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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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... The added value of robust standardised exercise tests (SETs) that show high reproducibility and from which an optimal training load can be derived based upon individual internal load capacity is increasingly recognised in human sports medicine and other animal models, such as dogs and mice. However, most equine athletes are still trained on an empiric basis and a one-approachfits-for all philosophy (Cunha et al., 2009;Ferasin and Marcora, 2009;Manzo et al., 2009;Alves et al., 2012;Rodrigues et al., 2016;Impellizzeri et al., 2019;Restan and Cerqueira, 2019). Creation of an optimal cardiovascular training programme for each horse without under-/or overtraining is not evident; however, it is of crucial importance, to avoid occurrence of sports injuries and to achieve maximum performance levels (Dyson, 2000;Singer et al., 2008). ...
... With the coming of a wide range of wearables for measuring the external load (speed, distance, etc.) and internal load (heart rate, blood/plasma lactate concentrations, heart rate variability, body temperature, gait symmetry, etc.), the first steps are made towards the objective assessment of performance capacity and individualisation of training programmes (Poole and Erickson, 2008;Peyré-Tartaruga and Coertjens, 2018;Impellizzeri et al., 2019). However, translating the obtained data and the derived performance parameters into effective training advice to induce the desired psychophysiological responses, is rarely applied in the different equestrian disciplines and still needs a lot of optimisation (Bourgela and Blais, 1991;Hauser et al., 2014;Arratibel-Imaz et al., 2016). ...
... No studies are available comparing SET vs. LMS deduced performance parameters in the field and no study has been looking into the effect of training on evolution of LMS test performance parameters. Lastly, almost no studies combine these internal and external loads in order to obtain objective training loads that create the most optimal psychophysical response (Impellizzeri et al., 2019). ...
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There is a great need for objective external training load prescription and performance capacity evaluation in equestrian disciplines. Therefore, reliable standardised exercise tests (SETs) are needed. Classic SETs require maximum intensities with associated risks to deduce training loads from pre-described cut-off values. The lactate minimum speed (LMS) test could be a valuable alternative. Our aim was to compare new performance parameters of a modified LMS-test with those of an incremental SET, to assess the effect of training on LMS-test parameters and curve-shape, and to identify the optimal mathematical approach for LMS-curve parameters. Six untrained standardbred mares (3–4 years) performed a SET and LMS-test at the start and end of the 8-week harness training. The SET-protocol contains 5 increments (4 km/h; 3 min/step). The LMS-test started with a 3-min trot at 36–40 km/h [until blood lactate (BL) > 5 mmol/L] followed by 8 incremental steps (2 km/h; 3 min/step). The maximum lactate steady state estimation (MLSS) entailed >10 km run at the LMS and 110% LMS. The GPS, heartrate (Polar®), and blood lactate (BL) were monitored and plotted. Curve-parameters (R core team, 3.6.0) were (SET) VLa1.5/2/4 and (LMS-test) area under the curve (AUC>/ 0.80), Bland-Altman method, and ordinary least products (OLP) regression analyses were determined for test-correlation and concordance. Training induced a significant increase in VLa1.5/2/4. The width of the AW increased significantly while the AUC>LMS and LMS decreased post-training (flattening U-curve). The LMS BL steady-state is reached earlier and maintained longer after training. BLmax was significantly lower for LMS vs. SET. The 40° angular method is the optimal approach. The correlation between LMS and VMLSS was significantly better compared to the SET. The VLa4 is unreliable for equine aerobic capacity assessment. The LMS-test allows more reliable individual performance capacity assessment at lower speed and BL compared to SETs. The LMS-test protocol can be further adapted, especially post-training; however, inducing modest hyperlactatemia prior to the incremental LMS-stages and omitting inclusion of a per-test recovery contributes to its robustness. This LMS-test is a promising tool for the development of tailored training programmes based on the AW, respecting animal welfare.
... *Correspondence: coach@josephcoyne.com 1 School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia Full list of author information is available at the end of the article ...
... Training load (TL) monitoring is normally applied to assess the physical work an athlete performs in training (i.e., external load) and the athlete's within-training response to that physical work (i.e., internal load) [1,2]. Sessional ratings of perceived exertion (sRPE) and differential ratings of perceived exertion (dRPE) are both subjective measures of the intensity of internal TL [1,3]. ...
... Training load (TL) monitoring is normally applied to assess the physical work an athlete performs in training (i.e., external load) and the athlete's within-training response to that physical work (i.e., internal load) [1,2]. Sessional ratings of perceived exertion (sRPE) and differential ratings of perceived exertion (dRPE) are both subjective measures of the intensity of internal TL [1,3]. Sessional ratings of perceived exertion, which are seen as a global measure of perceived exercise intensity [4,5], seem to be the most used measure in practice; being often recommended as the primary TL measure in team sports and being widely employed in endurance sports [6][7][8]. ...
Article
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This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.
... Training intensity (TI) has been defined as the input variable that is managed to elicit the desired training response. 1,2 For years, monitoring TI has been related to improving players' performance and reducing incidences of injury, while some authors have recently dismissed the relationship between TI management and injuries. 3,4 Therefore, monitoring training intensity is aimed to manage the training stimulus in impact in progressive intensity, adjustment of training plan, and managing the recovery strategies. ...
... To date, monitoring TI is one of the biggest concerns for soccer-specific sport scientists. [2][3][4][5][6][7][8][9][10] In 2003, Impellizzeri et al. 2 introduced a theoretical framework to establish the measurable construct of the training process, dividing this framework in two possible ways: internal and external TI. If the measured aspect is referring externally to the athlete it belongs to the external TI (i.e. ...
... To date, monitoring TI is one of the biggest concerns for soccer-specific sport scientists. [2][3][4][5][6][7][8][9][10] In 2003, Impellizzeri et al. 2 introduced a theoretical framework to establish the measurable construct of the training process, dividing this framework in two possible ways: internal and external TI. If the measured aspect is referring externally to the athlete it belongs to the external TI (i.e. ...
Article
Training intensity (TI) monitoring has become a necessary aspect of professional soccer training. This study systematically reviewed the original investigations that have reported values regarding TI across macrocycles, mesocycles, and one-, two-, and three-match day (MD) microcycles in professional soccer, to analyze TI variations among months, weeks, and training sessions, respectively. A systematic review of PubMed, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the PRISMA guidelines. The articles were included following these criteria: (i) professional soccer players, (ii) players monitored for TI values, (iii) TI distribution in, at least, 3 days, weeks, or months, (iv) variables related to TI (physical/physiological), and (v) original studies. The quality assessment of included articles was done using MINORS checklist. From the 473 studies initially identified, 19 were fully reviewed, and their outcome measures were extracted and analyzed. In microcycles, most articles showed lower values in MD+1 and progressively incremented until MD-4 or MD-3. As the number of days between matches decreased, TI values decreased, with values in MD-1 lower than 50% of MD’s intensity, or even values <50% in all sessions between matches in 3 MD microcycles. The variability standard in mesocycles was less clear. In macrocycles, TI was greater in the preseason than the values in the last stages of the in-season period. In conclusion, coaches may design a post-match recovery strategy followed by an increase in intensity until MD-3. Regarding mesocycles, a week-to-week TI prescription was the most common due to the different demands that are experienced during a match with different contextual factors. Finally, despite slight variations in intensity/volume between preseason and in-season periods during macrocycles, overall TI is similar for both time periods. However, training volume is usually reduced while intensity is kept high.
... Recently, Mellalieu et al. [8] emphasized relevant considerations for the conceptualization and measurement of what they defined "psychological load in sport" (i.e., the total environmental demands placed upon the individual inside and outside of sport). Commonly, load is referred as the net stimulus (that is 'dose') of a training session, combining fundamentally exercise intensity and volume indicators [9]. As load term is thought to be a problematic term [10], suitable terms were used thereafter in this review. ...
... Monitoring the global and cumulative amounts of various burdens placed on an individual over time is essential to effective training management, adaptation, and injury mitigation in sports [11]. However, we can observe currently that training monitoring is mainly focused on the external physical stimulus applied to the athlete and its psychophysiological responses [9], offering a massive amount of data collected day-by-day by athletic staff. Although being complimentary and relevant, the use of data on mental effort has been very restrained in sports environments. ...
... • perceptual (e.g., session rating of perceived exertion, sRPE [12,13]; and other psychological rating scales on the quality of the training sessions, recovery status, and wellbeing) • physiological (e.g., blood lactate and heart rate-HR-at rest, exercise, and recovery epochs [1,9]) • biological (cortisol [14] for stress, creatine kinase for muscle damage marker [15]) • biomechanical (stresses and strains on the musculoskeletal system [16]). ...
Article
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Appropriate training burden monitoring is still a challenge for the support staff, athletes, and coaches. Extensive research has been done in recent years that proposes several external and internal indicators. Among all measurements, the importance of cognitive factors has been indicated but has never been really considered in the training monitoring process. While there is strong evidence supporting the use of cognitive demand indicators in cognitive neuroscience, their importance in training monitoring for multiple sports settings must be better emphasized. The aims of this scoping review are to (1) provide an overview of the cognitive demand concept beside the physical demand in training; (2) highlight the current methods for assessing cognitive demand in an applied setting to sports in part through a neuroergonomics approach; (3) show how cognitive demand metrics can be exploited and applied to our better understanding of fatigue, sport injury, overtraining and individual performance capabilities. This review highlights also the potential new ways of brain imaging approaches for monitoring in situ. While assessment of cognitive demand is still in its infancy in sport, it may represent a very fruitful approach if applied with rigorous protocols and deep knowledge of both the neurobehavioral and cognitive aspects. It is time now to consider the cognitive demand to avoid underestimating the total training burden and its management.
... running distance) and internal load (e.g. heart rate; Impellizzeri, Marcora, & Coutts, 2019). With regard to studies using external load as a parameter for physical load (Bloß et al., 2020;Impellizzeri et al., 2019), findings neither indicate a relationship between running time (Paradis, Larkin, & O'Connor, 2015) nor distance covered and RDM (Emmonds et al., 2015;Mascarenhas et al., 2009). ...
... heart rate; Impellizzeri, Marcora, & Coutts, 2019). With regard to studies using external load as a parameter for physical load (Bloß et al., 2020;Impellizzeri et al., 2019), findings neither indicate a relationship between running time (Paradis, Larkin, & O'Connor, 2015) nor distance covered and RDM (Emmonds et al., 2015;Mascarenhas et al., 2009). While findings by Oudejans et al. (2005) indicate that more RDM errors occur at higher velocities (> 8 km/h), the study of Gomez-Carmona and Pino-Ortega (2016) point out less accurate RDM at slow velocities (< 8 km/h). ...
... Consequently, subsequent studies should conduct laboratory studies in which potential confounding variables can be controlled. This approach, in contrast to much of the research to date, would provide the added value of being able to control the internal load as the individual response to an external load (Bloß et al., 2020;Impellizzeri et al., 2019). It is important to note that these studies must be as externally valid as possible, meaning that the task and exercise are representative to examine the effects of physical load on RDM (Bloß et al., 2020;Hancock, Bennett, Roaten, Chapman, & Stanley, 2021). ...
Article
Referees are challenged for accurate decision-making under physical and psychological load. However, erroneous decision-making can lead referees to repeatedly negatively think about the error, i.e. they begin to ruminate. Previous studies focused on the relationship between decision-making and either physical or psychological load. Here, we examined the combined impact of physical and psychological load on top-class handball referees’ decision-making. N = 73 referees performed the Yo-Yo Test for Referees combined with psychological load induced through instantaneous feedback on the decisions to activate the referees’ rumination-trait, i.e. to test if low and high ruminating referees respond differently to load and feedback. Results indicate no effects of physical load on decision accuracy (i.e. foul vs. no foul), but referees’ reasonings accuracy (i.e. type of foul, punishment) decreased under medium external load. Moreover, low and high ruminating referees did statistically not differ in decision and reasoning accuracy. Results indicate that physical load may not affect decisions, but might affect reasonings. Thus, subsequent studies should incorporate decisions and reasonings in handball referees, especially against the background of referee’s development. For practitioners, the Yo-Yo Test for Referees seems a beneficial tool through the combination of physical likewise psychological load and a rule test in handball.
... While submission grappling techniques are a secondary skill in wrestling, judo and sambo [3][4][5], they are a key skill component in both MMA and BJJ respectively [6,7]. As with most sports, understanding and quantifying the physiological load imposed during grappling based training, sparring and competition is important in order to manipulate and manage training loads effectively [8]. ...
... This restricts the capacity of TMA to measure the external load of submission grappling. The absence of an accurate external load measurement may cause difficulties in understanding the relationship of external to internal load in this population, leading to suboptimal training prescription and implementation [8]. ...
... In order to support this proposition, further studies should be completed comparing PlayerLoad across multiple sessions of differing planned intensities to determine this variable's capability of distinguishing between sessions of higher and lower loads. In addition, within session PlayerLoad should be compared to internal load measurements such as HR or rating of perceived exertion (RPE) to determine a potential dose-response relationship to enable coaches to monitor the loadfatigue-recovery process [8]. ...
Article
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Submission grappling consists of skills and movements used in combat sports to physically control opponents whilst trying to apply choke holds and joint locks. There is currently no accepted method of monitoring external load in grappling-based sports due to the absence of key variables such as distance, velocity or time. The primary aim of this study was to determine whether PlayerLoad is a reliable variable for measuring external load of submission grappling movements, with a secondary aim of determining the between repetition variance of submission grappling movements. 7 experienced submission grapplers were recruited. Each wore a torso mounted Catapult® Optimeye S5 microelectromechanical systems (MEMS) device and completed 5 repetitions of each of the following: 4 submission techniques; 5 transition techniques; 2 guard pass techniques; 2 takedown techniques. Accumulated PlayerLoad (PLdACC) was recorded as a marker of absolute load, with accumulated PlayerLoad per minute (PLdACC∙min-1) representing relative load. Reliability of each was assessed using intraclass correlation coefficient (ICC(3,1)) (≥ 0.70). Between repetition movement variation was assessed via coefficient of variation with 95% confidence intervals (CV, 95%CI) (acceptable ≤ 15%, good ≤ 10%). PLdACC ICC(3,1) range = 0.78–0.98, with CV range = 9–22%. PLdACC∙min-1 ICC(3,1) range = 0.83–0.98, with CV range = 11–19%. Though several variables displayed CV > 15%, all had 95%CI lower boundaries ≤ 15%. Whilst PlayerLoad was found to be a reliable measure for submission grappling, relatively high CVs across most techniques examined suggest PlayerLoad may not be appropriate for measuring changes in external load for individual movements in submission grappling. However, it may prove a useful tool for monitoring the external load of full, grappling-based, training sessions within an individual.
... L a pallacanestro è uno sport di squadra dinamico, complesso, intermittente e ad alta intensità in cui la capacità di eseguire abilità tecnico-tattiche facendo fronte simultaneamente a ripetute accelerazioni, decelerazioni, cambi di direzione e salti, è di cruciale importanza per raggiungere il successo. [1][2][3] Uno degli obiettivi principali durante le sessioni di allenamento è prescrivere un adeguato carico esterno (EL), 4 che viene definito come il lavoro fisico eseguito, 5 per stimolare adattamenti specifici e 4 per promuovere la risposta desiderata da parte dell'organismo. 5 La risposta psicofisiologica durante l'esercizio (ad esempio la frequenza cardiaca) viene invece definita come carico interno (IL). ...
... [1][2][3] Uno degli obiettivi principali durante le sessioni di allenamento è prescrivere un adeguato carico esterno (EL), 4 che viene definito come il lavoro fisico eseguito, 5 per stimolare adattamenti specifici e 4 per promuovere la risposta desiderata da parte dell'organismo. 5 La risposta psicofisiologica durante l'esercizio (ad esempio la frequenza cardiaca) viene invece definita come carico interno (IL). 5 L'orientamento alla partita è il condizionamento più specifico basato sulle abilità, e coinvolge requisiti mentali, fisici e fisiologici maggiormente legati a situazioni reali. ...
... 5 La risposta psicofisiologica durante l'esercizio (ad esempio la frequenza cardiaca) viene invece definita come carico interno (IL). 5 L'orientamento alla partita è il condizionamento più specifico basato sulle abilità, e coinvolge requisiti mentali, fisici e fisiologici maggiormente legati a situazioni reali. 6 Conoscere le richieste fisiche e fisiologiche di una competizione sportiva è la chiave per la scelta dell'esercizio specifico più efficace in un contesto agonistico, durante l'allenamento. ...
Article
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BACKGROUND: The aim of this study was to compare the internal peak demands (PD) of different training sessions and official matches in professional basketball players. METHODS: PD for heart rate (HR), respiratory rate (RR) and ventilation (VE) were collected during six games and 49 training sessions. Linear mixed model (MLM) was accomplished to identify differences of HR, RR and VE among various sessions. RESULTS: Match day (MD) presents higher internal PD (small-moderate effects) for all variables compared to the practices. In turn, MD presents small to very large differences compared to the rest of the day codes for all variables. In addition, PD are substantially higher during MD than in season and pre-season. Moreover, for all variables, pre-season practices presented higher values (ES range: 0.15-0.29) than in-season practices. Additionally, PD were higher during friendly games and pre-season than during in-season practices. CONCLUSIONS: Internal PD for all parameters monitored were higher during games than during practices. Additionally, internal PD were higher during friendly games and pre-season than in-season practices. Despite being friendly games, these findings revealed that the game is the scenario where higher internal PD are reached. It is crucial to quantify the peak demands to allow for optimal training of players to cope with these internal PD and successfully execute key technical-tactical skills during games. KEY WORDS: Heart rate; Athletes; Basketball; Athletic performance; Team sports
... 4,5 Training loads are often classified as external or internal loads. External loads, defined as physical stresses applied to a runner (eg, duration, distance), 6 are most commonly monitored because they are simple to prescribe and measure. Monitoring external loads alone fails to consider a runner's physiological or psychological stress in response to external loads (ie, internal loads), such as running intensity. ...
... Monitoring external loads alone fails to consider a runner's physiological or psychological stress in response to external loads (ie, internal loads), such as running intensity. 6 Monitoring both external and internal loads provides a more comprehensive approach to stresses and may better estimate training loads experienced during each running session. [7][8][9] External loads can be self-reported by the runner using an electronic journal, [9][10][11][12][13][14] which is a cheap and easy method to collect data from a large sample. ...
Article
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Purpose: Running programs are designed to progress training loads by manipulating the duration, frequency, and/or intensity of running sessions. While some studies use journals to monitor training load, others have used wearable technology. The purpose of this study was to compare the validity of self-reported and global positioning system (GPS)-watch-derived measures of external and internal loads in high school cross-country runners. Methods: Twenty-two high school cross-country runners participated in the study during fall 2020. Participants recorded running sessions using a GPS watch and self-reported the running session using an electronic journal. External (distance and duration) and internal loads (session rating of perceived exertion [sRPE], average, and maximum heart rate) were retrieved from the GPS watch and electronic journal. Correlations compared relationships, and Bland-Altman plots compared agreements between GPS-watch-derived and self-reported measures of training loads. Results: We found moderate relationships between self-reported and GPS-watch-derived measures of external loads (distance: r = .76, moving duration: r = .74, and elapsed duration: r = .70) and poor relationships between internal loads (sRPE vs average heart rate: ρ = .11, sRPE vs maximal heart rate: ρ = .13). We found mean differences of -0.8 km (95% = -6.3 to +4.8 km) for distance, -4.5 minutes (95% = -27.8 to +33.2 min) for moving duration, and 2.7 minutes (95% = -27.8 min to +33.2 min) for elapsed duration. Conclusions: High school runners overreported running distance and duration using self-reports, and self-reported and GPS-watch-derived measures of internal loads demonstrated poor agreement. Coaches and clinicians should use caution when comparing results from studies using different methods of monitoring training loads.
... Two common classifications of training load are internal and external load measures [3]. External load is a measure of the load specific to the training undertaken and includes measures such as distance [total distance or distance within specific speed ranges], accelerations and tonnage for resistance training [4]. Internal loads reflect the actual psycho-physiological response by the athlete elicited by the external training load and includes measures related to heart rate (e.g., heart rate variability, heart rate reserve) and the product of session rating perceived exertion (sRPE) and duration (sRPE-TL (training load)) [4]. ...
... External load is a measure of the load specific to the training undertaken and includes measures such as distance [total distance or distance within specific speed ranges], accelerations and tonnage for resistance training [4]. Internal loads reflect the actual psycho-physiological response by the athlete elicited by the external training load and includes measures related to heart rate (e.g., heart rate variability, heart rate reserve) and the product of session rating perceived exertion (sRPE) and duration (sRPE-TL (training load)) [4]. ...
Article
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Studies of training and competition load in sport are usually based on data that represents a sample of a league and or annual training program. These studies sometimes explore important factors that are affected by load, such as training adaptations and injury risk. The generalisability of the conclusions of these studies, can depend on how much load varies between seasons, training phases and teams. The interpretation of previous load studies and the design of future load studies should be influenced by an understanding of how load can vary across seasons, training phases and between teams. The current study compared training loads (session rating of perceived exertion x session duration) between all (8) teams in an elite Netball competition for multiple (2) season phases and (2) seasons. A total of 29,545 records of athlete session training loads were included in the analysis. Linear mixed models identified differences between seasons and training phases (p < .05). There were also differences between teams and a complex set of interactions between these three factors (season, phase, and team) (p < .05). While the absolute value of the training loads reported here are only relevant to elite netball, these results illustrate that when data is sampled from a broader context, the range and variation in load may increase. This highlights the importance of cautiously interpreting and generalisation of findings from load studies that use limited data sets.
... Prescription of exercise intensity appears to be the most difficult part of exercise and training programing. Although training frequency and duration are important elements of creating the internal training load [1] to drive the adaptive responses to training, we have known since the pioneering work of Karvonen et al. [2] that prescription of training intensity is critical relative to achieving an adequate training stimulus. It is also central to the concept of training intensity distribution [3,4]. ...
... Rating of perceived exertion was shown to be related to physiological thresholds demarcating intensity domains in Table 6 Physiological and perceptual responses to continuous exercise "clamped" at the intensities of fairly good (1) Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
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Prescribing exercise intensity is crucial in achieving an adequate training stimulus. While numerous objective methods exist and are used in practical settings for exercise intensity prescription, they all require anchor measurements that are derived from a maximal or submaximal graded exercise test or a series of submaximal or supramaximal exercise bouts. Conversely, self-reported subjective methods such as the Talk Test (TT), Feeling Scale (FS) affect rating, and rating of perceived exertion (RPE) do not require exercise testing prior to commencement of the exercise training and therefore appear as more practical tools for exercise intensity prescription. This review is intended to provide basic information on reliability and construct validity of the TT, FS, and RPE measurements to delineate intensity domains. The TT and RPE appear to be valid measures of both the ventilatory threshold and the respiratory compensation threshold. Although not specifically examined, the FS showed tendency to demarcate ventilatory threshold, but its validity to demarcate the respiratory compensation threshold is limited. Equivocal stage of the TT, RPE of 10–11, and FS ratings between fairly good (+ 1) and good (+ 3) are reflective of the ventilatory threshold, while negative stage of the TT, RPE of 13–15, and FS ratings around neutral (0) are reflective of the respiratory compensation threshold. The TT and RPE can effectively be used to elicit homeostatic disturbances consistent with the moderate, heavy, and severe intensity domains, while physiological responses to constant FS ratings show extensive variability around ventilatory threshold to be considered effective in demarcating transition between moderate and heavy intensity domains.
... 1,2 Training load is the product of duration (DUR) and intensity and can be either external or internal. 3 External load refers to the performance output of players during training and match sessions. It is quantified using electronic performance and tracking systems (EPTSs) such as global positioning system or other global navigation satellite systems. ...
... Uncertainty of the effect is indicated by 95% CI. variables moderating the relationship between external and internal load. 3,4 Given the observational nature of our study, it remains unclear whether these findings represent an issue of validity or sensitivity, or whether it can be explained by the influence of conscious bias related to players' cognition (eg, miscomprehension of the constructs breathlessness and leg-muscle exertion) or players' motivational situation (eg, deliberate deception to reduce time-on-task). 7 While most players did not often provide different ratings, the few occasions where players deliberately chose to differentiate can provide useful information. ...
Article
Purpose: To examine the utility of differential ratings of perceived exertion (dRPE) for monitoring internal intensity and load in association football. Methods: Data were collected from 2 elite senior male football teams during 1 season (N = 55). External intensity and load data (duration × intensity) were collected during each training and match session using electronic performance and tracking systems. After each session, players rated their perceived breathlessness and leg-muscle exertion. Descriptive statistics were calculated to quantify how often players rated the 2 types of rating of perceived exertion differently (dRPEDIFF). In addition, the association between dRPEDIFF and external intensity and load was examined. First, the associations between single external variables and dRPEDIFF were analyzed using a mixed-effects logistic regression model. Second, the link between dRPEDIFF and session types with distinctive external profiles was examined using the Pearson chi-square test of independence. Results: On average, players rated their session perceived breathlessness and leg-muscle exertion differently in 22% of the sessions (range: 0%-64%). Confidence limits for the effect of single external variables on dRPEDIFF spanned across largely positive and negative values for all variables, indicating no conclusive findings. The analysis based on session type indicated that players differentiated more often in matches and intense training sessions, but there was no pattern in the direction of differentiation. Conclusions: The findings of this study provide no evidence supporting the utility of dRPE for monitoring internal intensity and load in football.
... Team Training in Sports Science: From the Individual Athlete to the Team Sport training is the process of systematically performing exercises to improve physical and cognitive abilities and to acquire specific sport skills (Impellizzeri et al., 2019). When delivered appropriately, training produces a functional adaptive response that induces shifts in various training outcomes such as physical, technical and/or tactical performance, injury resistance, or health (Impellizzeri et al., 2019). ...
... Team Training in Sports Science: From the Individual Athlete to the Team Sport training is the process of systematically performing exercises to improve physical and cognitive abilities and to acquire specific sport skills (Impellizzeri et al., 2019). When delivered appropriately, training produces a functional adaptive response that induces shifts in various training outcomes such as physical, technical and/or tactical performance, injury resistance, or health (Impellizzeri et al., 2019). Important differences in training philosophy and approach arise when individual and team sports are compared. ...
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The aim of this study was to assess drop jump (DJ) performance variables (jump height, contact time, and reactive strength index) concomitant to surface electromyography (sEMG) of lower limb muscles during DJs from different drop heights (intensities). The eccentric and concentric phase sEMG from the gastrocnemius medialis, biceps femoris, and vastus medialis muscles were assessed during all tests, with sEMG activity normalized to maximal voluntary isometric contraction (MVIC). In a cross-sectional, study, 10 amateur female volleyball players (age 22.1 years; body mass 72.9 kg; height 1.70 m) completed DJs from six heights [15–90 cm (DJ15 to DJ90)]. During DJs there was no jump-target box to rebound on to. Results of one-way analysis of variance (ANOVA) showed that the jump height, contact time, and reactive strength index were not significantly (p > 0.05) different between drop heights. Mean biceps femoris eccentric and concentric sEMG ranged from 27 to 50%, although without significant differences between drop heights. Mean gastrocnemius medialis eccentric and concentric sEMG remained relatively constant (60–80% MVIC) across DJs heights, although eccentric values reached 90–120% MVIC from DJ75 to DJ90. Mean variations of 50–100% MVIC for eccentric and 50–70% MVIC for concentric sEMG activations were observed in the vastus medialis across DJs heights. The biceps femoris eccentric/concentric sEMG ratio during DJ45 (i.e., 1.0) was lower (p = 0.03) compared to the ratio observed after DJ90 (i.e., 3.2). The gastrocnemius medialis and vastus medialis eccentric/concentric sEMG ratio were not significantly different between drop heights. In conclusion, jumping performance and most neuromuscular markers were not sensitive to DJ height (intensity) in amateur female volleyball athletes.
... In traditional exercise training, the external load (defined as the overload imposed by the ratio between the intensity and volume of the stimulus) is a determining parameter for the generation of muscle adaptation 15 . Taking into account the functional and histomorphological characteristics of lumbopelvic stabilizer muscles related to strength endurance, the dosimetric parameters should respect the principle of training specificity in order to induce muscle benefits. ...
... In order to calculate the external load through the NMES, the load control calculations used in weight training were adapted. For this, the terminology already proposed was used 15,19 , in which the load volume (LV) is the product of the intensity multiplied by the number of repetitions (the NMES intensity here would replace the endurance mass); the total of repetitions (TR) is the product of the number of sets multiplied by the number of repetitions (repetitions = number of contractions); and the stimulation intensity (SI) would be calculated by the result of the load volume divided by the number of repetitions. The stimulation density (SD) was obtained by the result of the volume divided by the stimulation duration. ...
... Team Training in Sports Science: From the Individual Athlete to the Team Sport training is the process of systematically performing exercises to improve physical and cognitive abilities and to acquire specific sport skills (Impellizzeri et al., 2019). When delivered appropriately, training produces a functional adaptive response that induces shifts in various training outcomes such as physical, technical and/or tactical performance, injury resistance, or health (Impellizzeri et al., 2019). ...
... Team Training in Sports Science: From the Individual Athlete to the Team Sport training is the process of systematically performing exercises to improve physical and cognitive abilities and to acquire specific sport skills (Impellizzeri et al., 2019). When delivered appropriately, training produces a functional adaptive response that induces shifts in various training outcomes such as physical, technical and/or tactical performance, injury resistance, or health (Impellizzeri et al., 2019). Important differences in training philosophy and approach arise when individual and team sports are compared. ...
Book
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The theory and methodology of training combine different factors that support the coach’s intervention for maximizing the athlete’s performance. Among these factors can be included the testing and monitoring, the definition of targets and structure of intervention, the planning, and the intervention itself or in a larger concept a hybrid model factor supporting performance as recovery strategies, psychological interventions, nutrition, or supplementation. Since performance is multidimensional, it seems interesting to look for this issue from a different perspective, namely considering interaction among factors and providing reports about those interactions. This was the main rationale for supporting the opening of this Frontiers topic. Overall, we have published 12 articles in our topic. Different approaches were received, as expected. Although the range of topics of research, it was obvious two major areas in which the articles were focused: (i) routes for integrating psychology into the sports training methodology; and (ii) testing and Monitoring into the sports training methodology. Among those included in the “routes for integrating psychology into the sports training methodology,” it was observed articles were more related with a description of psychological factors, while others were more focused on identifying how to use training interventions by using the psychology background. Regarding the articles related to testing and monitoring, it was obvious a specific concern in quantifying and qualifying the training intensities and the wellbeing of athletes across the season. Additionally, the characterization of specific exercises, tasks, or interventions was also focused on. Considering the interest of evidence presented in our topic, following the readers can briefly overview the multitude of topics and the main findings reported in the included articles.
... O objetivo deste estudo foi examinar os efeitos da distribuição da carga interna de treinamento no desempenho de salto vertical durante a pré-temporada em jogadores de voleibol. A amostra foi composta por 11 jogadores de voleibol da Superliga Masculina de Voleibol (26,4±5,7 anos; 96,6±9,0 kg; 197,6±7,8 cm;8,1±2,8% de gordura). A pré-temporada foi composta por 11 semanas de treinamento, dividida em três etapas: Etapa 1, com duração de 6 semanas; Etapa 2, com duração de 3 semanas; Etapa 3, com duração de 2 semanas. ...
... Nesse sentido, é importante considerar que as adaptações decorrentes do treinamento dependem diretamente da carga interna de treinamento (CIT), representada pelo estresse psicofisiológico imposto ao organismo do atleta em função do treinamento aplicado 8 . Dessa maneira, estudos que apontem possibilidades racionais de distribuição da CIT em momentos importantes da temporada, bem como os efeitos dessa distribuição em indicadores de desempenho relevantes para modalidade são importantes para orientar membros de comissão técnicas na elaboração de futuros planejamentos. ...
Article
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Resumo: O objetivo deste estudo foi examinar os efeitos da distribuição da carga interna de treinamento no desempenho de salto vertical durante a pré-temporada em jogadores de voleibol. A amostra foi composta por 11 jogadores de voleibol da Superliga Masculina de Voleibol (26,4±5,7 anos; 96,6±9,0 kg; 197,6±7,8 cm; 8,1±2,8% de gordura). A pré-temporada foi composta por 11 semanas de treinamento, dividida em três etapas: Etapa 1, com duração de 6 semanas; Etapa 2, com duração de 3 semanas; Etapa 3, com duração de 2 semanas. A carga interna de treinamento foi avaliada em todas sessões de treinamento através da percepção subjetiva de esforço da sessão, as percepções subjetivas de fadiga foram avaliadas ao final de cada semana, por meio da escala de bem-estar e o salto vertical com contra movimento foi avaliado ao final de cada etapa. Os resultados mostraram maiores valores de carga interna de treinamento em Etapa 1 em comparação a Etapa 2 (p = 0,02) e Etapa 3 (p = 0,01), alterações no estado de bem-estar dos atletas durante as três etapas analisadas, considerando os indicadores fadiga (F = 13,1; p < 0,001), estresse (F = 23,8; p < 0,001), humor (F = 16,7; p < 0,001) e bem-estar total (F = 11,2; p < 0,001) e também alterações significativas no salto vertical com contra movimento nos diferentes momentos de avaliação (F = 7,2; p < 0,01). Conclui-se que a distribuição de carga interna de treinamento, juntamente com as capacidades físicas trabalhadas, contribui para que ocorresse melhoria do desempenho no salto vertical com contra movimento e também redução da percepção de fadiga ao final da pré-temporada. Palavras-chave: Treinamento físico; Esportes de equipe; Desempenho esportivo Afiliação
... 28 The response to exercise -and any resultant adaptation (training outcome) -is driven by the exercise stimulus (the stress placed on the body), which is largely determined by the exercise dose ( Figure 1). 29 Exercise programmes, which are not individualised and fail to adhere to these fundamental principles of exercise prescription, will be suboptimal. ...
... The exercise training process (see Impellizzeri et al.29 ). ...
Article
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Exercise is an increasingly widely used treatment for older people across a range of clinical conditions including sarcopenia and physical frailty. Whilst exercise can have many benefits for older people, adaptations to exercise are specific to the exercise mode that is performed and not all exercise is created equal. The correct type of exercise, at the correct dose, needs to be prescribed to maximise effectiveness in treating sarcopenia and physical frailty where maintaining or improving muscle strength and physical function represent key aims. Resistance exercise (RE) is the most potent approach to improving muscle strength and physical function and should be prioritised within exercise programmes delivered to this group. Resistance exercise programme design should be underpinned by the fundamental principles of exercise prescription in order to deliver an appropriate and individualised exercise dose to maximise the potential of RE as a treatment for older people living with sarcopenia and physical frailty.
... Este aspecto es importante debido a que la respuesta del deportista ante un estímulo es específica según la naturaleza, intensidad, duración y tarea a realizar (Fox, Stanton, Sargent, Wintour & Scanlan, 2018) El diseño de la carga de trabajo es plasmado en carga externa ya que es aquella modificable y programable (Halson, 2014). Independientemente del método que se utilice para la cuantificación de la carga, los entrenadores prescriben el entrenamiento de acuerdo a la carga externa con el objetivo de obtener una respuesta psicofisiológica deseada (Impellizzeri, Marcora & Coutts, 2019). Por ello, la carga externa es definida como el estrés mecánico y locomotor que sufre un atleta durante la actividad (Boyd, Ball & Aughey, 2013), mientras que la carga interna es la reacción biológica del cuerpo del atleta al estímulo de carga externa durante las sesiones de entrenamiento y la competición, y que puede ser abordado desde el punto de vista fisiológico y psicológico (Halson, 2014). ...
... Esta se encuentra basada en la relación entre la contracción voluntaria de los músculos y la energía (generada con o sin oxígeno) necesaria para realizar dicho esfuerzo. Su monitorización ha sido realizada desde parámetros internos como frecuencia cardíaca, lactato o consumo de oxígeno muscular hasta parámetros de carga externa como velocidad, aceleración o distancia recorrida, entre otros (Impellizzeri et al., 2019). El problema de este modelo es que tiene en cuenta únicamente como se desplaza el individuo y que energía necesita para dicho esfuerzo, sin tener en cuenta como dicho movimiento afecta sus estructuras musculoesqueléticas . ...
Thesis
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La acelerometría es un método de cuantificación de la carga externa que está teniendo una aplicación exponencial gracias a su integración en dispositivos electrónicos para el análisis del rendimiento en deportes colectivos. La carga externa ha sido comúnmente cuantificada a través del desplazamiento (distancia y velocidad), no teniendo en cuenta su efecto a nivel neuromuscular. Por ello, el objetivo principal de la presente Tesis Doctoral es el análisis de la carga externa que soportan múltiples ubicaciones anatómicas de forma simultánea en los desplazamientos deportivos, específicamente en baloncesto. Para ello, se realiza una revisión sistemática detectando que diferentes aspectos técnicos requieren una evaluación previa al registro así como que los acelerómetros miden la aceleración del segmento al que están unidos. Para subsanar ambos aspectos, se realiza un análisis de la precisión y fiabilidad del sensor, se identifican los índices de carga y frecuencia de muestreo adecuados, así como se diseña y valida un protocolo de registro multi-ubicación y una batería de evaluaciones que representa los desplazamientos más comunes en los deportes de invasión. Finalmente, se realiza la evaluación multi-ubicación de la carga externa en test de laboratorio y test de campo para evaluar el efecto de la velocidad, sexo y tipo de desplazamiento, así como establecer perfiles de rendimiento individual. A partir de estos resultados, los entrenadores podrán identificar la carga externa específica de cada estructura musculoesquelética para diseñar programas individualizados de acondicionamiento físico, prevención de lesiones y recuperación adaptados a los grupos musculares con mayor carga externa. Accelerometry is a method for quantifying external load that is having an exponential application thanks to its integration in electronic performance and tracking systems in team sports. External load has been commonly quantified through displacement (distance and speed), not considering its effect at the neuromuscular level. Therefore, the main objective of this Doctoral Thesis is the analysis of the external load supported by multiple anatomical locations simultaneously in sports movements, specifically in basketball. To do this, a systematic review is carried out, detecting those different technical aspects that require an evaluation prior to registration, as well as that the accelerometers measure the acceleration of the segment to which they are attached. To correct both aspects, an analysis of the precision and reliability of the sensor was performed, the appropriate load index and sampling frequency were identified, as well as a multi-location registration protocol and a battery of evaluations that represent the most common displacements in invasion sports were designed and validated. Finally, the multi-location evaluation of the external load was performed in laboratory and field tests to evaluate the effect of speed, sex and type of movement, as well as to establish individual performance profiles. From these results, trainers will be able to identify the specific external load of each musculoskeletal structure to design individualized programs for physical conditioning, injury prevention and recovery adapted to the muscle groups with the highest external load.
... 270), leads to internal load, i.e., "the psychophysiological responses occurring during the execution of the exercise" (p. 270), (Impellizzeri, Marcora, & Coutts, 2019), causing, for example, an alteration of muscle activation patterns or a reduced level of voluntary muscle activation (Barber-Westin & Noyes, 2017;Santamaria & Webster, 2010). This, in turn, possibly affects risk factors and potentially increases the risk of injury. ...
... Such differences in the load types and protocols possibly explain the ensuing differences in the results. Different load types and protocols probably lead to different resulting internal loads because different body systems and structures (vestibular, visual, proprioception, sensorimotor, muscular) are not stressed equally due to the varying external loads or load types (Impellizzeri et al., 2019). ...
Article
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Dynamic postural control is one of the essential factors in situations where non-contact injuries mainly occur, i.e., landing, cutting, or stopping. Therefore, testing of dynamic postural control should be implemented in injury risk assessment. Moreover, non-contact injuries mainly occur under loaded conditions when the athlete is physically stressed. Therefore, risk factors and mechanisms of these injuries should also be regarded under loading conditions and not only when the athlete is recovered. Current studies examining the influence of physical load on risk factors, such as dynamic postural control, often use cycling protocols to stress the participants. Nevertheless, most types of sports require running as a central element and the induced internal load after cycling might not be the same after running. Therefore, the current study aimed to examine the influence of a running and a cycling protocol on dynamic postural control and to determine the potential injury risk under representative conditions. In total, 128 sport students (64 males and 64 females, age: 23.64 ± 2.44, height: 176.54 ± 8.96 cm, weight: 68.85 ± 10.98 kg) participated in the study. They were tested with the Y Balance Test before and after one loading protocol. A total of 64 participants completed a protocol on a cycle ergometer and the other 64 on a treadmill. A mixed ANOVA showed significant interactions of time and load type. Dynamic postural control was reduced immediately after cycling but did not change after running. These findings indicate a load type dependence of dynamic postural control that must be considered while assessing an athlete’s potential injury risk and they support the need for more representative designs.
... The monitoring of training load is an important process within team sports. 1 Insights from load monitoring are used to optimise training with regard to players' performance and health. Training load is the product of training volume and intensity. ...
... 2 Here, a distinction is made between external and internal variables of intensity. 1 External variables describe facets of intensity that occur externally to the player and are related to the performance output (e.g., running speed and number of jumps within a given timeframe). The same external intensity does, however, induce a different psycho-physiological stress to the body for every player and context, known as the internal intensity. ...
Article
The rating of perceived exertion method allows to describe training intensity in a single value. To better understand the underlying components, the separate rating of perceived breathlessness (RPE-B) and leg-muscle exertion (RPE-L) has been proposed. Here we hypothesized that the separation between the two components may (partly) be determined by the impacts on the lower extremities. In this study, we aimed to experimentally evaluate the differential effect of high versus low impact running and jumping on RPE-B and RPE-L in team sport activities by manipulating the movement strategy (heel strike and passive landing pattern versus forefoot strike and active landing pattern). Eighteen recreational team sport players participated in two submaximal tests consisting of a sequence of running and jumping bouts, whilst ground reaction forces (GRF) were collected. RPE-B and RPE-L data were collected after each bout using the CR100 scale. Paired-samples t-tests were used to analyse between-session differences in these variables. GRF analysis showed that absorption mechanics differed considerably between the two sessions. RPE-L was on average 6.50 AU higher in the low impact session (p = 0.006). However, RPE-B was also increased by 4.96 AU with low impact (p = 0.009). We conclude that the extent to which the lower extremities are being exposed to high or low impacts does not explain a possible separation between the two RPE types.
... From a physical perspective, it is common for practitioners to monitor the training load (TL) undertaken by their players in order to understand both the external load and internal response of each individual player. 5 External load is defined as the physical work prescribed in the training plan, whereas internal load refers to the psychophysiological responses to the external load. 5 The quantification of external load is often monitored using micro-electro-mechanical (MEMS) devices containing a global positioning system (GPS) processor and inertial sensors to collect information of variables such as distances covered at different velocities, acceleration and deceleration efforts and estimated metabolic power. ...
... 5 External load is defined as the physical work prescribed in the training plan, whereas internal load refers to the psychophysiological responses to the external load. 5 The quantification of external load is often monitored using micro-electro-mechanical (MEMS) devices containing a global positioning system (GPS) processor and inertial sensors to collect information of variables such as distances covered at different velocities, acceleration and deceleration efforts and estimated metabolic power. 6 The internal load is typically quantified using heart rate telemetry and subjective scales, such as the rating of perceived exertion (RPE) and wellness ratings. ...
Article
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Objectives 1) To evaluate current physical performance tests used within professional male youth soccer; 2) to understand the relationship of these tests performance in relation to specific measures of external and internal training load (TL) to conclude if there is a subsequent change in test performance. Methods Relevant literature was searched using five electronic databases (PubMed Medline, SPORTDiscus, Web of Science, CINAHL and Scopus), with additional articles identified by the authors. Articles relating to TL and physical development assessment within professional male youth soccer players were evaluated. Results Database searches yielded 5683 articles following removal of duplicates. After screening the titles, abstracts and full texts, 28 articles were identified. Both external TL (total distance, high speed distance, duration) and internal TL (rating of perceived exertion, training impulse) measures were found to be associated with improvements in physical test performance across both pre-season and in-season phases. Field-based testing was found to be sensitive to changes in physical performance for aerobic capacity, lower body power/strength and sprint performance. However, limited sensitivity to change was found when assessing player agility performance. Conclusion Future research in this area should look to enhance our understanding of the dose-response of TL with changes in fitness across different age groups in professional male youth soccer.
... This TL variation is crucial to the effective delivery of the training stimulus for both individual players and the team (Morgans et al., 2014), so may be applied to both professional (Martín-García et al., 2018;Rago et al., 2019) and youth soccer players (Coutinho et al., 2015;Wrigley et al., 2012). In this context, monitoring TL may help coaches and sports scientists to maximize the adaptive response of players, optimizing therefore the training program (Impellizzeri et al., 2019). ...
... (i) TM -training monotony [16,19,32] T M = mean of training load during the seven days of the week standard deviation of training load during the seven days of the week (ii) TS -training strain [16,19,32] T S = sum of the training loads for all training sessions during a week × TM (iii) ACWRacute:chronic workload ratio [36][37][38] ACW R = acute workload (most recent week) chronic workload (last 4 weeks) ...
Article
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Background: The aims of this study were to describe the variations of training monotony (TM), training strain (TS), and acute:chronic workload ratio (ACWR) through Hooper Index categories (fatigue, stress, DOMS, and sleep quality) and to compare those variations between player status and player positions. Methods: Seventeen male professional soccer players participated in this study. Considering player status, participants were divided in nine starters and eight non-starters. Additionally, participants were divided by playing positions: three wide defenders, four central defenders, three wide midfielders, four central midfielders, and three strikers. They were followed during 40-week in-season period. TM, TS, and ACWR were calculated for each HI category, respectively. Data were grouped in 10 mesocycles for further analysis. Results: Results showed variations across the mesocycles. In general, starters showed higher values for TM, TS, and ACWR calculations than non-starters, although there were some exceptions. Regarding player positions, significant differences were found in stress between wide defenders vs central midfielders for TM (p = 0.033, ES = 5.16), central defenders vs wide defenders for ACWR (p = 0.044, ES = 4.95), and in sleep between wide defenders and strikers for TM (p = 0.015, ES = 5.80). Conclusions: This study revealed that an analysis of players' well-being parameters according to player status and positions can provide clear information to the coaches and their staff to complement the tasks of training monitoring.
... Typical training sessions can result in high successive training loads during the training weeks, generating high-load demands on athletes (Teixeira et al., 2020;Tibana, Sousa, Prestes, Feito et al., 2019). The magnitude of training load is determined by the external training load (organization, quality, and quantity of exercise performed) and by the internal training load (psychophysiological response of the body to the external load applied) (Impellizzeri, Marcora, & Coutts, 2019). Some studies have demonstrated the usefulness of using the session-RPE method to determine the internal load (Teixeira et al., 2020; together with heart rate monitoring during functional-fitness training sessions (Carreker & Grosicki, 2020;. ...
Article
Purpose: This study aimed to analyze the effects of training load on stress tolerance (ST) and secretory immunoglobulin A (SIgA) in male and female high-intensity functional fitness (HIFF) athletes during two different 10 and consecutive weekly training volume loads [higher (week 1) and lower volume (week 2)]. Methods: 14 athletes [7 males: 29.3 (±5.8) years; 86.3 (±8.2) kg and 176.8 (±3.8) cm and 7 females: 32.7 (±4.4) years; 60.0 (±6.7) kg and 162.5 (±5.9) cm] participated. The ST, assessed by Daily Analysis of Life Demand in Athletes questionnaire (DALDA) and Saliva sampling were performed in four time-points (pre (T1) and post (T2) week 1; pre (T3) and post (T4) week 2). Results: Female athletes showed a decrease in ST (symptoms of stress) from 15 T1 to T3 [F(3,36) = 7.184, p˂ 0.001, ηp2 = 0.374], without difference in male athletes (p > .05). There is a significant difference of SIgA concentration [F(3.36) = 3.551; p = .024; ηp2 = 0.228], with a significant decrease in female athletes group in T2 compared to T1 (p = .013) and T4 (p = .023). In addition, the different training volume loads did not impact mucosal immunity in male athletes (p > .05). Conclusion: The current findings suggest that higher HIFF volume results in decreased ST and SIgA concentration in female 20 athletes and a subsequent decrease in training volume loads contributed to restoring these variables.
... Methods used to describe internal load are ratings of perceived exertion or heart rate [12]. Monitoring of player workload has recently received increasing interest to better understand the impact of workload in terms of athletic performance, fatigue, or injury risk [13,14]. ...
Article
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Beach handball is a young discipline that is characterized by numerous high-intensity actions. By following up on previous work, the objective was to perform in-depth analyses evaluating external load (e.g., distance traveled, velocity, changes in direction, etc.) in beach handball players. In cross-sectional analyses, data of 69 players belonging to the German national or prospective team were analyzed during official tournaments using a local positioning system (10 Hz) and inertial measurement units (100 Hz). Statistical analyses comprised the comparison of the first and second set and the effects of age and sex (female adolescents vs. male adolescents vs. male adults) and playing position (goalkeepers, defenders, wings, specialists, and pivots) on external load measures. We found evidence for reduced external workload during the second set of the matches (p = 0.005, ηp2 = 0.09), as indicated by a significantly lower player load per minute and number of changes in direction. Age/sex (p < 0.001, ηp2 = 0.22) and playing position (p < 0.001, ηp2 = 0.29) also had significant effects on external load. The present data comprehensively describe and analyze important external load measures in a sample of high-performing beach handball players, providing valuable information to practitioners and coaches aiming at improving athletic performance in this new sport.
... Load data is typically collected, interpreted, cleaned, analyzed, and disseminated with the goal of improving player performance and reducing injury risk. Practitioners seek to optimize training load at many points during the training process, such as planning and altering individual sessions, day-to-day, season periodization, and managing athletes with a long-term perspective to increase player performance and reduce injury risk [1]. Training load prediction can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing nonfunctional overreaching, illness, and injury [2]. ...
Article
Full-text available
Appropriate training load in physical education classes is conducive to improving students’ health. In this study, a training model is proposed for the prediction of the training load of middle school students in physical education based on the backpropagation neural network (BPNN). Ninety students in the seventh, eighth, and ninth grades (30 for each grade) are selected, and the training load is divided into type I, type II, and type III and combined with the average heart rate values of students in each grade during physical training. Next, the principal component analysis is used to select the main components whose cumulative contribution rate is greater than 90%. The corresponding score matrix is used for BPNN model training. Results show that, for most students in all grades, the training load intensity belongs to type II, and the training intensity is moderate. The variance contribution rates of the first, second, third, and fourth principal components of the seventh, eighth, and ninth grades reported are about 60%, 15%, 10%, and 5%, respectively, and the cumulative contribution rate of the first four principal components has reached more than 90%. Comparing the predicted value with the actual value, the proposed model showed the highest prediction performance and can accurately predict the training load in physical education.
... For several years, literature has reported widely on load terminology such as work rate (O'Donoghue, 2004;Carling et al., 2008), workload (Bowen et al., 2017;Williams et al., 2017;Gabbett et al., 2019), and training load (Impellizzeri et al., 2005;Bourdon et al., 2017;Vanrenterghem et al., 2017). These assumptions are based on a linear perspective where the smallest changes in the system input determine proportional and measurable changes in the output (Vanrenterghem et al., 2017;Impellizzeri et al., 2019). Therefore, the training load concept has been developed by setting up athlete monitoring as a linear system using cumulative effect as a key guidance . ...
Article
Full-text available
The aims of this study were 1) to analyze the influence of chronological age, relative age, and biological maturation on accumulated training load and perceived exertion in young sub-elite football players and 2) to understand the interaction effects amongst age grouping, maturation status, and birth quartiles on accumulated training load and perceived exertion in this target population. A 6-week period (18 training sessions and 324 observation cases) concerning 60 young male sub-elite football players grouped into relative age (Q1 to Q4), age group (U15, U17, and U19), and maturation status (Pre-peak height velocity (PHV), Mid-PHV, and Post-PHV) was established. External training load data were collected using 18 Hz global positioning system technology (GPS), heart-rate measures by a 1 Hz short-range telemetry system, and perceived exertion with total quality recovery (TQR) and rating of perceived exertion (RPE). U17 players and U15 players were 2.35 (95% CI: 1.25-4.51) and 1.60 (95% CI: 0.19-4.33) times more likely to pertain to Q1 and Q3, respectively. A negative magnitude for odds ratio was found in all four quartile comparisons within maturation status (95% CI: 6.72-0.64), except for Mid-PHV on Q2 (95% CI: 0.19-4.33). Between-and within-subject analysis reported significant differences in all variables on age group comparison measures (F = 0.439 to 26.636, p = 0.000 to 0.019, η 2 = 0.003-0.037), except for dynamic stress load (DSL). Between-subject analysis on maturity status comparison demonstrated significant differences for all training load measures (F = 6.593 to 14.424, p = 0.000 to 0.037, η 2 = 0.020-0.092). Interaction effects were found for age group x maturity band x relative age (Λ Pillai's = 0.391, Λ Wilk's = 0.609, F = 11.385, p = 0.000, η 2 = 0.391) and maturity band x relative age (Λ Pillai's = 0.252, Λ Wilk's = 0.769, F = 0.955, p = 0.004, η 2 = 0.112). Current research has confirmed the effects of chronological age, relative age, and biological maturation on accumulated training load. Perceived exertion does not seem to show any differences concerning age group or maturity status. Evidence should be helpful for professionals to optimize the training process and young football players' performance.
... The main objectives during training sessions are to 1) prescribe the optimal training load (Aoki et al., 2017); 2) stimulate specific adaptations (Aoki et al., 2017;Foster et al., 2001); and 3) obtain the desired responses (Impellizzeri et al., 2018). In turn, quantifying the physical and physiological loads is important to understand the dose-response nature of the training process when establishing optimal training procedures . ...
Article
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The purpose of this study was to compare external peak demands (PDs) across quarters(Q) in basketball. Thirteen elite, junior, male basketball players were monitored using electronic performance tracking systems. There were studied intervals for different time windows to determine the external PD for distance (m); player load; distance covered in four different zones; accelerations; and decelerations. A mixed linear model was run to identify differences among quarters, and the auto-correlation function was carried out to determine fluctuations across the whole game. The results showed significant differences between Q1 vs. Q2 for distance, player load, and standing–walking distance; between Q1 vs. Q3 for distance, player load, and HSR; between Q1 vs. Q4 for distance, player load,standing–walking, and HSR; and between Q3 vs. Q4 for distance and player load. These findings suggest that external PD for running-based demands (distance, player load, and high-speed running) decrease across basketball games with the most notable declines occurring between the first and fourth quarters. Nevertheless, it is important to note that non-significant differences were found between quarters for several external PD variables (jogging, running, acceleration, and deceleration) across different time windows. Findings from the present study reinforce the importance of considering specific PD variables for different functions due to the specific insight each provides.
... 4,5 Training intensity is a complex process, as it depends on the mechanical stimuli provided by the training drills, and such stimuli influence the physiological responses of players differently. 6 Thus, the concept of training intensity is usually classified into internal and external dimensions. 7 The external dimension in soccer usually characterises the physical demands that are imposed during exercise. ...
Article
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One of the possibilities for organising different studies and providing some reference values or benchmarks is to summarise all information. Such a possibility could help coaches and practitioners identify typical values based on specific conditions and eventually use benchmark values to compare players against The current systematic review was carried out to identify and summarise studies that have examined external and internal training intensity monitoring and to provide references values for the main measures in professional male soccer players. A systematic review of EBSCO, PubMed, Scielo, Scopus, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 2404 studies initially identified, 25 were fully reviewed, and their outcome measures were extracted and analysed. From these, the following range intervals by overall team were found: session rated perceived exertion (s-RPE)=26–936 AU, total distance=2143–9540 m and distance >14 km/h=410–1884 m, distance >18 km/h=7–541 m, distance >24 km/h=1–190 m, acceleration number >3 m.s−2=9–195, deceleration number >-3 m.s−2=10–157 and player load=310–774 AU. Additionally, range intervals for player positions and a match�day minus approach were provided. This study provided reference values of professional male players for the main internal and external intensity measures. Altogether, they can be used by coaches, their staff, or practitioners to achieve desired competitive levels. They can replicate such values or even increase the numbers presented in training sessions.
... HIIT-SM require athletes to exert a great deal of effort due to repetitive HIIT sessions in a short period of time leading to muscular [24] and mental fatigue [25] as well as a transient inflammatory response as an acute response to HIIT [26]. Therefore, monitoring the athlete's external (e.g., power output (PO), running speed, distance covered) and internal load (e.g., biochemical markers, heart rate (HR), and questionnaires [27,28]; but also the balance between stress and recovery [29][30][31] during these training periods is important to understand exercise-related fatigue to reduce the risk of injury or symptoms of overtraining [32]. Therefore, in this study we use a combination of subjective (e.g., questionnaires) and objective variables (e.g., biomarkers, neuromuscular performance) that provide us with a comprehensive picture of the athlete's training and corresponding psychophysiological response [33,34]. ...
Article
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Background Performing multiple high-intensity interval training (HIIT) sessions in a compressed period of time (approximately 7–14 days) is called a HIIT shock microcycle (SM) and promises a rapid increase in endurance performance. However, the efficacy of HIIT-SM, as well as knowledge about optimal training volumes during a SM in the endurance-trained population have not been adequately investigated. This study aims to examine the effects of two different types of HIIT-SM (with or without additional low-intensity training (LIT)) compared to a control group (CG) on key endurance performance variables. Moreover, participants are closely monitored for stress, fatigue, recovery, and sleep before, during and after the intervention using innovative biomarkers, questionnaires, and wearable devices. Methods This is a study protocol of a randomized controlled trial that includes the results of a pilot participant. Thirty-six endurance trained athletes will be recruited and randomly assigned to either a HIIT-SM (HSM) group, HIIT-SM with additional LIT (HSM + LIT) group or a CG. All participants will be monitored before (9 days), during (7 days), and after (14 days) a 7-day intervention, for a total of 30 days. Participants in both intervention groups will complete 10 HIIT sessions over 7 consecutive days, with an additional 30 min of LIT in the HSM + LIT group. HIIT sessions consist of aerobic HIIT, i.e., 5 × 4 min at 90–95% of maximal heart rate interspersed by recovery periods of 2.5 min. To determine the effects of the intervention, physiological exercise testing, and a 5 km time trial will be conducted before and after the intervention. Results The feasibility study indicates good adherence and performance improvement of the pilot participant. Load monitoring tools, i.e., biomarkers and questionnaires showed increased values during the intervention period, indicating sensitive variables. Conclusion This study will be the first to examine the effects of different total training volumes of HIIT-SM, especially the combination of LIT and HIIT in the HSM + LIT group. In addition, different assessments to monitor the athletes' load during such an exhaustive training period will allow the identification of load monitoring tools such as innovative biomarkers, questionnaires, and wearable technology. Trial Registration : clinicaltrials.gov, NCT05067426. Registered 05 October 2021—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05067426 . Protocol Version Issue date: 1 Dec 2021. Original protocol. Authors: TLS, NH.
... 5 6 It is a multidimensional construct that can be measured in multiple ways. 7 Hypotheses suggest that not only the amount of training load, but also the relative change in training load affect injury risk. 5 Balanced training load exposure may both cause and protect against injury through building fitness and fatigue. ...
Article
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Objectives Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports. Methods First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice. Results DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure. Conclusion DLNM can be used to assess the cumulative effect of training load on injury risk.
... Athletes were given Polar Team Pro GPS sensors (Polar Electro, Co, Kempele, Finland) to monitor the dependent variables which include distances and speeds covered during the competitive season. Additionally, average and maximal heart rates were considered as internal load, which is defined as the psychophysiological responses to exercise (20). WBGT was recorded from each match's start time. ...
Article
The purpose of this study was to evaluate the effects of environmental conditions on running performance and performance efficiency index (Effindex). Performance data recorded using Polar Team Pro sensors from eight collegiate female soccer players in nine matches were analyzed during the 2019 competitive season. Effindex and running performance, including total distance covered (TDREL) and distance covered in five speed thresholds relative to minutes played, were examined for indications of fatigue with respect to environmental conditions, including ambient temperature and relative humidity. Matches were separated into three groups based on environmental conditions: Low-Risk (n = 2 matches), Moderate-Risk (n = 3 matches), or High-Risk (n = 4 matches). Speed thresholds were grouped as follows: walking (WALKREL), jogging (JOGREL), low-speed running (LSRREL), high-speed running (HSRREL), and sprinting (SPRINTREL). A significant effect was observed for TDREL in all environmental conditions (η2 = 0.614). TDREL was significantly lower in the High-Risk (p = 0.002; 95.32 ± 12.04 m/min) and Moderate-Risk conditions (p = 0.004; 94.85 ± 9.94 m/min) when compared to Low-Risk (105.61 ± 9.95 m/min). WALKREL (p = 0.005), JOGREL (p = 0.005) LSRREL (p = 0.001), HSRREL (p = 0.035), SPRINTREL (p = 0.017), and Effindex (p = 0.0004) were significantly greater in Low-Risk conditions when compared to Moderate-Risk conditions. WALKREL (p = 0.005), HSRREL (p = 0.029), SPRINTREL (p = 0.005), and Effindex (p = 0.0004) were significantly greater in Low-Risk conditions when compared to High-Risk conditions. High-Risk environmental conditions may result in adverse performance in female collegiate soccer players.
... However, stressing the athlete's biological systems has to be balanced with appropriate recovery periods to allow for positive adaptations to occur. This highlights the importance of both measuring and managing training load and the associated acute responses (Impellizzeri et al., 2019). ...
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PurposeThe aim of this study was to assess the short-term responsiveness of measurement instruments aiming at quantifying the acute psycho-physiological response to load in high-level adolescent soccer players.Methods Data were collected from 16 high-level male youth soccer players from the Under 15 age group. Players were assessed on two occasions during the week: after 2 days of load accumulation (“high load”) and after at least 48 h of rest. Measurements consisted of the Short Recovery and Stress Scale (SRSS), a countermovement jump (CMJ) and a sub-maximal run to assess exercise heart-rate (HRex) and heart-rate recovery (HRR60s). Training load was quantified using total distance and high-speed running distance to express external and sRPE training load to express internal load. It was expected that good instruments can distinguish reliably between high load and rest.ResultsOdd ratios (0.74–1.73) of rating one unit higher or lower were very low for athlete-reported ratings of stress and recovery of the SRSS. Standardized mean high load vs. rest differences for CMJ parameters were trivial to small (−0.31 to 0.34). The degree of evidence against the null hypothesis that changes are interchangeable ranged from p = 0.04 to p = 0.83. Moderate changes were observed for HRex (−0.62; 90% Cl −0.78 to −0.47; p = 3.24 × 10−9), while small changes were evident for HRR60s (0.45; 90% Cl 0.08–0.80; p = 0.04). Only small to moderate repeated-measures correlations were found between the accumulation of load and acute responses across all measurement instruments. The strongest relationships were observed between HRex and total distance (rm-r = −0.48; 90% Cl −0.76 to −0.25).Conclusion Results suggest that most of the investigated measurement instruments to assess acute psycho-physiological responses in adolescent soccer players have limited short-term responsiveness. This questions their potential usefulness to detect meaningful changes and manage subsequent training load and program adequate recovery.
... This adapted exercise prescription suggests that the exercise parameters are operationalized and adapted to the individual by tailoring external training loads (e.g., by manipulating exercise intensity) using specific markers of the internal training load to provide comparable inter-individual exercise doses (Herold et al., 2019a). The internal training load can be described as acute individual response [i.e., biomechanical, physiological, and/or psychological response(s)] to training components (e.g., external training load) and other influencing factors (e.g., climatic conditions, equipment, ground condition) (Impellizzeri et al., 2019). This adapted exercise prescription approach is believed allowing further insights into dose-response relationships and to result in more distinct training effects (Herold et al., 2019a;Stojan and Voelcker-Rehage, 2019). ...
Book
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Technological Advancements in Aging and Neurological Conditions to Improve Physical Activity, Cognitive Functions, and Postural Control.
... Bu etkinin optimize ve monitörize edilmesi, özellikle performansın ilerlemesi ve korunmasında oldukça önemli görünmektedir. Uzun yıllardır bu ilerlemenin ve korumanın sağlanması adına spor ve egzersiz bilimi alanında çalışan birçok isim antrenman yükünün optimizasyonu ve monitörizasyonu konusunda incelemeler ve görüşler sunmaktadırlar (Impellizzeri, 2020a;Impellizzeri, 2020b;Impellizzeri, 2019;Burgess, 2017;Halson, 2014). Özellikle antrenman yükünün bir sporcu özelinde belirlenmesinin sadece sporcuların yaralanmaları değil, hastalık, depresif ruh hali, genel ilgisizlik, sinirlilik, uyku bozukluğu, yaralanmaya karşı artan hassasiyet, endokrin değişiklikler ve aşırı antrenman etkisinden korunmak adına da önemi göz önüne alındığında (Kalkhoven, 2021), bu alanın özel olarak her antrenör ve spor bilimciler tarafından özel bir ilgiyle incelenmesi ve irdelenmesini gerektirmiştir. ...
... There are several advantages to using this technology, including the capability to quantify the external loads of several players simultaneously to gather monitoring data efficiently in real time [4]. In this regard, external load is regarded as the physical load performed (e.g., duration, distance), which is determined by the organization, intensity, and quantity of exercise [5]. ...
Article
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To quantify and compare the external peak demands (PD) encountered according to game result (win vs. loss), quarter result (win vs. tie vs. loss), and quarter point difference (± difference in score) in under-18 years (U18), male basketball players. Thirteen basketball players had external load variables monitored across 9 games using local positioning system technology, including distance covered, distance covered in different intensity zones, accelerations, decelerations, and PlayerLoad™. PD were calculated across 30-s, 1-min, and 5-min time windows for each variable. Linear mixed models were used to compare PD for each variable according to game result (win vs. loss), quarter result (win vs tie vs loss), and quarter point difference (high vs. low). External PD were comparable between games that were won and lost for all variables and between quarters that were won and lost for most variables (p > 0.05, trivial-small effects). In contrast, players produced higher (p < 0.05, small effects) 1-min high-speed running distance and 5-min PlayerLoadTM in quarters that were won compared to quarters that were lost. Additionally, high quarter point differences (7.51 ± 3.75 points) elicited greater (p < 0.05, small effects) external PD (30-s PlayerLoadTM, 30-s and 5-min decelerations, and 1-min and 5-min high-speed running distance) than low quarter point differences (-2.47 ± 2.67 points). External PD remain consistent (trivialsmall effects) regardless of game result, quarter result, and quarter point difference in U18, male basketball players. Accordingly, external PD attained during games may not be a key indicator of team success.
... This result suggests that accelerations, decelerations and changes of direction occur even when speed is low 7 . To improve soccer players' performance while reducing injury risk, coaches should assess training and match loads in relation to players' position on the field to optimize training planning [8][9][10][11][12][13][14][15][16][17] . Specifically, external load (EL) is usually described by the total distance, range of speed covered, accelerations, metabolic power 7 , and other derived measures. ...
Preprint
Background: Global Positioning System (GPS) devices are widely used in soccer for monitoring external load indicators (ELi) with the aim of maximizing sports performance, while potentially reducing the risk of injury. The aim of this study was to investigate the ELi differences in players of different playing positions (i.e., central backs, external strikers, fullbacks, midfielders, strikers, wide midfielder) and among different sport-specific tasks. Methods: 1932 observations from 28 semi-professional soccer players (FC Palermo, Italy, age: 25±6 years, height: 183±6 cm, weight: 75.2±7 kg) were collected through GPS devices (Qstarz BT-Q1000EX, 10 Hz) during the season 2019-2020. Participants were monitored during Official Match (OM), Friendly Matches (FM), Small Sided Games (SSG), and Match-Based Exercises (MBE). Metabolic (i.e., metabolic power, percentage of metabolic power > 35w, number of intense actions per minute , distance per minute, passive recovery time per minute) and neuromuscular indicators (i.e., percentage of intense accelerations, percentage of intense decelerations, change of direction per min > 30°) were recorded during each task. Results: ANOVA showed significant differences in ELi between player positions within each task and between tasks ( p <0.05). Conclusion: In semi-professional soccer players, different ELis were detected for players’ positions showing the highest values during OM. Coaches should consider the different physical responses to different physical tasks and player position to design the most appropriate training program.
... 6 Measures collected from monitoring systems allow determining the physiological and physical impact of training drills on the players. 7 However, aiming to use these measures for the interpretation of impact within and between sessions, some intensity measures have been proposed to determine how training principles have been achieved. 8 For example, in 1998, Carl Foster introduced two concepts 9 : (i) training monotony (TM); and (ii) training strain. ...
Article
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Since acute:chronic workload ratio and training monotony have been criticized as injury risk predictors, the use of intensity measures should be more oriented to understand the variations of intensity across the season. The aim of this systematic review is to summarize the main evidence about the acute:chronic workload ratio and training monotony variations over the season in youth soccer players. The search was made in PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 225 studies initially identified, 13 were fully reviewed, and their outcome measures were extracted and analyzed. Nine analyzed acute:chronic workload ratio, seven analyzed monotony , and four studies analyzed both acute:chronic workload ratio and monotony. Overall, the range values for acute: chronic workload ratio were 0.58-17.5 AU, while for monotony were 0.83-23.0 AU which showed a higher variability. Few studies showed an association between higher values of acute:chronic workload ratio and/monotony with injury risk or to prevent health problems. These measures could be used to understand the variations of the data through the in-season periods. However, caution is necessary due to the scarce studies performed in young soccer players.
... A limitation of this study is the lack of internal load indicators evaluated during exercise. Future studies including an analysis of internal load of exercising calves, such as heart rate response, can aid in characterizing more of the psychophysiological response of calves to exercise [43]. ...
Article
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Circular exercise is used in many equestrian disciplines and this study aimed to determine if circle diameter impacts juvenile animal forelimb bone and joint health. On day 0, 24 calves at 9 weeks of age were assigned the following exercise treatments: small circle (12 m clockwise), large circle (18-m clockwise), treadmill, or non-exercised control. Exercise was initiated at 1.1–1.5 m/s for 5 min/d and increased 5 min weekly until reaching 30 min/d. On day 49, synovial fluid was collected from multiple joints, cartilage was collected from the proximal surface of fused third and fourth metacarpi (MC III and IV), and forelimbs underwent computed tomography scans. A statistical analysis (PROC mixed) was performed in SAS 9.4. The inside leg of the small circle treatment had a larger MC III and IV dorsopalmar external diameter than the outside (p = 0.05). The medial proximal phalanx had a greater mediolateral diameter than the lateral proximal phalanx of the small circle treatment (p = 0.01). Fetlock nitric oxide was greater in the large circle and treadmill treatments (p < 0.0001). Cartilage glycosaminoglycan concentration was greater in the outside leg of the small circle exercise treatment than the inside leg (p = 0.03). Even at slow speeds, circular exercise diameter can impact joint and bone health, but faster speeds may have greater alterations.
... R epeated exposure to specific training demands is necessary to elicit adaptations (150) that underpin improvements in physical performance capacities (77). However, single bouts of exercise also have the potential to produce a positive transient performance response (130,132). ...
... (Mujika et al., 2018. Future research not only needs to consider contextual variables in speed zone threshold; other variables that influence the workload of the training sessions and official matches should be integrated, such as the degree of opposition, the density of the task, the number of players, the competitive load, the space, the tactical load, since we should not fall into a one-dimensional analysis, a bias that does not allow us to have a holistic view of the subject-object of study (Halouani et al., 2014;Impellizzeri et al., 2019. ...
Article
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The quantification of training load has become mandatory for coaches and team staff. Within the analyzed variables, distance covered at different speed zones is one of the most studied. However, there is no consensus in the definition of speed zones in the published articles about each team sport, so it makes difficult a comparison between them. Therefore, the purposes of this article were to establish a criteria standard in the classification of speed zones in team sports and to analyze its practical application in basketball. Five speed categories were established for basketball following the methodology described above: 0-10km/h, >10-13.8 km/h, >13.8-17.6 km/h, >17.6-21.5 km/h, and >21.5 km/h. The results showed differences between periods in high-speed displacements and sprints, maximum speed, total decelerations, and accelerations. In conclusion, the categorization of workload based on standard deviation according to maximum speed could be a viable option to individualize the analysis of distance covered per speeds in team sports such as basketball. Its use may lead to a better understanding and contextualization of the locomotion data in specific sports and teams.
Preprint
Background: Sided-games (i.e., small- [SSG], medium- [MSG], large-sided [LSG]) involve tactical, technical, physical and psychological elements and are commonly implemented in soccer training. Although soccer sided-games research is plentiful, a meta-analytical synthesis of external load exposure during sided-games is lacking. Objective: The objective of this systematic review and meta-analysis was to: 1) synthesise the evidence on high-speed and sprint running exposure induced by sided-games in adult soccer players, 2) establish pooled estimates and intra-individual reliability for high-speed and sprint running exposure, and 3) explore the moderating effects of game format and playing constraints. Methods: A literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Four databases (PubMed/MEDLINE, Scopus, SPORTDiscus, Web of Science) were systematically searched up to 25 January 2022. Eligibility criteria were adult soccer players (population); training programmes incorporating sided-games (intervention); game manipulations including number of players, pitch dimension, game orientation (comparator); and high-, very high-speed and sprint relative (m∙min-1) running distances and associated intra-individual reliability (outcome). Eligible study risk of bias was evaluated using RoBANS. Pooled estimates for high-speed and sprint running exposure, and their intra-individual reliability, along with the moderating effect of tracking device running velocity thresholds, pitch dimension (i.e., area per player), and game orientation (i.e., score or possession), were determined via multilevel mixed effects meta-analysis. Estimate uncertainty is presented as 95% compatibility intervals (CI) with the likely range of relative distances in similar future studies determined via 95% prediction intervals (PI). Results: A total of 104 and 7 studies met our eligibility criteria for the main and reliability analyses, respectively. The range of relative distances covered across SSG, MSG and LSG was 14.8 m∙min-1 (95% CI: 12.3 to 17.4) to 17.2 m∙min-1 (95% CI: 13.5 to 20.8) for high-speed running, 2.7 m∙min-1 (95% CI: 1.8 to 3.5) to 3.6 m∙min-1 (95% CI: 2.3 to 4.8) for very high-speed running, and 0.2 m∙min-1 (95% CI: 0.1 to 0.4) to 0.7 m∙min-1 (95% CI: 0.5 to 0.9) for sprinting. Across different game formats, 95% PI’s showed future exposure for high-speed, very high-speed running, and sprinting to be from 0 m∙min-1 to 46.5 m∙min-1, 0 m∙min-1 to 14.2 m∙min-1, and 0 m∙min-1 to 2.6 m∙min-1, respectively. High-speed, very high-speed running, and sprinting showed poor reliability with a pooled coefficient of variation of 22.8% with distances being moderated by device speed thresholds, pitch dimension and game orientation. Conclusions: This study is the first to provide a detailed synthesis of exposure and intra-individual reliability of high-speed and sprint running during soccer sided-games. Our estimates, along with the moderating influence of common programming variables such as velocity thresholds, area per player and game orientation should be considered for informed planning of SSG, MSG and LSG soccer training.
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The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
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The purpose of the study was to examine the relationships of different training load variables and wellness responses, with in-game basketball performance across playing positions (backcourt and frontcourt players). External load variables (e.g., total distance, accelerations, etc), internal responses (e.g., rate of perceived exertion [RPE]) and wellness status (e.g., Hooper index) were monitored during 7 consecutive in-season months on 15 professional male basketball players. Besides, game-related individual statistics (performance index rating [PIR], and player total contribution [PTC]) were used to assess the performance during competition. Although no positive relationship was found between training load variables and basketball game performance, some wellness questionnaire parameters were significantly associated to game-related individual statistics. In particular, we found that only competing against direct rivals, players that reported stable values of stress stability achieved significantly higher PTC and PIR scores than players with high variability in stress values (8.53 IQR [6.09, 14.8] vs 0.00 IQR [-0.46, 0.84], and 0.47 ± 0.40 vs 0.10 ± 0.50; respectively; P < 0.05). Similarly, players with variable values of stress managed to maximize PIR scores during losses compared to players that presented high variability in stress levels (0.42 IQR [0.27, 0.55] vs 0.00 IQR [-0.12, 0.37], P < 0.05). Regarding playing positions, backcourt players showed higher PTC scores compared to frontcourt players when the fatigue levels are stable during the microcycle (8.27 ± 5.75 vs 4.77 ± 4.42; P < 0.05). Because basketball teams tend to accumulate more backcourt players that frontcourt players, it would be advisable to control training load maintaining it stable and avoiding load spikes during microcycle to allow team performance optimization. In conclusion, the results suggest that the best performances during official competition are not associated with higher training external and internal loads. Nevertheless, the wellness status could provide useful information when assessing player’s training responses and approaching possible peak performance during basketball competition. This shows the necessity to assess basketball performance from a holistic approach and consider more than just physical and physiological parameters, such as decision-making and psychological capacities, to better understand player’s performance during basketball competition.
Article
This study examined the association between different methods for training load (TL) monitoring during youth handball training. Distance covered, heart rate and session rating of perceived exertion [SRPE] were recorded during 12 training sessions in 14 youth women handball athletes (16.9±1.1 years). Internal load models based on SRPE and Edwards’ Trimp were calculated. An oscillatory feature was observed for the three methods of TL assessment (SRPE: 383±159 A.U., Edwards’ Trimp: 252±71 A.U., total distance: 3997±1291 m). A large correlation was found between Edwards’ Trimp and distance covered (r=0.59). A moderate correlation was observed for Edwards’ Trimp vs. SRPE (r=0.36), and between SRPE vs. distance covered (r=0.49). Shared variances of 13–35% were observed between TL methods comparisons. The results suggest that different constructs seem to be measured by each load model. Additionally, SRPE is a simple and low-cost method that might be used for TL monitoring in handball.
Article
Background: Elite junior Australian football players experience high training loads across levels of competition and training. This, in conjunction with impaired wellness, can predispose athletes to injury. Hypothesis: Elite junior Australian football players exposed to high loads with poor wellness are more likely to be at risk of injury than those with improved wellness. Study Design: Longitudinal prospective cohort study. Level of Evidence: Level 3. Methods: Data were collected and analyzed from 280 players across the 2014 season. Internal load was measured via session rating of perceived exertion. Player wellness was reported according to ratings of sleep quality, fatigue, soreness, stress, and mood. Week- and month-based training load measures were calculated, representing a combination of absolute and relative load variables. Principal component analysis factor loadings, based on 17 load and wellness variables, were used to calculate summed variable covariates. Injury was defined as “any injury leading to a missed training session or competitive match.” Associations between covariates and injury risk (yes/no) were determined via logistic generalized estimating equations. Results: A significant interaction term between load and wellness on injury was found [odds ratio (OR) 0.76; 95% CI 0.62-0.92; P < 0.01), indicating that wellness acts as a “dimmer switch” of load on injury. Further, there was evidence of moderated mediation (OR 0.71; 95% CI 0.57-0.87; P < 0.01). When wellness was low, injury risk started to increase substantially at a 1-week load of 3250 au. Conclusions: Subjective measures of training load are associated with injury risk through a nonlinear relationship. This relationship is further influenced by player wellness, which can amplify the risk of injury. There is evidence that higher stress is linked with injury and that soreness and sleep mediate any stress-injury relationship. Clinical Relevance: Coaching efforts to manage training load and player adaptive responses, including wellness, may reduce the risk of injury, with stress, soreness, and sleep particularly relevant at this level.
Article
Background Neuromuscular electrical stimulation (NMES) with kiloHertz currents (kHz) is a resource used in rehabilitation for producing muscle contractions with functional objectives, resulting from the optimization of the performance of aspects of muscle function (AOMF). However, parameters such as inadequate frequency, phase duration, amplitude, and therapy time may limit the effectiveness of NMES by the absence of adequate stimuli to generate positive adaptations in the AOMF. This study aimed to present an overview of the effectiveness and dosimetry of NMES by kHz on AOMF, such as torque and hypertrophy, in healthy people. Methods The study was outlined as a scoping review. From the search, 3892 studies were found of which were incorporated into Rayyan software for exclusion of duplicates and further selection by titles and abstracts, which resulted in 33 articles for this review. Results According to the included studies, kHz can increase torque and generate hypertrophy. Only the studies with Russian current showed hypertrophy gains. Dosimetry was not always detailed in the studies, which hinders stipulating optimal parameters for kHz. Conclusion From this review, it is concluded that NMSC by kHz is a valid resource to optimize AOMF, although the dosimetric parameters are still inconsistent.
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The aim of this single case study was to monitor the external workload of a professional female tennis player between 314 training sessions and 115 matches. A wearable device was used during two fully consecutive tennis seasons (24 months). External workload was determined using time indicators (total and active session times), shots indicators (shots per week, session, hour, rally and minute) and frequency distribution of rallies. This case study showed that the workload during practice sessions was higher compared to matches in terms of active time, percentage of active time, shots per hour and rally, and frequency distribution of rallies with more than nine shots. The number of shots executed per minute was lower in the practice sessions than in the match. It is concluded that the recommended number of shots per hour in a 90-min practice session is for the player to perform 400 to 800 shots. The recommended average number of rallies in practice sessions is 144 and 70% of the rallies should consist of four shots. The pace of rallies in open match situations in the practice sessions should reach the level of official matches. These conclusions could be useful guidelines for determining the workload of female tennis players participating in entry-level professional tournaments.
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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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Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.
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High-intensity interval training (HIT) is a well-known, time-efficient training method for improving cardiorespiratory and metabolic function and, in turn, physical performance in athletes. HIT involves repeated short (<45 s) to long (2-4 min) bouts of rather high-intensity exercise interspersed with recovery periods (refer to the previously published first part of this review). While athletes have used 'classical' HIT formats for nearly a century (e.g. repetitions of 30 s of exercise interspersed with 30 s of rest, or 2-4-min interval repetitions ran at high but still submaximal intensities), there is today a surge of research interest focused on examining the effects of short sprints and all-out efforts, both in the field and in the laboratory. Prescription of HIT consists of the manipulation of at least nine variables (e.g. work interval intensity and duration, relief interval intensity and duration, exercise modality, number of repetitions, number of series, between-series recovery duration and intensity); any of which has a likely effect on the acute physiological response. Manipulating HIT appropriately is important, not only with respect to the expected middle- to long-term physiological and performance adaptations, but also to maximize daily and/or weekly training periodization. Cardiopulmonary responses are typically the first variables to consider when programming HIT (refer to Part I). However, anaerobic glycolytic energy contribution and neuromuscular load should also be considered to maximize the training outcome. Contrasting HIT formats that elicit similar (and maximal) cardiorespiratory responses have been associated with distinctly different anaerobic energy contributions. The high locomotor speed/power requirements of HIT (i.e. ≥95 % of the minimal velocity/power that elicits maximal oxygen uptake [v/p[Formula: see text]O2max] to 100 % of maximal sprinting speed or power) and the accumulation of high-training volumes at high-exercise intensity (runners can cover up to 6-8 km at v[Formula: see text]O2max per session) can cause significant strain on the neuromuscular/musculoskeletal system. For athletes training twice a day, and/or in team sport players training a number of metabolic and neuromuscular systems within a weekly microcycle, this added physiological strain should be considered in light of the other physical and technical/tactical sessions, so as to avoid overload and optimize adaptation (i.e. maximize a given training stimulus and minimize musculoskeletal pain and/or injury risk). In this part of the review, the different aspects of HIT programming are discussed, from work/relief interval manipulation to HIT periodization, using different examples of training cycles from different sports, with continued reference to the cardiorespiratory adaptations outlined in Part I, as well as to anaerobic glycolytic contribution and neuromuscular/musculoskeletal load.
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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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Mental fatigue is a psychobiological state caused by prolonged periods of demanding cognitive activity. Although the impact of mental fatigue on cognitive and skilled performance is well known, its effect on physical performance has not been thoroughly investigated. In this randomized crossover study, 16 subjects cycled to exhaustion at 80% of their peak power output after 90 min of a demanding cognitive task (mental fatigue) or 90 min of watching emotionally neutral documentaries (control). After experimental treatment, a mood questionnaire revealed a state of mental fatigue (P = 0.005) that significantly reduced time to exhaustion (640 +/- 316 s) compared with the control condition (754 +/- 339 s) (P = 0.003). This negative effect was not mediated by cardiorespiratory and musculoenergetic factors as physiological responses to intense exercise remained largely unaffected. Self-reported success and intrinsic motivation related to the physical task were also unaffected by prior cognitive activity. However, mentally fatigued subjects rated perception of effort during exercise to be significantly higher compared with the control condition (P = 0.007). As ratings of perceived exertion increased similarly over time in both conditions (P < 0.001), mentally fatigued subjects reached their maximal level of perceived exertion and disengaged from the physical task earlier than in the control condition. In conclusion, our study provides experimental evidence that mental fatigue limits exercise tolerance in humans through higher perception of effort rather than cardiorespiratory and musculoenergetic mechanisms. Future research in this area should investigate the common neurocognitive resources shared by physical and mental activity.
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Locomotor muscle fatigue, defined as an exercise-induced reduction in maximal voluntary force, occurs during prolonged exercise, but its effects on cardiorespiratory responses and exercise performance are unknown. In this investigation, a significant reduction in locomotor muscle force (-18%, P < 0.05) was isolated from the metabolic stress usually associated with fatiguing exercise using a 100-drop-jumps protocol consisting of one jump every 20 s from a 40-cm-high platform. The effect of this treatment on time to exhaustion during high-intensity constant-power cycling was measured in study 1 (n = 10). In study 2 (n = 14), test duration (871 +/- 280 s) was matched between fatigue and control condition (rest). In study 1, locomotor muscle fatigue caused a significant curtailment in time to exhaustion (636 +/- 278 s) compared with control (750 +/- 281 s) (P = 0.003) and increased cardiac output. Breathing frequency was significantly higher in the fatigue condition in both studies despite similar oxygen consumption and blood lactate accumulation. In study 2, high-intensity cycling did not induce further fatigue to eccentrically-fatigued locomotor muscles. In both studies, there was a significant increase in heart rate in the fatigue condition, and perceived exertion was significantly increased in study 2 compared with control. These results suggest that locomotor muscle fatigue has a significant influence on cardiorespiratory responses and exercise performance during high-intensity cycling independently from metabolic stress. These effects seem to be mediated by the increased central motor command and perception of effort required to exercise with weaker locomotor muscles.
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The global epidemic of obesity and its associated chronic diseases is largely attributed to an imbalance between caloric intake and energy expenditure. While physical exercise remains the best solution, the development of muscle-targeted "exercise mimetics" may soon provide a pharmaceutical alternative to battle an increasingly sedentary lifestyle. At the same time, these advances are fueling a raging debate on their escalating use as performance-enhancing drugs in high-profile competitions such as the Olympics.
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Purpose: To describe the training demands of contemporary dance and determine the validity of using the session-RPE (sRPE) method to monitor exercise intensity and training load in this activity. In addition, we examined the contribution of training (i.e. accelerometry and heart rate) and non-training related factors (i.e. sleep and wellness) to perceived exertion during dance training. Methods: Training load and actigraphy for sixteen elite amateur contemporary dancers were collected during a 49 day period, using heart rate monitors, accelerometry and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and several other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during dance training. Results: Average weekly training load was 4283 ±2442 AU, monotony 2.13 ±0.92 AU, strain 10677± 9438 AU, and average weekly vector magnitude load 1809707 ±1015402 AU. There were large-to-very large within-individual correlations between sRPE-TL and various other internal and external measures of intensity and load. The stepwise multiple regression analysis also revealed that 49.7% of the adjusted variance in sRPE-TL was explained by HRpeak, METs, soreness, motivation and sleep quality (Y = -4.637 + 13.817 %HRpeak + 0.316 METS + 0.100 soreness + 0.116 motivation - 0.204 sleep quality). Conclusion: The current findings demonstrate; the validity of the sRPE method for quantifying training load in dance, that dancers undertake very high training loads and a combination of training and non-training factors contribute to perceived exertion in dance training.
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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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The response to an exercise intervention is often described in general terms, with the assumption that the group average represents a typical response for most individuals. In reality, however, it is more common for individuals to show a wide range of responses to an intervention rather than a similar response. This phenomenon of 'high responders' and 'low responders' following a standardized training intervention may provide helpful insights into mechanisms of training adaptation and methods of training prescription. Therefore, the aim of this review was to discuss factors associated with inter-individual variation in response to standardized, endurance-type training. It is well-known that genetic influences make an important contribution to individual variation in certain training responses. The association between genotype and training response has often been supported using heritability estimates; however, recent studies have been able to link variation in some training responses to specific single nucleotide polymorphisms. It would appear that hereditary influences are often expressed through hereditary influences on the pre-training phenotype, with some parameters showing a hereditary influence in the pre-training phenotype but not in the subsequent training response. In most cases, the pre-training phenotype appears to predict only a small amount of variation in the subsequent training response of that phenotype. However, the relationship between pre-training autonomic activity and subsequent maximal oxygen uptake response appears to show relatively stronger predictive potential. Individual variation in response to standardized training that cannot be explained by genetic influences may be related to the characteristics of the training program or lifestyle factors. Although standardized programs usually involve training prescribed by relative intensity and duration, some methods of relative exercise intensity prescription may be more successful in creating an equivalent homeostatic stress between individuals than other methods. Individual variation in the homeostatic stress associated with each training session would result in individuals experiencing a different exercise 'stimulus' and contribute to individual variation in the adaptive responses incurred over the course of the training program. Furthermore, recovery between the sessions of a standardized training program may vary amongst individuals due to factors such as training status, sleep, psychological stress, and habitual physical activity. If there is an imbalance between overall stress and recovery, some individuals may develop fatigue and even maladaptation, contributing to variation in pre-post training responses. There is some evidence that training response can be modulated by the timing and composition of dietary intake, and hence nutritional factors could also potentially contribute to individual variation in training responses. Finally, a certain amount of individual variation in responses may also be attributed to measurement error, a factor that should be accounted for wherever possible in future studies. In conclusion, there are several factors that could contribute to individual variation in response to standardized training. However, more studies are required to help clarify and quantify the role of these factors. Future studies addressing such topics may aid in the early prediction of high or low training responses and provide further insight into the mechanisms of training adaptation.
Article
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.
Article
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