected from 45 professional Australian footballers from one club during the 2019 competition season. External load was measured by GPS technology. Internal load was measured via session rate of perceived exertion (sRPE). Perceptual wellness was measured via pre-training questionnaires (1–5 Likert scale rating of soreness, sleep, fatigue, stress and motivation). Percentage of maximum speed was calculated relative to individual maximum recorded during preseason testing. Rolling derivative training load measures (7-day and 28-day) were calculated. Principal Component Analysis (PCA) identified eight uncorrelated components. PCA factor loadings were used to calculate summed variable covariates and single variables were chosen from components based on practicality and statistical contribution. Associations between covariates and performance were determined via linear Generalised Estimating Equations. Performance was assessed via Player Ratings from a commercial statistics company. Seven-day total distance, IMA event count and sRPE load showed significant positive relationships with performance (18–23% increase in performance z-score). No other covariates displayed significant associations with performance. Individual relative increases in training load within the 7-day period prior to a match may be beneficial for enhancing individual performance.

This study investigated the association between talent selection criteria, draft order and match performance in professional Australian Football players. Physical performance results from the Australian Football League (AFL) National Draft combine and non-performance based talent selection criteria were collated for all players drafted in the National Draft with selections 1-80 between 2003 and 2008 (n = 318). Match performance was assessed via the AFL Player Ranking metric that was provided by a commercial statistical provider (Champion Data Pty Ltd). A combination of stepwise multiple regression and linear mixed model analyses examined the influence of National Draft combine physical performance assessments and non-performance based talent selection criteria on draft order and future match performance. Earlier draft selection was associated with Under-18 all Australian team selection, height, Under-18 National Championship participation and indigenous status. The 20 m sprint and state of origin were associated with later draft selection. Under-18 all Australian team selection increased both Player Ranking/game and total Player Ranking. Under-18 all Australian team selection displays efficacy for selecting players with the potential for success.

In part 2 of this clinical commentary, we highlight the conceptual and methodologic pitfalls evident in current training-load-injury research. These limitations make these studies unsuitable for determining how to use new metrics such as acute workload, chronic workload, and their ratio for reducing injury risk. The main overarching concerns are the lack of a conceptual framework and reference models that do not allow for appropriate interpretation of the results to define a causal structure. The lack of any conceptual framework also gives investigators too many degrees of freedom, which can dramatically increase the risk of false discoveries and confirmation bias by forcing the interpretation of results toward common beliefs and accepted training principles. Specifically, we underline methodologic concerns relating to (1) measure of exposures, (2) pitfalls of using ratios, (3) training-load measures, (4) time windows, (5) discretization and reference category, (6) injury definitions, (7) unclear analyses, (8) sample size and generalizability, (9) missing data, and (10) standards and quality of reporting. Given the pitfalls of previous studies, we need to return to our practices before this research influx began, when practitioners relied on traditional training principles (eg, overload progression) and adjusted training loads based on athletes' responses. Training-load measures cannot tell us whether the variations are increasing or decreasing the injury risk; we recommend that practitioners still rely on their expert knowledge and experience.

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This study's purpose was to examine heart rate variability (HRV) and direct current potential (DC) measures' sensitivity and correlations between changes in the acute recovery and stress scale (ARSS) and the previous day's training load. Training load, HRV, DC and ARSS data were collected from fourteen professional mixed martial arts athletes (32.6 ± 5.3 years, 174.8 ± 8.8 cm, 79.2 ± 17.5 kg) the following morning after hard, easy and rest days. Sensitivity was expressed as a signal-to-noise ratio (SNR, inter-day typical error (TE) or coefficient of variation (%CV) divided by intra-day TE or %CV). Correlations between HRV, DC and ARSS with training load were also examined. The SNRs for the various HRV and DC measures were acceptable to good (1.02-2.85). There was a 23.1% CV average increase between measures taken between different locations versus the same location. Training load changes were not correlated with HRV/DC but were correlated with ARSS stress variables. Practitioners should be aware of HRV/DC variability; however the daily training signal was greater than the test-retest error in this investigation. Upon awakening, HRV/DC measures appear superior for standardization and planning. HRV and DC measures were less sensitive to the previous day's training load than ARSS measures.

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BACKGROUND:The balance between training stress and recovery is important for inducing adaptations to improve athletic performance. However, continuously high training loads with insufficient recovery may cause fatigue to accumulate and result in overtraining. A comprehensive systematic review is required to collate overtraining literature and improve the current understanding of the mechanisms underlying functional overreaching (FOR), non-functional overreaching (NFOR) and the overtraining syndrome (OTS) in resistance training. OBJECTIVE:The objective of this systematic review was to establish markers of overtraining and elucidate the mechanisms underlying maladaptive resistance training conditions. Furthermore, this review aims to critically evaluate the methodological approaches of the overtraining literature. METHODS:A systematic literature search was performed on PubMed, Web of Science and SPORTDiscus to identify studies up to June 2019. Electronic databases were searched using terms related to resistance training and overtraining. Records were included if they attempted to induce a state of overreaching or overtraining through resistance exercise in healthy participants. RESULTS:A total of 22 studies were selected for review. Among these studies, eight resulted in decrements in performance and measured changes in performance during a follow-up period. There were four studies that reported decrease in performance yet failed to implement follow-up measures. A total of 10 studies reported no decline in performance. Overall, a lack of standardisation in methodology (follow-up performance testing) and diagnostic criteria prevents consistent determination of FOR, NFOR and OTS in resistance training. CONCLUSIONS:Few studies have appropriately established FOR, NFOR or OTS in resistance training. Overtraining may be related to frequent high-intensity and monotonous resistance training. However, no marker other than a sustained decrease in performance has been establishe