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High Responders and Low Responders: Factors Associated with Individual Variation in Response to Standardized Training
Abstract
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.
... within-person variability; Walsh et al., 2020). This includes genetic (Mann et al., 2014;Sparks, 2017;Bonafiglia et al., 2020;Del Coso et al., 2020), climatic (Corbett et al., 2018), cognitive (Atkinson and Batterham, 2015), stress and sleep status (Mann et al., 2014), gender, age, time of day variation (Mann et al., 2014;Sparks, 2017), training status (Pickering and Kiely, 2019), physiological (Williamson et al., 2017;Atkinson et al., 2019), and statistical (Swinton et al., 2018;Chrzanowski-Smith et al., 2020). ...
... within-person variability; Walsh et al., 2020). This includes genetic (Mann et al., 2014;Sparks, 2017;Bonafiglia et al., 2020;Del Coso et al., 2020), climatic (Corbett et al., 2018), cognitive (Atkinson and Batterham, 2015), stress and sleep status (Mann et al., 2014), gender, age, time of day variation (Mann et al., 2014;Sparks, 2017), training status (Pickering and Kiely, 2019), physiological (Williamson et al., 2017;Atkinson et al., 2019), and statistical (Swinton et al., 2018;Chrzanowski-Smith et al., 2020). ...
... within-person variability; Walsh et al., 2020). This includes genetic (Mann et al., 2014;Sparks, 2017;Bonafiglia et al., 2020;Del Coso et al., 2020), climatic (Corbett et al., 2018), cognitive (Atkinson and Batterham, 2015), stress and sleep status (Mann et al., 2014), gender, age, time of day variation (Mann et al., 2014;Sparks, 2017), training status (Pickering and Kiely, 2019), physiological (Williamson et al., 2017;Atkinson et al., 2019), and statistical (Swinton et al., 2018;Chrzanowski-Smith et al., 2020). ...
This study investigated the effect of 4weeks of high-intensity interval training (HIIT) with specific techniques and analyzed inter-individual variability [classified in responders (Rs) and non-responders (NRs)] on jumping ability and change of direction speed (CODS) in youth karate athletes. Athletes of both genders ( n =10) were randomly assigned into experimental group (EG; n =5) and the control group (CG; n =5). The EG trained 2–3days per week applying HIIT (three rounds [15 sets of 4s all-out specific efforts with 8s of dynamical pauses] with 3min of recovery between rounds) during their usual training during 4weeks. Assessments included squat jump (SJ) and countermovement jump (CMJ) and CODS by T-test. No significant interaction effect group by time was found. Although, in percentage and effect size (ES) terms increases were reported in both groups for SJ (EG: 15.2%, ES=0.91 vs. CG: 12.4%, ES=0.02) and only in EG for the T-test (−1.7%; ES=−0.35). In turn, a trend toward a higher proportion of Rs was observed in the EG (40% Rs) vs. CG (20% Rs) for SJ and CODS, respectively. In conclusion, the addition to regular training of a HIIT with specific techniques and based on the temporal combat structure after 4weeks was not a sufficient stimulus to increase jumping ability and CODS in karate athletes.
... Genetics makes an important contribution to individual variation in training responses, like change in body mass index (Bae et al. 2007), body composition (Duoqi et al. 2015), improvement in insulin activity for glucose utilization during exercise (Weiss et al. 2005), a favorable response to endurance training for diastolic blood pressure (DBP) and mean arterial pressure (MAP) (Jayewardene et al. 2016;Rankinen et al. 2000), and improvement of VO 2 max following endurance exercise training (Timmons et al. 2010). The association between genotype and training response has been supported using heritability estimates; however, recent studies have shown variation in some training responses to specific SNPs (Mann et al. 2014;He et al. 2007He et al. , 2010Rice et al. 2012;Thomaes et al. 2011). Heritability is often expressed through the influence of innate factors on the pre-training phenotype, with some parameters showing a hereditary effect on the pre-training phenotype but not on the subsequent training response (Mann et al. 2014). ...
... The association between genotype and training response has been supported using heritability estimates; however, recent studies have shown variation in some training responses to specific SNPs (Mann et al. 2014;He et al. 2007He et al. , 2010Rice et al. 2012;Thomaes et al. 2011). Heritability is often expressed through the influence of innate factors on the pre-training phenotype, with some parameters showing a hereditary effect on the pre-training phenotype but not on the subsequent training response (Mann et al. 2014). Individual variation in response to standardized training that cannot be explained by genetic influences may be related to the characteristics of the training program, compliance with the program or differences in lifestyle factors among individuals (Mann et al. 2014;Ahtiainen et al. 2020;Zubair et al. 2019). ...
... Heritability is often expressed through the influence of innate factors on the pre-training phenotype, with some parameters showing a hereditary effect on the pre-training phenotype but not on the subsequent training response (Mann et al. 2014). Individual variation in response to standardized training that cannot be explained by genetic influences may be related to the characteristics of the training program, compliance with the program or differences in lifestyle factors among individuals (Mann et al. 2014;Ahtiainen et al. 2020;Zubair et al. 2019). Interestingly, the level of recovery between exercise sessions in endurance athletes has recently been shown to be related to liver-metabolizing genes, such as Cytochrome P450 2D6 (CYP2D6) and other genes encoding the family of G-glutathione transferases (Varillas Delgado et al. 2019), suggesting that future studies should associate genetic variants with the capacity of recovery by scavenging free radicals, or the neutralization of pH (Lewis et al. 2015). ...
The impact of genetics on physiology and sports performance is one of the most debated research aspects in sports sciences. Nearly 200 genetic polymorphisms have been found to influence sports performance traits, and over 20 polymorphisms may condition the status of the elite athlete. However, with the current evidence, it is certainly too early a stage to determine how to use genotyping as a tool for predicting exercise/sports performance or improving current methods of training. Research on this topic presents methodological limitations such as the lack of measurement of valid exercise performance phenotypes that make the study results difficult to interpret. Additionally, many studies present an insufficient cohort of athletes, or their classification as elite is dubious, which may introduce expectancy effects. Finally, the assessment of a progressively higher number of polymorphisms in the studies and the introduction of new analysis tools, such as the total genotype score (TGS) and genome-wide association studies (GWAS), have produced a considerable advance in the power of the analyses and a change from the study of single variants to determine pathways and systems associated with performance. The purpose of the present study was to comprehensively review evidence on the impact of genetics on endurance- and power-based exercise performance to clearly determine the potential utility of genotyping for detecting sports talent, enhancing training, or preventing exercise-related injuries, and to present an overview of recent research that has attempted to correct the methodological issues found in previous investigations.
... Many exercise training studies have interpreted wide ranges of observed changes in physiological outcomes as evidence that individuals demonstrate varying degrees of trainability-the change in a given variable directly attributable to an effect of exercise training per se [1][2][3]. However, these interpretations ignore the confounding influence of measurement error and/or variability introduced by changes in behavioural/environmental factors not related to exercise training including changes in sleep, diet, stress, etc. [4]. The confounding influences of behavioural and environmental factors are collectively referred to as "within-subject variability", and recognizing this source of variation challenges the assumption that interindividual differences in trainability exist following ostensibly the same exercise training stimulus [5,6]. ...
... For instance, the standard deviation of change scores for both exercise and control groups exceeded the typical errors of measurement reported in the literature (~ 1-2 mL/kg/min for CRF [18,51]; ~ 0.5 cm for waist circumference [52,53], and ~ 0.5 kg for body mass [52]). Our findings therefore indicate that individuals experienced real physiological differences in changes in CRF, waist circumference, and body mass, and that behavioural factors (e.g., sleep, stress, external physical activity, etc. [4]) may underlie this variance rather than exercise per se. Future work is needed to investigate the contribution of various behavioural Table 4 Analysis of relative body mass change scores and moderator analyses involving exercise vs. control and exercise only comparisons N number of individuals included in the IPD model Proportion > MCID: The proportion estimated to meet or exceed the minimal clinically important clinical difference, with 90% credible intervals denoting Bayesian subjective probabilities a Combines participants from intervention durations of 8 and 9 months b Intensities were prescribed as percentages of different variables across studies (see Table 1 for details) c Low, mid, and high exercise amounts categorized as less than 500 kcal, between 500 and 1000, and greater than 1000 kcal prescribed per sessions factors on observed changes following standardized and controlled exercise interventions. ...
... Our study design, and the designs of the included trials, did not allow us to determine the extent to which certain individual behavioural/environmental factors contributed to within-subject variability. The evidence that subtle changes in sleep quality, stress levels, or other behavioural/environmental factors impact training adaptations is indirect at best [4], warranting the need for future work designed to test the effects of individual behavioural/ environmental on observed variability. Second, we unfortunately do not have measures of measurement error, such as coefficients of variation, for CRF, waist circumference, or body mass for each trial and it is possible that measurement errors varied across trial sites. ...
Although many studies have assumed variability reflects variance caused by exercise training, few studies have examined whether interindividual differences in trainability are present following exercise training. The present individual participant data (IPD) meta-analysis sought to: (1) investigate the presence of interindividual differences in trainability for cardiorespiratory fitness (CRF), waist circumference, and body mass; and (2) examine the influence of exercise training and potential moderators on the probability that an individual will experience clinically important differences. The IPD meta-analysis combined data from 1879 participants from eight previously published randomized controlled trials. We implemented a Bayesian framework to: (1) test the hypothesis of interindividual differences in trainability by comparing variability in change scores between exercise and control using Bayes factors; and (2) compare posterior predictions of control and exercise across a range of moderators (baseline body mass index (BMI) and exercise duration, intensity, amount, mode, and adherence) to estimate the proportions of participants expected to exceed minimum clinically important differences (MCIDs) for all three outcomes. Bayes factors demonstrated a lack of evidence supporting a high degree of variance attributable to interindividual differences in trainability across all three outcomes. These findings indicate that interindividual variability in observed changes are likely due to measurement error and external behavioural factors, not interindividual differences in trainability. Additionally, we found that a larger proportion of exercise participants were expected to exceed MCIDs compared with controls for all three outcomes. Moderator analyses identified that larger proportions were associated with a range of factors consistent with standard exercise theory and were driven by mean changes. Practitioners should prescribe exercise interventions known to elicit large mean changes to increase the probability that individuals will experience beneficial changes in CRF, waist circumference and body mass.
... Accordingly, the inter-individual variability of observed responses to training, such as HIIT, according to the study by Walsh et al. (2020), is a combination of: (i) individual responses to perseverative exercise training (subject-training interaction), (ii) day-to-day biological variation and TE (random variation), and (iii) physiological responses associated with behavioral/maturational changes, not attributable to exercise (e.g., within-person variability) (Walsh et al., 2020). This includes genetic (Mann et al., 2014;Sparks, 2017;Bonafiglia et al., 2020;Del Coso et al., 2020), climatic (Corbett et al., 2018), cognitive (Atkinson and Batterham, 2015), stress and sleep status (Mann et al., 2014), gender, age, time of day variation (Mann et al., 2014;Sparks, 2017), training status (Pickering and Kiely, 2019), physiological (Williamson et al., 2017;Atkinson et al., 2019), and statistical outcomes (Swinton et al., 2018;Bonafiglia et al., 2020;Chrzanowski-Smith et al., 2020). ...
... Accordingly, the inter-individual variability of observed responses to training, such as HIIT, according to the study by Walsh et al. (2020), is a combination of: (i) individual responses to perseverative exercise training (subject-training interaction), (ii) day-to-day biological variation and TE (random variation), and (iii) physiological responses associated with behavioral/maturational changes, not attributable to exercise (e.g., within-person variability) (Walsh et al., 2020). This includes genetic (Mann et al., 2014;Sparks, 2017;Bonafiglia et al., 2020;Del Coso et al., 2020), climatic (Corbett et al., 2018), cognitive (Atkinson and Batterham, 2015), stress and sleep status (Mann et al., 2014), gender, age, time of day variation (Mann et al., 2014;Sparks, 2017), training status (Pickering and Kiely, 2019), physiological (Williamson et al., 2017;Atkinson et al., 2019), and statistical outcomes (Swinton et al., 2018;Bonafiglia et al., 2020;Chrzanowski-Smith et al., 2020). ...
... Accordingly, the inter-individual variability of observed responses to training, such as HIIT, according to the study by Walsh et al. (2020), is a combination of: (i) individual responses to perseverative exercise training (subject-training interaction), (ii) day-to-day biological variation and TE (random variation), and (iii) physiological responses associated with behavioral/maturational changes, not attributable to exercise (e.g., within-person variability) (Walsh et al., 2020). This includes genetic (Mann et al., 2014;Sparks, 2017;Bonafiglia et al., 2020;Del Coso et al., 2020), climatic (Corbett et al., 2018), cognitive (Atkinson and Batterham, 2015), stress and sleep status (Mann et al., 2014), gender, age, time of day variation (Mann et al., 2014;Sparks, 2017), training status (Pickering and Kiely, 2019), physiological (Williamson et al., 2017;Atkinson et al., 2019), and statistical outcomes (Swinton et al., 2018;Bonafiglia et al., 2020;Chrzanowski-Smith et al., 2020). ...
This study investigated the effect of 4 weeks of high-intensity interval training (HIIT) with specific techniques (TS-G) vs. repeated sprints (RS-G) and analyzed the inter-individual variability [classified into responders (Rs) and non-responders (NRs)] on sport-related fitness in taekwondo (TKD) athletes. Athletes of both genders (n = 12) were randomly assigned into TS-G and RS-G groups. Both groups trained 3 days/week for 4 weeks [two blocks of three rounds of 2 min of activity (4-s of all-out efforts with 28-s dynamical pauses) with 1 min of recovery in between and 5 min between blocks] during their regular training. The related sport fitness assessments included squat jump (SJ), countermovement jump (CMJ), multiple frequency speed of kick test (FSKTMULT), specifically total kicks and Kick Decrement Index (KDI), and 20-m shuttle run (20MSR). Relevant results indicate a significant effect of the time factor in both groups for SJ performance and a significant decrease for KDI in RS-G. In addition, an improvement in performance according to the effect size analysis in the TS-G in total kicks, KDI, and 20MSR. Complementarily, a higher proportion of athlete Rs was reported in TS-G vs. RS-G for SJ (50% vs. 30.3%, respectively), CMJ, and total kicks (16.6% vs. 0%). In conclusion, the addition to the regular training of a HIIT with specific-techniques and repeated-sprints associated with intervals and similar structure of the combat during 4 weeks of training can improve the concentric characteristics of lower limb performance, although they were not the sufficient stimuli in the other components of TKD-related fitness.
... These training responses are typically described in terms of fatigue and recovery trajectories, functional adaptations, and athletic performance outcomes Sands & Stone, 2006). Regardless of the metric, it is considered well established that (acute) responses or adaptations to training stimuli can vary widely between athletes in terms of magnitude, direction, and temporal dynamics Halson, 2014;Hautala et al., 2009;Hecksteden et al., 2015;Horn, 2003;Mann et al., 2014;Plews et al., 2012;Scharhag-Rosenberger et al., 2010;Scharhag-Rosenberger et al., 2012). This, in turn, poses substantial challenges for data analysis and interpretation of individual cases. ...
... This is probably due to the multitude of internal and external factors influencing internal load and training response. At the same time, there is increasing evidence for substantial variability in training and recovery responses between subjects (Barth et al., 2019;Buchheit et al., 2010;Halson, 2014;Hautala et al., 2009;Hecksteden et al., 2015;Hecksteden et al., 2017;Hecksteden et al., 2018;Horn, 2003;Mann et al., 2014;Plews et al., 2012;Scharhag-Rosenberger et al., 2010;Scharhag-Rosenberger et al., 2012). To address the complexity and individuality of fatigue, recovery, and performance, there is a clear call for multidimensional approaches that describe different functional systems of the organism (Akenhead & Nassis, 2016;Buchheit, 2014;Coutts et al., 2018;Coutts & Cormack, 2014;Halson, 2014;Heidari et al., 2019;Meeusen et al., 2013;Sands & Stone, 2006). ...
... Finally, future research directions are discussed that will follow from the research project presented here. (Achten & Jeukendrup, 2003;Aubert et al., 2003;Bellenger, Fuller, et al., 2016;Borresen & Lambert, 2008, 2009Bosquet et al., 2008;Buchheit, 2014;Capostagno et al., 2016;Carter et al., 2003;Daanen et al., 2012;Fatisson et al., 2016;Kingsley & Figueroa, 2016;Peçanha et al., 2017;Plews et al., 2013;Stanley et al., 2013) and athlete monitoring (Akenhead & Nassis, 2016;Gabbett et al., 2016;Gabbett, 2016;Halson, 2014;Haugen & Buchheit, 2016;Kinugasa et al., 2004;Mann et al., 2014;Mujika, 2017;Sands, 1991;Saw et al., 2016;Scott et al., 2016;Smith et al., 2002;Thorpe et al., 2017) were identified, which were intensively reviewed, and the bibliographies were used for collecting further studies. ...
The monitoring of heart rate (HR) and heart rate variability (HRV) can contribute significantly to the individualization and optimization of training and recovery. However, the practical interpretation of longitudinal data is still challenging in some cases. The results of this dissertation can be summarized as follows: Practical interpretation of HR(V) data requires consideration of contextual factors such as training context and more detailed analysis of HR(V) time courses (Study 1). Orthostatic tests appear to be useful in identifying complex and training-specific HR(V) responses following short-term overload training and recovery (Study 2). Submaximal HR during standardized warm-up is sensitive to short-term periods of training and recovery, contrary to previous assumptions (Study 3). A future challenge is to effectively separate potential short-term from long-term effects.
--- Das Monitoring von Herzfrequenz (HR) und Herzfrequenzvariabilität (HRV) wird zur Individualisierung und Optimierung von Training und Regeneration empfohlen. Die trainingspraktische Interpretation der Daten stellt jedoch nach wie vor eine Herausforderung dar. Die im Rahmen der Dissertation veröffentlichten Ergebnisse können wie folgt zusammengefasst werden: Die praxisnahe Interpretation von HR(V) Daten erfordert die Berücksichtigung kontextualer Faktoren wie die Trainingsstruktur und eine differenziertere Analyse von HR(V) Zeitverläufen (Studie 1). Orthostase Tests scheinen hilfreich zu sein, um die komple-xen, belastungsspezifischen HR(V) Reaktionen nach Kurzzeit-Überlastungstraining und Erholung identifizieren zu können (Studie 2). Die standardisiert im Training erfasste submaximale Belastungs-HR reagiert entgegen früherer Annahmen sensitiv auf kurze Trainings- und Erholungsphasen (Studie 3). Eine zukünftige Herausforderung besteht darin, Kurzzeit- von Langzeiteffekten zu isolieren.
... Yet, different training effort using such countermeasures can lead to variations in the musculoskeletal response (Timmons, 2011;Rittweger et al., 2018), however, in contrast, comparable training effort can also lead to BSV in muscle loss (English et al., 2015) or bone loss (Sibonga et al., 2015). The variation may be explained by the fact that there are responders and non-responders toward a training intervention (Mann et al., 2014;Hecksteden et al., 2015;Ahtiainen et al., 2016) resulting in a BSV (McPhee et al., 2010;Ross et al., 2019). ...
... We therefore speculate that gains in bone mass and muscle CSA measurements may have been produced by a combination of small true changes in the study groups and limited reliability of individual percent changes. However, given the substantial between-subject variation observed in this study, by Scott et al. (2021) and the repeated observation of responders and non-responders to training interventions (McPhee et al., 2010;Mann et al., 2014;Hecksteden et al., 2015;Ahtiainen et al., 2016;Ross et al., 2019), blunted or even paradoxical responses to bed rest cannot be ruled out. We suggest that future bed rest studies should make further attempts at improving and unifying standard operating procedures for pQCT. ...
... In many cases that diet will considerably deviate from the habitual intake patterns with expected effects on metabolism and adaptive processes. In addition, the well-established view of individual responsiveness to exercise interventions (McPhee et al., 2010;Mann et al., 2014;Hecksteden et al., 2015;Ahtiainen et al., 2016;Ross et al., 2019) also needs to be considered. This variable responsiveness may result from genetic and epigenetic pre-dispositions, which has been demonstrated, e.g., for the ACE ii/dd polymorphism (Montgomery et al., 1999;Valdivieso et al., 2017). ...
To improve quantification of individual responses to bed rest interventions, we analyzed peripheral quantitative computer tomography (pQCT) datasets of the lower leg of 76 participants, who took part in eight different bed rest studies. A newly developed statistical approach differentiated measurement uncertainty U Meas from between-subject-variation (BSV) and within-subject variation (WSV). The results showed that U Meas decreased 59.3–80% over the two decades of bed rest studies ( p < 0.01), and that it was higher for muscles than for bones. The reduction of U Meas could be explained by improved measurement procedures as well as a higher standardization. The vast majority (89.6%) of the individual responses pc i exceeded the 95% confidence interval defined by U Meas , indicating significant and substantial BSV, which was greater for bones than for muscles, especially at the epiphyseal measurement sites. Non-significant to small positive inter-site correlations between bone sites, but very large positive inter-site correlation between muscle sites suggests that substantial WSV exists in the tibia bone, but much less so in the calf musculature. Furthermore, endocortical circumference, an indicator of the individual’s bone geometry could partly explain WSV and BSV. These results demonstrate the existence of substantial BSV bone, and that it is partly driven by WSV, and likely also by physical activity and dietary habits prior to bed rest. In addition, genetic and epigenetic variation could potentially explain BSV, but not WSV. As to the latter, differences of bone characteristics and the bone resorption process could offer an explanation for its existence. The study has also demonstrated the importance of duplicate baseline measurements. Finally, we provide here a rationale for worst case scenarios with partly effective countermeasures in long-term space missions.
... Mann et al. [22] proposed baseline muscle phenotypes as a critical factor contributing to individual variation in response to acute exercise. They hypothesized that the relationship between baseline parameters and subsequent training responses may be related to the capacity for improvement [22]. ...
... Mann et al. [22] proposed baseline muscle phenotypes as a critical factor contributing to individual variation in response to acute exercise. They hypothesized that the relationship between baseline parameters and subsequent training responses may be related to the capacity for improvement [22]. The substantial interindividual variations in response to specific exercise doses might clarify valuable insights into the adaptations caused by eccentric exercise, thus promoting health. ...
... Considering the effect of MQI on IL-6 and CK response, a previous study stated that responsiveness might not be a common occurrence but the result of inappropriate statistical analyses [63]. Furthermore, responsiveness might depend on hereditary factors, baseline phenotype, readiness to train, training status, sleep and stress, and nutritional status [22]. ...
This study aimed to evaluate the time course and responsiveness of plasma interleukin-6 (IL-6) and creatine kinase (CK) levels following acute eccentric resistance exercise in sedentary obese older women with a different muscle quality index (MQI). Eighty-eight participants (69.4 ± 6.06 years) completed an acute eccentric resistance exercise (7 sets of 10 repetitions at 110% of 10-repetition maximum with 3 min rest interval). Participants were divided into two groups: high or low MQI according to 50th percentile cut-off. The responsiveness was based on minimal clinical important difference. There were no differences between groups and time on IL-6 and CK levels (p > 0.05). However, the high MQI group displayed a lower proportion of low responders (1 for laboratory and 2 for field-based vs. 5 and 4) and a higher proportion of high responders for IL-6 (7 for laboratory and 6 for field-based vs. 4 and 5) compared to low MQI group. In addition, the high MQI group showed a higher proportion of high responders for CK (11 for laboratory and 9 for field-based vs. 6 and 6) compared to low MQI. A prior MQI screening can provide feedback to understand the magnitude response. Individual responsiveness should be taken into consideration for maximizing eccentric exercise prescription
... Therefore, TE ∆ was calculated by multiplying the mean baseline V O 2peak/kg (19.3 mL · min -1 · kg -1 ) by a previously published coefficient of variation (CV) of 5.6 % [33], as this value represents the TE M as a percentage of the mean. The use of a CV of 5.6 % has been suggested and applied by others [15,16,34,35]. In conclusion, TE ∆ as a cut off value for response was calculated by 5.6 % x 2 x 19.3 mL · min -1 · kg -1 . ...
... A response heterogeneity in cardiorespiratory, mobility and cognitive outcomes following standardized exercise interventions has been observed in healthy [13,14,34,43,44] and clinical [6,12,24,[45][46][47] populations. We are not aware of studies that directly compare training response between pwMS and healthy individuals. ...
... Against the background, that previous literature as well as this study indicated that HIIT taxes the cardiorespiratory system on acute [50] and chronic [8,39] basis more than MCT in pwMS, it is not surprising that intensity plays a crucial role for training response. Moreover, people with low baseline levels of cardiorespiratory fitness seem more likely to show greater response to an exercise intervention than individuals with limited capabilities for improvement, potentially explained by the "ceiling effect" [34]. Additionally, a more pronounced absolute change in V O 2peak /kg after a three-week exercise intervention was observed in dependence to younger age. ...
Exercise is described to provoke enhancements of cardiorespiratory fitness in persons with Multiple Sclerosis (pwMS). However, a high inter-individual variability in training responses has been observed. This analysis investigates response heterogeneity in cardiorespiratory fitness following high intensity interval (HIIT) and moderate continuous training (MCT) and analyzes potential predictors of cardiorespiratory training effects in pwMS. 131 pwMS performed HIIT or MCT 3-5x/ week on a cycle ergometer for three weeks. Individual responses were classified. Finally, a multiple linear regression was conducted to examine potential associations between changes of absolute peak oxygen consumption (absolute ∆V̇O2peak/kg), training modality and participant's characteristics. Results show a time and interaction effect for ∆V̇O2peak/kg. Absolute changes of cardiorespiratory responses were larger and the non-response proportions smaller in HIIT vs. MCT. The model accounting for 8.6% of the variance of ∆V̇O2peak/kg suggests that HIIT, younger age and lower baseline fitness predict a higher absolute ∆V̇O2peak/kg following an exercise intervention. Thus, this work implements a novel approach that investigates potential determinants of cardiorespiratory response heterogeneity within a clinical setting and analyzes a remarkable bigger sample. Further predictors need to be identified to increase the knowledge about response heterogeneity, thereby supporting the development of individualized training recommendations for pwMS.
... Human adaptive response to exercise interventions is often described in general terms, assuming that the group average and SD are sufficient to represent the typical response for most individuals (Mann et al., 2014). However, studies reporting individual responses to exercise are usually heterogeneous, showing a wide range of responses to the interventions rather than a similar response Álvarez et al., 2017a;Sparks, 2017;. ...
... Following this, recent literature has dichotomously classified individuals as either "responders/non-responders" or "high responders (HiRes)/low responders (LoRes)" using a pre-determined threshold. The most commonly used criteria are: clinical cut-off points (Mann et al., 2014;, withinsubjects coefficient of variation (CV) , typical error of measurement (TE) , or two times the typical error (2x TE) Álvarez et al., 2017a;. Concerning exercise, most studies have focused on heterogeneity of the cardiorespiratory fitness (CRF) adaptations to training . ...
The novel coronavirus disease (COVID-19) has emerged at the end of 2019 and caused
a global pandemic. The disease predominantly affects the respiratory system; however,
there is evidence that it is a multisystem disease that also impacts the cardiovascular
system. Although the long-term consequences of COVID-19 are not well-known,
evidence from similar diseases alerts for the possibility of long-term impaired physical
function and reduced quality of life, especially in those requiring critical care. Therefore,
rehabilitation strategies are needed to improve outcomes in COVID-19 survivors. Among
the possible strategies, resistance training (RT) might be particularly interesting, since it
has been shown to increase functional capacity both in acute and chronic respiratory
conditions and in cardiac patients. The present article aims to propose evidence-based
and practical suggestions for RT prescription for people who have been diagnosed with
COVID-19 with a special focus on immune, respiratory, and cardiovascular systems.
Based on the current literature, we present RT as a possible safe and feasible activity
that can be time-efficient and easy to be implemented in different settings.
... Human adaptive response to exercise interventions is often described in general terms, assuming that the group average and SD are sufficient to represent the typical response for most individuals (Mann et al., 2014). However, studies reporting individual responses to exercise are usually heterogeneous, showing a wide range of responses to the interventions rather than a similar response Álvarez et al., 2017a;Sparks, 2017;. ...
... Following this, recent literature has dichotomously classified individuals as either "responders/non-responders" or "high responders (HiRes)/low responders (LoRes)" using a pre-determined threshold. The most commonly used criteria are: clinical cut-off points (Mann et al., 2014;, withinsubjects coefficient of variation (CV) , typical error of measurement (TE) , or two times the typical error (2x TE) Álvarez et al., 2017a;. Concerning exercise, most studies have focused on heterogeneity of the cardiorespiratory fitness (CRF) adaptations to training . ...
The second volume of the Research Topic entitled “Precision Physical Activity and Exercise Prescriptions for Disease Prevention: The Effect of Interindividual Variability Under Different Training Approaches” has been successfully completed, as expected. As stated in the preface to the first volume, this Research Topic was initially intended to address a challenge in this field, but this topic is becoming, over time, an important cornerstone for scientists who are exploring the fascinating subject of “Precision Physical Activity and Exercise Prescriptions for Disease Prevention” (Ramírez-Vélez et al., 2017). This Research Topic consists of 10 articles, of which seven contain original data, one is a systematic review with meta-analysis and two are opinion/hypothesis articles.
... Human adaptive response to exercise interventions is often described in general terms, assuming that the group average and SD are sufficient to represent the typical response for most individuals (Mann et al., 2014). However, studies reporting individual responses to exercise are usually heterogeneous, showing a wide range of responses to the interventions rather than a similar response (Bouchard and Rankinen, 2001;King et al., 2008;Scharhag-Rosenberger et al., 2012;Bonafiglia et al., 2016;Gurd et al., 2016;Parr et al., 2016;Álvarez et al., 2017a;de Lannoy et al., 2017;Sparks, 2017;Williamson et al., 2017;Chrzanowski-Smith et al., 2019;Ross et al., 2019). ...
... Following this, recent literature has dichotomously classified individuals as either "responders/non-responders" or "high responders (HiRes)/low responders (LoRes)" using a pre-determined threshold. The most commonly used criteria are: clinical cut-off points (Mann et al., 2014;Parr et al., 2016;Álvarez et al., 2019), withinsubjects coefficient of variation (CV) (Scharhag-Rosenberger et al., 2012;Astorino and Schubert, 2014), typical error of measurement (TE) (Ross et al., 2015;Montero and Lundby, 2017), or two times the typical error (2x TE) (Bouchard et al., 2012;Bonafiglia et al., 2016Bonafiglia et al., , 2018Gurd et al., 2016;Raleigh et al., 2016;Álvarez et al., 2017a;de Lannoy et al., 2017;Astorino et al., 2018). Concerning exercise, most studies have focused on heterogeneity of the cardiorespiratory fitness (CRF) adaptations to training (Bouchard and Rankinen, 2001;Scharhag-Rosenberger et al., 2012;Ross et al., 2015Ross et al., , 2019Williamson et al., 2017). ...
Background: Human adaptive response to exercise interventions is often described as group average and standard deviation to represent the typical response for most individuals, but studies reporting individual responses to exercise show a wide range of responses.
Objective: To characterize the physiological effects and inter-individual variability on fat mass and other health-related and physical performance outcomes after 12 weeks of high-intensity interval training (HIIT) and dietary energy restriction in overweight/obese adult women.
Methods: Thirty untrained adult overweight and obese women (age= 27.4 ± 7.9 yrs; BMI= 29.9 ± 3.3 kg/m2) successfully completed a 12-week supervised HIIT program and an individually prescribed home hypocaloric diet (75% of daily energy requirements) throughout the whole intervention.
Results: The prevalence for high and low responders was 33% (N=11) and 66% (N=19), respectively. At the whole group level, the intervention was effective to reduce the absolute fat mass (30.9±7.2 vs. 28.5±7.2 kg; P<0.0001), body fat percentage (39.8±4.3 vs. 37.8±4.9 %; P<0.0001), total body mass (76.7±10.1 vs. 74.4±9.9 kg; P<0.0001). In addition, there were improvements in systolic blood pressure (Δ%= -5.1%), diastolic blood pressure (Δ%= -6.4%), absolute VO2peak (Δ%= +14.0%), relative VO2peak (Δ%= +13.8%), peak power output (Δ%= +19.8%), anaerobic threshold (Δ%= +16.7%), maximal ventilation (Δ%= +14.1%), and peak oxygen pulse (Δ%= +10.4%). However, at the individual level, a wide range of effects was appreciated on all variables, and the magnitude of the fat mass changes did not correlate with baseline body mass or fat mass.
Conclusions: A 12-week supervised HIIT program added to a slight dietary energy restriction effectively improved fat mass, body mass, blood pressure, and cardiorespiratory fitness. However, a wide range of inter-individual variability was observed in the adaptative response to the intervention. Furthermore, subjects classified as low responders for fat mass reduction could be high responders in many other health-related and physical performance outcomes. Thus, the beneficial effects of exercise in obese and overweight women go further beyond the adaptive response to a single outcome variable such as fat mass or total body mass reduction.
... This knowledge is then amalgamated (consciously or unconsciously) within a mental framework (or painted by coaches as a training system) comprising their own experience and intuition, and an athletes available training history. However, large inter-individual variability exists in response to virtually all training interventions (Mann, Lamberts and Lambert, 2014;Swinton et al., 2018). Furthermore, most intervention based and observational research available to coaches has been conducted on non-elite populations (e.g., university students, youth athletes, or recreational trainees) (Haff, 2010), and is frequently weak with regard to aspects of experimental design (e.g., crosssectional, low statistical power, unrepresentative or non-specific training programs, short familiarisation period and duration) (Cissik, Hedrick and Barnes, 2008). ...
... Coaches will typically apply standardised maximal-effort testing protocols to try and identify if change has occurred, that may otherwise be masked if using submaximal effort tasks. However, all maximal effort tests comprise noise made up of instrumentation error and/or the effects of biological variability (Mann, Lamberts and Lambert, 2014;Swinton et al., 2018). Even with standardised conditions and reliable measurement instruments, the influence of normal biological variability makes it unlikely that a given measurement will reflect a completely an accurate estimate of the latent state of physical capability (Swinton et al., 2018). ...
... "Autoregulation" (i.e., the purposeful and frequent adjustment of programming that corresponds to measurable changes in an individual's response to training-and nontraining-related stressors) is a recognized and continually developing concept within competitive sport contexts (Greig et al., 2020). The routine monitoring of stressors is purported to guide training in a way that maximizes performance outcomes, reduces risk of negative acute experiences (e.g., overtraining, injury, incompletion, and psychological distress), and minimizes training response variance (Kraemer and Fleck, 2007;Borresen and Lambert, 2009;Mann et al., 2014;Thorpe et al., 2017). Flexible Nonlinear Periodization (FNLP) is an autoregulation strategy where training workloads, which are goal-specific and range from low-to high-demand, are chosen each day based on each individual's "readiness-to-train" (pre-exercise mental and physical states; Kraemer and Fleck, 2007). ...
... A particularly glaring gap relates to the operationalization of "readiness." In their foundational text, Kraemer and Fleck (2007) provided a six-factor checklist (coach-trainee interactions; injury status; hydration; mental and physical fatigue; vertical jump power; initial workload performance), whereas Mann et al. (2014) suggested that training status, sleep, stress, and habitual physical activity would indicate readiness-to-train. While these indices make intuitive sense, neither source presented empirical evidence to support their use in training scenarios. ...
Recent research in exercise prescription and periodization has emphasized the importance of subjective experience, both in medium- and long-term monitoring, but also in the acute experience. Emerging evidence also highlights an important role of subjective readiness (pre-exercise mental and physical states) in determining how exercise is experienced, and in acutely modifying the prescribed exercise intensity. The concept of “readiness-to-exercise” shows promise in enabling and informing this acute decision-making to optimize the experiences and outcomes of exercise. While subjective experiences can be effectively assessed using psychometric scales and instruments, these are often developed and deployed using cross-sectional samples, with resulting structures that reflect a normative pattern (nomothetic). These patterns may fail to reflect individual differences in sensitivity, experience and saliency (idiographic). We conducted this research with the primary aim of comparing the nomothetical and idiographic approaches to modeling the relatively novel concept of readiness-to-exercise. Study 1 (nomothetic) therefore analyzed data collected from 572 participants who completed a one-time survey using R-technique factor analysis. Results indicated a four-factor structure that explained 60% of the variance: “health and fitness;” “fatigue;” “vitality” and “physical discomfort.” Study 2 (idiographic) included a sample of 29 participants who completed the scale multiple times, between 42 and 56 times: permitting intra-individual analysis using separate P-technique factor analyses. Our analyses suggested that many individuals displayed personal signature, or “profiles” of readiness-to-exercise that differed in structure from the nomothetic form: only two participants' personal signatures contained four structures as modeled in Study 1, whereas the majority demonstrated either two or three factors. These findings raise important questions about how experiential data should be collected and modeled, for use in research (conceptual development and measurement) and applied practice (prescribing, monitoring)—as well as in more applied research (implementation, effectiveness).
... The magnitude of these adaptations depends on the duration, intensity, volume, and type of exercise training (Hawley et al., 2014). Although the benefits of exercise are well described, large interindividual variability in the observed responses to well standardized exercise training is consistently reported (Atkinson & Batterham, 2015;Bouchard & Rankinen, 2001;Hecksteden et al., 2015;Mann et al., 2014;Timmons et al., 2005), for all exerciserelated phenotypes (Mann et al., 2014), independently of the intervention duration (Atkinson & Batterham, 2015). Furthermore, gross measures of variability in response to exercise interventions, commonly measured by pre-post approaches are not a conclusive representation of individual response variability (Hecksteden et al., 2015). ...
... The magnitude of these adaptations depends on the duration, intensity, volume, and type of exercise training (Hawley et al., 2014). Although the benefits of exercise are well described, large interindividual variability in the observed responses to well standardized exercise training is consistently reported (Atkinson & Batterham, 2015;Bouchard & Rankinen, 2001;Hecksteden et al., 2015;Mann et al., 2014;Timmons et al., 2005), for all exerciserelated phenotypes (Mann et al., 2014), independently of the intervention duration (Atkinson & Batterham, 2015). Furthermore, gross measures of variability in response to exercise interventions, commonly measured by pre-post approaches are not a conclusive representation of individual response variability (Hecksteden et al., 2015). ...
Aim:
Observed effects of exercise are highly variable between individuals, and subject-by-training interaction (i.e., individual response variability) is often not estimated. Here, we measured mitochondrial (citrate synthetase, cytochrome-c oxidase, succinate dehydrogenase, and mitochondrial copy-number), performance markers (Wpeak , lactate threshold [LT], and VO2peak ), and fiber type proportions/expression (type I, type IIa, and type IIx) in multiple time points during 12-week of high-intensity interval training (HIIT) to investigate effects of exercise at the individual level.
Methods:
Sixteen young (age: 33.1 ± 9.0 years), healthy men (VO2peak 35-60 ml/min/kg and BMI: 26.4 ± 4.2) from the Gene SMART study completed 12-week of progressive HIIT. Performance markers and muscle biopsies were collected every 4 weeks. We used mixed-models and bivariate growth models to quantify individual response and to estimate correlations between variables.
Results:
All performance markers exhibited significant (Wpeak 0.56 ± 0.33 p = 0.003, LT 0.37 ± 0.35 p = 0.007, VO2peak 3.81 ± 6.13 p = 0.02) increases overtime, with subject-by-training interaction being present (95% CI: Wpeak 0.09-0.24, LT 0.06-0.18, VO2peak 0.27-2.32). All other measurements did not exhibit significant changes. Fiber type IIa proportions at baseline was significantly associated with all physiological variables (p < 0.05), and citrate synthetase and cytochrome-c oxidase levels at baseline and overtime (i.e., intercept and slope) presented significant covariance (p < 0.05). Finally, low correlations between performance and mitochondrial markers were observed.
Conclusion:
We identified a significant subject-by-training interaction for the performance markers. While for all other measures within-subject variability was too large and interindividual differences in training efficacy could not be verified. Changes in measurements in response to exercise were not correlated, and such disconnection should be further investigated by future studies.
... aerobic, resistance training, isometric, combined) have been employed to improve clinical hypertension [6][7][8][9]. In this regard, a range of individuals may display exceptionally wide-ranging or lowranging responses to training interventions [10], commonly termed 'responders' and 'nonresponders', respectively, and recent studies have highlighted the importance of exercise responsiveness. ...
... Our study presents some limitations. For example, it is important to note that responsiveness may also be explained by nutritional and training statuses, sleep and stress, medication, and the D/D genotype for angiotensinconverting enzyme [10,33] Thus, further assessments are required to evaluate the effects of these variables on blood pressure response variability in aerobic training and resistance training programs. Furthermore, blood pressure responsiveness may also be the result of inappropriate statistical analyses concerning SBP, and training time may be able to predict post-SBP responses [26]. ...
Background:
Hemodynamic responses to physical training are not homogenous and uniform, and considerable inter-individual variations in the blood pressure of hypertensive individuals are noted in both aerobic and resistance training protocols. In this context, this study aimed to evaluate the effects of resistance and aerobic exercise on the blood pressure responses of hypertensive older adults.
Methods:
Groups were randomly divided into resistance training, n = 20; aerobic training, n = 20; control group, n = 21). After the first intervention period (12 weeks), individuals underwent a washout period (six detraining weeks), followed by a second intervention. This process is called the 'cross-over' model, where individuals who performed the aerobic exercise protocol also performed resistance training and vice-versa, constituting another 12 weeks of intervention. Blood pressure, functional performance, glycated hemoglobin and lipid profiles were evaluated preintervention and postintervention.
Results:
Varying responses to resistance training or aerobic training stimuli were observed in the hypertensive older adult participants. Both resistance training (pre 133.2 ± 14.1; post 122.4 ± 7.3; P < 0.05) and aerobic training (pre 134.2 ± 14.4; post 123 ± 9.4; P < 0.0.5) were effective in decreasing SBP, but only aerobic training (pre 9955.3 ± 1769.4; post 8800.9 ± 1316.1; P < 0.05) resulted in a decreased double product, and only the resistance training group improved functional performance.
Conclusion:
Responses to resistance training or aerobic training stimuli varied noticeably between hypertensive older adults and both resistance training and aerobic training were effective in reducing SBP. This knowledge may be useful in providing individually tailored exercise prescriptions for hypertensive older adults.
... Large individual variation is observed in CRF responses to a standardised training programme with variation ranging from high responders, exhibiting CRF improvements above a predetermined response threshold to a given training stimulus to non-responders where no improvements is observed and even negative responders where a decrease in CRF can be seen. 3 A comprehensive evaluation and careful analyses of the distribution of responders to a standardised ...
... While many lifestyle, behavioural and environmental factors may come into play, the impact of physical recovery, nutritional status and stress can largely influence phenotyping effects of training. 3 The degree of homeostatic perturbation from a predetermined exercise load regulates the timeline associated with musculoskeletal, neural and metabolic recovery necessary to initiate the next training load. Low intensity and volume exercise can require less than 24 hours, whereas high-intensity and volume exercise may need more than 48 hours to adequately recover. ...
Physical activity (PA) guidelines for the general population are designed to mitigate the rise of chronic and debilitating diseases brought by inactivity and sedentariness. Although essential, they are insufficient as rates of cardiovascular, pulmonary, renal, metabolic and other devastating and lifelong diseases remain on the rise. This systemic failure supports the need for an improved exercise prescription approach that targets the individual. Significant interindividual variability of cardiorespiratory fitness (CRF) responses to exercise are partly explained by biological and methodological factors, and the modulation of exercise volume and intensity seem to be key in improving prescription guidelines. The use of physiological thresholds, such as lactate, ventilation, as well as critical power, have demonstrated excellent results to improve CRF in those struggling to respond to the current homogenous prescription of exercise. However, assessing physiological thresholds requires laboratory resources and expertise and is incompatible for a general population approach. A case must be made that balances the effectiveness of an exercise programme to improve CRF and accessibility of resources. A population-wide approach of exercise prescription guidelines should include free and accessible self-assessed threshold tools, such as rate of perceived exertion, where the homeostatic perturbation induced by exercise reflects physiological thresholds. The present critical review outlines factors for individuals exercise prescription and proposes a new theoretical hierarchal framework to help shape PA guidelines based on accessibility and effectiveness as part of a personalised exercise prescription that targets the individual.
... Prior use of the term "nonresponder" has been applied to subjects in studies investigating primarily aerobic training responses, where post-test variables (such as VO 2 max) worsened or remained unchanged following training interventions, or those whose responses did not exceed day-to-day variation [23]. It should be noted that the terms "nonresponder" and "responder" are misnomers when used to refer to training program adaptations. ...
... However, in adapting this definition to athletic contexts for practical purposes based upon prior use, the term "nonresponder" is defined herein as an athlete who fails to achieve a certain level of performance at the end of a desired training period that was designed to improve specific performance variables. Classification as a nonresponder tends to not be conclusive or permanent-an individual identified as a nonresponder in one variable may demonstrate improvement in other variables [23,24] or adapt well through one training period and not the next. This demonstrates the importance of conducting a regular testing battery and employing a comprehensive monitoring program for athletes. ...
Ideally an athlete would continue to improve performance indefinitely over time, however improvement slows as the athlete approaches their genetic limits. Measuring performance is complex—performance may be temporarily depressed following aggressive training for multiple reasons, physiological and psychosocial. This reality may be vexing to the strength and conditioning coach, who, as a service provider, must answer to sport coaches about an athlete’s progress. Recently an evaluation mechanism for strength and conditioning coaches was proposed, in part to help coaches establish their effectiveness within the organization. Without formal guidance and realistic expectations, if an athlete is not bigger, leaner, stronger, etc. as a result of training within a specified timeframe, blame is often placed upon the strength and conditioning coach. The purpose of this article is to explore possible causes of what may be perceived as athlete non-responses to training and to provide guidance for the coach on how to handle those issues within their domain. A process of investigation is recommended, along with resources to assist coaches as they consider a broad range of issues, including enhancing existing testing methods, improving athlete behaviors, and adjusting processes designed to bring about performance improvement.
... It is also possible that zonulin is less sensitive to changes in cardiorespiratory fitness as compared to FABP2 and it has previously been noted that variability in zonulin assays may have contributed to conflicting findings in previous analyses [23]. We note that a small proportion of participants (19%) did not exhibit an improvement in VO2peak which may reflect lower adherence or reduced responsivity to exercise given individual variability in cardiorespiratory adjustment to exercise [24]. Omega 3 fatty acids are known to promote the growth of bacterial species associated with enhanced gut barrier integrity through increased production of protective bacterial products such as the short chain fatty acid, butyrate [25]. ...
Introduction
Breakdown of gut barrier integrity has been associated with inflammatory activation and is implicated in the etiology of several chronic medical conditions. Acute exercise is known to increase gut barrier permeability but the impact of chronic exercise is not clear. Most studies to date have examined how acute exercise impacts gut barrier integrity in healthy adults, while few studies have examined the impact of chronic exercise in older adults with comorbidities. We aim to investigate the impact of a 12-week program of aerobic and resistance training on biomarkers of gut barrier integrity in a sample of older adults with coronary artery disease.
Methods
Participants were adults with coronary artery disease undergoing a moderate-intensity 12-week cardiac rehabilitation exercise program. Fasting blood samples were taken at baseline and study termination. Serum levels of biomarkers of gut barrier integrity (zonulin and fatty acid-binding protein 2 (FABP2)) were measured by ELISA. Cardiorespiratory fitness was assessed by peak oxygen uptake (VO 2peak ) at study start & completion. Data analyses were performed using SPSS software version 24.0.
Results
Among study participants (n = 41, 70% male, age = 62.7± 9.35) we found a significant negative association between baseline FABP2 levels and baseline VO 2peak in a multiple linear regression model adjusting for covariates ( B = -0.3, p = 0.009). Over the course of the exercise program an increase in VO 2peak (≥ 5 mL/kg/min) was independently associated with a relative decrease in FABP2 ( B = -0.45, p = 0.018) after controlling for medical covariates.
Conclusion
Our findings indicate that an increase in cardiorespiratory fitness during a 12-week exercise program resulted in a relative improvement in a biomarker of gut barrier integrity. This indicates a potential mechanism by which longer term exercise may improve gut barrier integrity.
... Interindividual differences in the physiological responses to an intervention (e.g., exercise or drugs) have received great research attention in the last decades with the aim to identify "responders" and "non-responders, " to explore the mechanisms that influence the individual responsiveness, and to promote "personalized medicine" (Mann et al., 2014;Hecksteden et al., 2015;Ross et al., 2019). However, some of the approaches used to analyze individual variability have not taken into account the variability explained by technical and/or random errors, and thus have not reported biological variability alone (Atkinson and Batterham, 2015). ...
To evaluate the individual responses in skeletal muscle outcomes following bed rest, data from three studies (21-day PlanHab; 10-day FemHab and LunHab) were combined. Subjects (n=35) participated in three cross-over campaigns within each study: normoxic and hypoxic bed rest, and hypoxic ambulation (used as control). Individual variability (SDIR) was investigated as √(SDExp2–SDCon2), where SDExp and SDCon are the standard deviations of the change score (i.e., post - pre) in the experimental (normoxic and hypoxic bed rest) and the control (hypoxic ambulation) groups. Repeatability and moderators of the individual variability were explored. Significant SDIR was detected for knee extension torque, and thigh and calf muscle area, which translated into an individual response ranging from 3% to -17% for knee extension torque, -2% to -12% for calf muscle area, and -1% to -8% for thigh muscle area. Strong correlations were found for changes in normoxic vs. hypoxic bed rest (i.e., repeatability) in thigh and calf muscle area (r=0.65-0.75, P<0.0001). Change-scores in knee extension torque, and thigh and calf muscle area strongly correlated with baseline values (P<0.001; r between -0.5 and -0.9). Orthogonal partial least squares (OPLS) regression analysis explored if changes in the investigated variables could predict calf muscle area alterations. This analysis indicated that 43% of the variance in calf muscle area could be attributed to changes in all of the other variables. This is the first study using a validated methodology to report clinically relevant individual variability after bed rest in knee extension torque, calf muscle area, and (to a lower extent) thigh muscle area. Baseline values emerged as a moderator of the individual response, and a global bed rest signature served as a moderately strong predictor of the individual variation in calf muscle area alterations.
... Collectively, these findings support consideration of more holistic approaches to intervention delivery. Factors that may influence an individual's response to intervention include nutritional status (both acute and chronic), physical activity levels, sleep, environmental conditions, and external sources of motivation (Mann et al., 2014) such as intervention expectancy (Marticorena et al., 2021). A logical next step for future research would be to attempt to parse out the relative influence of these factors on individual response variation, although this is undoubtedly challenging. ...
Large inter-individual variability is present in most health and performance interventions but little is known about the factors that underpin this variation.
PURPOSE: To estimate the average group effect and intervention response variation from beta-alanine (BA) supplementation on high intensity cycling capacity.
METHODS: Individual participant data on the effect of BA on a high intensity cycling capacity test (the CCT110%) were meta-analysed. Changes in time to exhaustion (TTE) and muscle carnosine (MCarn) were the primary and secondary outcomes. Multi-level distributional Bayesian models were used to estimate both the mean and standard deviation of BA and placebo (PLA) group change scores. The probability that the intervention change score standard deviation was larger than placebo was calculated and, when appropriate, so was the intervention response standard deviation (σIR).
RESULTS: Six eligible studies were found, and individual participant data were obtained from four of these. Analyses showed a group effect of BA supplementation on TTE (7.7 [95%CrI:1.3 to 14.3 s]) and MCarn (18.1 [95%CrI:14.5 to 21.9 mmol·kgDM-1]). While intervention response variation was identified for changes in MCarn (σIR = 5.8 [95%CrI:4.2 to 7.4 mmol·kgDM-1]), this statistic was not calculated for TTE, since equivalent standard deviation of change scores were shown in PLA (16.1 [95%CrI:13.0 to 21.3 s]) and BA groups (15.9 [95%CrI:13.0 to 20.0 s], with the probability that standard deviation was greater with PLA being: p = 0.643.
CONCLUSION: While evidence of a group effect of BA on high intensity cycling capacity was shown, there was no evidence of individual response variation. The similarity in observed score variance between the PLA and BA groups indicates that the source of variation is common to both groups and not related to the BA supplementation. This means that observed variation originates in factors outside of the intervention, such as sleep habits, motivation, nutrition and training schedules. This finding has important practical implications and suggests that consideration of modifiable lifestyle factors, in addition to following evidence-based supplementation recommendations, may be required to enhance an individual’s probability of a positive response to supplementation.
... The inconsistent responses could be explained by a possible inability of the MMR protocol to accelerate recovery, at least in terms of muscle performance and CK activity, as well as the complexity of the kinematics of exercise-induced fatigue, which may cause different effects within individual body systems. Genetic influences make an important contribution to these variations, while factors, such as sleep, psychological stress, habitual physical activity, and dietary intake, may also play important roles (Mann et al., 2014). Only dietary intake was standardized and controlled during the present study. ...
The aim of this study was to investigate whether recovery from eccentric squat exercise varies depending on age and to assess whether the use of a mixed-method recovery (MMR) consisting of cold water immersion and compression tights benefits recovery. Sixteen healthy and resistance-trained young (age, 22.1 ± 2.1 years; N = 8) and master male athletes (age, 52.4 ± 3.5 years; N = 8), who had a similar half squat 1-repetition maximum relative to body weight, completed two identical squat exercise training sessions, separated by a 2-week washout period. Training sessions were followed by either MMR or passive recovery (PR). Internal training loads [heart rate and blood lactate concentration (BLa)] were recorded during and after squat sessions. Furthermore, maximal voluntary isometric contraction (MVIC) force, countermovement jump (CMJ) height, resting twitch force of the knee extensors, serum concentration of creatine kinase (CK), muscle soreness (MS), and perceived physical performance capability (PPC) were determined before and after training as well as after 24, 48, and 72 h of recovery. A three-way mixed ANOVA revealed a significant time effect of the squat protocol on markers of fatigue and recovery (p < 0.05; decreased MVIC, CMJ, twitch force, and PPC; increased CK and MS). Age-related differences were found for BLa, MS, and PPC (higher post-exercise fatigue in younger athletes). A significant two-way interaction between recovery strategy and time of measurement was found for MS and PPC (p < 0.05; faster recovery after MMR). In three participants (two young and one master athlete), the individual results revealed a consistently positive response to MMR. In conclusion, master athletes neither reach higher fatigue levels nor recover more slowly than the younger athletes. Furthermore, the results indicate that MMR after resistance exercise does not contribute to a faster recovery of Schmidt et al. Recovery in Young and Master Athletes Frontiers in Physiology | www.frontiersin.org 2 September 2021 | Volume 12 | Article 665204 physical performance, neuromuscular function, or muscle damage, but promotes recovery of perceptual measures regardless of age.
... Современные технологии глобального позиционирования (GPS) широко используются для контроля тренировочных и соревновательных нагрузок посредством оценки параметров внешней нагрузки футболистов [11], это стало необходимостью для рационального управления тренировочным процессом. Однако, стандартизированные блоковые программы тренировок в командных видах спорта часто дают смешанные результаты [7]. Желание индивидуализировать тренировочный процесс для игроков командных видов спорта привело к внедрению множества раз-личных стратегий мониторинга, позволяющих тренерам получать информацию по каналам биологической обратной связи. ...
The study established the conjugation of statistical and spectral parameters of the heart rate with the parame- ters of the load and the reaction of the cardiovascular system of young players during football games.
... However, an analysis of changes in the maximal oxygen uptake at certain ages of Polish male rowers showed substantial improvement at 19-19.9 and 21-22 years (Klusiewicz et al., 2014). Maximal oxygen uptake, which is the gold standard for cardiorespiratory fitness, is a multifactorial trait influenced by environmental factors (e.g., exercise training) and genetic factors (Rankinen, 2011;Mann et al., 2014;Williams et al., 2017). However, improvements in cardiorespiratory fitness in response to exercise training vary greatly between individuals, with some people responding well or very well ("responders" or "high-responders") to exercise training, whereas others do not respond so well following similar exercise training (Mori et al., 2009;Bouchard et al., 2015;Williams et al., 2017). ...
Background: Little is known about the anthropometric and physiological profiles of lower-ranking athletes who aspire to rise to the pinnacle of their profession.
Aim: The aim of this study was to create anthropometric and physiological profiles of Hungarian male rowers of different age categories (15–16, 17–18, and over 18 years), sports rankings and career lengths.
Materials and Methods: Anthropometric and physiological profiles were created for 55 juniors, 52 older juniors and 23 seniors representing seven of the largest Hungarian rowing clubs. One-way independent analysis of variance (ANOVA) was used to compare arithmetic means.
Results: Rowers in older age categories were significantly taller (185.0 ± 5.0 cm vs. 183.0 ± 7.3 cm vs. 178.7 ± 7.2 cm) and heavier (81.1 ± 8.8 kg vs. 73.7 ± 8.4 kg vs. 66.8 ± 12.3 kg) than their younger peers, with significantly higher BMI values and larger body dimensions. Compared to younger athletes, rowers in older age categories also covered 2,000 m significantly faster (6.6 ± 0.3 min vs. 6.9 ± 0.4 min vs. 7.5 ± 0.5 min) while developing significantly more power (372.2 ± 53.0 W vs. 326.8 ± 54.5 W vs. 250.6 ± 44.6 W). Similarly, seniors and older juniors had higher values of maximal oxygen uptake and force max (by 6.2 and 7.0 ml/kg/min, and by 263.4 and 169.8 N). Within the older juniors, internationally ranked rowers had significantly greater body height (+ 5.9 cm), body mass (+ 6.1 kg), sitting height (+ 2.7 cm), arm span (+ 7.9 cm), limb length (+ 3.73 cm) and body surface area (+ 0.21 m ² ). They also rowed 2,000 m significantly faster (–0.43 min, p < 0.001) and had significantly higher values of power (+ 58.3 W), relative power (+ 0.41 W/kg), jump height (+ 4.5 cm), speed max (+ 0.18 m/s) and force max (+ 163.22 N).
Conclusion: The study demonstrated that potential differences in anthropometric and physiological profiles are more difficult to capture in non-elite rowers, and that the final outcome may be determined by external factors. Therefore, athletes with superior aptitude for rowing are more difficult to select from among lower-ranking rowers, and further research is needed to determine specific training requirements to achieve the maximum rowing performance.
... Therefore, recognizing that between this homogenous group we may have some relatively "untrained vs. trained and/or high-responders-vs. low-responders" to standardized training, may explain the different performance trends in physical fitness scores that we observed [82]. This phenomenon is also highlighted by the large variation in the mean responses that were observed in many of the examined variables, pointing out that the one-size-fits-all model of training was not that optimal [51,52]. ...
Law enforcement agencies generally employ the “one-size-fits-all” education-training
model. Its effectiveness compared to alternative training models has been under scrutiny. Physical fitness scores of Serbian male (n = 98) and female (n = 79) police cadets during their yearly evaluation were compared. Cadets trained for the first 3 years with the “one-size-fits-all” model. In the fourth year, they self-prescribed an individualized exercise program based on the obtained curriculum knowledge. A two-way MANOVA revealed a significant effect of academic years on combined variables (p < 0.001) and significant differences between academic years for deadlift, half squat, standing long jump, sit-ups and 12-min Cooper test time (p < 0.001). Sex also had a significant main effect on combined variables (p < 0.001) with males outscoring females on all of the fitness
assessments. For pull-ups, there was a significant year * sex interaction (p = 0.01) with the third year to be pivotal for female and male performance, respectively. In conclusion, the use of a “one-size-fits-all” model, presented differences in physical fitness scores between the years one to three, pointing to its questionable effectiveness. On the contrary, the self-prescribed individualized exercise program of the fourth year elicited greater fitness scores, indicating the need to evaluate the applicability of such a training model more.
... Coaches typically design an annual plan using insights taught by a previously successful coach [79], using a scientific basis, or both (e.g., polarized training [22]). Whilst there are merits for these approaches, a single model can often result in the outcome where some athletes benefit optimally from the prescription, while others do not [80][81][82]. ...
Many individual and team sport events require extended periods of exercise above the speed or power associated with maximal oxygen uptake (i.e., maximal aerobic speed/power, MAS/MAP). In the absence of valid and reliable measures of anaerobic metabolism, the anaerobic speed/power reserve (ASR/APR) concept, defined as the difference between an athlete’s MAS/MAP and their maximal sprinting speed (MSS)/peak power (MPP), advances our understanding of athlete tolerance to high speed/power efforts in this range. When exercising at speeds above MAS/MAP, what likely matters most, irrespective of athlete profile or locomotor mode, is the proportion of the ASR/APR used, rather than the more commonly used reference to percent MAS/MAP. The locomotor construct of ASR/APR offers numerous underexplored opportunities. In particular, how differences in underlying athlete profiles (e.g., fiber typology) impact the training response for different ‘speed’, ‘endurance’ or ‘hybrid’ profiles is now emerging. Such an individualized approach to athlete training may be necessary to avoid ‘maladaptive’ or ‘non-responses’. As a starting point for coaches and practitioners, we recommend upfront locomotor profiling to guide training content at both the macro (understanding athlete profile variability and training model selection, e.g., annual periodization) and micro levels (weekly daily planning of individual workouts, e.g., short vs long intervals vs repeated sprint training and recovery time between workouts). More specifically, we argue that high-intensity interval training formats should be tailored to the locomotor profile accordingly. New focus and appreciation for the ASR/APR is required to individualize training appropriately so as to maximize athlete preparation for elite competition.
... age, sex, fat mass, fat free mass, weight, and race) (28, 29), respond positively to a speci c dose of standardised exercise, with considerable individual variability in training adaptations including so-termed 'non-responders' and, in some cases, 'adverse responders'. The absence of a personalised approach to the exercise prescription has been put forth to explain the variability in response to exercise (30). It has been purported that a more individualised approach to exercise prescription may enhance training e cacy and limit training unresponsiveness. ...
Background Cardiorespiratory fitness and fatness (notably central obesity) are mediating factors of the metabolic syndrome (MetS), and consequent cardiovascular disease (CVD)/mortality risk. The fitness-fatness index (FFI) combines these factors and has been reported to be a better indicator of CVD and all-cause mortality risk, beyond the capacity of either fitness or fatness alone.
Objective This study sought to investigate the effects of different exercise volumes on FFI in adults with MetS.
Methods This was a sub-study of the ‘Exercise in the prevention of Metabolic Syndrome’ (EX-MET) multicenter trial. Ninety-nine adults diagnosed with MetS according to the International Diabetes Federation criteria were randomized to one of the following 16-week exercise interventions: i) moderate-intensity continuous training (MICT) at 60-70% HRpeak for 30 min/session (n=34, 150 min/week); ii) 4 x 4 min bouts of high-intensity interval training at 85-95% HRpeak, interspersed with 3-min active recovery at 50-70% HRpeak (n=34, 38min/session, 114 mins/week); and iii) 1 x 4 min bout of HIIT at 85-95% HRpeak (n=31, 17 min/session, 51 min/week). Cardiorespiratory fitness (peak oxygen uptake, V̇O2peak) was determined via indirect calorimetry during maximal exercise testing and fatness was the ratio of waist circumference-to-height (WHtR). FFI was calculated as V̇O2peak in metabolic equivalents (METs) divided by WHtR. A clinically meaningful response to the exercise intervention was taken as a 1 FFI unit increase.
Results Seventy-seven participants completed pre and post testing to determine FFI. There was a greater proportion of participants who had a clinically meaningful change in FFI following high-volume HIIT (60%, 15/25) and low-volume HIIT (65%, 17/26) compared to MICT (38%, 10/26), but with no significant between-group difference (p=0.12). A similar trend was found when a sub-analysis comparing the FFI between those with type 2 diabetes (MICT, 33%, 3/9; high-volume HIIT, 64%, 7/11; and low-volume HIIT, 58%, 7/12) and without type 2 diabetes (MICT, 41%, 7/17; high-volume HIIT, 57%, 8/14; low-volume HIIT, 71%, 10/14).
Conclusion This study suggests that the response to changes in FFI in adults with MetS is affected by aerobic exercise intensity.
... 13 Therefore, monitoring the fatigue status of players in team sports is a topic of growing interest today. 14,15 In a phenomenon as complex as fatigue, with a high number of interdependent variables of a diverse nature, 12,16 as well as individualized responses to the same training load, 17 it makes monitoring of fatigue must comprise a wide spectrum of parameters (mental, physical and emotional), to minimize injuries and illnesses. 11,18 Monitoring tools have been widely used as valid and reliable indicators for monitoring the recovery status of elite athletes, 19 allowing for greater efficacy in injury/illness prevention by prescribing training and recovery loads. ...
The aims of this study were: (1) to analyze how the periodization of workloads can induce states of accumulated fatigue in the short, medium and long term in indoor team sports and (2) to identify these periods of fatigue through the interpretation internal and external performance variables. This systematic review was carried out under PRISMA guidelines. The Web of Science, PubMed and Scopus databases were searched for relevant published studies between 1st January 2010 and 25th April 2021. The STROBE scale and MINORS checklist were used to assess the reporting and methodological risk of bias, respectively. Of the 2219 studies initially identified, 20 were selected for a full review. The main conclusions were that a periodic and integrative evaluation of monitoring variables of a different nature is needed to identify states of fatigue accurately and rigorously. The end of the preparatory periods (PPs) and the second phase of the competitive periods (CPs) seem to be the most exhausting moments of the season (high values of RPE, CK, LDH, and oxidative stress markers, and decrease in the T/C ratio). Specifically, congested weeks promote the development of high levels of acute and subacute fatigue (high levels of DOMS and fatigue along with low levels of RPE). Therefore, it is recommended to extend the duration of the preseason and the implementation of more active recovery days during congested weeks, in order to improve resistance to acute and subacute fatigue, and therefore avoid reaching a state of overtraining.
... 40 It has been suggested that non-responders to changes in VO 2peak are not necessarily non-responders in other measurements of training response. 41,42 There may be other mechanisms occurring through the pathogenesis of CKD which are dampening the physiological adaptations to exercise training, but not exercise capacity. Indeed, a 12-month analysis of the current cohort demonstrated no change in autonomic function between the LI and UC group 43 . ...
Background Supervised lifestyle interventions have the potential to significantly improve physical activity and fitness in patients with chronic kidney disease (CKD).
Methods To assess the efficacy of a lifestyle intervention in patients with CKD to improve cardiorespiratory fitness and exercise capacity over 36 months, we conducted a randomized clinical trial, enrolling 160 patients with stage 3-4 CKD, with 81 randomized to usual care and 79 to 3-year lifestyle intervention. The lifestyle intervention comprised care from a multidisciplinary team, including a nephrologist, nurse practitioner, exercise physiologist, dietitian, diabetes educator, psychologist, and social worker. The exercise training component consisted of an 8-week individualized and supervised gym-based exercise intervention followed by 34 months of a predominantly home-based program. Self-reported physical activity (metabolic equivalent of tasks [METs] min/wk), cardiorespiratory fitness (peak O 2 consumption [VO 2peak ]), exercise capacity (maximum METs and 6-minute walk distance) and neuromuscular fitness (grip strength and get-up-and-go time) were evaluated at 12, 24, and 36 months.
Results The intervention increased the percentage of patients meeting physical activity guideline targets of 500 MET min/wk from 29% at baseline to 63% at 3 years. At 12 months, both VO 2peak and METs increased significantly in the intervention group by 9.7% and 30%, respectively, without change in the usual care group. Thereafter, VO 2peak declined to near baseline levels, whereas METs remained elevated in the intervention group at 24 and 36 months. After 3 years, the intervention had increased the 6-minute walk distance and blunted declines in the get-up-and-go test.
Conclusions A 3-year lifestyle intervention doubled the percentage of CKD patients meeting physical activity guidelines, improved exercise capacity, and ameliorated losses in neuromuscular and cardiorespiratory fitness.
... This data may be linked to an individual profile suitable to excel in both mixed (aerobic and anaerobic) and predominantly anaerobic sport disciplines. Higher aerobic fitness is an important requirement, but not determinant since VO 2max may be influenced by genetic and environmental variables with marked inter-individual response to training 37 . Studies have shown no difference between I and X alleles, and their respective genotypes with VO 2max 35,38 , emphasizing our finding of ID and RX genotypes to VO 2max . ...
Abstract – It is known that angiotensin-converting enzyme (ACE) I/D and α-actinin-3 (ACTN3) R577X genes act in combination to exert great influence on athletic performance due to their impact on endurance, strength and power. While the ACE D allele would favors performance in power or strength tasks, the a-actinin-3-deficient XX genotype is believed to increased athletic performance in endurance sports. Aim: This study analyzed the influences of ACE and ACTN3 gene variants in sprinters, jumpers, and endurance young athletes of track and field. Methods: 36 school-level competitors of both sex (15 girls and 21 boys; aged 16.4 ± 1.2 years; training experience 4 ± 1.2 years) practitioners different sport disciplines (i.e. sprint, jump, and endurance athletes) participated in the study. The deoxyribonucleic acid (DNA) was extracted from peripheral blood using standard protocol. Anthropometric measurements, 30 m sprint, squat jump, and maximal oxygen uptake (VO2max) tests were measured. Results: Genotype distribution of the ACE and ACTN3 genes did not differ between groups. In ACE DD and ACTN3 RX genotypes, the squat jump (SJ) test were bigger in sprinters and jumpers than in the endurance runners. In contrast, when analyzed the ACE ID genotype, sprinters had higher SJ than the endurance athletes. Moreover, in ACE DD genotype, the sprinters and jumpers’ athletes had lower time in 30 m tests with compared to endurance runners. However, the ACE ID and ACTN3 RX genotypes was greater aerobic fitness in endurance runners than in jumpers’ athletes. Conclusion: Although genetic profile is not unique factor for determine athletic performance, the ACE DD and ACTN3 RX genotypes seem favor athletic performance in power and sprint versus endurance sports. Thus, this study evidenced that assessing genetic variants could be used as an auxiliary way to predict a favorable profile the identification of young talents of track and field.
... age, sex, fat mass, fat free mass, weight, and race) [36,37], respond positively to a specific dose of standardized exercise, with considerable individual variability in training adaptations including so-termed 'non-responders' and, in some cases, 'adverse responders' . The absence of a personalized approach to the exercise prescription has been put forth to explain the variability in response to exercise [38]. It has been purported that a more individualized approach to exercise prescription may enhance training efficacy and limit training unresponsiveness. ...
Background
Cardiorespiratory fitness and fatness (notably central obesity) are mediating factors of the metabolic syndrome (MetS) and consequent cardiovascular disease (CVD)/mortality risk. The fitness-fatness index (FFI) combines these factors and has been reported to be a better indicator of CVD and all-cause mortality risk, beyond the capacity of either fitness or fatness alone.
Objective
This study sought to investigate the effects of different exercise intensities on FFI in adults with MetS.
Methods
This was a sub-study of the ‘Exercise in the prevention of Metabolic Syndrome’ (EX-MET) multicentre trial. Ninety-nine adults diagnosed with MetS according to the International Diabetes Federation criteria were randomized to one of the following 16-week exercise interventions: i) moderate-intensity continuous training (MICT) at 60–70% HRpeak for 30 min/session ( n = 34, 150 min/week); ii) 4 × 4 min bouts of high-intensity interval training at 85–95% HRpeak, interspersed with 3-min active recovery at 50–70% HRpeak ( n = 34, 38 min/session, 114 min/week); and iii) 1 × 4 min bout of HIIT at 85–95% HRpeak ( n = 31, 17 min/session, 51 min/week). Cardiorespiratory fitness (peak oxygen uptake, V̇O 2 peak) was determined via indirect calorimetry during maximal exercise testing and fatness was the ratio of waist circumference-to-height (WtHR). FFI was calculated as V̇O 2 peak in metabolic equivalents (METs) divided by WtHR. A clinically meaningful response to the exercise intervention was taken as a 1 FFI unit increase.
Results
Seventy-seven participants completed pre and post testing to determine FFI. While there was no significant between group difference ( p = 0.30), there was a small group x time interaction effect on FFI [ F (2, 73) = 1.226; η ² = 0.01], with numerically greater improvements following HIIT (4HIIT, + 16%; 1HIIT, + 11%) relative to MICT (+ 7%). There was a greater proportion of participants who had a clinically meaningful change in FFI following high-volume HIIT (60%, 15/25) and low-volume HIIT (65%, 17/26) compared to MICT (38%, 10/26), but with no significant between-group difference ( p = 0.12). A similar trend was found when a sub-analysis comparing the FFI between those with type 2 diabetes (MICT, 33%, 3/9; high-volume HIIT, 64%, 7/11; and low-volume HIIT, 58%, 7/12) and without type 2 diabetes (MICT, 41%, 7/17; high-volume HIIT, 57%, 8/14; low-volume HIIT, 71%, 10/14).
Conclusion
Although there were no statistically significant differences detected between groups, this study suggests that the response to changes in FFI in adults with MetS may be affected by exercise intensity, when numerical differences between exercise groups are considered. Further research is warranted.
Trial registration number and date of registration : ClinicalTrials.gov NCT01676870; 31/08/2012.
... A stratified randomized procedure was performed based on the concept of responders and non-responders to adaptation capacity. 25 The training adaptation capacity was recorded before and after the first six-week of the exercise training program. The adaptation capacity was calculated using the equation AC ¼ 0:5 FP þ 0:5 CP, where AC is adaptation capacity, FP is the functional parameter (i.e. ...
Background
Previous studies have shown positive results of photobiomodulation (PBM) for improving performance and accelerating post-exercise recovery. However, the effects of PBM in healthy individuals who underwent a neuromuscular adaptation training remain unclear.
Objective
To investigate the effects of PBM during a training program combining sprints and explosive squats exercises on clinical, functional, and systemic outcomes in trained healthy individuals compared to a placebo intervention and a control.
Methods
We conducted a randomized placebo-controlled trial. Healthy males were randomly assigned to three groups: active PBM (30 J per site), placebo, or control (passive recovery). The participants performed a six-week (12 sessions) of a training program consisting of a combination of sprints and squats with recovery applied between sprints and squats. To prevent the influence of the primary neuromuscular adaptation to exercise on the results, all participants had to participate in a period of six weeks of exercise training program. Functional, clinical, and psychological outcomes and vascular endothelial growth factor (VEGF) were assessed at baseline and after six weeks. Results are expressed as mean difference (MD) and 95% confidence intervals (CI).
Results
Thirty-nine healthy male volunteers (aged 18–30 years; body mass index 23.9 ± 3 kg/m²) were recruited. There was no significant time by group interaction, and no significant effect of group, but there was a significant effect of time for maximal voluntary isometric contraction (primary outcome) (MD=22 Nm/kg; 95%CI: 3.9, 40) and for squat jump (MD=1.6 cm; 95CI%: 0.7, 2.5). There was no significant interaction (time*group), time, or group effect for the other outcomes.
Conclusion
The addition of PBM to a combined training performed for 6 weeks in previously trained individuals did not result in additional benefits compared to placebo or no additional intervention.
... Brojne studije posvećene su problemima doktrine upravljanja trenažnim procesom, prepoznavanja onih fizioloških markera koji sa dovoljno autoriteta informišu o kvalitativno-kvantitativnim posledicama dizajniranog trenažnog uticaja. Rezultati takvih istraživanja doprinose organizaciji treninga koji omogućava bolju adaptaciju na trenažne nadražaje, uz smanjenje rizika od pretreniranosti i prenaprezanja (Scharhag-Rosenberger et al., 2012;Mann et al., 2014;Wolpern et al., 2015). Jedna skorašnja studija ukazuje na činjenicu da slični trenažni stimulusi izazivaju različitu efikasnost i adaptaciju sportista, kao i da dinamika promena zavisi od pripremljenosti sportiste, trenažne discipline, pozicije u timu, itd. ...
Cardiorespiratory performance is one of the most important determinants of success in sports activities. In order to better prepare for sports challenges, the athletes must be exposed to appropriate training which should be based on individualized physiological parameters during activity. Even though training intensity can be determined in many different ways, the endurance training intensity is often quantified by the lactate thresholds obtained from the blood sampling or the ventilator thresholds obtained from the gas exchange. These data represent delayed indirect indicators of an increased anaerobic ATP resynthesis. The muscle oximetry, based on near-infrared spectroscopy (NIRS), represents non-invasive method that enables the information about the changes in oxygenation in hemoglobin, and potentially represents a very suitable technique to detect a critical exercise threshold directly in the exercising muscle.
... There can be significant variations in daily strength as high as 20%, resulting in a variable performance when using a fixed method (Hoover et al., 2016). Individual variability in factors, such as genetics, sleep, nutrition, psychological stress, lifestyle factors, total training load, and heart rate variability, may also affect the training performance (Mann et al., 2014;Costa et al., 2019). Fixed periodization also does not account for the individual training response and as such the potential for strength gains may be greater than the potential this model facilitates (Timmons, 2011;Fisher et al., 2017). ...
Musculoskeletal disorders such as tendinopathy are having an increasing burden on society and health systems. Tendinopathy is responsible for up to 30% of musculoskeletal disorders, having a high incidence in athletes and the general population. Although resistance training has shown short-term effectiveness for treating lower limb tendinopathy, more comprehensive exercise protocols and progression methods are required due to poor long-term outcomes. The most common resistance training protocols are pre-determined and standardised, which presents significant limitations. Current standardized protocols do not adhere to scientific resistance training principles and do not consider individual factors or take the importance of individualised training into account. Resistance training programs in tendinopathy are currently not achieving required intensity and dosage, leading to high recurrence rates. Therefore, better methods for individualising and progressing resistance training are required to improve outcomes. One potential method is autoregulation, which allows individuals to progress training at their own rate, taking individual factors into account. Despite being found effective for increasing strength in healthy athletes, autoregulation methods have not been investigated in tendinopathy. The purpose of this narrative review was threefold: first to give an overview and critical analysis of individual factors involved in tendinopathy and current resistance training protocols and their limitations. Secondly, to give an overview of the history, methods and application of autoregulation strategies both in sports performance and physiotherapy. Finally, a theoretical adaptation of a current tendinopathy resistance training protocol with autoregulation methods is presented, providing an example of how the method could be implemented in clinical practice or future research.
... In additional to the intraindividual variation in hormonal status and metabolism throughout the menstrual cycle, there is also potential for interindividual differences in physiological response to supplementation and exercise protocols that can influence the generalizability of results. Factors influencing interindividual variation include genetics, sleep, stress, and training status and modality (Mann et al., 2014). These factors affect outcome variation in both men and women and must be noted as a limitation to all nutrition and exercise metabolism studies, including those in this review. ...
Beta-alanine, caffeine, and nitrate are dietary supplements generally recognized by the sport and exercise science community as evidence-based ergogenic performance aids. Evidence supporting the efficacy of these supplements, however, is greatly skewed due to research being conducted primarily in men. The physiological differences between men and women, most notably in sex hormones and menstrual cycle fluctuations, make generalizing male data to the female athlete inappropriate, and potentially harmful to women. This narrative review outlines the studies conducted in women regarding the efficacy of beta-alanine, caffeine, and nitrate supplementation for performance enhancement. Only nine studies on beta-alanine, 15 on caffeine, and 10 on nitrate in healthy women under the age of 40 years conducted in normoxia conditions were identified as relevant to this research question. Evidence suggests that beta-alanine may lower the rate of perceived exertion and extend training bouts in women, leading to greater functional adaptations. Studies of caffeine in women suggest the physiological responder status and caffeine habituation may contribute to caffeine’s efficacy, with a potential plateau in the dose–response relationship of performance enhancement. Nitrate appears to vary in influence based on activity type and primary muscle group examined. However, the results summarized in the limited literature for each of these three supplements provide no consensus on dosage, timing, or efficacy for women. Furthermore, the literature lacks considerations for hormonal status and its role in metabolism. This gap in sex-based knowledge necessitates further research on these ergogenic supplements in women with greater considerations for the effects of hormonal status.
... Landmark studies such as the HERITAGE (n = 481) and FAMuSS (n = 585) have highlighted large variability of physiological adaptations following aerobic [7] and resistance exercise training [8]. Subsequent studies that have observed exercise response variability categorise participants depending on the magnitude of change and are classified accordingly as "high responders", "low responders", "non-responders" or "adverse responders" [9,10]. As such, this has guided the individualisation of exercise interventions to maximise the magnitude and rate of positive responses [11,12]. ...
Participation in resistance training improves muscle strength and size, as well as reduced risk of chronic disease and frailty. However, the exercise response to resistance training is highly variable. In part this may be attributed to individual physiological differences. Identification of biomarkers that can distinguish between high and low responders to exercise are therefore of interest. Exhaled volatile organic compounds may provide a non-invasive method of monitoring the physiological response to resistance training. However, the relationship between exhaled organic compounds and the acute response to resistance exercise is not fully understood. Therefore, this research will investigate exhaled volatile organic compounds in acute response to resistance exercise with an aim to discover a common group of compounds that can predict high and low responders to standardised resistance training.
... 13 Sports science research is becoming increasingly attuned to the fact that after an exercise intervention which is overall statistically beneficial, some participants-known in the literature as "nonresponders"-can show no benefits or even negative adaptations. 14 In this regard, although research supports the benefits of sport interventions-and particularly karate-on psychosocial functioning and academic performance, to the best of or knowledge no previous studies have determined whether inter-individual variability exists in response to these interventions. Moreover, in case that inter-individual variability exists, the analysis of those variables associated with a greater responsiveness could be of major relevance in order to individualize sport interventions so as to maximize responsiveness. ...
Introduction
School‐based sport interventions have shown beneficial effects on psychosocial functioning and academic performance in children. However, the inter‐individual variability in response to these types of interventions remains unclear. We aimed to determine which children benefit most from a school‐based sport intervention.
Methods
This is an ancillary analysis of a randomized controlled trial assessing the effects of a 1‐year school‐based karate intervention (versus “traditional” physical education lessons) in children (7–8 years) from twenty schools across five European countries. Outcomes included psychosocial functioning (Strengths and Difficulties Questionnaire [SDQ] for parents) and academic performance (grade point average). Only participants of the intervention group were included in the present ancillary analysis, and were categorized as responders or non‐responders for the analyzed outcomes attending to whether improvements surpassed a minimal clinically important difference.
Results
About 388 children (187 girls) from the intervention group completed the study, of which 17% and 46% were considered responders for SDQ and academic performance, respectively. Responders for the SDQ presented higher SDQ scores (i.e., higher psychosocial difficulties) at baseline than non‐responders (p < 0.001). Responders for academic performance were mostly males (p = 0.017), with an older age (p = 0.030), and with worse academic performance (p < 0.001) at baseline compared with non‐responders, and tended to present higher SDQ scores (p = 0.055). Responders for one outcome obtained greater benefits from the intervention on the other outcome (e.g., responders for SDQ improved academic performance [p < 0.001] compared with non‐responders).
Conclusions
A school‐based sport intervention (karate) seems particularly effective for children with psychosocial difficulties and low academic performance.
... It is well known that there is a high variability in the adaptive response to exercise, which in rare cases can even be adverse (36). This variability originates from differences in age, gender, genotype, nutritional status, exercise modality, and homeostatic stress conferred by the exercise stimulus (37). A genome-wide association study found 21 single nucleotide polymorphisms (SNPs) that account for half of the trainability in VO 2 max, with an SNP in an enzyme involved in lipid metabolism being the most prominent (38). ...
SignificancePrimary mitochondrial diseases (PMDs) are the most prevalent inborn metabolic disorders, affecting an estimated 1 in 4,200 individuals. Endurance exercise is generally known to improve mitochondrial function, but its indication in the heterogeneous group of PMDs is unclear. We determined the relationship between mitochondrial mutations, endurance exercise response, and the underlying molecular pathways in mice with distinct mitochondrial mutations. This revealed that mitochondria are crucial regulators of exercise capacity and exercise response. Endurance exercise proved to be mostly beneficial across the different mitochondrial mutant mice with the exception of a worsened dilated cardiomyopathy in ANT1-deficient mice. Thus, therapeutic exercises, especially in patients with PMDs, should take into account the physical and mitochondrial genetic status of the patient.
... A mixed program of aerobic and strength training demonstrated higher improvements in the studied markers of performance, with lower interindividual response variability, and longer detraining effects compared with aerobic or strength programs. low training response in one performance or physiological marker does not necessary imply a low training response in others [4][5][6][7]. This inter-individual response variability has been previously studied for AER and STR training programs. ...
Purpose: To compare inter-individual response variability and detraining effects on markers attributed to aerobic and anaerobic performance after short-term standardized aerobic, strength and mixed training programs.
Methods: Thirty-six male students were randomly assigned to either an aerobic, strength, mixed, or control program (9 per group). They performed two consecutive cycling tests (incremental and plateau) to exhaustion at three points: 1 week before training, after 6 weeks of training, and 3 weeks after the training was finished. Maximal oxygen consumption (VO 2max), maximal workload (W max), and time to exhaustion performed at W max (W × time) were compared between groups by repeated-measures ANOVA with Bonferroni post-hoc tests. The inter-subject response variability within each training group was evaluated by comparison with the 95% confidence interval of the control group. Detraining effects were evaluated using the hysteresis areas, which were compared between each training group and the control group by Mann-Whitney U test.
Results: Differences were observed in W max for the aerobic (F(2,7)=19.562; p=0.001; n 2 =0.85) and mixed (F(2,7)=13.447; p=0.004; n 2 =0.99) programs, and in W × time for the mixed program (F(2,7)=15.432; p= 0.016; n 2 =0.89). There was high inter-subject response variability for all variables and training programs, except for a homogenous positive response to W max in the mixed program (X2 =6.27; p=0.04). Detraining effects of W max were also better maintained after the mixed program.
Conclusion: A mixed program of aerobic and strength training demonstrated higher improvements in the studied markers of performance, with lower inter-individual response variability, and longer detraining effects compared with aerobic or strength programs.
... Collectively, these findings support consideration of more holistic approaches to intervention delivery. Factors that may influence an individual's response to intervention include nutritional status (both acute and chronic), physical activity levels, sleep, environmental conditions, and external sources of motivation (Mann et al., 2014) such as intervention expectancy (Marticorena et al., 2021). A logical next step for future research would be to attempt to parse out the relative influence of these factors on individual response variation, although this is undoubtedly challenging. ...
Currently, little is known about the extent of interindividual variability in response to beta-alanine (BA) supplementation, nor what proportion of said variability can be attributed to external factors or to the intervention itself (intervention response). To investigate this, individual participant data on the effect of BA supplementation on a high-intensity cycling capacity test (CCT110%) were meta-analyzed. Changes in time to exhaustion (TTE) and muscle carnosine were the primary and secondary outcomes. Multilevel distributional Bayesian models were used to estimate the mean and SD of BA and placebo group change scores. The relative sizes of group SDs were used to infer whether observed variation in change scores were due to intervention or non-intervention-related effects. Six eligible studies were identified, and individual data were obtained from four of these. Analyses showed a group effect of BA supplementation on TTE (7.7, 95% credible interval [CrI] [1.3, 14.3] s) and muscle carnosine (18.1, 95% CrI [14.5, 21.9] mmol/kg DM). A large intervention response variation was identified for muscle carnosine (σIR = 5.8, 95% CrI [4.2, 7.4] mmol/kg DM) while equivalent change score SDs were shown for TTE in both the placebo (16.1, 95% CrI [13.0, 21.3] s) and BA (15.9, 95% CrI [13.0, 20.0] s) conditions, with the probability that SD was greater in placebo being 0.64. In conclusion, the similarity in observed change score SDs between groups for TTE indicates the source of variation is common to both groups, and therefore unrelated to the supplement itself, likely originating instead from external factors such as nutritional intake, sleep patterns, or training status.
... Although it is conventional to focus monitoring on group mean responses following a particular training intervention or competition, sport settings frequently produce diverse results with high and low responders being often lost in the averaged data reports [66,67]. As a consequence, an increased attention for individualization of monitoring in sport settings has growth to a variety of athlete-monitoring approaches, allowing coaches to better manage fatigue and planning training prescription on an individual basis [68]. ...
Sleep is an essential component for athletes' recovery from fatigue, due especially to its physiological and psychological restorative effects. Moreover, sleep is extremely important for numerous biological functions and sleep deprivation can have significant effects on athletic performance on short-, medium-, and long-term. For example, and considering the physiology of sleep for athletes, some hormonal responses that take place in the lead up to and during sleep (e.g., growth hormone – important role in muscle growth and repair), may be affected following exercise (i.e., training and competition), especially when compared to non-athlete’s populations. Thus, monitoring sleep is also crucial to understand responses to training and readiness, enabling appropriate planning. Importantly, sleep monitoring also intends to reduce the risk of injury, illness, and non-functional overreaching. Moreover, an “individual approach” in athletes monitoring, could help in better prescribe training contents and more adequately manage fatigue, as well as recommend pertinent post-match recovery strategies, such as sleep hygiene interventions. Overall, for understanding the athlete’s sleep patterns/responses and to optimize the recovery strategies it is crucial a comprehensive monitoring of his/her health, performance, fitness and fatigue status.
Keywords: Athletes, Sleep interventions, Sleep Technology, Performance, Health.
Pontos-chaves:
•As demandas físicas, técnicas e táticas do futebol, assim como métodos e ferramentas para controle do treino e a aplicação de Jogos Reduzidos (JR) são importantes no processo de treinamento no Futebol.
• Através do conhecimento das características das demandas físicas e fisiológicas no futebol e terminologias no que concerne ao monitoramento da carga de treinamento (CT), contribuem para o entendimento das medições da carga interna e da carga externa de treinamento.
• A aplicação de Jogos Reduzidos no processo de treino e como a manipulação de constrangimentos, como dimensão do campo de jogo, número de jogadores, nível de desempenho tático, regras e mais variáveis contextuais (placar, vantagem momentânea, etc.) podem gerar adaptações físicas, técnicas e táticas além da possibilidade de mensuração através do uso da carga externa e ainda conhecer os efeitos agudos dos JR e como esses podem contribuir nos processos de treino.
Treatment response heterogeneity and individual responses following exercise training are topics of interest for personalized medicine. Proposed methods to determine the contribution of exercise to the magnitude of treatment response heterogeneity and categorizing participants have expanded and evolved. Setting clear research objectives and having a comprehensive understanding of the strengths and weaknesses of the available methods are vital to ensure the correct study design and analytical approach are used. Doing so will ensure contributions to the field are conducted as rigorously as possible. Nonetheless, concerns have emerged regarding the ability to truly isolate the impact of exercise training, and the nature of individual responses in relation to mean group changes. The purpose of this review is threefold. First, the strengths and limitations associated with current methods for quantifying the contribution of exercise to observed treatment response heterogeneity will be discussed. Second, current methods used to categorize participants based on their response to exercise will be outlined, as well as proposed mechanisms for factors that contribute to response variation. Finally, this review will provide an overview of some current issues at the forefront of individual response research.
Background
Cardiorespiratory fitness (CRF) prompts antiatherogenic adaptations in vascular function and structure. However, there is an extraordinary interindividual variability in response to a standard dose of exercise, wherein a substantial number of adults with intellectual and developmental disabilities (IDD) do not improve CRF. We (1) evaluated the effects of 12-month of moderate-intensity continuous training (MICT) on CRF and arterial stiffness and (2) tested whether an additional 3-month of high-intensity interval training (HIIT) would add to improvements in CRF responsiveness and arterial stiffness.
Methods
Fifteen adults with mild-to-moderate IDD (male adults = 9, 30.1 ± 7.5 years old) met 3 days per week for 30 min MICT for 12 months, after which the incidence of CRF responsiveness was calculated (≥5.0% change in absolute peak VO2). Thereafter, responders and non-responders started HIIT for 3 months with identical daily training load/frequency. Peak VO2, local and regional indices of arterial stiffness were assessed prior to and after each period.
Results
Sixty per cent of the participants were non-responders following MICT, but the incidence dropped to 20% following HIIT (P = 0.03). Absolute peak VO2 values reached significant difference from pre-intervention (+0.38 ± 0.08 L min⁻¹, P = 0.001) only when HIIT was added. Lower limb pulse wave velocity (PWV) decreased following MICT (−0.8 ± 1.1 m s⁻¹, P = 0.049), whereas central PWV only decreased following HIIT (−0.8 ± 0.9 m s⁻¹, P = 0.013).
Conclusions
Cardiorespiratory fitness responsiveness and reductions in PWV to a 12-month MICT period in adults with IDD improved following a period of HIIT programme inducing higher metabolic stress.
Background and purpose: Most studies on heart rate variability (HRV) in professional athletes concerned linear, time- and frequency-domain indices and there is lack of studies on nonlinear parameters in this group. The study aimed to determine the inter-day reliability, and group-related and individual changes of short-term symbolic dynamics (SymDyn) measures during sympathetic nervous system activity (SNSa) stimulation among elite modern pentathletes.
Methods: Short-term electrocardiographic recordings were performed in stable measurement conditions with a 7-days interval between tests. SNSa stimulation via isometric handgrip strength test was conducted on the second day of study. The occurrence rate of patterns without variations (0V), with one variation (1V), two like (2LV) and two unlike variations (2UV) obtained using three approaches (the Max-min, the σ and the Equal-probability methods) were analyzed. Relative and absolute reliability were evaluated.
Results: All SymDyn indices obtained using the Max-min method, 0V and 2UV obtained using the σ method, 2UV obtained using the Equal-probability method presented acceptable inter-day reliability (the intraclass correlation coefficient between 0.91 and 0.99, Cohen’s d between -0.08 and 0.10, the within-subject coefficient of variation between 4% and 22%). 2LV, 2UV and 0V obtained using the Max-min and σ methods significantly decreased and increased, respectively, during SNSa stimulation – such changes were noted for all athletes. There was no significant association between differences in SymDyn parameters and respiratory rate in stable conditions and while comparing stable conditions and SNSa stimulation.
Conclusion: SymDyn indices may be used as reliable non-respiratory-associated parameters in laboratory settings to detect ANS activity modulations in elite endurance athletes. These findings provide a potential solution for addressing the confounding influence of respiration frequency on HRV-derived inferences of cardiac-autonomic function. For this reason, SymDyn may prove to be preferable for field-based monitoring where measurements are unsupervised.
Various health disciplines have expressed a growing interest in personalized medicine in order to achieve better outcomes of medical interventions. The physiological responses to change are often characterized by substantial inter-individual variability both on Earth and even in Space. Such variability is frequently underplayed or ignored in most scientific studies. However, by failing to characterize such variability, vital information regarding the mechanisms that underline such responses, in addition to normal regulatory processes, is potentially lost.
Goals
The proposed new Frontiers Research Topic seeks to provide an opportunity to present, describe and explore inter-individual variability (and methods of assessment) in response to changes in activity levels, gravity loading, nutritional status, pharmaceuticals and exposure to radiation. In the process we hope to provide novel data that can inform fundamental science and also practitioners faced with managing (or even harnessing) such variability. Therefore, the aims of this new Frontiers Research Topic are to provide a platform:
1) to present, describe and explore inter-individual variability in the physiological response to change in fields such as exercise science, gravitational physiology, nutrition, pharmacology and radiation biology;
2) to expose and evaluate potential mechanisms e.g. genetic predispositions contributing to inter-individual variability of physiological responses to change;
3) for statisticians to present novel mathematical approaches to expose and evaluate inter-individual variability of physiological responses to change;
4) to highlight the importance of the exposure and evaluation of inter-individual variability in the physiological responses to change.
Scope and information for Authors
The proposed new Research Topic seeks to investigate inter-individual variability of physiological responses in the following disciplines:
1) Inter-individual variability in response to physical exercise
2) Inter-individual variability in response to gravitational unloading
3) Inter-individual variability in pharmacology
4) Inter-individual variability in response to dietary interventions
5) Inter-individual variability in response to radiation exposure
Roete, AJ, Stoter, IK, Lamberts, RP, Elferink-Gemser, MT, and Otter, RTA. Introducing a method to quantify the specificity of training for races in speed skating. J Strength Cond Res XX(X): 000-000, 2022-The specificity of training for races is believed to be important for performance development. However, measuring specificity is challenging. This study aimed to develop a method to quantify the specificity of speed skating training for sprint races (i.e., 500 and 1,000 m), and explore the amount of training specificity with a pilot study. On-ice training and races of 10 subelite-to-elite speed skaters were analyzed during 1 season (i.e., 26 weeks). Intensity was mapped using 5 equal zones, between 4 m·s-1 to peak velocity and 50% to peak heart rate. Training specificity was defined as skating in the intensity zone most representative for the race for a similar period as during the race. During the season, eight 500 m races, seven 1,000 m races, and 509 training sessions were analyzed, of which 414 contained heart rate and 375 sessions contained velocity measures. Within-subject analyses were performed. During races, most time was spent in the highest intensity zone (Vz5 and HRz5). In training, the highest velocity zone Vz5 was reached 107 ± 28 times, with 9 ± 3 efforts (0.3 ± 0.1% training) long enough to be considered 500 m specific, 6 ± 5 efforts (0.3 ± 0.3% training) were considered 1,000 m specific. For heart rate, HRz5 was reached 151 ± 89 times in training, 43 ± 33 efforts (1.3 ± 0.9% training) were considered 500 m specific, and 36 ± 23 efforts (3.2 ± 1.7% training) were considered 1,000 m specific. This newly developed method enables the examination of training specificity so that coaches can control whether their intended specificity was reached. It also opens doors to further explore the impact of training specificity on performance development.
Training sollte bestimmten Prinzipien folgen. Die Wissenschaft zeigt, dass sie auch in nicht-leistungssportlichen Settings, wie der klinischen Bewegungstherapie, relevant sind und zunehmend gefordert werden. Die Sportwissenschaftler Lars Donath und Oliver Faude haben die wichtigsten Prinzipien einem aktuellen Prüfstand unterzogen.
A poor Fitness Fatness Index (FFI) is associated with type 2 diabetes incidence, other chronic conditions (Alzheimer’s, cancer, and cardiovascular disease) and all-cause mortality. Recent investigations have proposed that an individualised exercise prescription based on ventilatory thresholds is more effective than a standardised prescription in improving cardiorespiratory fitness (CRF), a key mediator of FFI. Thus, the aim of the current study was to determine the effectiveness of individualised versus standardised exercise prescription on FFI in sedentary adults. Thirty-eight sedentary individuals were randomised to 12-weeks of: (1) individualised exercise training using ventilatory thresholds (n = 19) or (2) standardised exercise training using a percentage of heart rate reserve (n = 19). A convenience sample was also recruited as a control group (n=8). Participants completed CRF exercise training three days per week, for 12-weeks on a motorised treadmill. FFI was calculated as CRF in metabolic equivalents (METs), divided by fatness determined by waist to height ratio (WtHR). A graded exercise test was used to measure CRF, and anthropometric measures (height and waist circumference) were assessed to ascertain WtHR. There was a difference in FFI change between study groups, whilst controlling for baseline FFI, F (2, 42) = 19.382 p < .001, partial η2 = 0.480. The magnitude of FFI increase from baseline was significantly higher in the individualised (+15%) compared to the standardised (+10%) (p = 0.028) and control group (+4%) (p = <.001). The main finding of the present study is that individualised exercise prescription had the greatest effect on improving FFI in sedentary adults compared to a standardised prescription. Therefore, an individualised based exercise prescription should be considered a viable and practical method of improving FFI in sedentary adults.
Die Arbeit beleuchtet den Einsatz algorithmischer Datenbearbeitungen bei sportwissenschaftlichen Spiroergometrien aus praktischen und theoretischen Gesichtspunkten. Die aktuelle Verbreitung von algorithmischen Datenbearbeitungen aus Breath-by-Breath Untersuchungen wird über die Ergebnisse eines Fragebogens und einer systematischen Literaturübersicht dargestellt. Zudem erfolgt die Analyse der durch Algorithmen verursachten Messwertvarianzen der Sauerstoffaufnahme in diskontinuierlichen Belastungsuntersuchungen, bei Jugendlichen und im submaximalen Belastungsbereich.
Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.
Physical exercise induces several metabolic adaptations to meet increased energy requirements. Promoter DNA methylation, histone post-translational modifications, or microRNA expression are involved in the gene expression changes implicated in metabolic adaptation after exercise. Epigenetic modifications and many epigenetic enzymes are potentially dependent on changes in the levels of metabolites, such as oxygen, tricarboxylic acid cycle intermediates, 2-oxoglutarate, 2-hydroxyglutarate, and β-hydroxybutyrate, and are therefore susceptible to the changes induced by exercise in a tissue-dependent manner. Most of these changes are regulated by important epigenetic modifiers that control DNA methylation (DNA methyl transferases, and ten-eleven-translocation proteins) and post-translational modifications in histone tails controlled by histone acetyltransferases, histone deacetylases, and histone demethylases (jumonji C proteins, lysine-specific histone demethylase, etc.), among others. Developments in mass spectrometry approaches and the comprehension of the interconnections between epigenetics and metabolism further increase our understanding of underlying epigenetic mechanisms, not only of exercise, but also of disease and aging. In this article, we describe several of these substrates and signaling molecules regulated by exercise that affect some of the most important epigenetic mechanisms, which, in turn, control the gene expression involved in metabolism.
The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0-90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1-48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24-48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.
Purpose:
To determine whether a submaximal cycling test could be used to monitor and prescribe high-intensity interval training (HIT).
Methods:
Two groups of male cyclists completed 4 HIT sessions over a 2-wk period. The structured-training group (SG; n = 8, VO2max = 58.4 ± 4.2 mL · min-1 · kg-1) followed a predetermined training program while the flexible-training group (FG; n = 7, VO2max = 53.9 ± 5.0 mL · min-1 · kg-1) had the timing of their HIT sessions prescribed based on the data of the Lamberts and Lambert Submaximal Cycle Test (LSCT).
Results:
Effect-size calculations showed large differences in the improvements in 40-km time-trial performance after the HIT training between SG (8 ± 45 s) and FG (48 ± 42 s). Heart-rate recovery, monitored during the study, tended to increase in FG and remain unchanged in SG.
Conclusions:
The results of the current study suggest that the LSCT may be a useful tool for coaches to monitor and prescribe HIT.
The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.
It is well established that physical exercise modulates the function of many physiological systems, such as the musculoskeletal, the cardiovascular and the nervous system, by inducing various adaptations to the increased mechanical load and/or metabolic stress of exercise. Many of these changes occur through epigenetic alterations to DNA, such as histone modifications, DNA methylations, expression of microRNAs and changes of the chromatin structure. All these epigenetic alterations may have clinical relevance, thus playing an important role in the prevention and confrontation of neurophysiological disorders, metabolic syndrome, cardiovascular diseases and cancer. Herein we review the known epigenetic modifications induced by physical exercise in various physiological systems and pathologies, and discuss their potential clinical implications.
Abstract Major individual differences in the maximal oxygen uptake response to aerobic training have been documented. Vagal influence on the heart has been shown to contribute to changes in aerobic fitness. Whether vagal influence on the heart also predicts maximal oxygen uptake response to interval-sprinting training, however, is undetermined. Thus, the relationship between baseline vagal activity and the maximal oxygen uptake response to interval-sprinting training was examined. Exercisers (n = 16) exercised three times a week for 12 weeks, whereas controls did no exercise (n = 16). Interval-sprinting consisted of 20 min of intermittent sprinting on a cycle ergometer (8 s sprint, 12 s recovery). Maximal oxygen uptake was assessed using open-circuit spirometry. Vagal influence was assessed through frequency analysis of heart rate variability. Participants were aged 22 ± 4.5 years and had a body mass of 72.7 ± 18.9 kg, a body mass index of 26.9 ± 3.9 kg · m(-2), and a maximal oxygen uptake of 28 ± 7.4 ml · kg(-1) · min(-1). Overall increase in maximal oxygen uptake after the training programme, despite being anaerobic in nature, was 19 ± 1.2%. Change in maximal oxygen uptake was correlated with initial baseline heart rate variability high-frequency power in normalised units (r = 0.58; P < 0.05). Thus, cardiac vagal modulation of heart rate was associated with the aerobic training response after 12 weeks of high-intensity intermittent-exercise. The mechanisms underlying the relationship between the aerobic training response and resting heart rate variability need to be established before practical implications can be identified.
Is determination of exercise intensities as percentages of V̇O2max or HRmax adequate? Med. Sci. Sports Exerc., Vol. 31, No. 9, pp. 1342-1345, 1999. Often exercise intensities are defined as percentages of maximal oxygen uptake (V̇O2max) or heart rate (HRmax).
Purpose: The purpose of this investigation was to test the applicability of these criteria in comparison with the individual anaerobic threshold.
Methods: One progressive cycling test to exhaustion (initial stage 100 W, increment 50 W every 3 min) was analyzed in a group of 36 male cyclists and triathletes (24.9 ± 5.5 yr; 71.6 ± 5.7 kg; V̇O2max: 62.2 ± 5.0 mL·min-1·kg-1; individual anaerobic threshold = IAT: 3.64 ± 0.41 W·kg-1; HRmax: 188 ± 8 min). Power output and lactate concentrations for 60 and 75% of V̇O2max as well as for 70 and 85% of HRmax were related to the LAT.
Results: There was no significant difference between the mean value of IAT (261 ± 34 W, 2.92 ± 0.65 mmol·L-1), 75% of V̇O2max (257 ± 24 W, 2.84 ± 0.92 mmol·L-1), and 85% of HRmax (259 ± 30 W, 2.98 ± 0.87 mmol·L-1). However, the percentages of the IAT ranged between 86 and 118% for 75% V̇O2max and 87 and 116% for 85% HRmax (corresponding lactate concentrations: 1.41-4.57 mmol·L-1 and 1.25-4.93 mmol·L-1, respectively). The mean values at 60% of V̇O2max (198 ± 19 W, 1.55 ± 0.67 mmol·L-1) and 70% of HRmax (180 ± 27 W, 1.45 ± 0.57 mmol·L-1) differed significantly (P < 0.0001) from the IAT and represented a wide range of intensities (66-91% and 53-85% of the IAT, 0.70-3.16 and 0.70-2.91 mmol·L-1, respectively).
Conclusions: In a moderately to highly endurance-trained group, the percentages of V̇O2max and HRmax vary considerably in relation to the IAT. As most physiological responses to exercise are intensity dependent, reliance on these parameters alone without considering the IAT is not sufficient.
Heart-rate recovery (HRR) has been proposed as a marker of autonomic function and training status in athletes. The authors performed a systematic review of studies that examined HRR after training. Five cross-sectional studies and 8 studies investigating changes over time (longitudinal) met our criteria. Three out of 5 cross-sectional studies observed a faster HRR in trained compared with untrained subjects, while 2 articles showed no change as a result of training. Most longitudinal studies observed a corresponding increase in HRR and power output (training status). Although confounding factors such as age, ambient temperature, and the intensity and duration of the exercise period preceding HRR make it difficult to compare these studies, the available studies indicated that HRR was related to training status. Therefore, the authors conclude that HRR has the potential to become a valuable tool to monitor changes in training status in athletes and less well-trained subjects, but more studies and better standardization are required to match this potential.
Individuals differ in the response to regular exercise. Whether there are people who experience adverse changes in cardiovascular and diabetes risk factors has never been addressed.
An adverse response is defined as an exercise-induced change that worsens a risk factor beyond measurement error and expected day-to-day variation. Sixty subjects were measured three times over a period of three weeks, and variation in resting systolic blood pressure (SBP) and in fasting plasma HDL-cholesterol (HDL-C), triglycerides (TG), and insulin (FI) was quantified. The technical error (TE) defined as the within-subject standard deviation derived from these measurements was computed. An adverse response for a given risk factor was defined as a change that was at least two TEs away from no change but in an adverse direction. Thus an adverse response was recorded if an increase reached 10 mm Hg or more for SBP, 0.42 mmol/L or more for TG, or 24 pmol/L or more for FI or if a decrease reached 0.12 mmol/L or more for HDL-C. Completers from six exercise studies were used in the present analysis: Whites (N = 473) and Blacks (N = 250) from the HERITAGE Family Study; Whites and Blacks from DREW (N = 326), from INFLAME (N = 70), and from STRRIDE (N = 303); and Whites from a University of Maryland cohort (N = 160) and from a University of Jyvaskyla study (N = 105), for a total of 1,687 men and women. Using the above definitions, 126 subjects (8.4%) had an adverse change in FI. Numbers of adverse responders reached 12.2% for SBP, 10.4% for TG, and 13.3% for HDL-C. About 7% of participants experienced adverse responses in two or more risk factors.
Adverse responses to regular exercise in cardiovascular and diabetes risk factors occur. Identifying the predictors of such unwarranted responses and how to prevent them will provide the foundation for personalized exercise prescription.
We hypothesised that habitual physical activity (PA) together with progressive endurance training contributes to the differences in training response (Δ[V(·)]O(2max)) in healthy and physically active male participants. Twenty volunteers (age 30±3 years and [V(·)]O(2max) 54±7 ml·kg⁻¹·min⁻¹) participated in an eight-week training program which included four to six heart rate-guided exercise sessions weekly. PA data over the whole period were collected by an accelerometer-equipped wristwatch. Individual relative intensities of endurance training and PA were separately determined by adjusting to [V(·)]O(2max) reserve and calculated as mean daily duration (min) of training and PA at light, moderate, high and very high intensity levels. [V(·)]O(2max) increased 6.4±4.1% (p < 0.0001) during the training period. Δ[V(·)]O(2max) correlated with the amount of habitual PA that was mainly of light intensity (r = 0.53, p = 0.016), but not with the duration of moderate, high or very high intensity PA (p = ns for all). Age, body mass index, and daily amount of training at any intensity level of exercise were not related to Δ[V(·)]O(2max) (p = ns for all). In conclusion, a high amount of habitual PA together with prescribed endurance training was associated with good training response in physically active males.
Purpose:
Individual responses to aerobic training vary from almost none to a 40% increase in aerobic fitness in healthy subjects. We hypothesized that the baseline self-rated mental stress may influence to the training response.
Methods:
The study population included 44 healthy sedentary subjects (22 women) and 14 controls. The laboratory controlled training period was 2 weeks, including five sessions a week at an intensity of 75% of the maximum heart rate for 40 min/session. Self-rated mental stress was assessed by inquiry prior to the training period from 1 (low psychological resources and a lot of stressors in my life) to 10 (high psychological resources and no stressors in my life), respectively.
Results:
Mean peak oxygen uptake [Formula: see text] increased from 34 ± 7 to 37 ± 7 ml kg(-1) min(-1) in training group (p < 0.001) and did not change in control group (from 34 ± 7 to 34 ± 7 ml kg(-1) min(-1)). Among the training group, the self-rated stress at the baseline condition correlated with the change in fitness after training intervention, e.g., with the change in maximal power (r = 0.45, p = 0.002, W/kg) and with the change in [Formula: see text] (r = 0.32, p = 0.039, ml kg(-1) min(-1)). The self-rated stress at the baseline correlated with the change in fitness in both female and male, e.g., r = 0.44, p = 0.039 and r = 0.43, p = 0.045 for ΔW/kg in female and male, respectively.
Conclusion:
As a novel finding the baseline self-rated mental stress is associated with the individual training response among healthy females and males after highly controlled aerobic training intervention. The changes in fitness were very low or absent in the subjects who experience their psychological resources low and a lot of stressors in their life at the beginning of aerobic training intervention.
Endurance training-induced changes in hemodynamic traits are heritable. However, few genes associated with heart rate training responses have been identified. The purpose of our study was to perform a genome-wide association study to uncover DNA sequence variants associated with submaximal exercise heart rate training responses in the HERITAGE Family Study. Heart rate was measured during steady-state exercise at 50 W (HR50) on 2 separate days before and after a 20-wk endurance training program in 483 white subjects from 99 families. Illumina HumanCNV370-Quad v3.0 BeadChips were genotyped using the Illumina BeadStation 500GX platform. After quality control procedures, 320,000 single-nucleotide polymorphisms (SNPs) were available for the genome-wide association study analyses, which were performed using the MERLIN software package (single-SNP analyses and conditional heritability tests) and standard regression models (multivariate analyses). The strongest associations for HR50 training response adjusted for age, sex, body mass index, and baseline HR50 were detected with SNPs at the YWHAQ locus on chromosome 2p25 (P = 8.1 × 10(-7)), the RBPMS locus on chromosome 8p12 (P = 3.8 × 10(-6)), and the CREB1 locus on chromosome 2q34 (P = 1.6 × 10(-5)). In addition, 37 other SNPs showed P values <9.9 × 10(-5). After removal of redundant SNPs, the 10 most significant SNPs explained 35.9% of the ΔHR50 variance in a multivariate regression model. Conditional heritability tests showed that nine of these SNPs (all intragenic) accounted for 100% of the ΔHR50 heritability. Our results indicate that SNPs in nine genes related to cardiomyocyte and neuronal functions, as well as cardiac memory formation, fully account for the heritability of the submaximal heart rate training response.
The concept of 'lifestyle' includes different factors such as nutrition, behavior, stress, physical activity, working habits, smoking and alcohol consumption. Increasing evidence shows that environmental and lifestyle factors may influence epigenetic mechanisms, such as DNA methylation, histone acetylation and miRNA expression. It has been identified that several lifestyle factors such as diet, obesity, physical activity, tobacco smoking, alcohol consumption, environmental pollutants, psychological stress and working on night shifts might modify epigenetic patterns. Most of the studies conducted so far have been centered on DNA methylation, whereas only a few investigations have studied lifestyle factors in relation to histone modifications and miRNAs. This article reviews current evidence indicating that lifestyle factors might affect human health via epigenetic mechanisms.
The common inheritance of approximately 20 000 genes defines each of us as human. However, subst