No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
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.
... While it may be difficult to normalise exercise intensity for HIIT across individuals, it has been generally assumed that longitudinal HIIT interventions elicit adaptive variability between participants (Astorino and Schubert 2014;Coakley and Passfield 2018;Montero and Lundby 2017;Williams et al. 2019). It could be argued that this outcome results, at least in part, from the methodology associated with how training work rates are set for each participant (Mann et al. 2014;Iannetta et al. 2020;Jamnick et al. 2020;Mann et al. 2013). ...
... Besides the fact that cyclists one and two might question the coach's ability to appropriately prescribe HIIT, it is possible that only cyclist three will manifest the desired adaptive effect (i.e. V O2max increase), based on the premise that the magnitude of adaptive responses reflects, at least partially, the magnitude of the training stimulus (Mann et al. 2014;Flück 2006;Perry et al. 2010). Another possible problem may arise when scientists prescribe HIIT to a group of research participants and some are unable to complete the session, potentially leading to their exclusion from the sample, and ultimately biasing estimates of variables under investigation. ...
... Finally, our findings are in line with the contention that some of the inter-individual variability in adaptive responses following HIIT programmes (Astorino and Schubert 2014;Coakley and Passfield 2018;Montero and Lundby 2017;Williams et al. 2019) may result from how intensity was normalised across participants (Mann et al. 2014;Iannetta et al. 2020;Jamnick et al. 2020;Mann et al. 2013). ...
Purpose:
To compare methods of relative intensity prescription for their ability to normalise performance (i.e., time to exhaustion), physiological, and perceptual responses to high-intensity interval training (HIIT) between individuals.
Methods:
Sixteen male and two female cyclists (age: 38 ± 11 years, height: 177 ± 7 cm, body mass: 71.6 ± 7.9 kg, maximal oxygen uptake ([Formula: see text]O2max): 54.3 ± 8.9 ml·kg-1 min-1) initially undertook an incremental test to exhaustion, a 3 min all-out test, and a 20 min time-trial to determine prescription benchmarks. Then, four HIIT sessions (4 min on, 2 min off) were each performed to exhaustion at: the work rate associated with the gas exchange threshold ([Formula: see text]GET) plus 70% of the difference between [Formula: see text]GET and the work rate associated with [Formula: see text]O2max; 85% of the maximal work rate of the incremental test (85%[Formula: see text]max); 120% of the mean work rate of the 20 min time-trial (120%TT); and the work rate predicted to expend, in 4 min, 80% of the work capacity above critical power. Acute HIIT responses were modelled with participant as a random effect to provide estimates of inter-individual variability.
Results:
For all dependent variables, the magnitude of inter-individual variability was high, and confidence intervals overlapped substantially, indicating that the relative intensity normalisation methods were similarly poor. Inter-individual coefficients of variation for time to exhaustion varied from 44.2% (85%[Formula: see text]max) to 59.1% (120%TT), making it difficult to predict acute HIIT responses for an individual.
Conclusion:
The present study suggests that the methods of intensity prescription investigated do not normalise acute responses to HIIT between individuals.
... The requirements for higher intensity in uphill terrain in XC skiing drives metabolic demands considerably above those required to elicit the maximal oxygen uptake (VO2max). This includes work rates as high as ~40% and ~15-20% above VO2max during the uphill sections in sprint and distance XC skiing, receptively [15,17,[30][31][32][33]. Therefore, XC skiing requires high anaerobic energy turnover rates (i.e. ...
... The individual response magnitudes revealed that some athletes in LIG also improved their VO 2 max to the same extent as HIG, indicating individual variations in how athletes respond to different endurance training in eliciting VO 2 max. 24,30 The present sample of athletes, including both sexes and different initial levels, could, in part, have contributed to the subsequent variations Abbreviations: ES, effect size; GE, gross efficiency; HIG, high-intensity training group; HR, heart rate; HR peak , peak heart rate; [La − ], blood lactate; LIG, low-intensity training group; RER, respiratory exchange ratio; SKATE, laboratory test roller-ski skating; TTE, time to exhaustion; VO 2 , oxygen uptake; VO 2 peak, peak oxygen uptake; V peak , peak velocity. *Significantly different from pre (P < .05). ...
... Individual training responses among endurance athletes are determined by a complex interplay between training load (i.e., volume, intensity and frequency) and the subsequent recovery (e.g., sleep, nutrition and non-training daily stressors), in which pre-training status and genetic influence plays additional roles (Mann et al., 2014). Accordingly, training responses to standardized training programs demonstrates large inter-individual variations with individuals represented in both ends of the response range, a phenomenon often referred to as "high-and low-responders" (Mann et al., 2014). ...
Cross-country (XC) skiing is an Olympic Winter sport combining upper-and lower-body work to cross varied terrain in endurance competitions lasting from multiple ∼3 min (∼1.3–1.8 km) efforts in the sprint discipline to more than 2 hours (≤50 km) in the longest distance competitions. Over the last decades, retrospective training analyses of world-class XC skiers combined with more experimental designs have led to a well-developed theoretical framework of endurance training in XC skiing, although there is an ongoing discussion on how training volume and intensity should be progressed throughout the preparation period to optimize performance development. These training methods have elicited some of the highest maximal oxygen uptake (VO2max) values reported in the literature, with concurrent high peak oxygen uptakes (VO2peak) within the main sub-techniques of the skating and classical technique. In this context, it would be interesting to understand how athletes originating from other endurance sports would progress their VO2max/VO2peak values, in addition to improving their technique and efficiency, while transferring to XC skiing and adopting these training methods. Accordingly, the overall objectives of the present dissertation were to: (1) investigate both the short-term and more subsequent effects of increased low (LIT)- vs. high-intensity endurance training (HIT) on performance and physiological adaptations in the preparation period of junior XC skiers (study I-II), and (2) investigate the influence of adopting state-of-the-art training methods in XC skiing to endurance athletes originating from other sports in a talent transfer program (study III-IV).
Studies I-II are based on a randomized, experimental design which investigated the effects of increased load of LIT vs. HIT during an 8-week intervention (simulating general preparation period) followed by 5 weeks of standardized training with similar intensity distribution (simulating specific preparation period), and thereafter 14 weeks of self-chosen training and competitions (competition period) in junior XC skiers. Study I demonstrated that performance adaptations, including uphill running time-trial performance and peak speed when incremental running and roller-ski skating to exhaustion in the laboratory, did not differ significantly between the two groups. However, increased HIT elicited ~3-4% greater changes in VO2max running and VO2peak roller-ski skating compared to increased LIT. Study II was a follow-up study, demonstrating that the observed differences in physiological adaptations between the two groups during the 8-week intervention were outbalanced following 5 weeks of standardized training with similar intensity distribution across groups. Lastly, no further changes in any performance or physiological indices neither within nor between groups were found 14 weeks into the subsequent competition period.
Studies III-IV are based on a prospective, observational design investigating the development of performance, physiological, and technical indices of endurance athletes (i.e. runners, kayakers, and rowers) transferring to XC skiing during a talent transfer program. Study III demonstrated that the 6-month training period elicited large improvements in sport-specific performance indices (i.e. roller-ski skating and double-poling ergometry), whereas performance indices in a general mode (i.e. running) were unchanged. Improvements in sport-specific performance indices were coincided by better skiing efficiency/work economy and longer cycle lengths while roller-ski skating, as well as increased upper-body one-repetition maximum-strength (1RM) in ski-specific exercises. However, no changes in VO2max running and VO2peak roller-ski skating and double-poling ergometry were found at a group level. Moreover, larger development in sport-specific performance indices were found in runners compared to kayakers/rowers, which coincided with improved VO2peak and overall better physiological adaptations in roller-ski skating. Study IV was a follow-up study, comparing high- and low-performance responders to the 6-month training period using a multidisciplinary approach. Here, high-responders demonstrated superior physiological adaptations both at submaximal and maximal workloads (e.g. power at 4 mmol·L-1 and VO2max running and VO2peak roller-ski skating) than low-responders. These findings were coincided with higher training loads, greater perceived effort during sessions, and lower incidents of injury and illness during the 6-month period in comparison to their lower-responding counterparts. Lastly, qualitative interviews with the athletes coaches highlighted that greater motivation and passion for XC skiing together with the ability to build a strong coach-athlete relationship separated high- from low-responders.
Conclusively, the present dissertation demonstrates that performance development can successfully be achieved both by increased low- and high-intensity endurance training during the preparation period in XC skiers, although increased high-intensity training may provide short-term benefits for maximal aerobic energy turnover. However, these different ways of progressing training load had little or no effects on the subsequent performance and physiological development following a period of similar training regimes. Moreover, adopting the theoretical framework of training (i.e. state-of-the-art) in XC skiing on endurance athletes (i.e. runners, kayakers, and rowers) transferring to XC skiing elicits large sport-specific performance improvements, while improvements in aerobic energy turnover were limited. Here, the athletes with largest development had a background from running and the ability to concurrently develop high aerobic energy turnover rates together with skiing efficiency, cycle length, and upper-body specific strength. However, a more long-term approach than employed in the present studies is clearly needed to reach a high international level in XC skiing following talent transfer. Overall, the present data provides novel understanding of both the short-term and more subsequent effects of progressing endurance training volume and intensity in XC skiing, as well as the effects of applying state-of the-art XC skiing training to endurance athletes originating from other sports.
... 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.
... While prior work has provided many situational answers, the nuance of individual circumstances likely means that there is no hard and fast set of rules that dictates what will be most effective for all learners at all times. Different athletes will respond differently to the same training stimulus [1,2], and the response of each athlete may vary by day or by session due to a range of factors that are out of the practitioner's control [2]. It is therefore futile to assume that any "one size fits all" approach exists, and instead general principles for training must be adapted to the needs of the individual or group a practitioner is working with to achieve the greatest outcomes. ...
... While prior work has provided many situational answers, the nuance of individual circumstances likely means that there is no hard and fast set of rules that dictates what will be most effective for all learners at all times. Different athletes will respond differently to the same training stimulus [1,2], and the response of each athlete may vary by day or by session due to a range of factors that are out of the practitioner's control [2]. It is therefore futile to assume that any "one size fits all" approach exists, and instead general principles for training must be adapted to the needs of the individual or group a practitioner is working with to achieve the greatest outcomes. ...
Representative learning design (RLD) in sport is a well-established concept in both theory and practice. The goal of RLD is to faithfully replicate competition environments in training settings to benefit improvement in athletic performance. There is currently little research that considers how representative an activity needs to be to facilitate learning transfer, and how that level of representativeness might fluctuate between activities or sessions, and across competitive cycles. Similarly, there is no existing research that specifically considers the elevated workload (in cognitive and physical load) of highly representative training, and the potential impacts of chronic overuse of these highly demanding activities. This paper addresses these limitations, making a case for the application of RLD that considers the level of representativeness (fidelity) and the demands placed on athletes (load) from both a cognitive and physical perspective. This paper also suggests several categorisations of training activities that are based on their relative representativeness, level of imposed demands, and the intended outcomes of the activity with reference to the perception–action cycle. The two core concepts of fidelity and load are combined for a new approach to representative training that allows practitioners to balance the benefits of representative training with the risks of imposing excessive load on athletes.
... Physical activity (PA) increases energy expenditure and thus is often prescribed for body weight management [6]. Although weight loss is often reported after PA intervention [7,8], PA does not always result in expected weight loss and there is high variability in body weight responses to PA [9][10][11][12][13][14][15][16][17]. ...
... Developing effective strategies for body weight management remains a challenge worldwide [1,2]. Although PA offers a cost-effective way for body weight control [6], there is high inter-individual variability in body weight response to PA [9][10][11][12][13][14][15][16][17]. In the current study among generally healthy individuals in middle-to-late adulthood, (See figure on next page.) ...
Background
The gut microbiome regulates host energy balance and adiposity-related metabolic consequences, but it remains unknown how the gut microbiome modulates body weight response to physical activity (PA).
Methods
Nested in the Health Professionals Follow-up Study, a subcohort of 307 healthy men (mean[SD] age, 70[4] years) provided stool and blood samples in 2012–2013. Data from cohort long-term follow-ups and from the accelerometer, doubly labeled water, and plasma biomarker measurements during the time of stool collection were used to assess long-term and short-term associations of PA with adiposity. The gut microbiome was profiled by shotgun metagenomics and metatranscriptomics. A subcohort of 209 healthy women from the Nurses’ Health Study II was used for validation.
Results
The microbial species Alistipes putredinis was found to modify the association between PA and body weight. Specifically, in individuals with higher abundance of A. putredinis, each 15-MET-hour/week increment in long-term PA was associated with 2.26 kg (95% CI, 1.53–2.98 kg) less weight gain from age 21 to the time of stool collection, whereas those with lower abundance of A. putredinis only had 1.01 kg (95% CI, 0.41–1.61 kg) less weight gain (pinteraction = 0.019). Consistent modification associated with A. putredinis was observed for short-term PA in relation to BMI, fat mass%, plasma HbA1c, and 6-month weight change. This modification effect might be partly attributable to four metabolic pathways encoded by A. putredinis, including folate transformation, fatty acid β-oxidation, gluconeogenesis, and stearate biosynthesis.
Conclusions
A greater abundance of A. putredinis may strengthen the beneficial association of PA with body weight change, suggesting the potential of gut microbial intervention to improve the efficacy of PA in body weight management.
F8yFRh4iDnUGbtAWGbxr-RVideo Abstract
... The limited evidence regarding biomechanical changes with CSET might also stem from individual differences in response to training, where high responders show large responses to an intervention, whereas low responders show small to no responses (Mann et al., 2014). Furthermore, low responders to a specific intervention or training can be high responders to another type of training or intervention (Hautala et al., 2006). ...
... The prevalence of high responders in terms of RE was 42%. This result corroborates previous observations showing that individuals could either demonstrate a large, small, or even no response to a training intervention (Mann et al., 2014). The high responders to the CSET-PLY showed no changes in running biomechanics from pre-to postintervention (based on effect size calculation) despite RE improvements of 7.0 ± 2.3% (Table 5). ...
We compared the effects of two 8-week concurrent strength and endurance trainings (CSETs) on running economy (RE) and running biomechanics, and we explored whether the effects on running biomechanics were mediated by responder status [high vs low responder based on -2.6% change in RE]. Thirty-one male recreational runners were randomly assigned to a standard endurance running training combined with either plyometric (CSET-PLY) or dynamic body-weight (CSET-DYN) training. RE and running biomechanics [contact (tc) and flight (tf) time, step frequency (SF), duty factor (DF), and leg stiffness (kleg)] were measured pre- and post-intervention. RE significantly improved following CSET (RE = -2.1 ± 3.9%; p = 0.005) and no changes in tc, DF, SF, and kleg (p ≥ 0.10) but a shorter tf (p ≥ 0.03) from pre- to post-intervention were seen. The prevalence of high responders was 42% (RE = -5.7 ± 2.4%). Among high responders, there were no changes in running biomechanics except participants following CSET-DYN who increased their SF (+3%). These results indicate that improvements in RE obtained through CSET-PLY and CSET-DYN involve minimal to no changes in running biomechanics and that there was not a training modality, which was better than the other. More detailed biomechanical assessments involving kinematics, kinetics, and electromyography could shed light on the underlying mechanisms of RE improvement.
... 13 Other than methodological factors, previous studies have identified multiple biological contributors, including genetics, age, sex, blood oxygencarrying capacity, baseline fitness and autonomic function. 5,13,20 A better understanding of which variable or combination of variables predisposing some individuals to have a better training response than others may allow more effective exercise prescriptions, and thus would be particularly meaningful in clinical practice. 21 Although numerous exercise training studies are available that support this concept, a number of methodological issues impeded the synthesis of data to identify such mechanisms. ...
... Investigating which phenotypes or combinations of a number of phenotypes could be used to predict an individual's training responsiveness will be practically meaningful for clinicians to identify and design personalized training regimens, given the considerable interindividual variation in exercise responsiveness. 20 However, the presence of a surge of input factors and their potential interactions not only makes it difficult to develop an accurate statistical model but also brings the multiple testing problem. 6 Consequently, inferences from traditional statistical methods become less precise. ...
Background
Considerable attention has been paid to interindividual differences in the cardiorespiratory fitness (CRF) response to exercise. However, the complex multifactorial nature of CRF response variability poses a significant challenge to our understanding of this issue. We aimed to explore whether unsupervised clustering can take advantage of large amounts of clinical data and identify latent subgroups with different CRF exercise responses within a healthy population.
Methods
252 healthy participants (99 men, 153 women; 36.8 ± 13.4 yr) completed moderate endurance training on 3 days/week for 4 months, with exercise intensity prescribed based on anaerobic threshold (AT). Detailed clinical measures, including resting vital signs, ECG, cardiorespiratory parameters, echocardiography, heart rate variability, spirometry and laboratory data, were obtained before and after the exercise intervention. Baseline phenotypic variables that were significantly correlated with CRF exercise response were identified and subjected to selection steps, leaving 10 minimally redundant variables, including age, BMI, maximal oxygen uptake (VO2max), maximal heart rate, VO2 at AT as a percentage of VO2max, minute ventilation at AT, interventricular septal thickness of end-systole, E velocity, root mean square of heart rate variability, and hematocrit. Agglomerative hierarchical clustering was performed on these variables to detect latent subgroups that may be associated with different CRF exercise responses.
Results
Unsupervised clustering revealed two mutually exclusive groups with distinct baseline phenotypes and CRF exercise responses. The two groups differed markedly in baseline characteristics, initial fitness, echocardiographic measurements, laboratory values, and heart rate variability parameters. A significant improvement in CRF following the 16-week endurance training, expressed by the absolute change in VO2max, was observed only in one of the two groups (3.42 ± 0.4 vs 0.58 ± 0.65 ml kg⁻¹∙min⁻¹, P = 0.002). Assuming a minimal clinically important difference of 3.5 ml kg⁻¹∙min⁻¹ in VO2max, the proportion of population response was 56.1% and 13.9% for group 1 and group 2, respectively (P<0.001). Although the group 1 exhibited no significant improvement in CRF at group level, a significant decrease in diastolic blood pressure (70.4 ± 7.8 vs 68.7 ± 7.2 mm Hg, P = 0.027) was observed.
Conclusions
Unsupervised learning based on dense phenotypic characteristics identified meaningful subgroups within a healthy population with different CRF responses following standardized aerobic training. Our model could serve as a useful tool for clinicians to develop personalized exercise prescriptions and optimize training effects.
... Sports performance has many natural features through exercise training, recovery, health, nutrition, mental skills and acquisition skills as important factors in athletic preparation [12]. Systematic training prepares the athlete for his or her athletic needs to develop physical and athletic skills. ...
... Mathematical modeling and data analysis have helped trainers, sports science and sports medicine practitioners better understand the relationship-response capacity of top Australian athletes. Significant findings support previous anecdotal evidence of effective planning and monitoring of individual training response [12]. The availability of consistent training increases the athlete"s ability to work on both the team and each sport [14]. ...
International result-oriented performance in sports requires systematic scientific training. A proper pedagogical approach is required for systematic loading of the athletes in order to record-breaking performance. The performance of an athlete largely depends upon a progressive training load for a relatively long period of time. The degree of mechanical tension, subcellular damage, and metabolic stress can all play a role in exercise-induced muscle adaptations. The process of adaptation largely depended upon the ratio of load and recovery stimulus. Thus the load dynamics and proper adaptation is reflected in the achievement of an athlete. The present research review-based article discussed systematically the procedure of training load, importance of recovery, and adaptation of load.
... 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.
... 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.
... This individual analysis approach in exercise science is particularly relevant, because there is considerable within-subject variability in key performance variables that is otherwise obscured by looking at average values (Mann, 2011). When the individual responses to a training regime have been studied, performance changes differently from one individual to another (Barquero & Salazar, 2020;Mann et al., 2014). These individual responses are lost when performance variables are estimated using regression equations based on group data. ...
Jump height continues to be widely used to predict power in humans. Individual progress is often monitored on the basis of estimated power, but prediction equations are based on group data. The objective of the study was to show that vertical jump performance (VJP) and mechanical power are poorly associated, particularly within individuals. Two experiments are presented. First, 52 physically active male college students performed five maximal vertical jumps each. Second, three young male participants performed 50 maximal jumps each. Participants rested for 1 minute between jumps. VJP was calculated from kinematic data as peak body center of mass (BCOM) minus standing BCOM; peak power (PEAKPWR) was calculated from the vertical ground reaction force registered by a force plate, and average power (MEANPWR) during propulsion from the change in potential energy of BCOM. Regression analyses were performed using standardized VJP scores as the predictor variable and standardized power scores as the resulting variables, expecting an identity function of y = x (intercept = 0, slope = 1) and R2 = 1. In experiment 1, the model for zPEAKPWR R2 = 0.9707 (p < 0.0001) but slope (0.3452) ≠ 1 (p < 0.0001). The model for zMEANPWR R2 = 0.9239 (p < 0.0001); nevertheless, slope (0.4257) ≠ 1 (p < 0.0001). In experiment 2, all individual models for zPEAKPWR and zMEANPWR resulted in poor associations (R2 ≤ 0.21) and slopes ≠ 1 (p≤0.001). In conclusion, regression analysis for individuals, and even for groups, confirms that VJP is a poor predictor of mechanical power.
... Este abordaje de análisis individual en las ciencias del ejercicio es particularmente relevante, ya que existe una variabilidad intra-sujeto considerable en variables clave de rendimiento; esta variabilidad pasa desapercibida al tomar en cuenta los valores promedio (Mann, 2011). Cuando se han estudiado las respuestas individuales a un régimen de entrenamiento, el rendimiento cambia en forma distinta para cada individuo (Barquero y Salazar, 2020;Mann et al., 2014). Estas respuestas individuales se pierden cuando las variables de rendimiento son calculadas utilizando ecuaciones de regresión desarrolladas a partir de datos grupales. ...
La altura del salto se sigue usando ampliamente para predecir la potencia en seres humanos. El progreso individual, a menudo, se monitorea usando una estimación de la potencia, pero las ecuaciones de predicción se basan en datos grupales. El estudio pretende demostrar que la altura del salto vertical (VJP) y la potencia mecánica tienen una pobre correlación, particularmente en un mismo individuo. Se presentan dos experimentos; primero, 52 estudiantes universitarios físicamente activos ejecutaron cinco saltos verticales máximos cada uno; segundo, tres participantes masculinos ejecutaron 50 saltos máximos cada uno. Los participantes descansaron 1 minuto entre saltos. VJP se calculó a partir de los datos cinemáticos como posición más alta del centro de masa corporal (BCOM) menos BCOM de pie; la potencia pico (PEAKPWR) se calculó a partir de la fuerza vertical de reacción registrada por una plataforma de fuerza y la potencia promedio (MEANPWR) durante la propulsión a partir del cambio en la energía potencial del BCOM. Se realizaron análisis de regresión usando puntajes estandarizados de VJP como la variable predictora y puntajes estandarizados de potencia como las variables resultantes, con la expectativa de obtener una función de identidad y = x (intercepto = 0, pendiente = 1) y R2 = 1. En el experimento 1, el modelo para zPEAKPWR arrojó R2 = 0.9707 (p < .0001) pero la pendiente (0.3452) ≠ 1 (p = 8.7x10-15). El modelo para zMEANPWR dio R2 = 0.9239 (p < .0001); sin embargo, la pendiente (0.4257) ≠ 1 (p = 1.15x10-5). En el experimento 2, todos los modelos individuales para zPEAKPWR y zMEANPWR arrojaron asociaciones débiles (R2 ≤ 0.21) y pendientes ≠ 1 (p ≤ .001). En conclusión, el análisis de regresión para individuos y aun para grupos confirma que la altura de salto vertical es un pobre predictor de la potencia mecánica.
... Este abordaje de análisis individual en las ciencias del ejercicio es particularmente relevante, ya que existe una variabilidad intra-sujeto considerable en variables clave de rendimiento; esta variabilidad pasa desapercibida al tomar en cuenta los valores promedio (Mann, 2011). Cuando se han estudiado las respuestas individuales a un régimen de entrenamiento, el rendimiento cambia en forma distinta para cada individuo (Barquero y Salazar, 2020;Mann et al., 2014). Estas respuestas individuales se pierden cuando las variables de rendimiento son calculadas utilizando ecuaciones de regresión desarrolladas a partir de datos grupales. ...
La altura del salto se sigue usando ampliamente para predecir la potencia en seres humanos. El progreso individual, a menudo, se monitorea usando una estimación de la potencia, pero las ecuaciones de predicción se basan en datos grupales. El estudio pretende demostrar que la altura del salto vertical (VJP) y la potencia mecánica tienen una pobre correlación, particularmente en un mismo individuo. Se presentan dos experimentos; primero, 52 estudiantes universitarios físicamente activos ejecutaron cinco saltos verticales máximos cada uno; segundo, tres participantes masculinos ejecutaron 50 saltos máximos cada uno. Los participantes descansaron 1 minuto entre saltos. VJP se calculó a partir de los datos cinemáticos como posición más alta del centro de masa corporal (BCOM) menos BCOM de pie; la potencia pico (PEAKPWR) se calculó a partir de la fuerza vertical de reacción registrada por una plataforma de fuerza y la potencia promedio (MEANPWR) durante la propulsión a partir del cambio en la energía potencial del BCOM. Se realizaron análisis de regresión usando puntajes estandarizados de VJP como la variable predictora y puntajes estandarizados de potencia como las variables resultantes, con la expectativa de obtener una función de identidad y = x (intercepto = 0, pendiente = 1) y R2 = 1. En el experimento 1, el modelo para zPEAKPWR arrojó R2 = 0.9707 (p <.0001) pero la pendiente (0.3452) ≠ 1 (p = 8.7x10-15). El modelo para zMEANPWR dio R2 = 0.9239 (p < .0001); sin embargo, la pendiente (0.4257) ≠ 1 (p = 1.15x10-5). En el experimento 2, todos los modelos individuales para zPEAKPWR y zMEANPWR arrojaron asociaciones débiles (R2 ≤ 0.21) y pendientes ≠ 1 (p ≤ .001). En conclusión, el análisis de regresión para individuos y aún para grupos confirma que la altura de salto vertical es un pobre predictor de la potencia mecánica.
... It is widely accepted that athletic performance improvement has a very high genetic component for complex traits such as endurance, muscle strength, power, speed, agility, recovery rate, and risk of injury [6]. It is indisputable that genetic diversity affects both exercise performance and adaptation [7,8]. Elite athletes can compete at the highest level due to the advantages of correct genetic marker matches. ...
Background Current research on athletic performance focuses on genetic variants that contribute significantly
to individuals’ performance. ACTN3 rs1815739 and PPARA-α rs4253778 gene polymorphisms are variants frequently associated with athletic performance among different populations. However, there is limited research examining the pre-and post-test results of some variants of athletic performance in soccer players. Therefore, the presented research is to examine the relationships between the ACTN3 rs1815739 and PPARA-α rs4253778 gene polymorphisms and athletic performance improvement rates in adaptations to six weeks of training in elite soccer players using some athletic performance tests.
Methodology Twenty-two soccer players between the ages of 18 and 35 voluntarily participated in the study.
All participants were actively engaged in a rigorous six-day-a-week training program during the pre-season preparation period. Preceding and following the training program, a battery of diverse athletic performance tests was administered to the participants. Moreover, Genomic DNA was extracted from oral epithelial cells using the Invitrogen DNA isolation kit (Invitrogen, USA), following the manufacturer’s protocol. Genotyping was conducted using real-time PCR. To assess the pre- and post-test performance differences of soccer players, the Wilcoxon Signed Rank test was employed.
Results Upon analyzing the results of the soccer players based on the ACTN3 genotype variable, it was observed that there were no statistically significant differences in the SJ (Squat Jump), 30m sprint, CMJ (Counter Movement Jump), and DJ (Drop Jump) performance tests (p > 0.05). However, a statistically significant difference was identified in the YOYO IRT 2 (Yo-Yo Intermittent Recovery Test Level 2) and 1RM (One Repetition Maximum) test outcomes (YOYO IRT 2: CC, CT, and TT, p = 0.028, 0.028, 0.008, 0.000, respectively; 1RM: CC, CT, and TT, p = 0.010, 0.34, 0.001, respectively). Regarding the PPARA-α genotype variable, the statistical analysis revealed no significant differences in the SJ, 30m sprint, CMJ, and DJ performance tests (p > 0.05). Nevertheless, a statistically significant difference was observed in the YOYO IRT 2 and 1RM test results (YOYO IRT 2: CC, CG p = 0.001, 0.020; 1RM: CC, p = 0.000)
Conclusions The current study demonstrated significant enhancements in only YOYO INT 2 and 1RM test outcomes across nearly all gene variants following the six-day-a-week training program. Other performance tests, such as
the 30m sprint, SJ, CMJ, and DJ tests did not exhibit statistically significant differences. These findings contribute novel insights into the molecular processes involving PPARA-α rs4253778 and ACTN3 rs1815739 that underpin enhancements in endurance (YOYO INT 2) and maximal strength (1RM) aspects of athletic performance. However,
to comprehensively elucidate the mechanisms responsible for the association between these polymorphisms and athletic performance, further investigations are warranted. It is thought that the use of field and genetic analyses together to support each other will be an important detail for athletes to reach high performance.
... 1 The work performed by an athlete during training and competitions, also known as the external load, can be assessed using measurements such as speed, accelerations, and total distance covered during a session. 2 The rise in popularity of monitoring athlete load on an individual basis stems from the idea that fatigue is a multifactorial phenomenon that each athlete experiences differently and at various points of training. Therefore, as each athlete has unique stressors, genetic compositions, and conditions outside of training, and monitoring each athlete's individual stress response to training is key to maintaining healthy and fit athletes [10][11] and assisting with decision-making for return to play after an injury. 12 The acute:chronic workload ratio (ACWR) is a model for analyzing athlete external load by providing insight on athlete preparedness and fitness. ...
The purpose of this study was to describe the in-season variations of acute:chronic workload ratio (ACWR) of distance, high intensity distance (HID), sprints, accelerations, and decelerations between player positions of a Division I collegiate women’s lacrosse team. Data were collected via wearable microtechnology across a total of 17 games and 64 training sessions on a total of 15 participants (attackers n=5, midfielders n=5, defenders n=5). ACWRs were calculated weekly by dividing the workload from the past seven days by the workload from the past 28 days for each metric. Two repeated measures analyses of variance (RM-ANOVA) were used to compare positional differences and weekly changes in all five metrics for 1) ACWR and 2) weekly training totals. There were several differences in weekly totals and ACWRs across all five metrics evaluated (p<.05), but no positional differences were noted. Apart from the early training weeks, ACWR primarily stayed within the optimal window of 0.8-1.5 to maximize performance and reduce injury risk. These data indicate that there is variation in weekly totals for the main five metrics studied that cause “spikes” and “valleys” in workload. However, the athletes had built enough of a base in their chronic workload that it did not affect their ACWR to move outside of the optimal window. Using this information, coaches and team physicians can more effectively program training not only to optimize performance, but also to limit injuries, fatigue, and lack of fitness.
... 33 Additionally, Iannetta et al 34 recently reported that the intensity of exercise training during CR predicts the increase of MET peak, highlighting that the heterogeneity in the metabolic stimulus of each exercise session can generate individual variation in training adaptations. [34][35][36] Novel equations for patients with CVD This study revealed a simple but crucial mathematic limitation inherent to the method for seeking a fixed percentage of peak parameters for prescribing exercise intensity. For example, if we consider 69% of HR peak as the lower limit of exercise prescription (value for HR at VT1 in our data), we will assume that the equation follows a linear equation (Y=A*X +B), in which Y=HR at VT1, A=0.69, X=HR peak and B=0 (HR at VT1=0.69 * HR peak +0). ...
Objectives
To compare the elicited exercise responses at ventilatory thresholds (VTs: VT1 and VT2) identified by cardiopulmonary exercise testing (CPET) in patients with cardiovascular disease (CVD) with the guideline-directed exercise intensity domains; to propose equations to predict heart rate (HR) at VTs; and to compare the accuracy of prescription methods.
Methods
A cross-sectional study was performed with 972 maximal treadmill CPET on patients with CVD. First, VTs were identified and compared with guideline-directed exercise intensity domains. Second, multivariate linear regression analyses were performed to generate prediction equations for HR at VTs. Finally, the accuracy of prescription methods was assessed by the mean absolute percentage error (MAPE).
Results
Significant dispersions of individual responses were found for VTs, with the same relative intensity of exercise corresponding to different guideline-directed exercise intensity domains. A mathematical error inherent to methods based on percentages of peak effort was identified, which may help to explain the dispersions. Tailored multivariable equations yielded r ² of 0.726 for VT1 and 0.901 for VT2. MAPE for the novel VT1 equation was 6.0%, lower than that for guideline-based prescription methods (9.5 to 23.8%). MAPE for the novel VT2 equation was 4.3%, lower than guideline-based methods (5.8%–19.3%).
Conclusion
The guideline-based exercise intensity domains for cardiovascular rehabilitation revealed inconsistencies and heterogeneity, which limits the currently used methods. New multivariable equations for patients with CVD were developed and demonstrated better accuracy, indicating that this methodology may be a valid alternative when CPET is unavailable.
... When the individual production of antioxidant substances is adequate, these molecules may partially offset the negative effects of FRs, restoring the release of Ca 2+ in the sarcoplasmic reticulum, thus delaying the occurrence of muscular fatigue [40]. The reasons why athletes exhibit a different individual pattern in the production of pro-and antioxidant substances under strenuous exercise remain unknown [41,42]. Excluding the possibility that individual behavior could be related to significant differences in diet, lifestyle habits (such as smoking and alcohol use), exposure to environmental toxins, and, above all, different training loads, as they were almost the same for all subjects, the intervention of genetic factors may be supposed. ...
Vitamins, hormones, free radicals, and antioxidant substances significantly influence athletic performance. The aim of this study was to evaluate whether these biological mediators changed during the season and if this was associated with the rate of improvement in performance after training, assessed by means of a standardized test. Professional male soccer players took part in the study. Two evaluations were performed: the first in the pre-season period and the second at the mid-point of the official season, after about 6 months of intensive training and weekly matches. Blood levels of vitamins D, B12, and folic acid, testosterone and cortisol, free radicals, and antioxidant substances were measured. Two hours after breakfast, a Yo-Yo test was performed. The relationships between the biological mediators and the rate of improvement after training (i.e., the increase in meters run in the Yo-Yo test between the pre-season and mid-season periods) were evaluated by means of a linear mixed models analysis. Results: Eighty-two paired tests were performed. The athletes showed better performance after training, with an increase in the meters run of about 20%. No significant relationships between the vitamin and hormone values and the gain in the performance test were observed. Plasmatic levels of free radicals increased significantly, as did the blood antioxidant potential. An indirect relationship between oxidative stress and the improvement in performance was observed (free radicals β ± SE: = −0.33 ± 0.10; p-value = 0.001), with lower levels of oxidative stress being associated with higher levels of performance in the Yo-Yo test. Monitoring the measures of oxidative stress could be a useful additional tool for coaches in training and/or recovery programs tailored to each player.
... Both situations may disadvantage the player's preparation if the big picture (overall training plan) is not considered: double daily sessions may result in different endocrine, perceptual, and neuromuscular states than when performing single sessions [92]. Recovery from previous training sessions and "readiness" for subsequent training sessions may affect the magnitude of the training response incurred [93]. ...
The increase in the economic value of soccer occurred in parallel with an increase in competing demands. Therefore, clubs and federations evolved to greater specialization (e.g., state-of-the-art facilities and high-profile expertise staff) to support players’ performance and health. Currently, player preparation is far from exclusively club or national team centered, and the lack of control in each player’s environment can be more prevalent than expected. For example, an elite group of professional players faces disruptions in the season club-oriented planification due to involvement in national teams. Moreover, as elite players’ financial resources grow, it is common for them to employ specialized personal staff (e.g., strength and conditioning, nutritionist, and sports psychologist) to assist in their preparation, resulting in complex three-fold relationships (i.e., club, player’s staff, national team). Although efforts have been made to improve communication with and transition from the club to the national team supervision, this new reality (club-players’ staff) may generate serious compound role-related problems and difficulties in monitoring load and training adaptation and having a unified message. Therefore, efforts must be implemented to ensure a more informed management of the players’ performance environment, where the existence and impact of these various personal staff are considered to avoid a long-term non-zero sum for all intervening parties. If left unchecked, current professional thinking may collide or overlap, potentially triggering conflict escalation and impairing athletic performance or health, especially if effective communication routes are not adequately established. Moreover, diluted personal responsibility regarding performance may ensue, resulting in decreased productivity from all involved, which may cause more harm than benefits for the player’s overall health and performance. This emerging reality calls for developing a joint working framework (i.e., between the player’s personalized support team and the clubs’ team) and better managing of a player-centered process.
... Nonetheless, the review's discoveries recommend that the organized actual preparation program effectively evoked positive transformations in strength and perseverance among the members. These enhancements enhance participants' capacity to meet the physical demands of their chosen activities or sports and contribute to overall physical well-being [44]. ...
Introduction: Optic disc swelling (ODS) is a pathological condition with a variety of causes, including optic neuritis (ON), anterior ischemic optic neuropathy, and papilledema. Determining the causes of ODS is critical due to the possibilities of vision-or life-threatening diseases, such as space-occupying lesions. Objective: To assess the clinical and radiology finding of Patients with Bilateral Optic Disc Swelling. Methods: This cross sectional study was conducted in the Department of Ophthalmology, Ad-din Akij Medical College Hospital, Khulna, Bangladesh from January to June 2019. One hundred patients with bilateral disc swelling were selected as study population where bilateral disc swelling due to congenital disc anomaly, pseudo disc edema or need emergency medical care had been purposively excluded from the study. All patients were subjected to detailed ophthalmic examination, including visual acuity (VA), red saturation, bright sensitivity, color vision, and detailed slit lamp examination. All the information’s were recorded in a pre-designed data collection sheet. Results: Total 100 patients included in our study. Most commonly affected age group was between 21-30 years in which 32% case are observed, least common affected age group was 51-60 yr. in which 6% cases are observed. Male patients were 33% and female were 67%. The patients by presenting complaints were headache 71.0% followed by dimness of vision 63.0%. Nausea/ vomiting were present in 42.0% patients and ocular pain had 21 (21.0%) patients. Few (5.0%) had transient loss of vision. Among the patients who had IIH 34 (34%), ICSOL were 25(25%) and grade 4 Hypertensive retinopathy 9% respectively. Among the patients who had bilateral optic neuritis and VKH majority of them 13% and 13% respectively. In right eye, 44 (44.0%) had Visual acuity >0.3 while in left eye, 40 (40.0%) had Visual acuity >0.3. One third had (33.0%) sluggish pupillary response in both eye. Majority of the patients (right: 67.0%, left: 64.0%) had only disc swelling. Seventy-three patients (73.0%) did not have any ocular manifestation other than optic disc swelling while 14 (14.0%) had diplopia, 12 (12.0%) had uveitis and 1 (1.0%) had ptosis. Out of 100 patients, 70 patients (70.0%) did not have any space occupying lesion while 9 (9.0%) had meningioma and 6 (6.0%) had Cerebellopontine (CP) angle tumor. Conclusions: Among them headache is the most common presenting complaint and IIH is the most common clinical diagnosis.
... Nonetheless, the review's discoveries recommend that the organized actual preparation program effectively evoked positive transformations in strength and perseverance among the members. These enhancements enhance participants' capacity to meet the physical demands of their chosen activities or sports and contribute to overall physical well-being [44]. ...
The purpose of this study is to determine whether a structured exercise program can improve students' motor skills. Everyday activities, including academic and professional ones, necessitate strong motor skills. In any case, numerous understudies need assistance with their coordinated movements which can influence their general exhibition. An experimental design with a randomized control group was used in this study. There were two groups of 100 students in the research sample: the exploratory gathering, which went through an organized actual activity program, and the benchmark group, which got no mediation. Before and after the intervention, students' motor skills were assessed using a tested that was proven to work. The outcomes showed that the organized actual activity program altogether further developed understudies' coordinated abilities contrasted with the benchmark group. Motor coordination, agility, and movement accuracy all improved significantly in the experimental group. They also said they performed better on tasks that required motor skills. This study's findings show that students' motor skills can be improved with a structured exercise program. This study's practical implications include the need to incorporate structured physical activity into higher education curriculum to ensure that students have sufficient motor skills for life success.
... A large debate exists in the literature about how to design studies, analyze data measured on a continuous scale and interpret them in the context of inter-individual variability in the responses after an intervention. 15,[22][23][24][46][47][48][49] It appears that this is not solely an issue within the field of exercise science, but rather a concern that applies to all areas of biomedical sciences (e.g., clinical medicine and pharmacology). 16,18 For a long time, the application of a strict binary dichotomization of participants into 'responders' and 'non-responders', often relative to a zero response, was the common approach. ...
Aim:
We aimed to investigate the inter-individual variability in redox and physiological responses of antioxidant-deficient subjects after antioxidant supplementation.
Methods:
Two hundred individuals were sorted by plasma vitamin C levels. A low vitamin C group (n = 22) and a control group (n = 22) were compared in terms of oxidative stress and performance. Subsequently, the low vitamin C group received for 30 days vitamin C (1 g) or placebo, in randomized, double-blind, crossover fashion, and the effects were examined through a mixed-effects model, while individual responses were calculated.
Results:
The low vitamin C group exhibited lower vitamin C (-25 μmol/L; 95%CI[-31.7, -18.3]; p < 0.001), higher F2 -isoprostanes (+17.1 pg/mL; 95%CI[6.5, 27.7]; p = 0.002), impaired VO2max (-8.2 mL/kg/min; 95%CI[-12.8, -3.6]; p < 0.001) and lower isometric peak torque (-41.5 Nm; 95%CI[-61.8, -21.2]; p < 0.001) compared to the control group. Regarding antioxidant supplementation, a significant treatment effect was found in vitamin C (+11.6 μmol/L; 95%CI[6.8, 17.1], p < 0.001), F2 -isoprostanes (-13.7 pg/mL; 95%CI[-18.9, -8.4], p < 0.001), VO2max (+5.4 mL/kg/min; 95%CI[2.7, 8.2], p = 0.001) and isometric peak torque (+18.7; 95%CI[11.8, 25.7 Nm], p < 0.001). The standard deviation for individual responses (SDir) was greater than the smallest worthwhile change (SWC) for all variables indicating meaningful inter-individual variability. When a minimal clinically important difference (MCID) was set, inter-individual variability remained for VO2max , but not for isometric peak torque.
Conclusion:
The proportion of response was generally high after supplementation (82.9%-95.3%); however, a few participants did not benefit from the treatment. This underlines the potential need for personalized nutritional interventions in an exercise physiology context.
... Often the difficulties with monitoring team sport athletes in particular, is the individual variability between athletes in their response to modifiable (health, sleep and training status) and non-modifiable (genetics, weather, and pressure and expectation from media and supporters) factors (Impellizzeri et al., 2019). Indeed, the prescription of the identical training load for one athlete may evoke a completely different internal, psychophysiological response in other athletes from the same team (Bouchard et al., 2011;Mann et al., 2014;Smith, 2003). ...
This systematic review and meta-analysis evaluated the validity of tests / markers of athletic readiness to predict physical performance in elite team and individual sport athletes. Ovid MEDLINE, Embase, Emcare, Scopus and SPORT Discus databases were searched from inception until 15 March 2023. Included articles examined physiological and psychological tests / markers of athletic readiness prior to a physical performance measure. 165 studies were included in the systematic review and 27 studies included in the meta-analysis. 20 markers / tests of athletic readiness were identified, of which five were meta-analysed. Countermovement jump (CMJ) jump height had a large correlation with improved 10m sprint speed / time (r = 0.69; p = .00), but not maximal velocity (r = 0.46; p = .57). Non-significant correlations were observed for peak power (r = 0.13; p = .87) and jump height (r = 0.70; p = .17) from squat jump, and 10m sprint speed / time. CMJ jump height (r = 0.38; p = .41) and salivary cortisol (r = -0.01; p = .99) did not correlate with total distance. Sub-maximal exercise heart rate (r = -0.65; p = .47) and heart rate variability (r = 0.66; p = .31) did not correlate with Yo-Yo Intermittent Recovery Test 1 performance. No correlation was observed between blood C-reactive protein and competition load (r = 0.33; p = .89). CMJ jump height can predict sprint and acceleration qualities in elite athletes. The validity of the other readiness tests / markers meta-analysed warrants further investigation.
... (2023), who indicated that similar physiological adaptations could be facilitated when HIIT is prescribed using the ASR. Many studies have shown a considerable individual variation in response to standardized programs consisting of exercise interventions prescribed using relative intensity and duration (Mann et al., 2014). have argued such variations in homeostatic stress may affect the magnitude of stimulus experienced by athletes with different profiles and result in different adaptive responses over a training period. ...
The current study investigated the efficacy of individualizing exercise intensity according to anaerobic power reserve (APR) on hormonal, physiological, and performance adaptations in athletes with different profiles. Sixteen highly-trained male rowers (age = 22 ± 3 years, height = 183 ± 6 cm, weight = 83 ± 7 kg, body fat = 11 ± 2%, experience = 12 ± 5 years) were randomized to a high-intensity interval training consisting of 2 × (6, 6, 8, 8, 10, 10 repetitions from 1st to 6th week, respectively) × 60 s intervals using a rowing ergometer at ∆%30 APR (APR∆%30) or the same sets and repetitions at 130% maximal aerobic power (MAP130%). In both groups, relief intervals were set at 1:1 with 3 min of rest between sets. On four occasions separated by 24 h recovery, participants attended the laboratory to assess 2000-m rowing ergometer performance, maximal oxygen uptake (V̇O2max) and related physiological adaptations, and hormonal parameters. Significant increases were observed in 2000-m performance, V̇O2max, ventilation at V̇O2max, first and second ventilatory threshold, MAP and maximal sprinting power (MSP), total testosterone, and testosterone to cortisol ratio in response to 6 weeks of APR∆%30 and MAP130% protocols. The coefficient of variation (inter-subject variability) in the adaptive response of cardiorespiratory parameters to HIIT performed using the APR∆%30 protocol was lower than those of the MAP130% group. However, this is not the case for hormonal changes. Prescribing HIIT based on an athlete's APR may help to create a more consistent level of the mechanical and physiological stimulus relative to the athlete's capacity, potentially leading to more similar adaptations across athletes with varying profiles. Mechanisms influencing total testosterone are multifactorial and are not affected by this approach.
... A potential limitation of manual adjustment of external workload is that HR is prescribed in zones, while this approach may not be valid when the maintenance of HR needs to be more accurate (i.e., a specific HR value). For example, given large differences in interindividual responses to standardized exercise training (prescribing HR in zones), the approach of prescribing exercise intensity must be tailored and carefully controlled to achieve the desired outcomes (Mann et al., 2014). Consequently, the inter-individual variability of exercise responses may be reduced when HR is controlled to a fixed value when compared to HR controlled to zones. ...
... Determinants of and correlates with CRF include sex, age, body composition, resting heart rate, physical activity (PA) habits, education, smoking habits, alcohol consumption, and genetics [11,12]. Although the response to exercise may be influenced by individual factors, performing aerobic exercise is essential to improve and maintain CRF [1,13]. Interventions involving regular aerobic exercise are associated with improved aerobic capacity, improved functional ability, reduced pain, and improved CVD risk profile in persons with RA [14,15]. ...
Objectives
Persons with rheumatoid arthritis (RA) have lower cardiorespiratory fitness (CRF) than healthy individuals. We sought to identify variables explaining the association between RA status and reduced CRF.
Methods
RA patients recruited from two Norwegian hospitals and blood donors recruited as controls filled in questionnaires about physical activity, physical symptoms, and psychological factors. Estimated CRF (eCRF) was calculated from non-exercise models. The relationship between RA status and reduced eCRF was explored with structural equation modelling. The latent variables physical symptoms (based on morning stiffness, joint pain, and pain in neck, back, or hips) and negative emotions (based on Hospital Anxiety and Depression Scale’s Depression score and Cohen’s perceived stress scale) were included as possible mediators between RA status and eCRF in separate and combined models adjusted for age and sex.
Results
Two-hundred-and-twenty-seven RA patients and 300 controls participated. The patients were older and had lower eCRF than controls (age- and sex-adjusted mean difference: 1.7 mL/kg/min, p=0.002). Both latent variables were significant mediators of the association between RA and reduced eCRF when included in separate models. The latent variables mediated 74% of the total effect of RA on eCRF in the combined model. Standardized coefficients: direct effect of RA -0.024 (p=0.46), indirect effect through physical symptoms -0.034 (p=0.051), and indirect effect through negative emotions -0.034 (p=0.039).
Conclusion
Both physical symptoms and negative emotions mediated the association between RA and reduced eCRF with similar effect sizes. To successfully increase CRF in RA patients, both physical and psychological factors should be addressed. Key Points • The RA patients in the present study had 1.7 mL/kg/min lower mean estimated cardiorespiratory fitness (CRF) compared to healthy controls. • Mediation analysis demonstrated that physical symptoms and negative emotions mediated 74% of the total negative effect of RA on estimated CRF in a combined, adjusted model. • This suggests that both physical and psychological factors should be addressed when supporting RA patients in improving their CRF.
... Neben den genetischen Voraussetzungen beschreiben Mann und Kollegen (2014) weitere Hauptmoderatorengruppen für die trainingsindizierte Anpassungsreaktion: Homöostatischer Stress (Beanspruchung) jeder Trainingseinheit, Erholtheit bzw. Readiness und Nutrition (Mann et al. 2014). Einen interessanten Ansatz für die Individualisierung und Adjustierung des Trainingsreizes stellt der Vergleich der Akutreaktion mit der Anpassungsreaktion dar. ...
Trainingsprinzipien gelten als Handlungsorientierungen für die Gestaltung des Trainingsprozesses. In den letzten Jahrzehnten haben sich eine Vielzahl dieser Prinzipien etabliert. Je nach Bezugssystem und Sprachraum existieren in der Literatur bis zu 20 dieser Prinzipien. Deren wissenschaftliche Evidenz und die Bedeutung für den Trainingsprozess ist nicht immer klar belegt. Einige dieser Prinzipien sind zudem unscharf von anderen abgrenzbar. Eine integrative und evidenzbasierte Berücksichtigung relevanter Prinzipien sollte im Planungs-, Umsetzungs- und Auswertungsprozess des Trainings unter effizientem Mittel- und Methodeneinsatz im Sinne eines optimalen Anpassungsprozesses gefördert werden. Diese intendierten Anpassungsreaktionen sind reizspezifisch, individuell und müssen unter Berücksichtigung des progressiven Overloads sowie der Belastungsnormativa (Frequenz, Intensität, Typ und Zeit) sorgfältig geplant und überwacht werden. Dieser Beitrag ist Teil der Sektion sportmotorische Fähigkeiten und sportliches Training, herausgegeben vom Teilherausgeber Michael Fröhlich, innerhalb des Handbuchs Sport und Sportwissenschaft, herausgegeben von Arne Güllich und Michael Krüger.
... The amount of time necessary to recover from the effects of detraining is widely debated [9]. The amount of time for training adaptations often differ between people based upon training levels, genetics, and a host of other factors [9,[16][17][18]. Many of these genetic and environmental factors are considerations in the variability of injury recovery times, and why some athletes may recover more quickly [19][20][21]. ...
Background
In 2020, COVID-19 spread across the world and brought the world to a halt, causing the shutdown of nearly everything in order to prevent its spread. The NFL, like most of the world, faced shutdowns leaving athletes unable to train in some of the most advanced facilities with many of the best trainers in the world. Through a previous study, COVID-19 Return to Sport Injury Prevalence Analysis, it was determined that there was increased injury prevalence during the 2020 season likely due to decreased physiological adaptations within athletes bodies that resulted from facility shutdowns. Understanding injury epidemiology is vital in the prevention of injuries and the development of return-to-play protocols.
Objective:
The objective of this study is to perform a follow up study to COVID-19 Return to Sport Injury Prevalence Analysis in order to to examine the longitudinal effects of the COVID-19 pandemic on injury epidemiology. This study will examine if there was a recovery to baseline or lingering effects from the COVID-19 pandemic-induced spike in injuries.
Methods
Injury tallies collected from the 17-week-long 2020 NFL regular season, played after COVID-19 restrictions, were compared with the injury tallies collected from the 18-week-long NFL regular seasons (2021, 2022), in order to determine if there was a change in injury prevalence. An unpaired t-test was conducted to compare the mean injuries per team per week between each of the 2020, 2021, and 2022 regular seasons.
Results
The 2022 and 2021 NFL regular seasons produced lower numbers of total injuries than the 2020 NFL regular season that was impacted by COVID-19. The comparison of the mean number of injuries per team per week of the 2020 season compared with the 2021 regular season was statistically significant (P=.03). The comparison of the 2020 and 2022 regular seasons was also statistically significant (P=.02).
Conclusions
The results of this follow-up study and our previous study show that extended training interruptions have the ability to induce detraining and lead to increased injuries. Additionally, the results of this study show that retraining can occur and lead to injury protective factors. This is the first large scale opportunity to demonstrate the effects of these principles and how they are important to understanding injury epidemiology.
... The two prior studies identifying the intensity of HIFT (Browne et al., 2020;Willis et al., 2019) used a percent max heart rate (%HR) method (Liguori et al., 2021), which opens the possibility of a wide variation in the individual metabolic responses attributed to physiological change (Mann et al., 2014). Recent investigations have proposed using ventilatory thresholds (VT), based on individual metabolic responses, as measures of exercise intensity, because it reduces this variation and leads to better training responsiveness (Weatherwax et al., 2019;Wolpern et al., 2015). ...
High intensity functional training (HIFT) provides a potential option to meet public exercise recommendations for both cardiorespiratory and strength outcomes in a time efficient manner. To better understand the potential for HIFT as an exercise approach, energy expenditure (EE) and relative intensity need quantifying. In thirteen sedentary men and women with metabolic syndrome (MetS), we used both indirect calorimetry and blood lactate levels to calculate EE of a single session of HIFT. The HIFT session included four, 6-minute sets of consecutive functional exercises. Examples of the exercises involved were squats, deadlifts, suspension rows, suspension chest press, and planks. Intensity is described relative to individual ventilatory thresholds. The total group EE was 270.3 ± 77.3 kcal with approximately 5% attributed anaerobic energy production. VO2 ranged between 88.8 ± 12.3% and 99 ± 12% of the second ventilatory threshold (VT2), indicating a vigorous effort. After each work interval, peak blood lactate ranged between 7.9 ± 1.9 and 9.3 ± 2.9 mmol, and rate of perceived exertion between 6.9 ± 1.0 and 8.7 ± 0.8 arbitrary units from 1-10. These were achieved in approximately 46 minutes of exercise per participant. In conclusion, HIFT elicits the energy expenditure and effort requisite to result in the adaptive responses to produce the known suite of benefits of exercise for individuals with MetS.
... 8 While population-level effect estimates demonstrate a protective effect of NMT warm-up programs in reducing injuries, individual response to the NMT warm-up programs can vary dramatically. 22 6,18 and injury history 6,44 that may influence the response to an NMT warmup program in youth who are exposed to NMT. ...
OBJECTIVES: To identify factors associated with non-response to neuromuscular training (NMT) warm-up programs among youth exposed to NMT warm-ups. METHODS: This is a secondary analysis of youth (aged 11-18 years) in the intervention groups of four randomized controlled trials in high school basketball, youth community soccer, and junior high school physical education (PE). Youth who were exposed to NMT and who sustained an injury during the study were considered 'non-responders.' Odds ratios (OR) were based on generalized estimating equations logistic regression controlling for clustering by team/class and adjusted for age, weight, height, balance performance, injury history, sex, and sport (soccer/basketball/PE). RESULTS: A total of 1793 youth were included. Youth with a history of injury in the previous year had higher odds (OR=1.64 95% CI: 1.14-2.37) of injury during the study and females were more likely (OR=1.67, 95% CI: 1.21-2.31) to sustain an injury than males who were participating in NMT. Age was not associated with the odds of sustaining an injury (OR=1.10, 95% CI: 0.93-1.30). Soccer players benefited most from greater adherence, with 81% lower odds of injury (OR=0.19, 95% CI: 0.06-0.57) when completing three NMT sessions a week compared with one session per week. CONCLUSIONS: Factors associated with non-response to an NMT warm-up program were female sex, history of injury during the previous 12 months, and lower weekly NMT session adherence in some sports (soccer).
... Responses to training doses throughout the season (the dose-response relationship) may vary depending on players' initial physical fitness characteristics, and these responses can vary widely among players; this is often described as "high and low responders" [33,34]. In this context, analysis of responding or non-responding players, i.e., determining the player's responsiveness and unresponsiveness to training load, can help explain physical fitness changes [35]. ...
Abstract Purpose This study aimed to (1) analyze the impact of a small-sided game training program in the locomotor profile of youth male soccer players (while interacting with the baseline level – higher and lower level); and (2) test the relationships between variation in locomotor profile and the accumulated demands in 3v3, 5v5 and match over the period of observation. Methods The cohort lasted 3-weeks. Twenty under-17 male amateur soccer players (16.8 ± 0.41 years; experience: 6.35 ± 0.67 years) were assessed twice for their final velocity at 30−15 intermittent fitness test (VIFT), peak speed at 30-m sprint test (PSS) and anaerobic speed reserve (ASR). The PSS was estimated using a Global Positioning System, while the VIFT was estimated using the maximum level attained by the players during the test. Based on the baseline levels, the scores were standardized using the Z-score. The total score of athleticism (TSA) was calculated per player to organize the players into two groups: lower TSA and higher TSA. Over the three weeks of observation, the small-sided games of 3v3 and 5v5 and match demands were monitored using polar team pro. The heart rate responses (mean and peak), distance covered (overall and split by speed thresholds), and peak speed in these games were obtained and summed over the weeks. The repeated measures ANCOVA tested the variations (time) of the locomotor profile of players while considering the baseline as covariable and the group as a factor. The Pearson-product correlation test analyzed the relationships between variations in locomotor profile (Δ, post-baseline) and the accumulated demands in 3v3, 5v5, and match. Results Between-groups analysis (lower TSA vs. higher TSA) revealed no significant differences on VIFT (p = 0.915), PSS (p = 0.269), ASR (p = 0.258) and TSA score (p = 0.138). Within-group (baseline vs. post-observation) analysis revealed significant difference on VIFT (p
... The two prior studies identifying the intensity of HIFT (Browne et al., 2020;Willis et al., 2019) used a percent max heart rate (%HR) method (Liguori et al., 2021), which opens the possibility of a wide variation in the individual metabolic responses attributed to physiological change (Mann et al., 2014). Recent investigations have proposed using ventilatory thresholds (VT), based on individual metabolic responses, as measures of exercise intensity, because it reduces this variation and leads to better training responsiveness (Weatherwax et al., 2019;Wolpern et al., 2015). ...
High intensity functional training (HIFT) provides a potential option to meet public exercise recommendations for both cardiorespiratory and strength outcomes in a time efficient manner. To better understand the potential for HIFT as an exercise approach, energy expenditure (EE) and relative intensity need quantifying. In thirteen sedentary men and women with metabolic syndrome (MetS), we used both indirect calorimetry and blood lactate levels to calculate EE of a single session of HIFT. The HIFT session included four, 6-minute sets of consecutive functional exercises. Examples of the exercises involved were squats, deadlifts, suspension rows, suspension chest press, and planks. Intensity is described relative to individual ventilatory thresholds. The total group EE was 270.3 ± 77.3 kcal with approximately 5% attributed anaerobic energy production. VO2 ranged between 88.8 ± 12.3% and 99 ± 12% of the second ventilatory threshold (VT2), indicating a vigorous effort. After each work interval, peak blood lactate ranged between 7.9 ± 1.9 and 9.3 ± 2.9 mmol, and rate of perceived exertion between 6.9 ± 1.0 and 8.7 ± 0.8 arbitrary units from 1-10. These were achieved in approximately 46 minutes of exercise per participant. In conclusion, HIFT elicits the energy expenditure and effort requisite to result in the adaptive responses to produce the known suite of benefits of exercise for individuals with MetS.
... According to these criteria, one of the biggest challenges is to find a suitable and tailored training modality, depending on the individual performance capacity, the risk profile, intended prehabilitation aim and the available time until the surgery. However, the level and speed of physical adaptation to exercise stress differs among individuals and is partially genetically determined [33,34]. For this purpose, performance-based indicators [e.g. ...
Purpose of review:
The purpose of this narrative review is to give an overview about the effects of multimodal prehabilitation and current existing and prospectively planned studies. The potential efficacy of exercise in the context of prehabilitation ranges from preoperatively improving patients' functional capacity to inducing cellular mechanisms that affect organ perfusion via endothelial regeneration, anti-inflammatory processes and tumour defense.
Recent findings:
Current studies show that prehabilitation is capable of reducing certain postoperative complications and length of hospital stay in certain patient populations. These findings are based on small to mid-size trials with large heterogeneity, lacking generalizability and evidence that prehabilitation has positive effects on long term survival.
Summary:
The concept of prehabilitation contains the features, namely preoperative exercise, nutritional intervention and psychological support. Preoperative exercise holds potential molecular effects that can be utilized in the perioperative period in order to improve patients' postoperative outcome. Future multimodal prehabilitation trials must specifically clarify the clinical impact of this concept on patients' quality of life after major cancer surgery and cancer-specific survival.
... Variability in training effects may be associated with variability in training volume. Furthermore, the concept of high versus low responders may provide insights about the mechanisms of training adaptation (Mann et al., 2014). ...
This study aimed to examine the association between interindividual variability in strength changes and in training volume. A total of 26 untrained men completed 4-weeks of isometric knee extension (KE group, n = 12) and hip flexion (HF group, n = 14) training. Each training session comprised four sets of ten isometric contractions, 3-s contractions every 20 s. Training volume, which was defined as impulse during contractions, and maximal voluntary contraction (MVC) torque during KE and HF were evaluated. Based on the magnitude of MVC torque changes, the participants were divided into the high and low responders (n = 13; KE = 6 and HF = 7 per responders). The MVC torque changes (KE, 20.8%; HF, 22.4%) and total training volume did not significantly differ between the two groups. A higher training volume was demonstrated in the low responders than the high responders. The total training volume was positively associated with the MVC torque changes in low responders (r = 0.869%, 95% confidence interval [0.610, 0.960], p < 0.001), but not in high responders [r = 0.229, 95% confidence interval (−0.368, 0.693), p = 0.451], KE or HF group. Results showed that training volume was an important factor in determining the magnitude of strength gains in low responders, and MVC torque could improve by approximately 20% with the use of the study protocol regardless of joint actions involved during training.
... While some studies have focused on exercise intensity and frequency in this regard [40,41], not all individuals are capable of safely performing exercise at ever more intense levels. We therefore investigated whether changing the modality of exercise could enhance the response to training and stimulate positive physiological and health adaptations [42]. Some previous cross-over designed studies have investigated changes in fitness (VO 2 max) in response to END and RES training. ...
Background
Individual variability in traditional cardiovascular risk factor responses to different exercise modalities has not been directly addressed in humans using a randomized cross-over design.
Methods
Body weight and body mass index, resting blood pressure, blood glucose, insulin and lipids were assessed in 68 healthy untrained adults (26±6 years) who underwent three-months of exercise training targeted at improving cardiopulmonary fitness (endurance) and skeletal muscle function (resistance), separated by three-months washout.
Results
There were significant increases in weight and body mass index following resistance (+0.8 kg, P<0.01; and +0.26 kg/m ² , P<0.01, respectively), but not endurance (+0.1 kg, P = 0.75; and +0.03 kg/m ² , P = 0.70, respectively). Although no significant group changes resulted from training in other cardiovascular risk factors, the positive response rate for all variables ranged from 27–49% for resistance and 42–58% for endurance. Between 39–59% of individuals who did not respond to resistance nonetheless responded to endurance, and 28–54% who did not respond to endurance responded to resistance.
Conclusion
Whilst, on average, 12 weeks of resistance or endurance did not change most cardiovascular risk factors, many subjects showed robust positive responses. Exercise modality had an impact on the proportion of subjects who responded to training, and non-response to one mode of training did not imply non-response to the alternate mode. Although the effect of exercise on a single risk factor may be modest, the effect on overall cardiovascular risk profile can be dramatic.
Study registration
The study was registered at the Australian New Zealand Clinical Trials Registry, which was published prior to recruitment and randomization ( ACTRN12616001095459 ).
... In contrast, a person reporting lower stress recovered in about 1 day. Such an effect may help to account for why some individuals respond to exercise with positive adaptations, while others have little to no change (83). Indeed, those reporting higher chronic stress have been shown to gain less strength over a multi-month resistance training program (16). ...
Chapter 13 of ACSM's Resources for the Exercise Physiologist. Health Stress Management.
... 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.
... The current physical activity guidelines targeting fitness and fatness alone utilise a standardised exercise prescription, using either relative percentages of maximal oxygen uptake (VO2 max), VO2 reserve (VO2R), or heart rate reserve (HRR) to establish exercise at moderate-(40 -60% HRR or VO2R) or vigorous to high-intensity (60 -80% HRR or VO2R) . This generic method of exercise prescription, however, often results in a wide variability in responses due to the large inter-individual variation in the metabolic responses to exercise training (Bouchard et al., 2012;Scharhag-Rosenberger et al., 2012;Mann et al., 2014). To better account for the individual metabolic responses when prescribing vigorous to high-intensity exercise, a more individualised approach has been proposed since a standardized method both over-and under-estimates the metabolic responses (Weatherwax et al., 2019). ...
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.
Purpose To investigate the influence of exercise intensity normalisation on intra- and inter-individual acute and adaptive responses to an interval training programme.
Methods Nineteen cyclists were split in two groups differing (only) in how exercise intensity was normalised: 80% of the maximal work rate achieved in an incremental test (%Ẇmax) vs. maximal sustainable work rate in a self-paced interval training session (%Ẇmax-SP). Testing duplicates were conducted before and after an initial control phase, during the training intervention, and at the end, enabling the estimation of inter-individual variability in adaptive responses devoid of intra-individual variability.
Results Due to premature exhaustion, the median training completion rate was 88.8% for the %Ẇmax group, but 100% for the %Ẇmax-SP group. Ratings of perceived exertion and heart rates were not sensitive to how intensity was normalised, manifesting similar inter-individual variability, although intra-individual variability was minimised for the %Ẇmax-SP group. Amongst six adaptive response variables, there was evidence of individual response for only maximal oxygen uptake (standard deviation: 0.027 L· min-1· week- 1) and self-paced interval training performance (standard deviation: 1.451 W· week-1). However, inter- individual variability magnitudes were similar between groups. Average adaptive responses were also similar between groups across all variables.
Conclusions To normalise completion rates of interval training, %Ẇmax-SP should be used to prescribe relative intensity. However, the variability in adaptive responses to training may not reflect how exercise intensity is normalised, underlining the complexity of the exercise dose-adaptation relationship. True inter- individual variability in adaptive responses cannot always be identified when intra-individual variability is accounted for.
Time-of-day differences in acute exercise performance in mice are well established with late active phase (afternoon) runners exhibiting significantly greater endurance performance compared to early active phase (morning) runners. In this study, we asked if performance adaptations would be different when training for 6 weeks at two different times of day, and if this corresponds to steady state changes in the phase of peripheral tissue clocks. To address these questions, we endurance trained female PER2::Luciferase mice, at the same relative workload, either in the morning, at ZT13, or in the afternoon, at ZT22. Then, after training, we recorded luminescence from tissues of PER2::Luciferase mice to report timing of tissue clocks in several peripheral tissues. After 6 weeks, we found that both groups exhibited significant improvements in maximal endurance capacity (total treadmill work)(p < 0.0001), but the morning runners exhibited an enhanced rate of adaptation as there was no detectable difference in maximal endurance capacity (p = 0.2182) between the morning and afternoon runners. In addition, morning and afternoon runners exhibited divergent clock phase shifts with a significant 5-hour phase advance in the EDL (p < 0.0001) and soleus (p < 0.0001) of morning runners, but a phase delay in the EDL (p < 0.0001) and Soleus (p < 0.0001) of afternoon runners. Therefore, our data demonstrate that morning training enhances endurance adaptations compared to afternoon training in mice, and we suggest this is due to phase advancement of muscle clocks to better align metabolism with exercise performance.
The proportion of individuals whose cardio-respiratory fitness change after endurance training does not exceed the test's measurement error can be 40 %. We determined if progressively increasing treadmill run intensity compared to maintaining the same run intensity, improved the responder proportion to a 6-week 20-minute treadmill training regimen. The intervention response standard deviation method estimated the proportion of responders attributable to progressively increasing run intensity. The mixed-effects model demonstrated V̇O2 peak improved significantly more in the progressive versus constant run intensity group. The proportion of V̇O2 peak responses above the smallest worthwhile change attributable to progressively increasing run intensity was 63.6 %.
Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.
There is a growing focus on developing person-adaptive strategies to support sustained exercise behavior, necessitating conceptual models to guide future research and applications. This paper introduces Flexible nonlinear periodization (FNLP) - a proposed, but underdeveloped person-adaptive model originating in sport-specific conditioning - that, pending empirical refinement and evaluation, may be applied in health promotion and disease prevention settings. To initiate such efforts, the procedures of FNLP (i.e., acutely and dynamically matching exercise demand to individual assessments of mental and physical readiness) are integrated with contemporary health behavior evidence and theory to propose a modified FNLP model and to show hypothesized pathways by which FNLP may support exercise adherence (e.g., flexible goal setting, management of affective responses, and provision of autonomy/variety-support). Considerations for future research are also provided to guide iterative, evidence-based efforts for further development, acceptability, implementation, and evaluation.
Evidence strongly shows that ACE I/D and ACTN3 R577X genetic polymorphisms are closely related to outstanding exercise performance. This study explored the relationships between the two polymorphisms and the response to short-term high-altitude exercise training. 49 young Han nationality male subjects who were newcomers to high-altitude were selected. At 3200-m high-altitude, the subjects were trained for 4 weeks, and 30-m × 2 snake run, pull-up, sit-up and 3000-m run were tested before and after training. ACE gene was grouped by II and ID + DD genotypes, and ACTN3 gene was grouped by RR and RX + XX genotypes. Results showed that the performances of ACE ID + DD groups were both slight lower than II groups in pull-up and sit-up before training, while no differences after adjusting for covariates (age, body mass index and pre-training baseline) after training. No differences existed between the groups in 30-m × 2 snake run and 3000-m run, whether before training or after training. No ACE gene × training interactions were found in all exercise indicators. For ACTN3 gene, no significances were observed. Results suggest that ACE I/D polymorphism maybe have slight effect on the response to short-term high-altitude strength training. The discussion on the results implies that enough training duration and intensity are probably important in achieving significant gene × training interaction, and there may be the difference of gender in the interaction for ACTN3 gene.
Muscle hypertrophy is the increase in the size of the transverse diameter of muscle fibers. Although the mechanisms and signaling pathways that regulate hypertrophy are known, it is still unclear how much genetics and epigenetics contribute to this process; whether both mechanisms participate jointly, or whether there is a more significant influence of one event than the other. Therefore, this study aimed to extensively review the literature and determine the role of genes and epigenetic mechanisms in regulating muscle hypertrophy associated with physical activity and sport. For this purpose, the Scopus and ScienceDirect databases were reviewed, and the PubMed and Google Scholar search engines were used, where 25 articles met the inclusion criteria. Results demonstrated that there are several regulatory genes of muscle hypertrophy, such as MSTN , PGC-1 α , STARS , and JunB , among others, as well as genetic polymorphisms and vital participation of DNA methylation, which together would control signaling pathways and gene networks necessary for the development of this process.
Diabetes alters numerous physiological functions and can lead to disastrous consequences in the long term. Neuromuscular function is particularly affected and is impacted early, offering an opportunity to detect the onset of diabetes-related dysfunctions and follow the advancement of the disease. The role of physical training for counteracting the deleterious effects of diabetes is well accepted but at the same time, it appears difficult to reliably assess the effects of exercise on functional capacity in patients with diabetic peripheral neuropathy (DPN). In this paper, we will review the specific characteristics of various neuromuscular dysfunctions associated with diabetes according to the DPN presence or not, and their changes over time. We present several propositions regarding the onset of neuromuscular alterations in people with diabetes compared to people with DPN. It appears that motor unit loss and neuromuscular transmission impairment are among the main mechanisms explaining the considerable degradation of neuromuscular function in the transition from a diabetic to neuropathic state. Rate of force development and contractile properties could start to decrease with the onset of preferential type II fiber atrophy, commonly reported in people with DPN. Finally, Mmax amplitude could decrease with neuromuscular fatigue only in people with DPN, reflecting the fatigue-related neuromuscular transmission impairment reported in people with DPN. In this review, we show that the different neuromuscular parameters are altered at different stages of diabetes, according to the presence of DPN or not. The precise evaluation of these parameters might participate in adapting the physical training prescription.
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.
Purpose:
To examine individual exercise response rates across a range of cardiometabolic variables, cardiorespiratory fitness, and body composition in adults.
Methods:
A retrospective analysis of data from three randomized controlled trials. Participants include those who completed the given trial (Control, n = 87; Intervention, n = 251). Anthropometric (weight, BMI, waist circumference), cardiorespiratory fitness (VO2 peak), MRI-measured total adipose tissue (AT), abdominal subcutaneous AT, and visceral AT and common cardiometabolic variables were assessed pre- and post-intervention using standard methodologies. The technical error (TE), which includes both the day-to-day variability and instrument error, was calculated using pre- and post-intervention data from the time-matched control group.
Results:
On average all anthropometric, MRI, and VO2 peak variables improved significantly following intervention compared to the control group (p < 0.05). With the exception of glucose disposal rate (37%), following intervention less than 13% of participants improved cardiometabolic outcome measures beyond the day-to-day variability of measurement. In other words, the individual response for 63-96% of participants fell within the uncertain range (2 TEs). Similarly, for absolute VO2 peak (l/min), only 45% of participants improved beyond 2 TEs. By comparison, for MRI-derived variables, the majority of participants (77%, 58% and 51% for total AT, abdominal subcutaneous AT and visceral AT, respectively), improved beyond 2 TEs. The observed reductions beyond 2 TEs for WC and body weight were 53 and 63% respectively.
Conclusions:
The findings suggest extreme caution when inferring that the cardiometabolic and cardiorespiratory fitness response for a given individual is attributable to the exercise dose prescribed.
Patients with heart failure and reduced ejection fraction (HFrEF) exhibit sympathetic activation and exercise intolerance,1,2 both of which independently predict foreshortened survival.3,4 We reported previously that their muscle sympathetic nerve activity (MSNA) is elevated at rest¹ and, unlike healthy individuals, also during dynamic leg exercise.⁵ Both values related inversely to peak oxygen uptake (V̇O2peak),1,6 and fell after 6 months of exercise-based cardiac rehabilitation.⁷ However, sympatho-inhibition was not evident in all participants.⁷ Such autonomic variability suggests that conventional programmes benefit some with HFrEF more than others. Which patients derive the most autonomic benefit from exercise training is presently unknown, and identified in our recent review as an important HFrEF research question.⁸
We tested the hypothesis that those HFrEF patients with V̇O2peak less than the cohort median, and thus higher MSNA during exercise, would exhibit a greater training-induced reduction in sympathetic discharge during mild-to-moderate one-leg cycling than those whose V̇O2peak exceeded this median.
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.
The primary aim of this study was to determine whether chronic mental stress moderates recovery of muscular function, perceived energy, fatigue, and soreness in the first hour after a bout of strenuous resistance exercise.
Thirty-one undergraduate resistance training students (age = 20.26 ± 1.34 yr) completed the Perceived Stress Scale and Undergraduate Stress Questionnaire (USQ; a measure of life event stress) and completed fitness testing. After 5 to 14 d of recovery, they performed an acute heavy-resistance exercise protocol (10-repetition maximum (RM) leg press test plus six sets: 80%-100% of 10 RM). Maximal isometric force (MIF) was assessed before exercise, after exercise, and at 20, 40, and 60 min postexercise. Participants also reported their levels of perceived energy, fatigue, and soreness. Recovery data were analyzed with hierarchical linear modeling growth curve analysis.
Life event stress significantly moderated linear (P = 0.013) and squared (P = 0.05) recovery of MIF. This relationship held even when the model was adjusted for fitness, workload, and training experience. Likewise, perceived stress moderated linear recovery of MIF (P = 0.023). Neither USQ nor Perceived Stress Scale significantly moderated changes in energy, fatigue, or soreness.
Life event stress and perceived stress both moderated the recovery of muscular function, but not psychological responses, in the first hour after strenuous resistance exercise.
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, substantial variation exists between individual human genomes, including 'replication' of gene sequences (copy number variation, tandem repeats), or changes in individual base pairs (mutations if <1% frequency and single nucleotide polymorphisms if >1% frequency). A vast array of human phenotypes (e.g. muscle strength, skeletal structure, tendon elasticity, and heart and lung size) influences sports performance, each itself the result of a complex interaction between a myriad of anatomical, biochemical and physiological systems. This article discusses the role for genetic influences in influencing sporting performance and injury, offering specific exemplars where these are known. Many of these preferable genotypes are uncommon, and their combination even rarer. In theory, the chances of an individual having a perfect sporting genotype are much lower than 1 in 20 million - as the number of associated polymorphisms increase, the odds decrease correspondingly. Many recently discovered polymorphisms that may affect sports performance have been described in animal or other human based models, and have been included in this review if they may apply to athletic populations. Muscle performance is heavily influenced by basal muscle mass and its dynamic response to training. Genetic factors account for approximately 50-80% of inter-individual variation in lean body mass, with impacts detected on both 'training-naive' muscle mass and its growth response. Several cytokines such as interleukin-6 and -15, cilliary neurotrophic factor and insulin-like growth factor (IGF) have myoanabolic effects. Genotype-associated differences in endocrine function, necessary for normal skeletal muscle growth and function, may also be of significance, with complex interactions existing between thyroxine, growth hormone and the downstream regulators of the anabolic pathways (such as IGF-1 and IGF-2). Almost 200 polymorphisms are known to exist in the vitamin D receptor (VDR) gene. VDR genotype is associated with differences in strength in premenopausal women. VDR expression decreases with age and VDR genotype is associated with fat-free mass and strength in elderly men and women. Muscle fibre type determination is complex. Whilst initial composition is likely to be strongly influenced by genetic factors, training has significant effects on fibre shifts. Polymorphisms of the peroxisome proliferator-activated receptor α (PPARα) gene and R577x polymorphism of the ACTN3 gene are both associated with specific fibre compositions. Alterations in cardiac size have been associated with both increased performance and excess cardiovascular mortality. PPARα is a ligand-activated transcription factor that regulates genes involved in fatty acid uptake and oxidation, lipid metabolism and inflammation. Psychology plays an important role in training, competition, tolerance of pain and motivation. However, the role of genetic variation in determining psychological state and responses remains poorly understood; only recently have specific genes been implicated in motivational behaviour and maintenance of exercise. Thyroid hormone receptors exist within the brain and influence both neurogenesis and behaviour. With the current state of knowledge, the field of genetic influences on sports performance remains in its infancy, despite over a decade of research.
The association between stress and cardiovascular disease (CVD) risk is becoming established. A mechanistic link clarifying the intermediate steps between the experience of stress and the development of CVD would support this association. We sought to examine the role of perceived stress as a factor associated with disturbed sleep with the goal of providing an explanation for the stress-CVD connection. We performed a cross-sectional analysis of data recorded by subjects at entry to our CVD prevention program. Data collection included questionnaire surveys, anthropometrics, and a CVD-relevant laboratory panel. Of 350 consecutively enrolled subjects (mean age 54.4 ± 12.4 [SD] years, 138 men, 39%), 165 (47%) scored above the mean for stress measures. These high-stress subjects displayed an increased cardiovascular risk profile including elevated body mass index (mean ± SD 31.1 ± 5.9 vs. 29.0 ± 5.9, r(s) = 0.175), increased waist circumference (102 ± 17 cm vs. 98 ± 14, r(s) = 0.135), and elevated high-sensitivity serum C-reactive protein (0.384 mg/dl vs. 0.356, r(s) = 0.109). High-stress subjects also demonstrated greater daytime sleepiness (Epworth Sleepiness Scale: 10.4 ± 5.0 vs. 7.8 ± 4.8, r(s) < 0.316), greater fatigue (fatigue scale: 5.4 ± 2.2 vs. 3.4 ± 2.4, r(s) = 0.484), poorer sleep quality (Pittsburgh Sleep Quality Index: 8.5 ± 4.4 vs. 5.9 ± 4.0, r(s) = 0.416), and shorter sleep duration (20 min less/24 h, r(s) = negative 0.177) with a higher risk for sleep apnea (60% at high risk vs. 40%, p = 0.003) than low-stress subjects. High stress was associated with significant disturbances in sleep duration and sleep quality. Stress levels also correlated with daytime consequences of disturbed sleep. The stress-sleep connection may be an important mechanistic mediator of the association between stress and CVD.
Overload principle of training states that training load (TL) must be sufficient to threaten the homeostasis of cells, tissues, organs and/or body. However, there is no “golden standard” for TL measurement. The aim of the present study was to investigate if post-exercise heart rate variability (HRV) could be used to evaluate TL of interval running exercises with different intensities and durations. Thirteen endurance-trained men (35 ± 5 years) performed MO250 [moderate intensity, 2 × 6 × 250 m/rec 30 s/5 min at 85% of the maximal velocity of the graded maximal test (V
max)], MO500 (2 × 3 × 500 m/rec 1 min/5 min at 85% V
max) and HI250 (high intensity, 2 × 6 × 250 m/rec 30 s/5 min at 105% V
max) interval exercises on a treadmill. HRV was analyzed during rest, exercise and immediate 15 min recovery. Fast recovery of LFP (P < 0.001), HFP (P < 0.01) and TP (P < 0.01) occurred during the first two recovery minutes after each exercise. Strong negative correlations (P < 0.01) were found between post-exercise HRV and perceived exertion as well as excess post-exercise oxygen consumption. Post-exercise HRV differentiated interval exercises of equal work, but varying intensity or distance of running bout. The results of the present study suggest that immediate post-exercise HRV may offer objective information on TL of interval exercises with different bout durations and intensities.
The incidences of diseases related to mental stress are increasing in Japan. Mental stress, unacknowledged for long periods, has been shown to lead to the development of a number of diseases. Thus, an index for mental stress is important to induce awareness of its presence. We focused on the relationship between cortisol and mental stress in this review. We will discuss both the usefulness and problems of cortisol as a mental stress index by summarizing the relationship between cortisol and mental stress. The present findings suggested that cortisol appears to be an adequate index for mental stress. However, there are several problems; the present group clarifies these problems and builds the comprehensive mental stress assessment systems by using saliva samples.
Low cardiorespiratory fitness is a powerful predictor of morbidity and cardiovascular mortality. In 473 sedentary adults, all whites, from 99 families of the Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study, the heritability of gains in maximal O(2) uptake (VO(2max)) after exposure to a standardized 20-wk exercise program was estimated at 47%. A genome-wide association study based on 324,611 single-nucleotide polymorphisms (SNPs) was undertaken to identify SNPs associated with improvements in VO(2max) Based on single-SNP analysis, 39 SNPs were associated with the gains with P < 1.5 × 10(-4). Stepwise multiple regression analysis of the 39 SNPs identified a panel of 21 SNPs that accounted for 49% of the variance in VO(2max) trainability. Subjects who carried ≤9 favorable alleles at these 21 SNPs improved their VO(2max) by 221 ml/min, whereas those who carried ≥19 of these alleles gained, on average, 604 ml/min. The strongest association was with rs6552828, located in the acyl-CoA synthase long-chain member 1 (ACSL1) gene, which accounted by itself for ~6% of the training response of VO(2max). The genes nearest to the SNPs that were the strongest predictors were PR domain-containing 1 with ZNF domain (PRDM1); glutamate receptor, ionotropic, N-methyl-D-aspartate 3A (GRIN3A); K(+) channel, voltage gated, subfamily H, member 8 (KCNH8); and zinc finger protein of the cerebellum 4 (ZIC4). The association with the SNP nearest to ZIC4 was replicated in 40- to 65-yr-old, sedentary, overweight, and dyslipidemic subjects trained in Studies of a Targeted Risk Reduction Intervention Through Defined Exercise (STRRIDE; n = 183). Two SNPs were replicated in sedentary obese white women exercise trained in the Dose Response to Exercise (DREW) study (n = 112): rs1956197 near dishevelled associated activator of morphogenesis 1 (DAAM1) and rs17117533 in the vicinity of necdin (NDN). The association of SNPs rs884736 in the calmodulin-binding transcription activator 1 (CAMTA1) locus and rs17581162 ~68 kb upstream from regulator of G protein signaling 18 (RGS18) with the gains in VO(2max) in HERITAGE whites were replicated in HERITAGE blacks (n = 247). These genomic predictors of the response of Vo(2max) to regular exercise provide new targets for the study of the biology of fitness and its adaptation to regular exercise. Large-scale replication studies are warranted.