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Abstract

Exercise is a stressor that induces various psychophysiological responses, which mediate cellular adaptations in many organ systems. To maximize this adaptive response, coaches and scientists need to control the stress applied to the athlete at the individual level. To achieve this, precise control and manipulation of the training load are required. In 2003, the authors introduced a theoretical framework to define and conceptualize the measurable constructs of the training process. They described training load as having 2 measurable components: internal and external load. The aim of this commentary is to extend, clarify, and refine both the theoretical framework and the definitions of internal and external training load to avoid misinterpretation of this concept.

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... Inadequate management or control of sport-specific demands and training loads has been shown to increase the risk of injury [3], and ABSTRACT: The quantification of physical demands placed upon handball players, segmented by their specific roles and duration of play, is crucial for sustaining high performance and minimizing the risk of injury. Leveraging advanced inertial measurement units, this investigation captured and analyzed the external load data of athletes participating in the EHF Women's EURO 2022. ...
... Beyond understanding training and match demands, another critical aspect is the recovery of players between matches or training sessions, which requires effective fatigue management [16]. Incomplete recovery from accumulated fatigue can lead to increased injury risk or diminished sport performance [3,4]. ...
... The high values of the coefficient when random effect (players) was included with a Conditional R² between 0.82 and 0.92, highlights the influence of individual player profiles (Table 3). Factors like aerobic capacity, physical attributes, and playing style play a crucial role in performance [3,35]. Individual characteristics, including size, body mass, and technical abilities, also contribute to performance variability [35,36]. ...
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The quantification of physical demands placed upon handball players, segmented by their specific roles and duration of play, is crucial for sustaining high performance and minimizing the risk of injury. Leveraging advanced inertial measurement units, this investigation captured and analyzed the external load data of athletes participating in the EHF Women’s EURO 2022. The aim of this study was to provide coaching staff with information on fatigue development during periods of high match density. The study evaluated the effects of playing position and cumulative playing time on external load metrics, using linear mixed models that treated individual players as random effects. The study employed a cutting-edge computational framework integrating sensor network technologies, Local Positioning Systems (LPS), and Big Data Analytics within a descriptive analytics methodology. From over half a billion raw records, we distilled 1,013 data entries from 47 matches for analysis. The findings reveal that the wings demonstrated the highest levels of total and high-speed running distances, though they sustained lower PlayerLoad relative to backs. Interestingly, cumulative playing time did not markedly alter load profiles, which may be attributed to strategic substitution decisions by coaches and the players’ own pacing strategies. Notable discrepancies within positional demands were observed over time, such as centers displaying increased distance coverage within the 2–3 hour play interval. This study underscores the efficacy of strategic load management and tailored pacing in sustaining player performance throughout high-stakes tournaments. It elucidates the relationship between managerial tactics and player-specific characteristics in the context of external load distribution.
... In the daily training environment, athletes and coaches routinely capture multiple training load indices, which can be measured and classified as either internal and/or external, based on the measurable aspects occurring internally or externally to the athlete [13]. Internal load reflects the relative physiological strain and disturbance in homeostasis of the metabolic processes in response to an external load, which is characterized by objective measures such as distance, power, or speed [14]. ...
... have been reported across multiple measures of internal and external training load in cyclists during racing and training [15], suggesting other metrics such as session rating of perceived exertion (sRPE) [16], Lucia training impulse (LuTRIMP) [17], and training stress score (TSS) [18] can also provide relevant information to athletes and coaches. However, there is no gold standard measure of training load [13], and measures of external and internal load are not always consistent. For example, TSS may overemphasize intensity compared with TWD, LuTRIMP, and sRPE [19,20], but without a standard for comparison it is unclear which measure may be over-emphasizing or underemphasizing intensity. ...
... Despite the ubiquity of training load quantification and variety of methods for measuring load, there is no consensus on which methods best represent the true load of a training session [13,31,44]. This is context dependent and relies heavily on the nature of the exercise stimulus (e.g., the sport or mode of training). ...
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Background Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions. Objectives We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load measures and easily quantified individual factors. Methods This study was undertaken in two parts: a primary study, where participants performed four different laboratory-based cycle training sessions, and a validation study where different participants performed a single laboratory-based training session using one of three exercise modalities (cycling, running, or kayaking). The primary study included 15 cyclists (five female; maximal oxygen consumption [V˙\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}O2max], 51.9 ± 7.2 mL/kg/min), the validation study included 21 cyclists (seven female; V˙\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}O2max 53.5 ± 11.0 mL/kg/min), 20 runners (six female; V˙\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}O2max 57.5 ± 7.2 mL/kg/min), and 18 kayakers (five female; V˙\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}O2max 45.6 ± 4.8 mL/kg/min). Training sessions were quantified using six training load metrics: two using heart rate, three using power, and one using perceived exertion. Carbohydrate use and EE were determined separately for aerobic (gas exchange) and anaerobic (net lactate accumulation, body mass, and O2 lactate equivalent method) energy systems and summed. Repeated-measures correlations were used to examine relationships between training load and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using training load alongside measures of fitness and sex. Models were built in the primary study and tested in the validation study. Model performance is reported as the coefficient of determination (R²) and mean absolute error, with measures of calibration used for model evaluation and for sport-specific model re-calibration. Results Very-large to near-perfect within-subject correlations (r = 0.76–0.96) were evident between all training load metrics and both CHO utilization and EE. In the primary study, all models explained a large amount of variance (R² = 0.77–0.96) and displayed good accuracy (mean absolute error; CHO = 16–21 g [10–14%], EE = 53–82 kcal [7–11%]). In the validation study, the mean absolute error ranged from 16–50 g [15–45%] for CHO models to 53–182 kcal [9–31%] for EE models. The calibrated mean absolute error ranged from 9–20 g [8–18%] for CHO models to 36–72 kcal [6–12%] for EE models. Conclusions At the individual level, there are strong linear relationships between all measures of training load and both CHO utilization and EE during cycling. When combined with other measures of fitness, EE and CHO utilization during cycling can be estimated accurately. These models can be applied in running and kayaking when used with a calibration adjustment.
... Athlete monitoring systems (AMSs) are increasingly used to assist the entire supporting team (coaches, medics, physiotherapists etc.) [4,6]. In addition to specifc demands of the sports, sport-and environment-specifc injuries and other sport-related health issues must be considered when planning and implementing AMS. ...
... In addition to injury monitoring as a cornerstone for injury prevention strategies, an AMS as a centralized software-based platform may include monitoring of athlete's external and internal loads, as well as their perceptual well-being and readiness for upcoming training sessions and competitions [7]. A variety of objective and subjective parameters are available for these purposes, especially since technological advances have brought new systems for tracking athletes, teams, and the ball indoors [4,6,8]. In a recent paper, Boullosa et al. pointed out that a specifc "fne-tuning approach" with a combination of appropriate monitoring parameters should be pursued for diferent sports [9]. ...
... External load leads to an individual psychophysiological response at various functional levels, which is referred to as internal load and considered as the actual stimulus for adaptation [6]. For example, the percentage of maximum oxygen consumption (VO 2max ), lactate measurements, or training heart rate (HR ex ) and training concepts derived from them can be used to assess internal load. ...
Article
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Athlete monitoring systems (AMSs) provide a centralized platform for integrating, processing, analyzing, and graphing various monitoring data to help coaches manage the rigorous demands of elite men’s basketball players, who frequently participate in high-stress games with minimal recovery time. This review synthesizes current challenges in deploying AMSs, underscores their role in injury prevention and performance optimization, and discusses technological advances that could enhance their utility. Key challenges include selecting appropriate monitoring methods based on human and financial resources, accuracy of data collection, real-time data processing, and personalization of training regimens. Due to the weaknesses and limitations of each monitoring method, it is recommended that both objective (e.g., external load data, heart rate measures, and biomarkers) and subjective (athlete-reported outcome measures) monitoring data be integrated into an AMS to provide a holistic insight of the athlete’s health and readiness. In addition, decision support systems integrated into an AMS can help coaches quickly gain an overview of their players’ current condition and make informed decisions about daily load and recovery management. In this context, future perspectives suggest the potential for AMSs to incorporate predictive analytics and artificial intelligence to further enhance decision-making processes in elite men’s basketball. Our findings underscore the need for continued innovation and rigorous validation of AMS technologies to ensure they meet the evolving demands of professional sports environments.
... Every person has distinct genetic, psychological, and physiological traits. In order to maximize the intended outcomes, training programs can be individually tailored to each person's unique needs and responses with the aid of training load monitoring (Impellizzeri et al., 2019). Monitoring has been shown to be useful in identifying functional limitations and asymmetries (Šć epanović T et al., 2020). ...
... Periodization, training, and training intensity pertain to the organization and scheduling of training programs, as well as the level of exertion imposed on athletes during training. Monitoring training load is crucial for ensuring appropriate periodization and achieving optimal training intensity (Bourdon et al., 2017 andImpellizzeri et al., 2019). Adolescents and elite athletes share similar characteristics when it comes to being the focus of training load monitoring. ...
... Individualization pertains to tailoring the training program to suit the unique characteristics and training load tolerance of each athlete. To prevent injuries resulting from excessive or insufficient training loads, training programs can be customized by closely monitoring and adjusting the training loads for each individual (Impellizzeri, 2019). Training load monitoring can assist in identifying high-risk phases or periods in a competition season or training program where the likelihood of injury is elevated. ...
Article
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Monitoring training load can help improve performance, predict injury risk, determine athlete readiness, and keep track of health conditions. By using bibliometric analysis methods, one can evaluate publications from institutions or countries and track the growth or decline of a specific field. The purpose of this study is to compile a bibliography of works on the subject of training load monitoring that were published between 1979 and 2023. This study analyzes publications on training load monitoring and uses scientific mapping to describe the structure and trends. Contributions from countries, authors, cited articles, frequently appearing keywords, and keyword trends are all covered in this study. According to the findings, research was scarce during the first two decades and significantly increased in the next. While Australia has the most publications, the European continent dominates this research field. Most articles are published in and referenced from the International Journal of Sports Physiology and Performance. Furthermore, due to its high citation count, Halson's article had the greatest influence. Some keywords are related and appear in this study. This article presents a trend visualization that academics can use as a reference guide. Keywords: monitoring, training load, bibiometric analysis.
... Training load can be divided into external load and internal loads [2]. External load is an objective measure of the work done by an athlete during training or competition, independent of internal load [3], it is determined by the organization, quality, and quantity of exercise [4]. Internal load refers to the relative physiological or psychological stress on an athlete during training or competition. ...
... As new technologies for measuring athletes' training loads become more available, sports scientists and basketball coaches are eager to adopt these innovations. External load measurement tools like electronic performance tracking systems (EPTS) [4] have become more prevalent in sports science [6]. Meanwhile, it is crucial to recognize that internal load determines the effectiveness of training [4,7]. ...
... External load measurement tools like electronic performance tracking systems (EPTS) [4] have become more prevalent in sports science [6]. Meanwhile, it is crucial to recognize that internal load determines the effectiveness of training [4,7]. Integrating training load monitoring methods is essential, as external and internal loads provide different information about the demands of training and competition [8]. ...
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Background Limited research has investigated the association between training load and performance of basketball players during games. Little is known about how different indicators of player performance are affected by internal and external loads. The purpose of this study was to determine whether external and internal loads influence basketball players’ performance during games. Method This longitudinal study involved 20 professional male basketball players from a single team, classified as first-level athletes by the Chinese Basketball Association. During 34 games, external load was measured as PlayerLoad using micro-sensors, while internal load was assessed using session rating of perceived exertion (sRPE). Player performance was quantified using three metrics: Efficiency, Player Index Rating (PIR), and Plus-Minus (PM). Pearson correlation coefficients were calculated to assess the strength of the relationships between training loads and performance metrics. Linear mixed-effects models were applied to further analyze the influence of internal and external loads on basketball performance. Results Pearson correlation analysis revealed moderate positive correlations between both sRPE and PlayerLoad with Efficiency and PIR. Specifically, sRPE (r = 0.52) and PlayerLoad (r = 0.54) were both significantly correlated with Efficiency. For PIR, sRPE (r = 0.50) and PlayerLoad (r = 0.56) also demonstrated moderate correlations. These correlations were further substantiated by linear mixed-effects models, which showed that sRPE (β = 2.21, p < 0.001) and PlayerLoad (β = 1.87, p = 0.004) had significant independent effects on Efficiency. Similarly, sRPE (β = 2.15, p < 0.001) and PlayerLoad (β = 2.36, p < 0.001) significantly predicted PIR. Additionally, a significant interaction effect between PlayerLoad and sRPE was found on Plus-Minus (β = -2.49, p < 0.001), indicating that the combination of high physical and psychological loads negatively impacted overall team performance. However, the correlation strengths for Plus-Minus were relatively low (sRPE: r = 0.16; PlayerLoad: r = 0.10). Conclusion Both external and internal loads positively contribute to performance, the integration of objective (accelerometry) and subjective (sRPE) measures of load provides a comprehensive understanding of the physiological and psychological demands on athletes, contributing to more effective training regimens and performance optimization.
... In this regard, the terms low-load BFR exercise [17][18][19] or low-intensity BFR exercise [20,21] are frequently used in the literature. However, the external load/ intensity is characterized by the exercise characteristics (e.g., external resistance, repetition scheme, volitional muscle failure), while the internal load/intensity (i.e., psychophysiological responses) is mirrored by multiple variables including heart rate or rate of perceived exertion (RPE) [22][23][24]. In this context, the application of BFR during low load/intensity exercise and its mode of action can lead to psychophysiological responses that are typically not associated with low but moderate or even high external load/intensity exercise. ...
... Physical activity and/or exercise (e.g., running, cycling, swimming) triggers acute psychophysiological responses and can lead to chronic adaptations when the exercise stimulus is applied repetitively, at sufficient time periods, and with appropriate magnitude [23,26]. To maximize long-term training adaptations, it is crucial to control and manipulate the stress applied to the exercising individual and the resulting psychophysiological strain. ...
... To maximize long-term training adaptations, it is crucial to control and manipulate the stress applied to the exercising individual and the resulting psychophysiological strain. Therefore, scientists have developed theoretical frameworks that distinguish between the physical work performed during exercise (i.e., external load/intensity) as well as indicators of the body's psychophysiological reactions and the strain experienced by specific tissues (i.e., internal load/intensity) in response to the applied external training load/intensity [22][23][24]27]. The terminology 'external and internal training load' was recently criticized from a biomechanical perspective pointing out that load is a mechanical variable, which describes forces [28][29][30]. ...
Article
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Physical exercise induces acute psychophysiological responses leading to chronic adaptations when the exercise stimulus is applied repeatedly, at sufficient time periods, and with appropriate magnitude. To maximize long-term training adaptations, it is crucial to control and manipulate the external load and the resulting psychophysiological strain. Therefore, scientists have developed a theoretical framework that distinguishes between the physical work performed during exercise (i.e., external load/intensity) and indicators of the body's psychophysiological response (i.e., internal load/intensity). However, the application of blood flow restriction (BFR) during exercise with low external loads/intensities (e.g., ≤ 30% of the one-repetition-maximum, ≤ 50% of maximum oxygen uptake) can induce physiological and perceptual responses, which are commonly associated with high external loads/intensities. This current opinion aimed to emphasize the mismatch between external and internal load/intensity when BFR is applied during exercise. In this regard, there is evidence that BFR can be used to manipulate both external load/intensity (by reducing total work when exercise is performed to exhaustion) and internal load/intensity (by leading to higher physiological and perceptual responses compared to exercise performed with the same external load/intensity without BFR). Furthermore, it is proposed to consider BFR as an additional exercise determinant, given that the amount of BFR pressure can determine not only the internal but also external load/intensity. Finally, terminological recommendations for the use of the proposed terms in the scientific context and for practitioners are given, which should be considered when designing, reporting, discussing, and presenting BFR studies, exercise, and/or training programs.
... Los programas de entrenamiento son diseñados bajo dos objetivos bien claros: (a) ayudar a los futbolistas a mejorar su rendimiento en competición y, (b) reducir el riesgo potencial de lesiones(Rodriguez et al., 2018). El estímulo de entrenamiento debe ser aplicado en los períodos con un tiempo suficiente y con una apropiada magnitud para prevenir una caída de esas adaptaciones antes de la competición(Impellizzeri et al., 2019).Para dar respuesta a este estímulo que se transforma para el futbolista como carga de entrenamiento,Banister et al. (1975) presentaron un modelo matemático(Fig.7.) que permitía simular como era la respuesta del impulso al stress de entrenamiento, el cual conocimos como, modelo fitness-fatigue (del inglés "fitness-fatigue model").Esto ha sido definido como la relación carga-respuesta, la cual es uno de los principios fundamentales de nuestra disciplina(Impellizzeri et al., 2018). Los entrenadores y preparadores físicos utilizan diferentes ejercicios y tareas de entrenamiento con sus jugadores durante las sesiones de entrenamiento, mientras que, simultáneamente, monitorizan la respuesta de esa carga de entrenamiento desde una doble categorización, la CI y la CEx. ...
... La carga de entrenamiento ha sido descrita como la variable de entrada que es manipulada para alcanzar la deseada respuesta al entrenamiento. En el marco teórico del proceso de entrenamiento(Fig.8.)la carga es representada como la CEx y CI, las cuales son definidas respectivamente, como el trabajo realizado por el deportista (CEx) y la respuesta fisiológica asociada a ella (CI)(Impellizzeri et al., 2019). ...
... Esquema del marco teórico del proceso de entrenamiento(Impellizzeri et al., 2019).Más recientemente, y aunque más adelante se profundizará en ello, Verheul et al., (2020) realizan una relación, con el hecho de que la CI ha sido ampliamente medida y monitorizada en fútbol, con el objeto de un mejor control del entrenamiento y en optimizar la relación carga-respuesta, nos muestran un doble camino para entender mejor dicha relación, en la línea del modelo presentado anteriormente por Verenterghem et al.,(2017). ...
Thesis
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El objetivo de esta tesis doctoral fue registrar, analizar y cuantificar, por primera vez, el perfil CT y CF mediante tecnología IMU en jugadores profesionales de fútbol durante partidos. La investigación está basada en un diseño observacional del análisis de la CT y CF durante partidos de un equipo profesional de fútbol. Un total de 18 futbolistas profesionales (n=18) de 24.6 ± 2.7 años, 1.78 ± 0.32 estatura (m), 74.6 ± 4.5 masa (kg), 23.54 ± 2.7 IMC (kg.m2), 9.76 ± 2.20 % graso, y 65.6 ± 2.7 VO2 Max (ml·kg·min2) pertenecientes a un mismo equipo, tomaron parte en este estudio. En el estudio se realizaron un total de 103 registros, los jugadores fueron clasificados de acuerdo a su posición específica: Defensa Lateral (DL: n=4), Defensa Central (DC: n=3), Mediocampista (MD: n=4), Extremo (EX: n=4) y Delantero (DT. n=3). En el análisis de las variables CF encontramos que WR (m/min) (p= ≤ 0.001, ES: 0.92), DR (m) (p= 0.02, ES: 0.57), DAI (m) (p= 0.01, ES: 0.27), DAI (m/min) (p= ≤ 0.001, ES:0.36), DS (m) (p= 0.02, ES: 0.21), DS (m/min) (p= 0.01, ES: 0.23), SP (#) (p= 0.04, ES:0.29), SP (#/min) (p= 0.01, ES: 0.37), DRZ3 (p= 0.01, ES: 0.31), DRZ4 (p= 0.01, ES:0.26) y DRZ5 (p= 0.02, ES: 0.24), generan valores significativamente mayores en la primera parte que en la segunda. Con respecto a las diferentes posiciones específicas, se observaron diferencias significativas por puestos tanto para variables de CT como para variables de CF. Atendiendo a variables de CT, por puestos específicos los MD son los que generaron un mayor registro de TT, sin alcanzar diferencias significativas con DC (p= 0.84), pero obteniendo diferencias significativas con DL (p = 0.01) y con DT (p ≤0.001). Estos resultados enfatizan la naturaleza independiente de CT desde la CF, y brindan información valiosa sobre las demandas técnicas de los jugadores en diferentes posiciones durante los partidos. Como conclusión principal, se afirma que el perfil de CF de los futbolistas profesionales, en función de su posición de juego, resulta independiente del desarrollo de CT observado durante los partidos. Por lo tanto, monitorear, cuantificar y controlar tanto el perfil de CT y CF de los jugadores de fútbol ofrece una comprensión más integral y holística de las demandas en los partidos en comparación con el análisis únicamente de CF.
... In the daily training environment, athletes and coaches routinely capture multiple training load indices, which can be measured and classified as either internal and/or external, based on the measurable aspects occurring internally or externally to the athlete [13]. Internal load reflects the relative physiological strain and disturbance in homeostasis of the metabolic processes in response to an external load, which is characterized by objective measures such as distance, power, or speed [14]. ...
... However, there is no gold standard measure of training load [13], and measures of external and internal load are not always consistent. For example, TSS may overemphasize intensity compared with TWD, LuTRIMP, and sRPE [19,20], but without a standard for comparison it is unclear which measure may be over-or under-emphasizing intensity. ...
... After applying the calibration adjustment to each predicted value in the validation study (calibration intercept + predicted value * calibration slope), accuracy of all models was improved as shown in Figure 7 (carbohydrate utilization) and Figure 8 (Table 4). there is no consensus on which methods best represent the true load of a training session [13,31,44]. This is context-dependent and relies heavily on the nature of the exercise stimulus (e.g., the sport or mode of training). ...
... I n the past 20 years, research on match and training load monitoring has increased considerably (19). In this context, several studies published to analyze "the most intense activity period (for an arbitrarily selected time frame) for a player within training or competition settings" (43) have also increased (e.g., Australian rules football (11), basketball (15), futsal (14), Gaelic football (24), handball (13), rink hockey (14), rugby union (10), and soccer (32,37,38)). ...
... Training load is "a multidimensional construct manifested by 2 causally related sub-dimensions: external and internal load" (20). Thus, the MDPs should fit into this framework because, strictly speaking, it is the manifestation of load within a specific period and can be expressed by external and internal parameters (19). Accordingly, the MDPs should integrate internal load measures with external load metrics (32,50) to provide a more holistic perspective. ...
... Whitehead et al. (51) noted that studies on the MDPs mostly used runningbased metrics across different team sports, which indicates that the narrow scope of metrics included in the MDPs analysis models is also a shortcoming. According to Impellizzeri et al. (19), the external load measures should be specific to the nature of the exercise undertaken. This suggests that the analysis models should be flexible to integrate the activities of each sport. ...
Article
In the context of training load monitoring, the most demanding periods of play (MDPs) have increasingly caught the interest of researchers. However, the MDPs analysis is currently embryonic, raising some conceptual and methodological questions. This current opinion synthesizes the methods used for the MDPs analysis while highlighting conceptual and methodological gaps and proposing a broader perspective on the topic. It is underlined that (a) the information available on the MDPs is mostly limited to external load (particularly running-based metrics), with scarce research focused on internal load; (b) the metrics have been analyzed in a univariate way, neglecting the multivariate scenarios from which the MDPs emerge; (c) the MDPs are highly variable over time due to the complex interaction between individual, tactical–technical, and contextual factors; and (d) scarce evidence is available regarding the contextualization of the MDPs from a tactical–technical perspective. Thus, the MDPs would benefit from cross-referencing external load with game moments and tactical actions while avoiding the idea of fixed benchmarks given the inherent match-to-match variability. Practitioners may consider replicating the MDPs (and their contexts) in (some?) training sessions as a complementary prescription strategy (metaphorically, the cherry on top, not the cake). However, the feasibility and effectiveness of such practices warrant investigation.
... Training load can be categorized into two primary dimensions: external and internal [3]. External load refers to the mechanical intensity imposed on players through various exercises or tasks [4], while internal load encompasses the psycho-physiological responses elicited by the external load [3]. ...
... Training load can be categorized into two primary dimensions: external and internal [3]. External load refers to the mechanical intensity imposed on players through various exercises or tasks [4], while internal load encompasses the psycho-physiological responses elicited by the external load [3]. Although these two dimensions do not always exhibit Therefore, this study aimed to assess changes in myometric parameters over a training microcycle in semi-professional female soccer players. ...
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This study aimed to evaluate changes in muscle contractile properties during a training microcycle in semi-professional female football players and explore their relationship with training load variables. Nineteen players (age: 23.9 ± 3.9 years; body mass: 60.6 ± 6.9 kg; height: 164.5 ± 6.7 cm) underwent myotonometric assessments of the biceps femoris (BF) and rectus femoris (RF) before and after the following training sessions: MD1 (i.e., 1 day after the match), MD3, MD4, and MD5. Training loads were quantified for each session, revealing significant variations, with MD4 exhibiting the highest values for high-speed running distance, number of sprints, and accelerations. Notably, MD3 showed the highest perceived exertion (RPE), while MD5 recorded the lowest total distance run. Myotonometric assessments indicated significant differences in stiffness of the RF in MD3 and BF in MD5, as well as RF tone in MD5. The findings underscore a notable relationship between training load and myotometric variables, particularly in muscle stiffness and tone. These results emphasize the need for further research to clarify how training loads affect muscle properties in female athletes.
... RPE is considered a reflection of an "internal" training load, as it reflects the athlete's own perceived effort, rooted in a subjective interpretation of their performance relative to their maximal effort [3]. It has been found that while RPE alone does not entirely reflect the load experienced by an athlete, multiplying this subjective measure by distance or duration, as is achieved when calculating sRPE, provides a valuable metric for load determination [5][6][7]. sRPE has been shown to have relationships to other internal training load measures, like training impulse (TRIMP), including Bannister's, Lucia's, and Edwards', as well as external load measures like total and high-speed distance [3]. ...
... There was also a significant interaction effect between game number and sRPE type, F(5, 1974) = 8.41, p < 0.01, accounting for within-player effects ( Figure 6). Pairwise compar- isons were performed to assess the differences between the sRPE type (actual and predicted) by game number (1)(2)(3)(4)(5)(6), and significant differences were identified between the sRPE type at games number 1, 2, 3, and 5 (p < 0.01). ...
Article
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The use of session rating of perceived exertion (sRPE) as a measure of workload is a popular athlete load monitoring tool. However, the nature of sRPE means the contribution of salient, sport-specific factors to athlete load in field sports is challenging to isolate and quantify. In rugby sevens, drivers of load include high-speed running and physical contact. In soccer and men’s rugby, union acceleration/deceleration also influences load. These metrics are evaluated using data from global navigation satellite system (GNSS) sensors worn by athletes. Research suggests that sensor data methods for identifying load in men’s rugby do not accurately quantify female athlete loads. This investigation examined how mass, contact, and accelerations and decelerations at different speeds contribute to load in women’s rugby sevens. The study evaluated 99 international matches, using data from 19 full-time athletes. GNSS measures, RPE, athlete mass, and contact count were evaluated using a linear mixed-model regression. The model demonstrated significant effects for low decelerations at low and high speeds, mass, distance, and contact count explaining 48.7% of the global variance of sRPE. The use of acceleration/deceleration and speed from GNSS sensors alongside mass, as well as contact count, presents a novel approach to quantifying load.
... In this regard, many women's basketball teams implement player monitoring systems 2 to measure the external loads (ie, physical stimuli imposed on players) and internal loads (ie, psychophysiological responses to the external load) experienced by players during training and games. 3 External and internal load data provide coaches with evidence to determine if the intended demands were adequately delivered to players in each session, players responded in desired ways, and stimuli were periodized appropriately across the season. 3 External and internal loads are commonly periodized using weekly microcycles among basketball teams, given competition rounds among many semiprofessional and professional competitions are arranged in a weekly fashion. ...
... 3 External and internal load data provide coaches with evidence to determine if the intended demands were adequately delivered to players in each session, players responded in desired ways, and stimuli were periodized appropriately across the season. 3 External and internal loads are commonly periodized using weekly microcycles among basketball teams, given competition rounds among many semiprofessional and professional competitions are arranged in a weekly fashion. 4 In turn, effective load periodization strategies may help reduce the risk of players experiencing maladaptive responses while optimizing their readiness to perform during games each week. ...
Article
Purpose : To quantify and compare loads encountered in individual training sessions and games during noncongested and congested weeks in semiprofessional women basketball players. Methods : Using an observational, longitudinal design, 12 players from the same team had their external (PlayerLoad, relative PlayerLoad, and total and high-intensity inertial movement analysis variables) and internal load (session rating of perceived exertion [sRPE], sRPE-load, percentage of heart rate peak, and modified summated-heart-rate-zones load) monitored across a regular season. Training and game data were categorized into noncongested (0–1 game) and congested weeks (2–3 games). Linear mixed models and Cohen d effect sizes were used for analyses. Results : Comparisons between training sessions revealed higher ( P < .05, d = 1.35–5.33) PlayerLoad, total inertial movement analysis, sRPE, and sRPE-load during training session 1 than training session 2 in congested weeks. Comparisons between training sessions and games revealed higher ( P ≤ .001, d = 1.10–1.66) sRPE and sRPE-load during games than training sessions 1 and 2 in noncongested weeks, alongside higher ( P ≤ .001, d = 1.87–3.55) sRPE during game 1 than training sessions 1 and 2 in congested weeks. Comparisons between games revealed higher ( P < .05, d = 0.57–2.82) loads in game 3 during congested weeks compared with all other games. Conclusions : Training appeared to be tapered in congested weeks, likely to account for upcoming increases in game loading, but remained relatively consistent across sessions during noncongested weeks. Individual game loads remained relatively consistent but were noticeably increased when a third game was played in the week.
... Global positioning systems (GPS) are typically used to monitor external load (EL) [6]. EL is described by Impellizzeri et al. [7] as the organization, quality, and quantity of the exercise, which encompasses the overall training plan. In team sports, EL can be measured by metrics such as distance covered, accelerations, and other performance indicators. ...
... The concept of internal load (IL) incorporates all the psychophysiological responses that occur during the execution of the exercise prescribed by the coach. These responses correspond to the internal training load [7]. Acute and chronic changes in training outcomes are ultimately the result of an athlete's cumulative IL. ...
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Methods A descriptive cross-sectional study design was employed. Twenty-eight professional athletes from the first division of the Brazilian national championship were monitored during a game-based training session, which consisted of varying durations of ball-in-play blocks. The training session comprised 4 blocks of 1 min, 2 blocks of 2 min, and 1 block of 3 min of ball-in-play, with 1-min intervals between blocks of the same duration and 2-min intervals between blocks of different durations. Results A reduction in EL was reported during longer bouts of ball-in-play. Specifically, a reduction was observed when comparing block 5 to block 1 (p = 0.021) and when comparing block 7 with blocks 1, 2, 3, 4, and 6 for distance per min. For accelerations and decelerations, blocks 5, 6, and 7 showed lower values than block 1 (p = 0.001 and p = 0.005, respectively). Block 4 showed an increase in rate of perceived exertion (rPE) values compared to blocks 1 (p = 0.010) and 2 (p = 0.004). Increased rPE values were also found in block 5 compared to block 1 (p = 0.001), as well as compared to blocks 2 (p = 0.001) and 3 (p = 0.002). RPE in block 7 was higher than in blocks 1, 2, 3, and 4 (p = 0.001). Conclusions In summary, higher rPE values were reported across blocks, and IL appeared to be more volume-dependent.
... Accordingly, it has been advocated that setting goals in sports settings leads to enhanced performance [42]. Indeed, monitoring was defined as a constant and regular form of testing both performance and health, differentiating between external and internal loads or between physical, psychological and social loads [6,9,11,43]. Recently, including in snow sports settings, athlete load monitoring has become a regular practice for determining the "dose-response" relationship to competition and training loads, considering objective measures, subjective outcomes, psychological measures and lifestyle-related factors [4,6,43]. ...
... Indeed, monitoring was defined as a constant and regular form of testing both performance and health, differentiating between external and internal loads or between physical, psychological and social loads [6,9,11,43]. Recently, including in snow sports settings, athlete load monitoring has become a regular practice for determining the "dose-response" relationship to competition and training loads, considering objective measures, subjective outcomes, psychological measures and lifestyle-related factors [4,6,43]. All these monitoring tools were employed to inform practitioners about practice, and they were defined as extremely valuable as they guided future actions. ...
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Background and Objective Competitive alpine skiing, snowboarding and freestyle skiing, all different in nature and risks, are known for their high injury and illness burden. Testing measures and training methods may be considered for athletes’ preparation to support performance enhancement while safeguarding their health. We explored the perspectives and perceptions of competitive alpine skiing, snowboarding and freestyle skiing stakeholders regarding testing and training practices in their competitive snow sports. Methods We conducted an exploratory qualitative study based on grounded theory principles through 13 semi-structured interviews about testing and training practices with athletes, on-snow and off-snow coaches, managers and healthcare providers from different national teams. The interviews were inductively analysed through a constant comparative data analysis. Results Participants described winning as the end goal of testing and training practices, which requires athletes to perform in their best condition. To do so, they mentioned two main targets: performance enhancement and health protection. Participants acknowledged health as a premise to perform optimally, considering testing and monitoring approaches, goal setting, and training to support and protect athlete performance. This continuous cyclic process is driven by communication and shared decision making among all stakeholders, using testing and monitoring outputs to inform goal setting, training (e.g. on-snow and off-snow) and injury prevention. Such an approach helps athletes achieve their goal of winning while being fit and healthy throughout their short-term and long-term athletic career development. Conclusions The ultimate goal of testing measures and training methods in such competitive snow sports is winning. Performance enhancement and health protection act as pillars in systematic, tailored and flexible processes to guarantee athletes’ best preparation to perform. Moreover, athletes’ assessments, goal setting, monitoring tools, open communication and shared decision making strongly guide this cyclic process.
... The absolute mean HR was calculated in two different 5-min time windows (i.e., 5-10 and 10-15 min of exercise). The mean HR of the 10-15 min interval was also reported as relative to peak HR and maximum predicted HR to determine the relative internal training load (Impellizzeri et al., 2019). The HRV indices were calculated during the 5-10 and 10-15 min of exercise using Kubios HRV Standard software (ver. ...
... Thus, an accurate determination of the exercise intensity is essential to guarantee that the effort is submaximal and therefore to optimize exercise-induced reduction in FM symptoms (Busch et al., 2007). In this context, cardiac autonomic responses are considered representative markers of individual internal training load (i.e., the acute individual response to the prescribed exercise) and can be used to guide exercise prescription (Impellizzeri et al., 2019;Manser et al., 2021). While the similar cardiac autonomic responses to exercise observed between the FM and CON indicate that submaximal cycling exercise provokes a similar internal training load in women with FM and CON women, monitoring HR and HRV indices during exercise could enable adjustment in the exercise intensity according to the physiological specificities and health status of FM patients. ...
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Fibromyalgia (FM) patients present impaired cardiac autonomic regulation during maximal exercise; however, it is unknown whether these alterations also manifest during submaximal exercise. The aim of this study was to compare the on-transient heart rate (HR) response and HR variability during a constant-load submaximal cycling exercise between FM and control (CON) women. Ten women with FM (age: 45.2± 9.3 years) and 10 age-matched CON women (age: 48.4± 6.1 years) performed a 15-min cycling exercise, with the work rate fixed at 50% of the individual peak power output attained in a maximal graded exercise test. The time intervals between consecutive heartbeats were recorded regularly during the exercise for subsequent analysis of on-transient HR response and HR variability indices. The on-transient HR time constant was similar (P= 0.83) between the FM (41.0± 14.1 sec) and CON (42.2± 10.4 sec). During the 5-10 and 10-15 min of exercise, HR variability indices indicating sympathetic and parasympathetic activities were similar (P> 0.05) between FM and CON groups. In conclusion, women with FM presented a normal cardiac autonomic response to submaximal cycling exercise. These findings have clinical relevance, as submaximal exercises are commonly prescribed for FM patients.
... Physical and performance demands refer to the physical, technical, and tactical activities undertaken during training and competition, determined by factors such as the organization, quality, and quantity of exercises [3]. Physical demands can be assessed using some external load parameters; specifically, they can be measured through tracking technologies such as radar-based local positioning systems (LPS), global positioning systems, and multiple-camera video technology [4]. These technologies enable the recording of metrics such as distance covered, player load (PL), steps, accelerations, dynamic stress load (DSL), and activity duration [3]. ...
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Background: This study examines how physical demands and individual performance influence quarter results in under-18 basketball players during a six-day basketball tournament. Methods: Twelve male players from an elite Spanish team were tracked using inertial microsensors to monitor external load variables (player load, steps, and dynamic stress load). Individual performance was assessed using the performance index rating (PIR). Results: The results showed significant differences in physical demands between quarters. Also, player load (F = 3.75, p = 0.012) and steps (F = 5.29, p = 0.001) were higher in the first quarter and decreased over time. Winning quarters had significantly higher physical demands compared to losing quarters (PL: F = 27.13, p < 0.001; steps: F = 16.70, p < 0.001; DSL: F = 9.50, p < 0.001). On the contrary, PIR did not show significant differences between winning and losing quarters (F = 2.15, p = 0.143), but tended to be higher in winning quarters. Conclusions: These results suggest that physical demands are stronger predictors of quarter results than individual performance scores, indicating that such parameters should be closely monitored by sport scientists and coaches since they can play a crucial role in team success.
... Para analizar la carga de entrenamiento, se debe diferenciar entre dos tipos de carga, la carga interna y la carga externa. Se conoce como carga externa la cantidad o el volumen total de actividades o ejercicios que realizan los deportistas, mientras que la carga interna es el efecto que le suponen esas actividades en el propio organismo (Impellizzeri, Marcora y Coutts, 2019). Actualmente, existen diferentes métodos aceptados para medir la carga interna de entrenamiento, como pueden ser los métodos subjetivos basados en escalas o cuestionarios utilizados para valorar la percepción de la carga de entrenamiento desde el punto de vista del deportista (Herman, Foster, Maher, Mikat, y Porcari, 2006) y métodos fisiológicos que permitan analizarla objetivamente a través de diferentes parámetros fisiológicos como la frecuencia cardíaca (FC; Castagna, Impellizzeri, Chaouachi, Bordon, y Manzi, 2011;Manzi et al., 2010). ...
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The aim of this study was to analyze the training load of the different tasks of a professional basketball team, based on the tasks and player positions. A total of 10 professional basketball players participated (Mage = 23.70; SD = 2.26) who carried out three different training tasks for four weeks: situation in the middle court (MC); situation in the middle court followed by a counterattack in the opposite court (FB); half court situation followed by two full court counterattack situations (CB). GPS Polar Team Pro© was used to monitoring the physical load. Internal physical load and external physical load variables were measured. One-way ANOVA was performed to explore the main differences between the different types of tasks and player positions on each of the dependent variables. The results show that FB tasks involve a higher internal load, while CB tasks involve a greater external load. Regarding the type of players, the exteriors reached higher values in the different variables analyzed. Therefore, it has been found that different training tasks imply different physical demands. Specifically, using training tasks that reproduce the dynamics of competition imply an increase in conditional load.
... Therefore, measures of external load in strength training can be expressed, for example, considering the load (external resistance) lifted or number of repetitions, and in aerobic training, external load can be described by measures such as displacement speed or the total distance covered. As for internal load, physiological parameters, such as heart rate, and psychophysiological ones, such as subjective perceived exertion (RPE), can be used [9]. ...
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Background: Few studies evaluate the behavior of training loads in clinical populations. Purpose: Analyze the behavior of external and internal load in combined progressive training (aerobic and strength), for nine weeks, in elderly of both sexes with cardiometabolic risk factors, participants in a Cardiorespiratory Rehabilitation Program. Methods: The program were composed of strong and moderate sessions, divided into three mesocycles composed of three microcycles. Parameters of external load (push-up and squat repetitions and distance covered) and internal load (RPE session) were collected. Data were analyzed using Student's t test for paired samples and one-way analysis of variance, with Tukey's post-hoc. The significance level adopted was 5%. Results: 31 participants (67.5 ± 5.53 years) were evaluated. Strong sessions presented greater external load (p < 0.001) than moderate sessions. Microcycles 3 had a higher number of squat repetitions than microcycles 1 (p = 0.030). Mesocycle 3 showed higher values in the two external load repetition variables (push-up, p = 0.001; squat, p < 0.001). The last strong session showed an increase in external load (push-up repetitions, p < 0.001; squat repetitions, p < 0.001; distance covered, p = 0.001) in relation to the first strong session. Conclusion: There was maintenance or decrease of the internal load and increase of the external training load, which demonstrates the effectiveness of training program in the adaptation and improvement of the physical capacity of trained elderly with cardiometabolic risk factors, being possible for the trainer to check the progression of his students' training.
... While heart rate serves as a recognized marker of vagal activity [6], heart rate variability (HRV) offers considerable versatility in providing insights into exercise-induced physiological stress, thereby enabling the monitoring of training load [7]. Evaluating the cardiac response, particularly HRV, using wearable devices, including real-time monitoring, has shown its reliability as a dependable field instrument [8,9]. However, it is important to note that these instruments do not directly facilitate on-field evaluation. ...
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Assessing respiratory frequency (f R) is practical in monitoring training progress in competitive athletes, especially during exercise. This study aimed to validate a new wearable chest strap (wCS) to estimate f R against ergospirometry as a criterion device in soccer players. A total of 26 elite professional soccer players (mean [standard deviation]: 23.6 [4.8] years; 180.6 [5.7] cm; 77.2 [5.4] kg) from three Italian Serie A League teams participated in this cross-sectional study. The sample included attackers, midfielders, and defenders. f R was assessed during a maximal cardiopulmonary exercise test (CPET) on a treadmill using (i) a breath-by-breath gas exchange analyzer (Vyntus ® CPX, Vyaire Medical) and (ii) a novel wCS with sensors designed to assess breath frequency following chest expansions. Pearson's correlation coefficient (r), adjusted coefficient of determination (aR 2), Bland-Altman plot analysis, and Lin's concordance correlation coefficient (ρ c) were used for comparative analysis (correlation and concordance) among the methods. The repeated measures correlation coefficient (r rm) was used to assess the strength of the linear association between the methods. The intraclass correlation coefficient (ICC) and the Finn coefficient (r F) were used for inter-rater reliability. All statistical analyses were performed within the R statistical computing environment, with 95% confidence intervals (95% CIs) reported and statistical significance set at p < 0.05. A total of 16529 comparisons were performed after collecting the CPET data. The robust time series analysis with Hodges-Lehmann estimation showed no significant differences between both methods (p > 0.05). Correlation among devices was statistically significant and very large (r [95% CI]: 0.970 [0.970, 0.971], p < 0.01; aR 2 [95% CI]: 0.942 [0.942, 0.943], p < 0.01) with strong evidence supporting consistency of the new wCS (BF 10 > 100). In addition, a high concordance was found (ρ c [95% CI]: 0.970 [0.969, 0.971], bias correction factor: 0.999). Vyntus TM CPX, as a standard criterion, showed moderate agreement with wCS after Bland-Altman analysis (bias [95% lower to the upper limit of agreement]; % agree: 0.170 [−4.582 to 4.923] breaths·min −1 ; 69.9%). A strong association between measurements (r rm [95% CI]: 0.960 [0.959, 0.961]), a high absolute agreement between methods (ICC [95% CI]: 0.970 [0.970, 0.971]), and high inter-rater reliability (r F : 0.947) were found. With an RMSE = 2.42 breaths·min −1 , the new wCS seems to be an valid and reliable in-field method to evaluate f R compared to a breath-by-breath gas exchange analyzer. Notwithstanding, caution is advised if methods are used interchangeably while further external validation occurs.
... La carga de entrenamiento se ha desarrollado en otras definiciones de la organización del Colegio Americano de Medicina del Deporte como intensidad absoluta del ejercicio e intensidad relativa del ejercicio (Staunton et al., 2022). Las demandas físicas (también conocidas como carga externa) representan las demandas locomotoras impuestas por los ejercicios de entrenamiento a los estudiantes que pueden ser monitoreadas por sistemas microelectromecánicos (Impellizzeri, Marcora, & Coutts, 2019). Las respuestas psicofisiológicas (también conocidas como carga interna) son las respuestas orgánicas a la carga externa que pueden variar de un estudiante a otro en función de factores como la aptitud física, la preparación y las condiciones psicológicas y/o ambientales (McLaren et al., 2018). ...
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This study aimed to analyse the types of relationship between internal and external intensities in fourth grade students (age: 9.4 ± 0.19; height: 1.37 ± 0.187; body mass 41.56 ± 7.95). Fifty-five students in two groups participated in fourteen sessions monitored with wGT3X accelerometers and Polar Team Pro® heart rate monitors. It showed a positive relationship for all variables, with average heart rate significantly related to total distance (r=0.441, p<0.001) and average speed (r=0.346, p<0.001). Maximumheart rate also showed a significant relationship with both total distance and average speed respectively (r=0.329; p<0.001; r=0.183; p<0.001). The use of alternative approaches with small-sided games and the use of playful force such as educational CrossFit exercises may positively promote a related improvement in exercise intensity already at a young age.
... Injuries should be viewed as opportunities for training (e.g., cannot play rugby, use wrestling or other exercise modalities, have an upper-body injury, and focus on the lower body using medically permissible equipment). Finally, training should be guided by the athlete's response to training (internal load) (57,66), looking for opportunities to regress and progress the external load. Other strategies (Table 9) can be beneficial to maximize recovery and be adapted based on the internal load responses centered on evidence-based practice. ...
Article
With increasing investments and resources, rugby sevens is growing internationally and domestically in many countries. Within Canada, women's rugby sevens is a popular sport at the regional and national levels in university settings and centralized training programs. Given the importance of strength and conditioning for success in sevens, the purpose of this article is to highlight some of the frameworks used to develop sevens athletes from the university to the international level within the Canadian context. As such, the match demands and physical characteristics relevant to rugby sevens are discussed based on a comprehensive needs analysis. This is further contextualized by a training philosophy and framework tailored for university-aged female athletes, which play a key role in the Canadian talent development pathway.
... In both male and female handball competitions, internal and external factors can lead to fatigue [13][14][15][16]. Specialists use measurements of external and internal load to determine an athlete's level of adaptation to exertion [17][18][19]. On the one hand, the external load represents the visible physical effort, expressed by volume and intensity parameters. ...
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In team handball, coaches can make unlimited substitutions, allowing players to enter the game at any time, even if they haven't been active on the bench. The aim of this study was to investigate the impact of inactivity following a warm-up on the physical performance and physiological responses of female elite team handball players. The secondary aim of the study was to examine a possible connection between the examined parameters. Twelve female adult elite field handball players (n = 12; age, 31.9 ± 4.05 years; weight, 66.1 ± 5.8 kg; height 173 ± 3.8 cm and body mass index, 2.2 ± 0.2 kg/cm2) were examined. All tests were assessed in two distinct situations: (a) immediately after warm-up (T1-AW) and (b) after a 15-minute inactivity period (T2-IP). The physical tests performed were: countermovement jump with arms fixed (CMJ AF), squat jump (SJ), medicinal ball rotational throw test right (MBTT-R) and medicinal ball rotational throw test left (MBTT-L) and 10 m acceleration test (TA 10m). Heart rate (HR) was me
... The total workload comprises training and match load, which can be categorized and quantified through internal and external measures [4]. Internal load refers to the psycho-physiological responses that occur during soccer training and competition [5]. These responses can be measured using objective methods, such as heart rate (HR), or subjective methods, such as the rating of perceived exertion (RPE) [6]. ...
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The incorporation of triaxial accelerometers into Global Positioning Systems (GPS) has significantly advanced our understanding of accelerations in sports. However, inter-positional differences are unknown. This study aimed to explore the variability of acceleration and deceleration (Acc) distribution curves according to players' positions during soccer matches. Thirty-seven male players from a national-level Portuguese club were monitored using 10 Hz GPS with an embedded accelerometer during the 2021/2022 season. Resultant Acc was obtained from the x (lateral), y (frontal/back), and z (vertical) axes and expressed in gravitational units (g). Statistical Parametric Mapping was employed to compare playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM), and strikers (ST). All positions exhibited a decreasing Acc distribution curve, very similar in shape, with a high frequency of events in the lower ranges (i.e., 0 to 1 g) and a lower frequency of events in the higher values (2 to 10 g). Post hoc comparisons revealed significant differences between all positions, except between FB and WM. Out of 1000 points in the curve, CD had 540, 535, 414, and 264 different points compared to FB, CM, WM, and ST, respectively. These findings indicate that players in different positions face distinct demands during matches, emphasizing the need for position-specific Acc analysis and training programming. By analyzing Acc as a continuous variable, this study highlights the importance of individualized monitoring to ensure the comprehensive and precise tracking of all player activities, without overlooking or omitting critical information.
... The external load can be considered the training "dose" which determines internal responses in the psychophysiological systems of players. 23 The present findings corroborate this notion, given that the higher physical demands of the Trap condition determined also higher perceived exertion scores. One previous study 24 examined the perceived load of playing only the offensive or only the defensive phase during a 3vs3 SSG and found a moderate difference between conditions, suggesting that different tactical tasks affect internal loads. ...
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Purpose: This study aimed to assess the effect of playing different pick-and-roll (PnR) defensive tactical options during small-sided games (SSGs) on external and internal loads in female basketball players. Methods: Twelve female basketball players (age 28 [2] y; stature 175 [6] cm; body mass 65 [7] kg; playing experience 18 [4] y) belonging to a team competing in the Lithuanian second division were recruited for this study. Across 3 experimental sessions and in a randomized order, players performed 3 SSGs sharing the same features but using 3 defensive strategies on the middle PnR action: Switch, Trap, and Drop. External load was measured using PlayerLoad (PL); the number of accelerations, decelerations, and changes of direction, classified based on their intensities as low (<2.5 m·s−2), medium (2.5–3.5 m·s−2), and high (>3.5 m·s−2); and jumps categorized as low (<40 cm) and high (≥40 cm). Internal load was measured via rating of perceived exertion (RPE). Results: Higher PL values in SSGs including Trap defense were found compared with Drop (P < .001, ES = 0.69, moderate) and Switch (P = .001, ES = 0.60, moderate). Additionally, a higher number of accelerations was registered in Trap defense compared with Drop defense (P = .027, ES = 0.99, moderate). Trap defense also led to higher RPE compared with Switch (P = .003, ES = 1.49, large) and Drop (P = .004, ES = 1.42, large) defense. Conclusions: Different defensive strategies on the middle PnR can influence the external and internal loads during SSGs, and female basketball coaches should consider the high demand of the Trap defense when designing SSGs.
... Contact sports such as rugby union involve locomotor activities (e.g., high-speed running, accelerations, decelerations) and collisions (e.g., tackles, ball-carries) which can be analysed using video analysis and microtechnology (i.e., global positioning systems, accelerometers, and gyroscopes) (Cunniffe et al., 2009;MacLeod et al., 2018;West et al., 2019). These actions contribute to the construct of external load which affects the physiological, biomechanical, and psychological responses of the player (considered subdimensions of the construct of internal load) (Impellizzeri et al., 2019;Vanrenterghem et al., 2017). In rugby union (and other team sports), match-play is typically reflected by an increased external and internal load experienced by the players, than their typical training. ...
... Data regarding acute and longitudinal external load training stimulus (Impellizzeri et al., 2019;Russell, McLean, Stolp, et al., 2021) imposed on highly trained youth basketball players is limited (Russell, McLean, Impellizzeri, et al., 2021). Multiple studies by Vazquez-Guerrero and colleagues (Pino-Ortega et al., 2019;Vázquez-Guerrero et al., 2019 described external loads experienced by international youth players in tournament settings (13 games). ...
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This study describes the training demands of highly trained male youth basketball players, based on training year, term and playing position. Data was collected from 41 male youth basketballers over two seasons from all on-court coach-led training sessions utilising an LPS. Linear mixed-models and pairwise comparisons were used to analyse by training year (Y1, Y2 and Y3), term (T1, T2, T3 and T4) and playing position (Backcourt, Frontcourt). Results showed no differences in external load metrics between training years. Significant differences existed between training terms, with total distance greater in both T3 and T4 than T1 and 2 (p < 0.03). Total PlayerLoad was significantly greater in T4 than T1 (p < 0.001) and T3 (p = 0.004). Distance/min was greater in T2, T3 and T4 than T1 (p < 0.01). PlayerLoad/min was higher in T4 than T1 and T2 (p < 0.01). Backcourt players showed significantly greater distance/min (p = 0.011), PlayerLoad/ min (p = 0.011) and deceleration counts (p < 0.001). Overall, limited year-on-year change existed in external training load metrics (p > 0.05), though volume (p < 0.001) and intensity (p < 0.001) differed between terms. Backcourt players completed higher intensities (p = 0.011) than Frontcourt players. This study provides a description of external loads of training in highly trained youth basketball players assisting coaches and performance practitioners to better understand physical demands within youth basketball development pathways. ARTICLE HISTORY
... Finally, since each TL method presents a different ability to detect TL variation, athletes and coaches should avoid varying between the TL tools due to their noninterchangeable characteristics. Historically, TL measures have been described as internal and external TL (Impellizzeri et al., 2019). The external TL measures involve traditional measures of volume such as distance covered, training time, and number of steps. ...
... Es por esta razón, que la monitorización de la carga que asumen las jugadoras durante una competición es importante (Impellizzeri et al., 2019). Este registro de la carga externa en el balonmano playa se ha estudiado con el objetivo de conocer que impacto tiene en la jugadora durante la competición (Sánchez-Sáez et al., 2021;Trindade et al., 2022), incluso diferenciando el tipo de partido al cual se somete a la jugadora (Lara et al., 2023). ...
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Official beach handball competitions usually take place over four to five consecutive days. The aim of this study was to describe the internal load of the international beach handball player during an official competition by assessing her perception of fatigue as well as pulse monitoring during night rest. Ten players of the Italian national team were assessed through questionnaires and by means of a device that monitored their pulse rate during night rest during the competition at the Mediterranean Beach Games in Heraklion -Greece 2023-. The RPE data during the first day reported a mean of 4.8±2.5 and on the last day of the competition a mean of 6.2±2.2. In the Hooper Well-being questionnaire (TC) the items of fatigue (4.1±0.9/5.5±0.7), muscle soreness (4.0±0.8/5.5±0.9) and stress level (4.5±0.8/5.5±0.7) showed a trend of increasing records as the days of competition passed. The Hooper index shows this trend towards a deteriorated state of well-being (20.5±0.4), with a significant difference between the first and last day's recordings of maximum heart rate (F=9.580; pbonf =.009**; h2=.444). This study provides information to the coaching staff on the fatigue control of the players during the official competition.
... El primero de ellos hace referencia a la medida objetiva del rendimiento realizado por el deportista tanto en los entrenamientos como en la competición, refiriéndose a este término como carga física (García-Calvo et al., 2019;Pérez-Contreras et al., 2022). Por el contrario, la carga interna es el estrés biológico (físico, fisiológico y psicológico) producido en el deportista (Impellizzeri et al., 2019;Mujika, 2017). Una distinción fundamental entre ambos indicadores radica en su cuantificación; mientras que, la carga externa es uniforme para todos los jugadores, la carga interna se caracteriza por ser específica e individualizada para cada uno de los deportistas (Bourdon et al., 2017;Reina et al., 2020). ...
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El objetivo principal de esta investigación fue comparar la carga interna, externa y la satisfacción intrínseca en jugadoras de fútbol a través de tres tipos de entrenamiento: circuito, juego reducido 3x3 y juego reducido 7x7. La muestra estuvo formada por 12 jugadoras, con una edad promedio de 17.17 (±2.83) años. Para la recogida de los datos, se utilizaron sensores de frecuencia cardíaca Polar H10 y un cuestionario de percepción subjetiva del esfuerzo para medir la carga interna, sensores ZEPP Play Football para la medición de la carga externa y un cuestionario para medir el nivel de satisfacción intrínseca. Los resultados indicaron que las jugadoras percibieron los juegos reducidos como menos extenuantes y más divertidos. Además, se ha observado que las mediocentros fueron las que más distancia recorrieron, independientemente del tipo de entrenamiento realizado. Además, las jugadoras con mayor experiencia y edad reportaron menor percepción subjetiva del esfuerzo en comparación con las jugadoras más jóvenes y menos experimentadas. Se concluye que, a la hora de proponer tareas de carácter condicional, las jugadoras perciben menor exigencia física en los juegos reducidos, siendo, además, más divertidos para ellas en comparación con el entrenamiento en circuito, lo que supone una mayor motivación a la hora de afrontar el entrenamiento. Palabras Clave: Fútbol femenino; Preparación física; Cuantificación de la carga; Juegos reducidos; Satisfacción intrínseca.
... To calculate the external load delivered by the NMES, the load control calculations used in counter-resistance training were adapted to NMES, using the terminology proposed by Impellizzeri et al. (17) and Marston et al. (18) (table 2). Table 2 Methodological procedures ...
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Introduction: Neuromuscular electrostimulation (NMES) represents a therapeutic approach for addressing chronic low back pain (CLBP); however, the influence of NMES dose on muscle activity remains subject to debate. Objective: To compare the impact of two distinct NMES protocols employing Aussie current, characterized by varying dosages emphasizing time cycle alterations, on electromyographic activity within the multifidus muscles in individuals afflicted with CLBP. Methods: A randomized clinical trial encompassed 18 volunteers diagnosed with mechanical CLBP. These volunteers were randomly assigned to two NMES intervention groups with dissimilar dosages: 15 repetitions ([Formula: see text][Formula: see text]) and 30 repetitions ([Formula: see text][Formula: see text] ). In both interventions, the current amplitude was tailored to individual perception and documented at the culmination of each session. Over the course of four weeks, two sessions took place per week. Electromyographic activity of the multifidus muscles was evaluated using surface electromyography before and after the intervention. The assessment focuses on both time-domain analysis using Root Mean Square (RMS) and frequency-domain analysis involving mean activation frequency (FREQ). Results: There are no interactions between the time and intervention, but there is the time effect on RMS, indicating that post-intervention muscle activity exceeded pre-intervention values in both groups. FREQ values did not exhibit statistically significant discrepancies. Conclusions: This study showed that NMES using the Aussie current is effective in increasing muscle activity in individuals with CLBP, and the results were not influenced by the different cycle times with equal volumes.
... Thus, a single measure cannot encapsulate all dimensions of the training load construct . To effectively quantify the training stimulus and identify how players are coping with the training programme, a combination of external and internal load measures is therefore essential (Impellizzeri et al., 2019). ...
Article
Microcycles are fundamental structures for training prescription and load management, helping to optimise training effects and performance. This study quantified external and internal loads of Italian Serie A youth soccer players across competitive weeks and their periodisation within microcycles. Data were collected from 90 players belonging to four age groups (under-19,-17,-16,-15) across a season. Methods of monitoring external [duration and global navigation satellite systems (GNSS)] and internal load [heart rate (HR) and rating of perceived exertion (RPE)] were employed. Linear mixed models determined differences in training loads across age groups, training days and player positions. Under-19 and under-17 players trained five times per week, while younger players trained four times. Late-stage academy players (under-19 and-17) demonstrated higher weekly accumulated external and sRPE training load compared to their younger counterparts (p < 0.05 between groups). Weekly accumulated HR internal loads were higher in under-15 players (p < 0.05 between groups). Marked fluctuations of daily load were observed across microcycles in under-19 and under-17 groups (p < 0.05 between days). These findings highlight progressive increases in training load throughout the development pathway, with late-stage academy players training with higher frequency, volume and marked periodisation compared to younger players.
... The external training load is the physical stimulus imposed on the athletes, such as jump count and height, movement speed and distance, or the weight used in resistance training. Internal training load reflects the psychophysiological stress caused by external training load [3]. The methods for quantifying training load should be sensitive to the specific sport while being convenient enough to be used frequently. ...
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Background The long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions. Methods Fourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1–7), competition 1 (C1, weeks 8–14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15–23), and competition 2 (C2, weeks 24–29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height. Results C2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p < 0.001). Jump count was negatively correlated with jump height (ρ = −0.089, p = 0.006) and the percentage of jumps exceeding 80% of maximal height (ρ = −0.388, p < 0.001). The internal and external training load variables were similar among different playing positions. Conclusion The participants exhibited significantly higher internal training load in C2 and higher jump height after P1. A high jump count was associated with higher internal training load and lower jump height. Excessive jumps may result in fatigue and reduce height.
... Es relevante mencionar que, dentro de las diferentes metodologías para el entrenamiento de la fuerza, actualmente no se utiliza ampliamente el umbral anaeróbico como método para el desarrollo de la fuerza, ya que se relaciona más con la capacidad aeróbica y no como una medida directa de la fuerza muscular (Brooks 2020). Sin embargo, la carga de entrenamiento asociada directamente al lactato proporciona un valor específico de fuerza vinculado a un porcentaje del umbral anaeróbico, lo que puede utilizarse como un índice del trabajo muscular interno guiado por la cinética del lactato, permitiendo así controlar la intensidad absoluta del entrenamiento (Casado et al., 2022;Impellizzeri et al., 2019). Esto se traduce en una dinámica de trabajo de baja a moderada intensidad. ...
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Objetivos: Desarrollar un protocolo de entrenamiento de la fuerza basado en la cinética del lactato en poblaciones con factores de riesgo. Materiales y Métodos: A través de muestreo probabilístico, 15 participantes, edad 35±53 años, estatura 1.78±0.09. peso 72± 15 kg. grasa 23±19%, masa muscular 41±47% y consumo de oxígeno máximo Vo2max 50±80 ml/kg/min. Realizaron dos pruebas de fuerza en sentadilla media, la primera progresiva hasta el umbral anaeróbico, de la cual se obtuvo la carga media de los resultados que se utilizó en la segunda prueba, donde se realizaron 15 series de 15 repeticiones con descansos de 1 minutos entre cada serie. La toma de la muestra de lactato fue en la serie 1, 3, 5, 7, 9, 11, 13, 15. Resultados; Los resultados de la prueba de carga constante describen un comportamiento con poca variabilidad entre las variables frecuencia cardiaca y lactato. media Fc; 133.27/2.36, lactato 3.01/0.19. Pearson (R) de 0.719. p<0.001. El análisis de varianza (ANOVA) no reveló diferencias estadísticamente significativas en las medias de los grupos en relación con las variables de lactato p<0.358 F1.110. y Frecuencia cardíaca. p<0.221 F 1.299. Conclusiones: Estos hallazgos subrayan la relación significativa entre lactato y frecuencia cardíaca durante el ejercicio. Los tamaños de efecto estimados indican que estas variables tienen un impacto moderado en las diferencias observadas entre los grupos. y respaldan la utilidad de estas variables, principalmente el lactato como método de entrenamiento. Palabras claves: Salud, condición física, fuerza muscular, adulto. Abstract. Objectives: To develop a strength training protocol based on lactate kinetics in populations with risk factors. Materials and Methods: Through probabilistic sampling, 15 participants (age 35±53 years, height 1.78±0.09 m, weight 72±15 kg, body fat 23±19%, muscle mass 41±47%, and maximum oxygen consumption Vo2max 50±80 ml/kg/min) performed two strength tests in the half squat. The first test was progressive until the anaerobic threshold, from which the average load was obtained and used in the second test, where 15 sets of 15 repetitions were performed with 1-minute rests between each set. Lactate samples were taken during sets 1, 3, 5, 7, 9, 11, 13, and 15. Results: The results of the constant load test describe behavior with little variability between the variables heart rate and lactate. Mean HR: 133.27 ± 2.36, lactate: 3.01 ± 0.19. Pearson (R) of 0.719, p < 0.001. The analysis of variance (ANOVA) did not reveal statistically significant differences in group means concerning lactate variables (p < 0.358, F = 1.110) and heart rate (p < 0.221, F = 1.299). Conclusions: These findings highlight the significant relationship between lactate and heart rate during exercise. The estimated effect sizes indicate that these variables have a moderate impact on the observed differences between groups and support the utility of these variables, primarily lactate, as a training method. Keywords: Health, physical condition, muscle strength, adult.
... In aesthetic sports like gymnastics or acrobatics, physical preparation determines the quality of technical elements judged, thus the number of elements of a given difficulty and type, or the time spent performing them in intensity zones related to energy sources, will influence the calculation of training load [10,11]. Combining internal and external load measures provides valuable insights into how an athlete copes with tasks included in the training plan [12,13]. Effective management and monitoring of loads are essential to assess the athletes' adaptation to acute and chronic training outcomes and optimize performance [14,15]. ...
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In the long-term performance development of rhythmic gymnasts aged 16–17, athletes enter a high-performance training phase, marked by increased training loads and preparation for international competitions. This study aimed to (1) provide an overview of methods used to capture external and internal training/competition loads in elite rhythmic gymnasts, and (2) identify measurements of external and internal training/competition loads and their responses during monitored periods. Conducted according to PRISMA guidelines, the systematic review included 6 studies out of the 815 initially identified. The most common methods for calculating external training load were hours or minutes per week. Internal measures varied and included objective methods such as heart rate monitoring and biochemical, hormonal, and hematological assessments from saliva and blood samples. Among subjective methods, session-RPE was most frequently used, along with other questionnaires examining recovery, well-being, sleep, and competition anxiety. The analyzed studies integrated diverse external and internal training load variables, delving into their impact on athlete’s biochemical parameters, recovery, and well-being. Pre-competitive and competitive training periods were the focal points of measuring loads. The complex training structure of rhythmic gymnastics can complicate the calculation of training loads. Therefore, more studies are needed to explore the dose-response relationships between training load and training adaptations, fatigue, and recovery.
... Precise control and manipulation of the training load are necessary to adjust the stress applied to the athlete at the individual level [3]. This training load can be described as being internal (all the psychological responses) and external (the physical work prescribe in the training plan) [4]. External and internal loads are therefore linked by a causal relationship [5]. ...
Article
This study aimed to create a training load index to measure physiological stress during breath-hold (BH) training and examine its relationship with memory performance. Eighteen well-trained BH divers (Age: 35.8±6.6 years, BH training practice: 5.3±4.5 years) participated in this study. During a standard 1.5-hour BH training in the pool, perceived exertion, heart rate, distance, and duration were measured. The training load index was modelled on the basis of a TRIMP (TRaining IMPulse) with four different equations and was used to measure the stress induced by this BH training. A reference value, based on the ratio between the average heart rate during all BHs and the lowest heart rate during BH training, was used for comparing training load index. Memory assessment was conducted both before and after this training. Of the four equations proposed, equation no. 4, named aTRIMP for “apnoea,” showed the strongest correlation with our reference value (r=0.652, p<0.01). No difference was found between any of the memory tests before and after the BH training. The aTRIMP was a new representative index for monitoring habitual training of well-trained BH divers. Furthermore, this training had no negative impact on memory performance.
... 3 Contextual factors, including scheduling, environmental, and psychoemotional factors may moderate HR responses and could contribute an unexplained percentage of variance in studies attempting to interpret HR ex solely as a function of external work. 3,[27][28][29] Indeed, in both protocols, HR i meaningfully impacted HR ex (Figure 1). As mentioned, the applied setting in which our study was conducted did not enable standardization of withinsession scheduling nor activity immediately prior to the SSD. ...
Article
Purpose : To examine associations between exercise heart rate (HR ex ) during a continuous-fixed submaximal fitness test (CF-SMFT) and an intermittent-variable protocol (semistandardized kicking drill [SSD]) in Australian Football athletes, controlling for external intensities, within-session scheduling, and environmental conditions. Methods : Forty-four professional male Australian Football athletes (22.8 [8.0] y) were monitored over 10 sessions involving a 3-minute CF-SMFT (12 km·h ⁻¹ ) as the first activity and a SSD administered 35.7 (8.0) minutes after the CF-SMFT. Initial heart rate and HR ex were collected, with external intensities measured as average velocity (in meters per minute) and average acceleration–deceleration (in meters per second squared). Environmental conditions were sampled. A penalized hierarchical linear mixed model was tuned for a Bayesian information criterion minima using a 10-fold cross-validation, with out-of-sample prediction accuracy assessed via root-mean-squared error. Results : SSD average acceleration–deceleration, initial heart rate, temperature, and ground hardness were significant moderators in the tuned model. When model covariates were held constant, a 1%-point change in SSD HR ex associated with a 0.4%-point change in CF-SMFT HR ex (95% CI, 0.3–0.5). The tuned model predicted CF-SMFT HR ex with an average root-mean-squared error of 2.64 (0.57) over the 10-fold cross-validation, with 74% and 86% of out-of-sample predictions falling within 2.7%-points and 3.7%-points, respectively, from observed values, representing the lower and upper limits for detecting meaningful changes in HR ex according to the documented typical error. Conclusions : Our findings support the use of an SSD to monitor physiological state in Australian Football athletes, despite varied scheduling within session. Model predictions of CF-SMFT HR ex from SSD HR ex closely aligned with observed values, considering measurement imprecision.
... Recent global positioning system (GPS) development in football has improved the understanding of the sport's physical and physiological aspects by accurately recording daily workloads during training sessions and games [1]. The training load (TL) is the input variable used to elicit the desired training response [2] and is differentiated into internal and external loads [3], both of which represent the cumulative exposure of each athlete to training sessions and games [3]. The internal load is the psycho-physiological stress of the player's body during exercise, while the external load refers to all the activities that a football player can perform during training or a game [4] and can be measured using a GPS, which has proven to be a valid and reliable tool [5]. ...
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The purpose of this study was to (a) correlate the weekly external training load with the game running performance in season microcycles and (b) specify the optimal training/game ratio of the weekly external load in elite youth soccer players. The total distance (TD), the high-speed running distance (HSRD) (19.8–25.2 km/h), the ZONE6 distance (>25.2 km/h), the acceleration (ACC) (≥+2 m/s2), and the deceleration (DEC) (≥−2 m/s2) were monitored with global positioning system (GPS) technology throughout 18 microcycles and official games. TD had a very high positive correlation average (r = 0.820, p = 0.001), the HSRD had a high positive correlation average (r = 0.658, p = 0.001), the ZONE6 distance and DEC had a moderate positive correlation average ((r = 0.473, p = 0.001) and (r = 0.478, p = 0.001), respectively), and the ACC had a low positive correlation average (r = 0.364, p = 0.001) between microcycles and games. Regarding the training/game ratio, the HSRD showed statistically significant differences between ratios 1.43 and 2.60 (p = 0.012, p ≤ 0.05), the ACC between ratios 2.42 and 4.45 (p = 0.050, p ≤ 0.05) and ratios 3.29 and 4.45 (p = 0.046, p ≤ 0.05), and the DEC between ratios 2.28 and 3.94 (p = 0.034, p ≤ 0.05). Considering the correlation between weekly training and game external load, high weekly training TD values correspond to higher game values, whereas HSRD, ZONE6 distance, ACC, and DEC, which determine training intensity, should be trained in a specific volume. Training/game ratios of 1.43, 2.42 to 3.29, and 2.28 to 3.11 seem to be optimal for HSRD, ACC, and DEC weekly training, respectively.
Chapter
Sports data analytics is a helpful tool for making important strategic decisions regarding gameplay and for athletes to monitor and improve their performance. For instance, in the American NBA, several teams, such as the Philadelphia 76ers, are capitalizing and utilizing intricate data analysis techniques and complex data analysis tools, such as data visualization and hypothesis testing, to analyze NBA games, to affect in-game coaching strategy, and for post-match assessments. While analysis of specific data variables aims to accurately identify patterns and trends of success, performance, and achievement, little consideration is given to the demands, stressors, and anxieties such data-driven analysis has on prepubescent athletes, especially young females. Furthermore, the increasing demand for actuarial data and predictive analytics in sports on performance and timely deliverables creates new and difficult developmental challenges for prepubescent players and their coaches. In a data-rich sports environment, the following questions need to be asked: what data points are necessary? What are the developmental ethics around data collection (i.e., at what age should this begin) and usage? What is the cost of the data, what transactional data is being used, what is the shelf life of the data, and who interprets and analyzes the data? These questions are addressed by highlighting concerns within the context of elite female swimming. The current chapter explores the implications of data-driven analytics in prepubescent athletes and whether it is detrimental to their mental well-being.
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Small-sided games (SSGs) are widely used drill-based games that mimic the dynamics of a match while allowing coaches to tailor specific objectives, such as inducing physiological/physical stimulus or developing technical/tactical behaviors. Interestingly, by designing the SSGs with different modifications, player's responses can occur with a potential impact on the physical adaptations of players. With growing evidence about using SSGs in handball, there is now an opportunity to summarize the main acute effects induced by different task constraints. Furthermore, there is also an increasing body of evidence that can help coaches decide on the effectiveness of SSGs compared with other types of high-intensity interval training for improving physical fitness. Thus, this narrative review aims to summarize the evidence regarding using SSGs in handball, particularly regarding the acute physiological and locomotor demands, and physical fitness adaptations after exposing players to SSG-based programs.
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The rate of muscle injuries in professional football has not decreased despite the implementation of preventive strategies. This is commonly attributed to the rise in the number of competitions per season and the rigorous demands of modern football. However, these factors seem insufficient to explain the absence of impact from preventive strategies. Adopting a Network Physiology perspective, we hypothesize that some strength programs focused on reinforcing the most susceptible musculoskeletal structures and increase the muscle mass might contribute to, rather than mitigate, injuries in some players. The aim of this work is twofold: a) explaining why some currently applied strength training methods may promote sports injuries in some players, and b) suggest the use of intermuscular connectivity measures to test the risk of injury. The stability of multilevel neuromuscular synergies operating at various timescales is a crucial factor for adapting to strength training workloads. This stability necessitates long-term adaptation and cannot be assured through rapid strength gains. When neuromuscular synergies become unstable, the vulnerability to injuries rises. Since performance tests offer limited insights into the stability of neuromuscular synergies, this commentary suggests employing recently developed intermuscular connectivity measures for monitoring and tracking players' progress.
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Purpose This study analyzed the weekly external load of professional soccer players with two main aims: 1) to describe the external load based on match-contextual difficulty and playing position; 2) to compare the external load in contexts of low and high match-contextual difficulty. Methods Eighteen professional soccer players were monitored over 13 weeks using GPS units and accelerometers. Players participating for at least 60 minutes in non-congested weeks were analyzed for total distance, distances covered at various speeds and acceleration levels, and Player Load. Match difficulties were categorized as "high" (score > 10) or "low" (score ≤ 10) and determined using match location and quality of opposition. Results The results revealed that the highest external loads occurred mid-week, decreasing towards the week's end, with external defenders and midfielders facing higher demands than central defenders and forwards. In low-difficulty scenarios (p < 0.001–0.030), external loads were higher than in high-difficulty scenarios, particularly on days preceding a match (MD-4 to MD-2). Conversely, high-difficulty matches increased distance and mechanical work on specific days (MD-3 and MD-1) (p < 0.001–0.020). Conclusion The findings suggest that external load varies significantly with match context, offering valuable insights for tailoring training loads according to position and upcoming match difficulty.
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The purpose of this study was to compare the workload of a maximal treadmill test (TREAD) and a fire suppression task (BURN) in firefighters and to examine their relationships to fitness as measured by body mass index (BMI), percent body fat (BF%), and peak aerobic capacity (VO2PEAK). The amount of time spent in the heart rate (HR) intensity ranges of 50–59% HRMAX (ZONE1), 60–69% HRMAX (ZONE2), 70–79% HRMAX (ZONE3), 80–89% HRMAX (ZONE4), and ≥90% HRMAX (ZONE5) quantified the workload as the Edward’s Training Impulse for TREAD (ETRIMPTREAD) and BURN (ETRIMPBURN). The ETRIMPTREAD was significantly less than ETRIMPBURN. For TREAD, ZONE5 > ZONE2 and ZONE3. For BURN, ZONE4 > ZONE1, ZONE2, and ZONE5 > ZONE1, ZONE2, and ZONE3. A lower BF% and greater VO2PEAK were related to a greater ETRIMPTREAD and unrelated to ETRIMPBURN. For BURN only, a lower BF% and greater VO2PEAK were related to less time in ZONE5. BMI was unrelated to all workload measures. Laboratory-based maximal exercise testing does not adequately reflect the workload of simulated fire suppression and therefore may not be indicative of firefighter readiness to meet job demands. Less-fit firefighters rely on higher cardiovascular intensities to complete the same workload, and practitioners should consider this when selecting strategies to reduce job-associated cardiovascular risk.
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Background High-Intensity Multimodal Training (HIMT) refers to all styles of high-intensity combined aerobic, resistance and/or bodyweight exercise. Previous heterogeneity in exercise prescription and reporting in HIMT reduces the understanding of which factors should be considered when prescribing HIMT (e.g., exercise volume, intensity, duration). Previous studies have demonstrated positive effects of HIMT on health and performance outcomes. However, methodological disparities limit comparisons between findings. The objective of this systematic mapping review was to examine which prescriptive considerations and health and performance outcomes have been reported on in HIMT. This review also examined the quantity and trends of research conducted on HIMT. Methods A systematic literature search was conducted using Ovid Medline, SPORTDiscus and Cochrane Library databases and additional sources to identify studies up until February 2023. A total of 37,090 records were retrieved, of which 220 were included for review. 246 individual HIMT protocols were included for categorical analysis against the Consensus on Exercise Reporting Template (CERT) and Applied Research Model for the Sport Sciences (ARMSS). Results A total of 85 unique terms were used to describe HIMT. Included studies most commonly prescribed HIMT using a consistent exercise selection and circuit format. Exercise intensity was inconsistently reported on and a large proportion of studies prescribed ‘high-intensity’ exercise at a level lower than the American College of Sports Medicine criteria for high-intensity (i.e., < 77% heart rate maximum). Participation location, supervision and participation format were the most commonly reported non-training variables. The most frequently reported outcomes were cardiovascular health, perceptual outcomes, body composition and biochemical outcomes. A large proportion of previous HIMT research was experimental in design. Conclusions Previous HIMT research demonstrates a lack of standardisation in reporting. Future studies should seek to follow guidelines (i.e., CERT) to improve reporting rigour. Additionally, forthcoming research should attempt to actively involve practitioners in implementation studies to improve ecological validity among interventions. Finally, future outcome measures should be accessible in practice and reflect common training goals of participants. Registration This review adhered to PRISMA-ScR guidelines. Preregistration: osf.io/yknq4.
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Musculoskeletal injuries are a complex multifactorial phenomenon, and several factors can contribute to their occurrence. This review aimed to discuss some relevant and often unexpected elements involved in musculoskeletal injuries and rehabilitation. One of the main factors discussed is the role of physiological adaptation to training in musculoskeletal injury susceptibility. This is probably the most modifiable factor in preventing and treating musculoskeletal injuries. Other factors discussed are the role of genetics in injury susceptibility; the effect of stressors and environmental factors and the way we deal with setbacks; anabolic steroid use as aesthetic and performance-enhancement drugs; nutrition, sleeping, and the imbalance between rest, energy intake, and training; anatomic and biomechanical factors; and the role of systemic disease. Moreover, the topic of unknown factors keeps an open door for future discoveries. This review highlights the importance of understanding the various factors contributing to musculoskeletal injuries and the need for an individualized approach to injury prevention and rehabilitation, from both a historical and a physiological point of view.
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Introducción: El cambio de categoría en baloncesto de U12 a U14 supone una gran dificultad de adaptación para muchos jugadores. La modificación de las variables de juego permitirá una mejor evolución de los jugadores, ayudando a reducir las exigencias y demandas técnicas. Objetivo: analizar la influencia de la modificación de las variables de juego sobre las variables de carga interna y externa a través de diferentes situaciones. Metodología: Ocho jugadores fueron analizados durante 4 situaciones de juego en las que se llevó a cabo la modificación de diferentes variables (espacio, número de canastas, número de jugadores y tiempo). Se realizaron en un espacio de 14 x 15 metros. Cada jugador fue equipado con un dispositivo inercial WimuProTM. Resultados y discusión: Los resultados muestran que la modificación de las variables en las diferentes situaciones de juego provoca cambios en las demandas físicas sobre la carga interna y externa. Conclusión: Cabe resaltar la importancia que tiene añadir una canasta más, aumentando la carga interna y externa, debido a que se produce un mayor movimiento en la pista. Mientras que disminuir el tamaño del campo, el tiempo y reducir el número de jugadores provocará que se recorran mayores distancias.
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Diverse strategies for manipulating acute training load can be employed in small-sided games. However, the impact of different scoring methods on the internal and external training loads of young athletes during such games remains a topic of debate. The present study aimed to compare external and internal training loads and perceive recovery in small-sided games played with dif - ferent scoring rules. The secondary objective was to analyse the correlation between internal and external training loads. On separate days, 13 young athletes ( Mage 14.7 ± 0.4 years) participated in three different small-sided game models: ball possession, score zone, and small-sided games with small goals. Accelerometers worn by the players recorded raw acceleration values, which were later transformed into external training loads measured with PlayerLoadTM. Approximate - ly 20 minutes after the conclusion of each small-sided game session, the participants’ internal training loads were obtained, using the session rate of perceived exertion (session-RPE) meth - od. Significant differences were found in external training load values ( F(2.28) = 35.046, p < .001; ŋ ² = .78). Ball possession resulted in higher external training loads compared to the score zone and the small-sided games with small goals. Similarly, ball possession caused higher internal training loads compared to the score zone, and the small-sided games with small goals ( F (2,28) = 19. 549, p ≤ .001; ŋ ² = .62). In conclusion, ball possession led to higher external training loads and internal training loads, while small-sided games with small goals led to higher external and internal train - ing loads than score zone. Coaches can opt to use ball possession in training intensifications and small-sided games with small goals in periods of reduced training loads. Keywords: Session-RPE. PlayerLoad. Training impulse. Adolescent.
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Objective: To establish whether a simple integration of selected internal and external training load (TL) metrics is useful for tracking and assessing training outcomes during team-sport training. Methods: Internal [heart rate training impulse (HR-TRIMP), session rating of perceived exertion (sRPE-TL)] and selected external (global positioning systems; GPS) metrics were monitored over seven weeks in 38 professional male rugby league players. Relationships between internal and external measures of TL were determined, and an integrated novel training efficiency index (TEI) was established. Changes in TEI were compared to changes in both running performance (1.2 km shuttle test) and external TL completed. Results: Moderate to almost perfect correlations (r = 0.35–0.96; ±~0.02; range ± 90% confidence limits) were observed between external TL and each measure of internal TL. The integration of HR-TRIMP and external TL measures incorporating both body mass and acceleration/deceleration were the most appropriate variables for calculating TEI, exhibiting moderate (ES= 0.87–0.89; ±~0.15) and small (ES = 0.29–0.33; ±~0.07) relationships with changes in running performance and completed external TL respectively. Conclusions: Combination of the TEI and an athlete monitoring system should reveal useful information for continuous monitoring of team-sport athletes over several weeks.
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Exercise training which meets the recommendations set by the National Physical Activity Guidelines ensues a multitude of health benefits towards the prevention and treatment of various chronic diseases. However, not all individuals respond well to exercise training. That is, some individuals have no response, while others respond poorly. Genetic background is known to contribute to the inter-individual (human) and -strain (e.g., mice, rats) variation with acute exercise and exercise training, though to date, no specific genetic factors have been identified that explain the differential responses to exercise. In this review, we provide an overview of studies in human and animal models that have shown a significant contribution of genetics in acute exercise and exercise training-induced adaptations with standardized endurance and resistance training regimens, and further describe the genetic approaches which have been used to demonstrate such responses. Finally, our current understanding of the role of genetics and exercise is limited primarily to the nuclear genome, while only a limited focus has been given to a potential role of the mitochondrial genome and its interactions with the nuclear genome to predict the exercise training-induced phenotype(s) responses. We therefore discuss the mitochondrial genome and literature that suggests it may play a significant role, particularly through interactions with the nuclear genome, in the inherent ability to respond to exercise.
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Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO 2 , and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved. Watching athletes perform well, set personal records or win competitions, are great pleasures for sports scientists. To think that the information that you have collected on the athlete, or synthesized from the literature, has helped the athlete achieve optimal performance is " as good as it gets " for support staff. Conversely, watching a poor performance inspires analysis of what went wrong, with preparation, tactics, or execution of the competitive plan. This provides the basis for the questions that drive sport-science research. Because so much of the preparation of athletes is related to the structure and details of the training program, there is a natural emphasis on how training influences performance. This interest goes into history, to Milo of Crotona, the Italian farm boy who lifted a growing bullock daily until he became the strongest man in the world and legend of the ancient Olympics. This story provides the historical grounding for the quest to understand the training response, most uniquely characterized by the concept of progression of the training load, and to the idea that training loads can be quantitatively expressed 1 and related to performance outcomes. 2–6 Although it is not known if Milo had a coach, most top athletes throughout history have had one, someone with more knowledge and experience, and the objectivity to evaluate their training and performance. The concept of training monitoring, regardless of historical time frame is in essence about the coach-athlete interface. Although not always appreciated, one can make the argument that the greatest value of sports science is related to optimizing the coach-athlete interface; to give the athlete a smarter, better-informed coach. Accepting the premise that the proper role of sport science is to inform and support the coach-athlete relationship, we need to ask what the coach needs from the sport-science community. A reasonable approximation is provided in Table 1. The reality is that sports scientists are rather good at providing the first 2 of these needs to the coach but less good at the last 2. As addressed previously, 9 index workouts could be performed routinely by groups of athletes as a normal part of the training program, giving the coach high-frequency data useful for predicting progress toward training goals, and decision making regarding when the training program needs to be modified. The laboratory, is hard to schedule, is not well suited to testing large numbers of athletes quickly, and is not available for high-frequency testing. It is also much harder to provide the information which the coach needs to " translate " the results of the training to specif-ics about the progress and performance of the athlete (Figure 1).
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Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of HR evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds, and the possibility of trackside measurement of HR, lactate, VO2 and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise, but often require laboratory testing for calibration, and tend to produce too much information, in too slow of a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training LOAD. Although the original TRIMP concept was mathematically complex, the development of the Session RPE and similar low tech methods has demonstrated a way to evaluate training LOAD, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high resolution data of the external training load. These methods are promising, but problems relative to information overload and turn-around time to coaches remain to be solved.
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The need to quantify aspects of training in order to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias towards subjective reports and/or quantifications of external load. It is evident the lack of extensive longitudinal studies employing objective internal load measurements possibly due to the cost/effectiveness and the invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges and opportunities of various wearable technologies.
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Often exercise intensities are defined as percentages of maximal oxygen uptake ((V) over dot O-2max) or heart rate (HRmax). Purpose: The purpose of this investigation was to test the applicability of these criteria in comparison with the individual anaerobic threshold. Methods: One progressive cycling test to exhaustion (initial stage 100 W. increment 50 W every 3 min) was analyzed in a group of 36 male cyclists and triathletes (24.9 +/- 5.5 yr; 71.6 +/- 5.7 kg; W. increment 50 W every 3 min) was analyzed in a group of 36 male cyclists and triathletes (24.9 +/- 5.5 yr; 71.6 +/- 5.7 kg; (V) over dot O-2max; 62.2 +/- 5.0 mL.min(-1).kg(-1); individual anaerobic threshold = IAT: 3.64 +/- 0.41 W.kg(-1); HRmax: 188 +/- 8 min). Power output and lactate concentrations for 60 and 75% of (V) over dot O-2max as well as for 70 and 85% of HRmax were related to the IAT. Results: There was no significant difference between the mean value of WT (261 +/- 34 W, 2.92 +/- 0.65 mmol.L-1), 75% of (V) over dot O-2max (257 +/- 24 W, 2.84 +/- 0.92 mmol.L-1), and 85% of HRmax (259 +/- 30 W, 2.98 +/- 0.87 mmol.L-1). However, the percentages of the IAT ranged between 86 and 118% for 75% (V) over dot O-2max and 87 and 116% for 85% HRmax (corresponding lactate concentrations: 1.41-4.57 mmol.L-1 and 1.25-4.93 mmol.L-1, respectively). The mean values at 60% of (V) over dot O-2max (198 +/- 19 W, 1.55 +/- 0.67 mmol.L-1) and 70% of HRmax (180 +/- 27 W, 1.45 +/- 0.57 mmol.L-1) differed significantly (P < 0.0001) from the WT and represented a wide range of intensities (66-91% and 53-85% of the IAT, 0.70-3.16 and 0.70-2.91 mmol.L-1, respectively). Conclusions: In a moderately to highly endurance-trained group, the percentages of (V) over dot O-2max and HRmax vary considerably in relation to the IAT. As most physiological responses to exercise are intensity dependent, reliance on these parameters alone without considering the IAT is not sufficient.
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This article is devoted to the role of genetic variation and gene-exercise interactions in the biology of adaptation to exercise. There is evidence from genetic epidemiology research that DNA sequence differences contribute to human variation in physical activity level, cardiorespiratory fitness in the untrained state, cardiovascular and metabolic response to acute exercise, and responsiveness to regular exercise. Methodological and technological advances have made it possible to undertake the molecular dissection of the genetic component of complex, multifactorial traits, such as those of interest to exercise biology, in terms of tissue expression profile, genes, and allelic variants. The evidence from animal models and human studies is considered. Data on candidate genes, genome-wide linkage results, genome-wide association findings, expression arrays, and combinations of these approaches are reviewed. Combining transcriptomic and genomic technologies has been shown to be more powerful as evidenced by the development of a recent molecular predictor of the ability to increase VO2max with exercise training. For exercise as a behavior and physiological fitness as a state to be major players in public health policies will require that the role of human individuality and the influence of DNA sequence differences be understood. Likewise, progress in the use of exercise in therapeutic medicine will depend to a large extent on our ability to identify the favorable responders for given physiological properties to a given exercise regimen. © 2011 American Physiological Society. Compr Physiol 1:1603-1648, 2011.
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Video match analysis is used for the assessment of physical performances of professional soccer players, particularly for the identification of "high intensities" considered as "high running speeds." However, accelerations are also essential elements setting metabolic loads, even when speed is low. We propose a more detailed assessment of soccer players' metabolic demands by video match analysis with the aim of also taking into account accelerations. A recent study showed that accelerated running on a flat terrain is equivalent to running uphill at constant speed, the incline being dictated by the acceleration. Because the energy cost of running uphill is known, this makes it possible to estimate the instantaneous energy cost of accelerated running, the corresponding instantaneous metabolic power, and the overall energy expenditure, provided that the speed (and acceleration) is known. Furthermore, the introduction of individual parameters makes it possible to customize performance profiles, especially as it concerns energy expenditure derived from anaerobic sources. Data from 399 "Serie-A" players (mean +/- SD; age = 27 +/- 4 yr, mass = 75.8 +/- 5.0 kg, stature = 1.80 +/- 0.06 m) were collected during the 2007-2008 season. Mean match distance was 10,950 +/- 1044 m, and average energy expenditure was 61.12 +/- 6.57 kJ x kg(-1). Total distance covered at high power (>20 W x kg(-1)) amounted to 26% and corresponding energy expenditure to approximately 42% of the total. "High intensities" expressed as high-power output are two to three times larger than those based only on running speed. The present approach for the assessment of top-level soccer players match performance through video analysis allowed us to assess instantaneous metabolic power, thus redefining the concept of "high intensity" on the basis of actual metabolic power rather than on speed alone.
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Detraining is the partial or complete loss of training-induced adaptations, in response to an insufficient training stimulus. Detraining characteristics may be different depending on the duration of training cessation or insufficient training. Short term detraining (less than 4 weeks of insufficient training stimulus) is analysed in part I of this review, whereas part II will deal with long term detraining (more than 4 weeks of insufficient training stimulus). Short term cardiorespiratory detraining is characterised in highly trained athletes by a rapid decline in maximal oxygen uptake (VO2max) and blood volume. Exercise heart rate increases insufficiently to counterbalance the decreased stroke volume, and maximal cardiac output is thus reduced. Ventilatory efficiency and endurance performance are also impaired. These changes are more moderate in recently trained individuals. From a metabolic viewpoint, short term inactivity implies an increased reliance on carbohydrate metabolism during exercise, as shown by a higher exercise respiratory exchange ratio, and lowered lipase activity, GLUT-4 content, glycogen level and lactate threshold. At the muscle level, capillary density and oxidative enzyme activities are reduced. Training-induced changes in fibre cross-sectional area are reversed, but strength performance declines are limited. Hormonal changes include a reduced insulin sensitivity, a possible increase in testosterone and growth hormone levels in strength athletes, and a reversal of short term training-induced adaptations in fluid-electrolyte regulating hormones.
Article
The development of performance in competition is achieved through a training process that is designed to induce automation of motor skills and enhance structural and metabolic functions. Training also promotes self-confidence and a tolerance for higher training levels and competition. In general, there are two broad categories of athletes that perform at the highest level: (i) the genetically talented (the thoroughbred); and (ii) those with a highly developed work ethic (the workhorse) with a system of training guiding their effort. The dynamics of training involve the manipulation of the training load through the variables: intensity, duration and frequency. In addition, sport activities are a combination of strength, speed and endurance executed in a coordinated and efficient manner with the development of sport-specific characteristics. Short- and long-term planning (periodisation) requires alternating periods of training load with recovery for avoiding excessive fatigue that may lead to overtraining. Overtraining is long-lasting performance incompetence due to an imbalance of training load, competition, non-training stressors and recovery. Furthermore, annual plans are normally constructed in macro-, meso- and microcycles around the competitive phases with the objective of improving performance for a peak at a predetermined time. Finally, at competition time, optimal performance requires a healthy body, and integration of not only the physiological elements but also the psychological, technical and tactical components.
Article
Electromyographic (EMG) activity of the leg muscles and the ground reaction forces were recorded in 17 elite male middle-distance runners, who performed isometric maximal voluntary contractions (MVC) as well as running at different speeds. Electromyograms were recorded from the gluteus maximus, vastus lateralis, biceps femoris, gastrocnemius and tibialis anterior. The results indicated that the averaged EMG (aEMG) activities of all the muscles studied increased (P < 0.05) with increasing running speed, especially in the pre-contact and braking phases. At higher speeds, the aEMG activities of the gastrocnemius, vastus lateralis, biceps femoris and gluteus maximus exceeded 100% MVC in these same phases. These results suggest that maximal voluntary contractions cannot be used as an indicator of the full activation potential of human skeletal muscle. Furthermore, the present results suggest that increased pre-contact EMG potentiates the functional role of stretch reflexes, which subsequently increases tendomuscular stiffness and enhances force production in the braking and/or propulsive phases in running. Furthermore, a more powerful force production in the optimal direction for increasing running speed effectively requires increased EMG activity of the two-joint muscles (biceps femoris, rectus femoris and gastrocnemius) during the entire running cycle.
Monitoring training load in Italian football. Paper presented at: 8th Annual Congress of the European College of Sport Science
  • F M Impellizzeri
Impellizzeri FM. Monitoring training load in Italian football. Paper presented at: 8th Annual Congress of the European College of Sport Science; 2003. Salzburg, Austria.
Developing athlete monitoring systems: theoretical basis and practical applications
  • A J Coutts
  • S Crowcroft
  • T Kempton
Coutts AJ, Crowcroft S, Kempton T. Developing athlete monitoring systems: theoretical basis and practical applications. In: Kellmann M, Beckmann J, eds. Sport, Recovery and Performance: Interdisciplinary Insights. Abingdon, UK: Routledge; 2018:19-32.
  • I D Pubmed
PubMed ID: 18184760 doi:10.1152/ajpregu.00678.2007