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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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Internal and External Training Load: 15 Years On
Franco M. Impellizzeri, Samuele M. Marcora, and Aaron J. Coutts
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 dene 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 rene both the theoretical framework and the denitions of internal and external training load to avoid misinterpretation of
this concept.
Keywords:workload, exercise training, stimulus, stressor, psychobiological response
The concepts of internal and external training load were rst
presented at the Eighth Annual Congress of the European College
of Sport Science in Salzburg, Austria (2003)
1
at an invited session
and symposium organized by Tom Reilly. The content of this
presentation was included in 2 follow-up papers, which rst
described the taxonomy of the training stimulus.
2,3
Although these
concepts were initially proposed in the context of team sports, the
terms internal and external training load are now used more
generally in both the research and practice.
47
In the original
article, the concepts of internal and external load were introduced,
but we did not address them in depth. Therefore, the intention of
this commentary is to extend, clarify, and rene both the theoretical
framework and the denitions of internal and external training load
to also avoid misapplication or misinterpretation of these concepts
as they were originally proposed.
Theoretical Framework:
The Training Process
Athletic or sport training has been dened as the process of sys-
tematically performing exercises to improve physical abilities and
to acquire specic sport skills.
8
When delivered appropriately,
exercises induce a functional adaptive response. It is these func-
tional adaptations that underpin changes in various training out-
comes such as physical performance, injury resistance, or health.
The exercise bout induces a psychophysiological response, and it is
this response (rather than the exercise task itself) that provides the
stimulus for adaptation.
9
Potentially, any strategy inducing the same
response would have a similar effect (eg, pharmacological interven-
tions).
10
The athletes response to the stimulus and the stimulus itself
is specic to the nature, intensity, and duration of the exercise task.
8
A single exercise bout can generate a stimulus that elicits a tran-
sient acute adaptive response while the systematic repetition of this
stimulus and the associated response are necessary to elicit chronic
adaptations. This training stimulus should also to be applied at
sufcient time periods and be of appropriate magnitude to prevent
decay of these adaptations prior to competition. According to the
principle of reversibility, if the stimulus discontinues, previous
adaptations revert and performance declines.
11
To obtain specic
performance adaptations, training needs to target the systems that
determine performance (Figure 1).
Training Load: Internal and External Load
The training load in the context of athletic training has been
described as the input variable that is manipulated to elicit the
desired training response.
12
Training load can be described as being
either external and/or internal,
2,3
depending if we are referring to
measurable aspects occurring internally or externally to the athlete.
The organization, quality, and quantity of exercise (training plan)
determine the external load, which is dened as the physical work
prescribed in the training plan.
2,3,12
Accordingly, measures of ex-
ternal load are specic to the nature of training undertaken. For
example, the external load in resistance training is usually consid-
ered the load (external resistance) lifted; however, it may also be
expressed as work completed or the velocity generated during
lifting.
13
Similarly, in team sports, external load can be described
by measures of total distance covered (or in specic speed bands),
accelerations, or metabolic power (as examples).
14
Despite its
name (ie, it infers metabolism, which is internal to the athlete),
the latter is mathematically derived from the speedtime prole and
therefore remains an external load indicator. Irrespective of how it
is quantied, coaches prescribe training according to external
load to elicit the desired psychophysiological response. It is this
response that corresponds to the internal training load. Accord-
ingly, measures of internal load can be indicators reecting the
actual psychophysiological response that the body initiates to cope
with the requirements elicited by the external load. Therefore, the
concept of internal load incorporates all the psychophysiological
responses occurring during the execution of the exercise (single or
sequence) prescribed by the coach. According to our denitions,
the concepts of external and internal load do not have a single or
gold standard measure, but rather these may be quantied by a
myriad of variables, which describe the external load or the internal
response during the exercise. In addition, the validity of a measure
Impellizzeri and Coutts are with the Facultyof Health, Human Performance Research
Centre, University of Technology Sydney, Sydney, NSW, Australia. Marcora is with
the School of Sport and Exercise Sciences, University of Kent, Chatham, United
Kingdom, and the Dept of Biomedical and NeuroMotor Sciences (DiBiNeM),
University of Bologna, Bologna, Italy. Impellizzeri (franco.impellizzeri@uts.
edu.au) is corresponding author.
1
International Journal of Sports Physiology and Performance, (Ahead of Print)
https://doi.org/10.1123/ijspp.2018-0935
© 2018 Human Kinetics, Inc. INVITED COMMENTARY
of a load indicator depends upon the context. For example, heart
rate is a valid measure of internal load for endurance training but
not for resistance training. Moreover, even within the same context,
a single load measure may not have the same level of validity
(eg, heart rate is a less valid indicator of internal load in short
duration, intermittent high-intensity efforts compared with long
distance or interval training).
Internal Over External Load
As the internal training load determines the training outcome, we
recommend that this can be used as primary measure when moni-
toring athletes. This is because the internal load experienced from
aspecic external load may vary depending on specic contextual
factors either between or within athletes. For example, specic
modiable and nonmodiable factors such as training status, nutri-
tion, health, psychological status, and genetics may result in indi-
vidual athletes experiencing a different internal load (and individual
differences in adaptive processes
15
) when provided same external
load (Figure 1).
1618
As some of these characteristics are not xed,
the internal load experienced by a specic athlete for a given
external load may also change when these factors are modied
(ie, changes in their training status, health, etc). In addition, the stress
response (ie, internal load) can be inuenced by other stressors
(eg, hot conditions during training) affecting the psychophysiologi-
cal response to exercise.
Therefore, from a practical point of view, it is difcult to
precisely estimate the individuals actual internal load prior to
exercise. This is especially the case during exercise bouts that are
characterized by spontaneous activities and/or those that are inu-
enced by self-pacing and sparing behaviors (eg, small-sided games,
match play, or sparring in combat sports). Due to these factors, we
recommend that internal load can be assessed directly so that we
can be sure that the intended psychophysiological response was
induced as planned.
The recent development of more sophisticated (micro)tech-
nology now allows for increasingly detailed information about
external load.
19
For example, with the use of GPS (global posi-
tioning systems), accelerometers, and gyroscopes, it is now rela-
tively simple to quantify accelerations, decelerations, speed, and
power during exercise. However, as a result of the increased
availability of these devices, the attention of coaches and scientists
appears to have shifted to examining the external load rather than
the actual psychophysiological response (ie, internal load). Caution
should be taken in shifting this attention if we are monitoring
athletes. In this case, it is the internal load rather than the external
load, which ultimately determines the functional outcome of
training and therefore should be monitored. However, the advan-
tage of having greater information on the external load is that this
may allow for more precise prescription of external load.
External Over Internal Load
In practice, it is not always possible to measure the internal load as
there are situations where there may not be a readily available valid
indicator of the internal load. For example, single and repeated
sprint interval training induces greater neuromuscular responses
(internal load) compared other forms of high-intensity training
that involve longer duration bouts completed at lower speeds.
20
However, at present, there are no established valid indicators of
neuromuscular involvement that are available to be used during
real training conditions. By contrast, there are other indicators
of external load such as velocity or time (to complete the sprints)
that are easily measurable and these are typically applied. It is
Figure 1 Theoretical framework of the training process.
(Ahead of Print)
2Impellizzeri, Marcora, and Coutts
Downloaded by aaron.coutts@uts.edu.au on 01/07/19
commonly assumed that there is a higher involvement of neuro-
muscular components with increasing running speed.
21
Similarly,
external load indicators such as weight lifted, work, and time under
tension are commonly used in resistance-based training. However,
also for strength training methods, internal load measures based on
perceived exertion have been proposed.
13
Practitioners often implic-
itly estimate internal load based on these measures of external load;
however, as explained previously, this approach is conceptually
limited as it cannot be assumed a direct correspondence between
external load and internal response.
Integrating Internal and External Load
Despite the increased availability of external load assessment tools,
we caution against the exclusive use of this load measure for
monitoring athletes as it has conceptual limitations. For example,
it is difcult to make accurate interindividual comparisons of how
athletes are responding to (or coping with) training (eg, low respon-
ders vs high responders). Indeed, by denition, a low responder is an
athlete who has a lower response to the same internal load, which
stipulates that internal load measures are required for such evalua-
tions. To this regard, also the use of an appropriate internal load
indicator is crucial. For example, it is well known that the percentage
of maximal oxygen uptake (VO
2
max) can correspond to different
percentages of the lactate thresholds.
22
Therefore, athletes exercising
at the same percentage of the VO
2
max can have different internal
load responses (lactate thresholds) explaining apparently different
training-induced adaptations (responder vs nonresponder when using
thepercentageofVO
2
max as internal load indicator).
23
In addition,
from a conceptual point of view, there are additional advantages in
integrating internal and external load measures for monitoring
training. For example, the uncoupling between internal and exter-
nal load may be used to identify how an athlete is coping with their
training program. Specically, athletes who exhibit a lower internal
load to standardized external load completed in similar conditions,
would be assumed to reect increased tness. By contrast, when
the internal load is increased in this situation, the athlete may be
losing tness or suffering from fatigue. Moreover, the combination
of psychological and physiological measures of internal training
load may suggest the kind of fatigue the athlete is suffering from.
Specically, muscle fatigue increases both heart rate and rating of
perceived exertion,
24
whereas mental fatigue increases only rating
of perceived exertion.
25
This knowledge may help to choose the
most appropriate intervention to reduce fatigue, for example, a
reduction in muscle-damaging exercise or better sleep hygiene.
Clarifying Internal-Load Indicators
As previously mentioned, internal load is dened as the psycho-
physiological response during exercise. There is a common mis-
conception that measures such as heart rate recovery or heart rate
variability collected after the exercise (immediately after or in the
following morning) are indicators of internal load. However,
according to the denition, these measures cannot be considered
internal load indicators as these are responses occurring after and
not during the exercise. We suggest that a postexercise response
can be used as an indirect (surrogate) measure of the internal load
when there is a strong association between these 2 variables
(ie, internal load and the surrogate). But even in this situation,
this measure is not strictly speaking a measure of the internal load
but a measure of the postexercise response to the internal load.
Similarly, other common athlete monitoring measures such as the
hormonal response after exercise, jump tests used to assess neuro-
muscular fatigue or self-reports about postexercise symptoms
(eg, fatigue and muscle soreness) should not be considered as
measures of internal load. As a rule of thumb, an indicator of the
internal training load is any indicator that can be used to prescribe
exercise intensity.
Practical Applications and Conclusions
After 15 years since the conceptual model was rst presented, the
concepts of internal and external load are now widespread and
common in both research and practice. In this commentary, we
have claried the denitions of internal and external load and also
explained the relevance of these constructs within the training
process. Furthermore, we have highlighted the importance of using
internal load, especially when monitoring athletes and discussed
the limitations of the exclusive use of external load for this purpose.
Finally, we presented the advantages (conceptually) of contextual-
izing internal and external load in understanding the training
process. As this model can be applied to understand the link be-
tween training and the individual adaptive response, it is suitable as
the theoretical framework for developing athlete monitoring sys-
tems. When these systems are implemented effectively, they can
assist coaches and scientists to better control and optimize the
training process.
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... In order to adjust the TL to their developmental stages and during competitive phases of the soccer season, two components of the TL are often conceptualized according to measurable parameters occurring internally or externally to the player: the external load (EL) is the physical work prescribed in the training plan (in soccer, i.e., total distance covered or in specific speed bands, accelerations), whilst the internal load (IL) reflects all the psychophysiological responses of the individual to the EL prescribed by the coach (Impellizzeri et al., 2019). The aforementioned constructs do not have a gold standard measure (Impellizzeri et al., 2019), but throughout history, there have been various methods employed for TL monitoring (Foster et al., 2017). ...
... In order to adjust the TL to their developmental stages and during competitive phases of the soccer season, two components of the TL are often conceptualized according to measurable parameters occurring internally or externally to the player: the external load (EL) is the physical work prescribed in the training plan (in soccer, i.e., total distance covered or in specific speed bands, accelerations), whilst the internal load (IL) reflects all the psychophysiological responses of the individual to the EL prescribed by the coach (Impellizzeri et al., 2019). The aforementioned constructs do not have a gold standard measure (Impellizzeri et al., 2019), but throughout history, there have been various methods employed for TL monitoring (Foster et al., 2017). Subjective, self-report measures are the most commonly used to express the details of IL quantitatively (Coyne et al., 2018;Halson, 2014). ...
... Although the IL determines the functional outcome of training (Impellizzeri et al., 2019), owing to its complex and multifactorial nature, its direct assessment is difficult using a single type of measure (McLaren et al., 2018). In spite of this apparent handicap, the sRPE method is recommended as the primary global measure of TL (Coyne et al., 2018). ...
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Objetivo: O estudo buscou verificar se existe diferença entre período de treinamentos e competição na carga, humor, fadiga, infecções, bem-estar e recuperação. Métodos: Vinte futebolistas homens (17,79 ± 1,23 anos; 72 ± 9,50 kg; altura 1,80 ± 0,08 m), foram monitorados durante a semana pré-competitiva (PCO) e competição (COM). Foram utilizados os instrumentos Profile of Mood States (POMS); Wisconsin Upper Respiratory Symptom Survey (WURSS); bem-estar geral e qualidade total de recuperação (QTR). A carga interna de treinamento (CIT) foi monitorada pela percepção subjetiva de esforço. Resultados: Houve diferença significativa para POMS-PTHESCORE (P = 0,001), fadiga (P = 0,001) e recuperação CV% (P = 0,001) da condição COM vs. PCO. A severidade do WURSS foi maior em PCO vs. COM (P = 0,011). Para PCO houve correlação da recuperação CV% r = 0,61) e fadiga (r = 0,62). Em COM, houve correlação da CIT com CV% de recuperação (r = 0,94), recuperação semanal (r = -0,84), fadiga (r = 0,57) PTHESCORE (r = 0,48). Conclusão: Conclui-se que apesar da CIT ser menor em COM, o humor, a fadiga e variação semanal da recuperação são maiores quando comparados a PCO. Além disto, independente do período, há relação da CIT e métodos de monitoramento.
... Although there are widely accepted guidelines for exercising patients with known heart disease [19,20] and a rich literature on monitoring exercise training in athletes [21][22][23][24], there is virtually no evidence of how much exercise is actually accomplished by patients enrolled in rehabilitation programs. It can be argued that guidelines without evidence of how well they are complied with are of limited value. ...
... Thirty-two participants in the phase III La Crosse Exercise and Health Program (LEHP) were recruited. They ranged in age from 35-90, 62% were male and, typical of middle-aged Americans, they generally had elevated values for body mass index (BMI) (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Twenty-two of the patients had experienced a documented cardiovascular event (myocardial infarction, revascularization surgery, angioplasty with stent), and the other 10 were >55 years of age and had risk factors for cardiovascular disease. ...
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Exercise training is an important component of clinical exercise programs. Although there are recognized guidelines for the amount of exercise to be accomplished (≥70,000 steps per week or ≥150 min per week at moderate intensity), there is virtually no documentation of how much exercise is actually accomplished in contemporary exercise programs. Having guidelines without evidence of whether they are being met is of limited value. We analyzed both the weekly step count and the session rating of perceived exertion (sRPE) of patients (n = 26) enrolled in a community clinical exercise (e.g., Phase III) program over a 3-week reference period. Step counts averaged 39,818 ± 18,612 per week, with 18% of the steps accomplished in the program and 82% of steps accomplished outside the program. Using the sRPE method, inside the program, the patients averaged 162.4 ± 93.1 min per week, at a sRPE of 12.5 ± 1.9 and a frequency of 1.8 ± 0.7 times per week, for a calculated exercise load of 2042.5 ± 1244.9 AU. Outside the program, the patients averaged 144.9 ± 126.4 min, at a sRPE of 11.8 ± 5.8 and a frequency of 2.4 ± 1.5 times per week, for a calculated exercise load of 1723.9 ± 1526.2 AU. The total exercise load using sRPE was 266.4 ± 170.8 min per week, at a sRPE of 12.6 ± 3.8, and frequency of 4.2 ± 1.1 times per week, for a calculated exercise load of 3359.8 ± 2145.9 AU. There was a non-linear relationship between steps per week and the sRPE derived training load, apparently attributable to the amount of non-walking exercise accomplished in the program. The results suggest that patients in a community clinical exercise program are achieving American College of Sports Medicine guidelines, based on the sRPE method, but are accomplishing less steps than recommended by guidelines.
... Therefore, for any athlete, the magnitude of the resultant fatigue response for each of these systems is likely mediated by an array of personal characteristics (e.g. training and injury history and psycho-emotional state) and the specific external training load that is prescribed over a period of time (Kiely, 2018;Impellizzeri et al., 2019). ...
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Establishing dose–response relationships between training load and fatigue can help the planning of training. The aim was to establish the relative importance of external training load measurements to relate to the musculoskeletal response on a group and individual player level. Sixteen elite male rugby league players were monitored across three seasons. Two- to seven-day exponential weighted averages (EWMA) were calculated for total distance, and individualised speed thresholds (via 30–15 Intermittent Fitness Test) derived from global positioning systems. The sit and reach, dorsiflexion lunge, and adductor squeeze tests represented the musculoskeletal response. Partial least squares and repeated measures correlation analyses established the relative importance of training load measures and then investigated their relationship to the collective musculoskeletal response for individual players through the construction of latent variables. On a group level, 2- and 3-day EWMA total distance had the highest relative importance to the collective musculoskeletal response (p < 0.0001). However, the magnitude of relationships on a group (r value = 0.20) and individual (r value = 0.06) level were trivial to small. The lack of variability in the musculoskeletal response over time suggest practitioners adopting such measures to understand acute musculoskeletal fatigue responses should do so with caution.
... While this may reduce the bias given to longer duration training sessions associated with other training load metrics, it is still conceivable that vastly different training sessions (i.e., long low-intensity or shorter highintensity) may register similar training loads despite imposing a very different training stress. Furthermore, training load metrics derived from heart rate may fail to account for the stress imposed by short, very high-intensity sprint work intervals, despite being associated with a very large psychological and/or tissue stress (Impellizzeri et al., 2019). As such, no single training load metric can be relied upon that can differentiate the stress imposed by a given training session. ...
... With a two-dimension system, it is possible to understand the importance of using both variables, creating different magnitudes of training and match demands. The Internal Training Load is defined by how an athlete responds to a particularly planned training load [21]; influenced by individual characteristics, training, physiological status, health, nutrition, environment, and genetics. It can be monitored using perceived exertion scales [39] or wellness questionnaires including the Perceived Recovery Status Scale [27,28] or Delayed Onset Muscle Soreness [44], which exhibit efficient and affordable mechanisms to monitor the internal training load [8]. ...
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This study aimed to compare the external and internal training load indicators according to playing positions and competitive periods during a season with professional Brazilian soccer players. Twenty-three professional soccer players were divided by playing positions and monitored over 16 weeks. Internal training load (ITL) was obtained using Rating of Perceived Exertion (RPE), Session Rating of Perceived Exertion (sRPE), Perceived Recovery Scale (PRS), and Delayed Onset Muscle Soreness Scale (DOMS). The external load (ETL) was registered using Global Positioning System (GPS Polar Team Pro 10 Hz), including the total distance covered (TD) and distance in different speed zones (High Speed Running-HSR; >16 km·h-1). Results displayed that season periods influenced PRS, DOMS, ITL and ETL for all playing positions. The competitive period showed higher values of PRS for Forwards (PSR: p < 0.001) and Midfielders (PSR: p < 0.001) and DOMS for Central Defenders (p < 0.001), Full Backs (p = 0.01), Midfielders (p < 0.001) and Forwards (p < 0.001). ITL and ETL were dissimilar only for Midfielders with higher values during competitive periods (sRPE: p = 0.001; TD: p = 0.018; HSR: p = 0.002). PRS and DOMS was influenced by the season's period based on playing positions, while ETL and ITL were disparate only for Midfielders. The 16 microcycles showed homogeneity concerning perceived recovery and DOMS, with non-linear ITL and ETL characteristics throughout the weeks.
... Importantly, participants spent more time in comfortable HR zones and showed lower strenuousness (load scores), despite the higher covered distance and speed. Given that external load and internal load are intertwined [67][68][69], especially considering that a higher external load (higher covered distance and average speed at t2 compared to t1) could be achieved with a lower internal load (lower mean heart rate) to buttress the positive influence of the Fitness-Dance training on physical capacity in older individuals with MCI. However, these positive changes were reversed after four weeks of COVID-19 induced detraining. ...
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Physical training is considered as a low-cost intervention to generate cardioprotective benefits and to promote physical and mental health, while reducing the severity of acute respiratory in-fection symptoms in older adults. However, lockdown measures during COVID-19 have limited people’s opportunity to exercise regularly. The aim of this study was to investigate the effect of eight weeks of Fitness and Dance training, followed by four weeks of COVID-19-induced de-training, on cardiac adaptations and physical performance indicators in older adults with mild cognitive impairment (MCI). Twelve older adults (6 males and 6 females) with MCI (age, 73 ± 4.4 y; body mass, 75.3 ± 6.4 kg; height, 172 ± 8 cm; MMSE score: 24–27) participated in eight weeks of a combined Fitness-Dance training intervention (two sessions/week) followed by four weeks of training cessation induced by COVID-19 lockdowns. Wireless Polar Team Pro and Polar heart rate sensors (H10) were used to monitor covered distance, speed, heart rate (HR min, avg and max), time in HR zone 1 to 5, strenuousness (load score), beat-to-beat interval (max RR and avg RR) and heart rate variability (HRV-RMSSD). One-way ANOVA was used to analyze the data of the three test sessions (T1: first training session, T2: last training session of the eight-week training program, and T3: first training session after the four-week training cessation). Statistical analysis showed that eight weeks of combined Fitness-Dance training induced beneficial cardiac adaptations by decreasing HR (HR min, HR avg and HR max) with p < 0.001, ES = 0.5–0.6 and Δ = −7 to−9 bpm, and increasing HRV related responses (max and avg RR and RMSSD), with p < 0.01 and ES = 0.4. Consequently, participants spent more time in comfortable HR zones (e.g., p < 0.0005; ES = 0.7; Δ = 25% for HR zone 1) and showed reduced strenuousness (p = 0.02, Δ = −15% for load score), despite the higher covered total distance and average speed (p < 0.01; ES = 0.4). However, these changes were reversed after only four weeks of COVID-19 induced detraining, with values of all parameters returning to their baseline levels. In conclusion, eight weeks of combined Fitness-Dance training seems to be an efficient strategy to promote cardioprotective benefits in older adults with MCI. Importantly, to maintain these health benefits, training has to be continued and detraining periods should be reduced. During a pandemic, home-based exercise programs may provide an effective and efficient alternative of physical training
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We examined how summated training and match load measures relate to salivary immunological and hormonal profile changes in professional football players. Data were collected from 18 elite-level professional male football players from one English Championship team across a complete 40 wk competitive season. Daily training (micro-technology) and match (computerised tracking) measures of total, high-speed and high-metabolic load running distance and sprint, acceleration, deceleration and sRPE load were converted into exponentially weighted moving average ‘acute’ (7d), ‘chronic’ (28d) and acute:chronic composite load measures. Bi-weekly morning saliva samples were analysed for immunoglobulin-A, alpha-amylase, testosterone, cortisol and testosterone:cortisol. A two-stage data reduction technique using partial least squares modelling and a backward stepwise selection procedure determined the most parsimonious model for each salivary variable. Testosterone had non-linear relationships with chronic total (P=0.015; Cohen’s D: large), high-metabolic load (P=0.001;small) and high-speed (P=0.001;trivial) running distance and linear relationships with chronic sRPE (P=0.002;moderate) and acute:chronic high-speed running distance (P=0.001; trivial). Cortisol had a non-linear relationship with chronic high-speed running distance (P=0.001;trivial). Testosterone:cortisol had non-linear relationships with chronic decelerations (P=0.039;small) and chronic summated acceleration and deceleration load (P=0.039;small). Non-linear relationships typically indicated optimal hormonal responses at squad mean loads. No load variables clearly related to salivary immunoglobulin-A or alpha-amylase changes. We conclude that chronic total and high-intensity load measures relate to hormonal changes and might be useful indicators of player readiness. Acute load variables were not related to immunological or hormonal changes and consequently, should not be used as surrogate measures of player readiness in isolation.
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Objective: To establish whether a simple integration of selected internal and external training load (TL) metrics is useful for tracking and assessing training outcomes during team-sport training. Methods: Internal [heart rate training impulse (HR-TRIMP), session rating of perceived exertion (sRPE-TL)] and selected external (global positioning systems; GPS) metrics were monitored over seven weeks in 38 professional male rugby league players. Relationships between internal and external measures of TL were determined, and an integrated novel training efficiency index (TEI) was established. Changes in TEI were compared to changes in both running performance (1.2 km shuttle test) and external TL completed. Results: Moderate to almost perfect correlations (r = 0.35–0.96; ±~0.02; range ± 90% confidence limits) were observed between external TL and each measure of internal TL. The integration of HR-TRIMP and external TL measures incorporating both body mass and acceleration/deceleration were the most appropriate variables for calculating TEI, exhibiting moderate (ES= 0.87–0.89; ±~0.15) and small (ES = 0.29–0.33; ±~0.07) relationships with changes in running performance and completed external TL respectively. Conclusions: Combination of the TEI and an athlete monitoring system should reveal useful information for continuous monitoring of team-sport athletes over several weeks.
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Exercise training which meets the recommendations set by the National Physical Activity Guidelines ensues a multitude of health benefits towards the prevention and treatment of various chronic diseases. However, not all individuals respond well to exercise training. That is, some individuals have no response, while others respond poorly. Genetic background is known to contribute to the inter-individual (human) and -strain (e.g., mice, rats) variation with acute exercise and exercise training, though to date, no specific genetic factors have been identified that explain the differential responses to exercise. In this review, we provide an overview of studies in human and animal models that have shown a significant contribution of genetics in acute exercise and exercise training-induced adaptations with standardized endurance and resistance training regimens, and further describe the genetic approaches which have been used to demonstrate such responses. Finally, our current understanding of the role of genetics and exercise is limited primarily to the nuclear genome, while only a limited focus has been given to a potential role of the mitochondrial genome and its interactions with the nuclear genome to predict the exercise training-induced phenotype(s) responses. We therefore discuss the mitochondrial genome and literature that suggests it may play a significant role, particularly through interactions with the nuclear genome, in the inherent ability to respond to exercise.
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Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO 2 , and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved. Watching athletes perform well, set personal records or win competitions, are great pleasures for sports scientists. To think that the information that you have collected on the athlete, or synthesized from the literature, has helped the athlete achieve optimal performance is " as good as it gets " for support staff. Conversely, watching a poor performance inspires analysis of what went wrong, with preparation, tactics, or execution of the competitive plan. This provides the basis for the questions that drive sport-science research. Because so much of the preparation of athletes is related to the structure and details of the training program, there is a natural emphasis on how training influences performance. This interest goes into history, to Milo of Crotona, the Italian farm boy who lifted a growing bullock daily until he became the strongest man in the world and legend of the ancient Olympics. This story provides the historical grounding for the quest to understand the training response, most uniquely characterized by the concept of progression of the training load, and to the idea that training loads can be quantitatively expressed 1 and related to performance outcomes. 2–6 Although it is not known if Milo had a coach, most top athletes throughout history have had one, someone with more knowledge and experience, and the objectivity to evaluate their training and performance. The concept of training monitoring, regardless of historical time frame is in essence about the coach-athlete interface. Although not always appreciated, one can make the argument that the greatest value of sports science is related to optimizing the coach-athlete interface; to give the athlete a smarter, better-informed coach. Accepting the premise that the proper role of sport science is to inform and support the coach-athlete relationship, we need to ask what the coach needs from the sport-science community. A reasonable approximation is provided in Table 1. The reality is that sports scientists are rather good at providing the first 2 of these needs to the coach but less good at the last 2. As addressed previously, 9 index workouts could be performed routinely by groups of athletes as a normal part of the training program, giving the coach high-frequency data useful for predicting progress toward training goals, and decision making regarding when the training program needs to be modified. The laboratory, is hard to schedule, is not well suited to testing large numbers of athletes quickly, and is not available for high-frequency testing. It is also much harder to provide the information which the coach needs to " translate " the results of the training to specif-ics about the progress and performance of the athlete (Figure 1).
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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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Resistance exercise is difficult to quantify owing to its inherent complexity with numerous training variables contributing to the training dose (type of exercise, load lifted, training volume, inter-set rest periods, and repetition velocity). In addition, the intensity of resistance training is often inadequately determined as the relative load lifted (% 1-repetition maximum), which does not account for the effects of inter-set recovery periods, repetition velocity, or the number of repetitions performed in each set at a given load. Methods to calculate the volume load associated with resistance training, as well as the perceived intensity of individual sets and entire training sessions have been shown to provide useful information regarding the actual training stimulus. In addition, questionnaires to subjectively assess how athletes are coping with the stressors of training and portable technologies to quantify performance variables such as concentric velocity may also be valuable. However, while several methods have been proposed to quantify resistance training, there is not yet a consensus regarding how these methods can be best implemented and integrated to complement each other. Therefore, the purpose of this review is to provide practical information for strength coaches to highlight effective methods to assess resistance training, and how they can be integrated into a comprehensive monitoring program.
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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.
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.
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.