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High Responders and Low Responders: Factors Associated with Individual Variation in Response to Standardized Training
Abstract
The response to an exercise intervention is often described in general terms, with the assumption that the group average represents a typical response for most individuals. In reality, however, it is more common for individuals to show a wide range of responses to an intervention rather than a similar response. This phenomenon of 'high responders' and 'low responders' following a standardized training intervention may provide helpful insights into mechanisms of training adaptation and methods of training prescription. Therefore, the aim of this review was to discuss factors associated with inter-individual variation in response to standardized, endurance-type training. It is well-known that genetic influences make an important contribution to individual variation in certain training responses. The association between genotype and training response has often been supported using heritability estimates; however, recent studies have been able to link variation in some training responses to specific single nucleotide polymorphisms. It would appear that hereditary influences are often expressed through hereditary influences on the pre-training phenotype, with some parameters showing a hereditary influence in the pre-training phenotype but not in the subsequent training response. In most cases, the pre-training phenotype appears to predict only a small amount of variation in the subsequent training response of that phenotype. However, the relationship between pre-training autonomic activity and subsequent maximal oxygen uptake response appears to show relatively stronger predictive potential. Individual variation in response to standardized training that cannot be explained by genetic influences may be related to the characteristics of the training program or lifestyle factors. Although standardized programs usually involve training prescribed by relative intensity and duration, some methods of relative exercise intensity prescription may be more successful in creating an equivalent homeostatic stress between individuals than other methods. Individual variation in the homeostatic stress associated with each training session would result in individuals experiencing a different exercise 'stimulus' and contribute to individual variation in the adaptive responses incurred over the course of the training program. Furthermore, recovery between the sessions of a standardized training program may vary amongst individuals due to factors such as training status, sleep, psychological stress, and habitual physical activity. If there is an imbalance between overall stress and recovery, some individuals may develop fatigue and even maladaptation, contributing to variation in pre-post training responses. There is some evidence that training response can be modulated by the timing and composition of dietary intake, and hence nutritional factors could also potentially contribute to individual variation in training responses. Finally, a certain amount of individual variation in responses may also be attributed to measurement error, a factor that should be accounted for wherever possible in future studies. In conclusion, there are several factors that could contribute to individual variation in response to standardized training. However, more studies are required to help clarify and quantify the role of these factors. Future studies addressing such topics may aid in the early prediction of high or low training responses and provide further insight into the mechanisms of training adaptation.
... High-intensity interval training (HIIT) is widely regarded as an effective method for enhancing athletic performance, with numerous studies demonstrating its significant effects on improving cardiorespiratory function, muscle strength, and metabolic health (Faelli et al., 2022). However, individualized training programs still face challenges in reducing variation in adaptation responses (Mann et al., 2014). Given the different physiological characteristics and training backgrounds of athletes, ensuring standardized training intensity while effectively accommodating individual differences remains an unresolved issue (Mann et al., 2014;Bossi et al., 2023;Bassett and Howley, 2000). ...
... However, individualized training programs still face challenges in reducing variation in adaptation responses (Mann et al., 2014). Given the different physiological characteristics and training backgrounds of athletes, ensuring standardized training intensity while effectively accommodating individual differences remains an unresolved issue (Mann et al., 2014;Bossi et al., 2023;Bassett and Howley, 2000). ...
... Based on the above discussion, this study aims to explore the impact of individualized HIIT on the physiological adaptation of rowers, particularly the potential of SIT to reduce variability in training outcomes. We hypothesize that, due to the highintensity demands of SIT, it will lead to more consistent adaptive responses and significantly improve anaerobic capacity and cardiorespiratory adaptations, thereby providing a scientific basis and practical guidance for individualized training (Mann et al., 2014;Bossi et al., 2023;Meyler et al., 2023;Bossi et al., 2023;Bagger et al., 2023;Thurlow et al., 2024). ...
Objective
This study aimed to compare the consistency of physiological adaptations and inter-individual variability in response to three distinct high-intensity interval training (HIIT) protocols—anaerobic power reserve (APR), maximal aerobic power (MAP), and sprint interval training (SIT)—among elite male rowers. By exploring the impact of individualized intensity prescriptions, we sought to identify the most effective protocol for enhancing training consistency, as well as improving both aerobic and anaerobic performance while minimizing variability in individual responses.
Methods
Thirty well-trained male rowers (mean age: 24.9 ± 3.1 years; height: 185 ± 4.4 cm; body mass: 86 ± 7.9 kg; body fat: 12.5% ± 2.4%) participated in the study. All participants were members of a national rowing team with an average of 6 years of competitive experience and regular participation in national and international championships. The intervention involved 6 weeks of individualized HIIT, performed three times per week, with pre- and post-tests assessing VO2max, cardiovascular efficiency (Qmax), anaerobic power (MSP, CP), and 2,000-m rowing performance.
Results
All interventions resulted in significant improvements in VO2max, Qmax, MSP, and 2,000-m rowing time trial performance (p < 0.05). The SIT group exhibited the largest relative improvements, with VO2max increasing by 6.3% (from 51.9 ± 3.2 to 55.2 ± 3.3 mL·kg⁻¹·min⁻¹, Cohen’s d = 1.05, 95% CI [0.57, 1.53]), Qmax by 6.4% (Cohen’s d = 1.15, 95% CI [0.66, 1.64]), and a 3.7% reduction in 2,000-m time (Cohen’s d = 0.86, 95% CI [0.39, 1.33]). Notably, SIT demonstrated the lowest variability across all measured outcomes, as evidenced by reduced coefficients of variation and narrower confidence intervals.
Conclusion
The SIT protocol, emphasizing maximal exertion, led to the most consistent adaptations and the greatest improvements across key performance metrics, including VO2max, Qmax, and 2,000-m rowing performance. These results suggest that SIT may be the optimal approach for improving performance consistency and maximizing physiological adaptations in elite rowers. Future research should explore the long-term applicability and potential integration of SIT with other training modalities to further enhance rowing performance.
... A time-regimen interaction was observed for VȮ 2max [F (2,27) No between-group difference was seen at the baseline for the measured variables (p > 0.05). Both groups significantly enhanced measures of cardiorespiratory fitness over time. ...
... Exercise intervention outcomes are typically presented as mean group values, assuming that these averages accurately reflect individual responses. Individual adaptive responses to a given intervention have been shown to vary widely among participants due to factors such as physiological ceilings, locomotor profiles, and trainability, as reported in previous studies [10][11][12]27 . Our findings corroborate earlier studies highlighting significant diversity in individual responses to diverse HIIT interventions 4,28,29 . ...
... The variability in physiological demands during training sessions is associated with a range of adaptive responses, as demonstrated in prior research 31 . The cumulative external load induced during an exercise is affected by parameters such as duration and intensity, acting as a stimulus that initiates subsequent changes 27 . Adaptive alterations in cellular levels to a given intervention are a consequence of the total collective effect of particular transcriptional and translational 'micro-adaptations' that manifest following intervention 32 . ...
This study aimed to investigate the uniformity of adaptive changes in cardiorespiratory fitness and anaerobic power to high-intensity interval interventions (HIIT) designed using techniques specified for soccer players. Thirty well-trained athletes (age = 25 ± 3.1 years; body mass = 82.4 ± 3.4 kg; height = 183 ± 2.1 cm) were randomly assigned to two experimental groups engaging in interval intervention individualized using 30–15 Intermittent Fitness Test [HIITvIFT (two sets of 5–8 min intervals, comprising 15 s of running at 95% of VIFT followed by 15 s of passive recovery)], and small-sided game with matched timing (SSG [4 sets of 2.5–4 min 3 v 3 efforts]), as well as an active control group. Before and after a 6-week intervention consisting of three sessions per week, participants underwent a lab-based cardiorespiratory fitness test using breath-by-breath gas analyzer and non-invasive impedance cardiography to evaluate aerobic fitness and cardiac function measures. Also, anaerobic power was measured using lower-body Wingate test. Both interventions resulted in significant enhancement in all measures of cardiorespiratory fitness and anaerobic power over the training period. Analyzing inter-individual variability through determining residuals in individual changes indicated HIITvIFT results in residuals in individual changes in ventilatory threshold (VT [first (VT1) and second (VT2)]), and peak and average power output than SSG (p = 0.02, 0.04, 0.02, and 0.01, respectively). In addition, the change in maximum oxygen uptake, maximal ventilation, and average power output following HIITvIFT was notably greater than SSG (p = 0.002, 0.006, and, 0.019, respectively). There was no significant difference between HIITvIFT and SSG in cardiac hemodynamics (cardiac output and stroke volume). Overall, by facilitating more homogenous stress, HIITvIFT results in more identical physiological demands and more uniform adaptations in ventilatory threshold and anaerobic power than SSG.
... An important concept provided by the study by Lahti et al. (2020) is the response capacity of athletes to OS training with TS. The concept of responders, or participants that respond to training in the expected sense, has been widely studied (Mann et al., 2014;Pickering & Kiely, 2017;Pickering & Kiely, 2019) and one of the main conclusions reached is that the problem does not lie in the existence of responders (or high-responders) and non-responders (or low-responders) (Pickering & Kiely, 2019), but in the training load used and its dosage (Mann et al., 2014). In other words, if an athlete does not respond to a certain training, it is possibly due to a poor choice and dosage of the training load (Pickering & Kiely, 2019). ...
... An important concept provided by the study by Lahti et al. (2020) is the response capacity of athletes to OS training with TS. The concept of responders, or participants that respond to training in the expected sense, has been widely studied (Mann et al., 2014;Pickering & Kiely, 2017;Pickering & Kiely, 2019) and one of the main conclusions reached is that the problem does not lie in the existence of responders (or high-responders) and non-responders (or low-responders) (Pickering & Kiely, 2019), but in the training load used and its dosage (Mann et al., 2014). In other words, if an athlete does not respond to a certain training, it is possibly due to a poor choice and dosage of the training load (Pickering & Kiely, 2019). ...
... 1st Quarter (January-March), p. 43-52 F4: -0.51%), while the rest of the athletes do improve, some considerably in percentage terms (F2: +5.87%; F3: +7.07%; M1: +1.48%; M2: +2.82%). Introducing follow-up tests for a few weeks could also provide us with more information about the effects of training (Bissas et al., 2022;Lahti et al., 2020), as well as taking into account the possible error in the measurement procedures, an aspect that is by no means negligible in research, and which can lead to possible false non-responder or vice versa (Mann et al., 2014;Pickering & Kiely, 2019). Pickering and Kiely (2019) argue that the most important aspect of training is the individual dosage of the training load and that the lack of response to the process may be because it was not adequate to its characteristics. ...
The current motorized towing system devices are highly precise when selecting loads and achieving results. An increased use could expand the theoretical body on the effects of overspeed methods. Our objectives were to analyze the results of an overspeed intervention with a motorized towing system on the maximum running speed (MRS), the step length and rate, the flight and contact time, and the distance to the first support from the vertical projection of the center of masses, as well as to make a methodological proposal. Six young athletes (age: 16.71 ± 2.00 years) performed ten overspeed sessions with the assistance of 5.05 ± 0.53% of body weight at 105.83 ± 1.79% of maximum running speed, using the 1080 Sprint device. After the intervention, non-significant (p > .05) increases of 2.94% (95% CI: 0.25-5.62) of the voluntary maximum running speed were obtained with a large effect size (r B : 0.71; 95% CI: 0.00-0.95). The distance to the first support from the vertical projection of the center of masses presented significant differences (p < .05; dr B : 1; 95% CI: 1-1). The non-significant maximum running speed increases cannot be neglected in high-level competition, where small differences in performance separate athletes. To choose the appropriate training load is key, and so a standardized methodology allowing the comparison of results is necessary.
... Typically, when presenting responses to various exercise interventions, the convention is to display average group values, assuming that these values accurately reflect individual reactions. However, it is important to acknowledge that individual responses to a standardized intervention can differ significantly among athletes with varying physiological profiles and locomotor abilities (Mann et al., 2014). Furthermore, the workload imposed by sport-specific interventions may be uneven due to the inability of people to control various factors influencing workload (Hill-Haas et al., 2008a;Hill-Haas et al., 2008b). ...
... One plausible explanation to diversity in the homogeneity of adaptations could be the difference in the intensity of the participants' explosive drop jumps, leading to inter-subject variability in the adaptive responses over the training period. The cumulative homeostatic stress encountered during exercise sessions is influenced by factors like exercise intensity and duration, acting as a triggering stimulus for adaptive responses (Mann et al., 2014). According to Flück (2006), at the cellular level, adaptation to exercise training arises from the collective impact of specific transcriptional and translational micro-adaptations occurring after each exercise session. ...
... According to Flück (2006), at the cellular level, adaptation to exercise training arises from the collective impact of specific transcriptional and translational micro-adaptations occurring after each exercise session. Consequently, the heterogeneous acute exercise stimulus received may explain the individual variabilities in the training responses that accumulate over time (Mann et al., 2014). Elevated anaerobic power and motor abilities were other important findings of this study. ...
This study compared inter-individual variability in the adaptive responses of cardiorespiratory fitness, anaerobic power, and motor abilities of male volleyball players to high-intensity interval training (HIIT) prescribed as repetitive drop jumps (interval jumping) and running-based intervals (interval running). Twenty-four collegiate volleyball players were equally randomized to two training groups executing 11 minutes of interval running or interval jumping during which they ran or repeated drop-jumps for 15 seconds, alternating with 15 seconds of passive recovery. Before and after the 6-week training period, aerobic fitness, cardiac function, and anaerobic power were evaluated using a graded exercise test, impedance cardiography, and a lower-body Wingate test, respectively. Additionally, linear speed, agility, and jumping tests determined motor abilities. Both interventions significantly enhanced maximum oxygen uptake (V̇O2max), velocity associated with V̇O2max, first and second ventilatory thresholds (VT1 & VT2), maximal cardiac output (Q̇max), stroke volume (SVmax), peak and average power output, vertical jump, change of direction, and linear sprint speed. Interval jumping group demonstrated a significantly greater improvement in squat jump (p = 0.001; 95% CI: 2.51-5.42) and countermovement jump (p = 0.001; 95% CI: 2.11-4.61) compared to interval running group. Conversely, interval running group elicited a greater enhancement in sprint speed (p = 0.002; 95% CI: 2.53-5.71) than interval jumping group. Examining the individual residual in the adaptive responses revealed that interval running induced more homogenized adaptations across individuals in VT1 (p = 0.04; 95% CI: 0.03-1.33), Q̇max (p = 0.03; 95% CI: 0.04-1.64), SVmax (p = 0.04; 95% CI: 0.02-1.75), and maximal sprint speed (p = 0.01; 95% CI: 0.72-1.95) in contrast to interval jumping. However, the uniformity of adaptations in countermovement jump in response to interval jumping surpassed that of interval running (p = 0.02; 95% CI: 0.08-1.32). Although both training modalities effectively improved the mentioned variables concurrently, tailoring the HIIT intervention to the reference intensity and training modality specific for each quality may enhance measured quality.
... The overall external load imposed by an exercise session results from the interplay of factors including duration and intensity of the exercise. This serves as stimulus that triggers adaptive responses (Mann et al., 2014). For instance, muscular adaptations to an exercise bout are manifested by the accumulative effects of "transcriptional and translational micro-adaptations that occur after each exercise bout (Flück, 2006)." ...
... For instance, muscular adaptations to an exercise bout are manifested by the accumulative effects of "transcriptional and translational micro-adaptations that occur after each exercise bout (Flück, 2006)." Consequently, discrepancies in the acute exercise stimulus received may partly account for the individual variations in the training responses that accrue over time (Mann et al., 2014). Regardless of the method of measurement, coaches prescribe training based on external load to achieve the desired physiological response. ...
... To overcome inter-subject variability in the adaptations, many authors employed various interventions to facilitate the same degrees of physical stress across athletes with varying profiles and ensure more homogenous adaptations (Bagger et al., 2003;Mann et al., 2014;Dai and Xie, 2023;Wang and Zhao, 2023). To be specific, they have utilized different reference intensities such as %MHR, %V O 2max, and different proportions of anaerobic speed reserve with a sport-specific duration and frequency. ...
This study compared the inter-individual variability in adaptive responses to six weeks of small-sided games (SSG) and short sprint interval training (sSIT) in young basketball players. Thirty well-trained young athletes (age: 16.4 ± 0.6 years; stature: 190 ± 8.4 cm; weight: 84.1 ± 8.2 kg) voluntarily participated and were randomly assigned to SSG (3 sets of 5 min 3v3 on full length (28 m) and half-width (7.5 m) court, with 2 minutes of passive recovery in-between), sSIT (3 sets of 12 × 5 s sprinting with 20 s recovery between efforts and 2 min of rest between sets), or CON (routine basketball-specific technical and tactical drills) groups, each of ten. Before and after the training period, participants underwent a series of laboratory- and field-based measurements to evaluate their maximum oxygen uptake (V̇O2max), first and second ventilatory threshold (VT1 and VT2), oxygen pulse, peak and average power output (PPO and APO), linear speed, change of direction (COD), countermovement jump (CMJ), and vertical jump (VJ). Both SSG and sSIT sufficiently stimulated adaptive mechanisms involved in enhancement of the mentioned variables (p < 0.05). However, sSIT resulted in lower residuals in percent changes in V̇O2max (p = 0.02), O2pulse (p = 0.005), VT1 (p = 0.001), PPO (p = 0.03), and linear speed (p = 0.01) across athletes compared to the SSG. Moreover, sSIT resulted in more responders than SSG in V̇O2max (p = 0.02, φ = 0.500), O2pulse (p = 0.003, φ = 0.655), VT1 (p = 0.003, φ = 0.655), VT2 (p = 0.05, φ = 0.436), and linear speed (p = 0.05, φ = 0.420). Our results indicate that sSIT creates a more consistent level of mechanical and physiological stimulus than SSG, potentially leading to more similar adaptations across team members.
... Moreover, it has been well established in the scientific literature that individual athletes adapt differently to the training load and those with relatively lower basal aerobic fitness levels show greater improvements than those with greater aerobic fitness levels following training interventions. 18 However, the effect of fitness level on doseresponse relationships has not yet been studied in soccer players. Therefore, the aims of the present study were to (1) examine within-individual player dose-response associations between selected training load measures and changes in fitness level (tracked by submaximal exercise HR [HR ex %] from a valid and reliable submaximal test 8 ) and (2) measure the relationships between these dose-response associations with basal HR ex % (to study the influence of fitness level on dose-response relationship). ...
... Factors including genetic, lifestyle, sleep quality, nutrition, and training background might also play roles. 18 Indeed, individual variation in the homeostatic stress following training results in different exercise stimulus and contributes to different adaptive response to the training phase among individuals. 18 This highlights the importance of individual monitoring of dose-response within the team sport setting. ...
... 18 Indeed, individual variation in the homeostatic stress following training results in different exercise stimulus and contributes to different adaptive response to the training phase among individuals. 18 This highlights the importance of individual monitoring of dose-response within the team sport setting. ...
Purpose:
To (1) examine within-individual player dose-response associations between selected training-load measures and changes in aerobic fitness level via submaximal exercise heart rate (HRex%) and (2) measure the relationships between these dose-response associations with basal HRex% (to study the influence of fitness level on dose-response relationship).
Methods:
During an in-season phase, selected training-load measures including total minutes, total distance, mechanical work (the sum number of accelerations and decelerations > 3 m2), high metabolic load distance, and Edwards' training impulse were collected via Global Positioning System and heart-rate sensors for analyzing accumulated load. A submaximal warm-up test was used repeatedly before and after 9 phases to elicit HRex% and track fitness changes at an individual level.
Results:
Negative to positive extensive ranges of within-individual associations were found among players for different metrics (r = -.84 to .89). The relationship between pooled HRex% (basal fitness) and dose-response correlations showed inverse very large (r = -.71) and large (r = -.65) values for accumulated weekly minutes and distance. However, moderate values were found for all other measures (r = -.35 to -.42).
Conclusions:
Individual players show extensive different ranges of dose-response associations with training measures. The dose-response association is influenced by players' fitness level, and players with lower fitness levels show stronger inverse relationships with accumulated minutes and total distance.
... Typically, responses to different exercise interventions are presented as average group values, with the presumption that these values represent individual responses. However, individual adaptations to a standardized intervention vary among athletes with different physiological profiles and locomotor abilities (Mann et al., 2014). Moreover, the quantity of workload imposed by some sport-specific interventions could be unequal due to the inability of intervention to control several influencing factors in workload (Hill-Haas et al., 2008a, 2008b. ...
... Divergence in the adaptations around mean alludes to inter-subject variation in the adaptive response to training intervention, a phenomenon frequently observed yet explicitly addressed in only a relatively small number of studies. At both ends of a spectrum representing individual responses, there are individuals who exhibit notably significant responses (high Rs) and those who demonstrate notably small responses (low Rs) to a given training intervention (Mann et al., 2014). Nevertheless, individuals exhibiting a limited response to an exercise intervention in one parameter (e.g., _ VO 2 max ) may not necessarily demonstrate the same response in other parameters (Vollaard et al., 2009;Scharhag-Rosenberger et al., 2012). ...
... Frontiers in Physiology frontiersin.org 08 strategies, sleep and stress, and fixed vs. flexible training prescription (Mann et al., 2014). ...
This study examined the uniformity of adaptations in cardiorespiratory fitness and bio-motor abilities by analyzing individual responses to measures representing the mentioned qualities. Twenty-four male well-trained soccer players (Age = 26 ± 4 years; stature = 181 ± 3.8; Weight = 84 ± 6.1) were randomized to two groups performing short sprint interval training [sSIT (3 sets of 10 × 4 s all-out sprints with 20 s of recovery between efforts and 3 min of rest intervals between sets)] or a time-matched small-sided game [SSG (3 sets of 3 v 3 efforts in a 20 × 15 m area with 3 min of relief in-between)]. Before and after the 6-week training period, aerobic fitness indices, cardiac hemodynamics, and anaerobic power were assessed through a graded exercise test utilizing a gas collection system, noninvasive impedance cardiography, and a lower-body Wingate test, respectively. Also, sport-specific bio-motor abilities were determined by measuring linear speed, change of direction, and jumping ability. Comparing inter-individual variability in the adaptive changes by analyzing residuals in individual adaptations indicated that sSIT induces more uniform changes in the first and second ventilatory threshold (VT1 & VT2), stroke volume, and peak power output across team members than SSG. SSG also yielded lower proportions of responders in V ˙ O 2 max , VT1, VT2, peak, and average power output compared to sSIT. Additionally, the coefficient of variation in mean group changes in measures of aerobic fitness and bio-motor abilities in response to sSIT were lower than in SSG. Short sprint interval training induces more homogenized adaptations in measures of cardiorespiratory fitness and anaerobic power than small-sided games across team members.
... While the application of threshold-based intensity domains is suggested to be a superior method in standardizing exercise in relation to metabolic stress (Egger et al. 2016;Mann et al. 2013;Scharhag-Rosenberger et al. 2010), also the chronic training effects seem to be more precisely targeted with this type of approach . There is evidence that the threshold-based individual exercise prescription would lead to superior training adaptations and decrease the likelihood of low training response (Weatherwax et al., 2019;Wolpern et al., 2015). ...
... There is evidence that the threshold-based individual exercise prescription would lead to superior training adaptations and decrease the likelihood of low training response (Weatherwax et al., 2019;Wolpern et al., 2015). It has even been suggested that fixed training intensities (e.g., based on maximum HR or VO 2 ) could be among the major determinants affecting interindividual differences in training adaptations (Mann et al. 2014;. Although certain fixed percentages of the maximum could generally be correct, there are substantial interindividual differences at which percentage of the maximum the metabolic thresholds are located (Iannetta et al. 2020). ...
Background
Endurance exercise intensities can be categorized into moderate, heavy, and severe domains based on physiological responses during incremental exercise testing. Since the evaluation of metabolic thresholds is not always possible, this study assessed the accuracy of fixed intensity anchors to estimate lactate thresholds.
Methods
165 (64 females, 101 males) recreational runners performed a maximal incremental treadmill test. The first (LT1) and second (LT2) lactate thresholds were determined as percentages of maximum heart rate (HR), oxygen consumption (VO2), and running speed, alongside the rating of perceived exertion (RPE). Sex-specific mean values were used to determine the threshold intensities and to analyze the error magnitude for each method.
Results
Females had a higher relative HR, VO2, and speed at LT1 compared to males (p < 0.001). In the pooled data, the mean absolute error for estimating LT1 varied from 0.6 to 0.8 km/h for speed and 4.9–7.4 bpm for HR, while LT2 errors ranged from 0.4 to 0.8 km/h and 2.8–5.2 bpm, respectively. The speed-derived estimations yielded the smallest error magnitudes, while the RPE-derived estimations were the least accurate. Estimating the maximum speed increased the respective errors to 1.0 km/h and 8.4 bpm for LT1, and to 1.1 km/h and 6.7 bpm for LT2.
Conclusion
LT1 occurred at higher relative intensity in females, suggesting a need for sex-specific intensity anchors. The speed-derived estimates were the most accurate, but the estimation of maximum values impaired the prediction accuracy. Thus, the optimal method also depends on the availability of the maximum values.
... Although this may indicate that the responsiveness to exercise is heritable to a certain extent, Mann and colleges (2014) described additional factors potentially affecting the training response. These included the homeostatic stress produced by the training, recovery and readiness to exercise, and nutrition [12]. ...
... Individualization plays an instrumental role in the design of training programs, which is mainly due to the variability in the exercise response. Non-modi able factors such as genomic predictors [38] or the in uence of sex hormones [39] have been proposed to explain different adaptations to the same intervention [12]. In addition, Pickering et al. [40] suggested that considering training variables including volume, duration, and intensity may help to reduce heterogeneity. ...
Background Exercise has numerous benefits for health, well-being and performance. However, due to factors such as genetics or training status, the individual response can be highly different. Force-velocity (FV) based training is a popular method to individualize exercise programs aiming to improve speed and power. This systematic review investigated the effects of FV based training on motor performance. Methods A systematic literature search was conducted by two independent examiners using PubMed, Web of Science, and Google Scholar. We included randomized controlled trials involving healthy adults and comparing individualized (FV) to non-individualized training programs with a minimal duration of four weeks. Study quality was evaluated using the PEDro scale, publication bias was checked by inspection of funnel plots. We used robust variance estimation to pool the effects of individualized vs. non-individualized training for sprint time, strength, and jump height. Results Searches returned 684 articles, and n = 10 papers were included. Study quality was good (5.3 ± 0.8 / 7 points on the PEDro scale) and no indication of publication bias was found. Meta-analysis did not reveal differences between FV based and non-individualized training for strength (SMD: -0.04, 95%CI: -0.34 to 0.26, p = 0.72, I2: 0%), sprint time (SMD: 0.28, 95%CI: -0.75 to 1.32, p = 0.49, I2: 69,7%), and jump height (SMD: 1.8, 95%CI: -0.57 to 4.2, p = 0.11, I2: 90.8%). Conclusion Although FV profiling represents a plausible approach to individualize speed and power training, our meta-analysis does not support its application for performance reasons at present. Future research should investigate more specific conditions and homogenous populations such as elite athletes.
... [16][17][18] This observation might indicate that simply increasing exercise intensity to strengthen the training stimulus is insufficient to minimise adaptive variability. Whilst cognizant of the role played by genetics, 7,8,19,20 it is possible that adaptive variability stems mostly from a methodological problem. Specifically, inconsistent levels of homeostatic disturbance across individuals, secondary to an inadequate intensity normalisation, may lead to inter-individual variability in training adaptations. ...
... Specifically, inconsistent levels of homeostatic disturbance across individuals, secondary to an inadequate intensity normalisation, may lead to inter-individual variability in training adaptations. [7][8][9] Assessing the utility of %Δ for HIIT prescription is therefore crucial in understanding whether a large and homogeneous training stimulus can be delivered to a group of individuals undertaking training. ...
Objectives This study was designed to quantify inter- and intra-individual variability in performance, physiological, and perceptual responses to high-intensity interval training (HIIT) prescribed using the percentage of delta (%Δ) method, in which the gas exchange threshold and maximal oxygen uptake (V̇O2max) are taken into account to normalise relative exercise intensity.
Design Repeated-measures, within-subjects design with mixed-effects modelling.
Method Eighteen male and four female cyclists (age: 36 ± 12 years, height: 178 ± 10 cm, body mass: 75.2 ± 13.7 kg, V̇O2max: 51.6 ± 5.3 ml·kg-1·min-1) undertook an incremental test to exhaustion to determine the gas exchange threshold and V̇O2max as prescription benchmarks. On separate occasions, participants then completed four HIIT sessions of identical intensity (70%Δ) and format (4-min on, 2-min off); all performed to exhaustion. Acute HIIT responses were modelled with participant as a random effect to provide estimates of inter- and intra-individual variability.
Results Greater variability was generally observed at the between- compared with the within-individual level, ranging from 50% to 89% and from 11% to 50% of the total variability, respectively. For the group mean time to exhaustion of 20.3 min, inter- and intra-individual standard deviations reached 9.3 min (CV = 46%) and 4.5 min (CV = 22%), respectively.
Conclusions Due to the high variability observed, the %Δ method does not effectively normalise the relative intensity of exhaustive HIIT across individuals. The generally larger inter- versus intra-individual variability suggests that day-to-day biological fluctuations and/or measurement errors cannot explain the identified shortcoming of the method.
... Some athletes may respond remarkably positively to certain types of HIIT, while others may not respond at all or even experience negative effects, i.e., insufficient recovery or overreaching (Stöggl and Sperlich, 2014;Casado et al., 2023;Strepp et al., 2024). A variety of predeterminants, including genetic predisposition, baseline phenotype, training status, recovery and "ready to train status", as well as lifestyle factors such as sleep and nutrition, may contribute to individual training responses (Mann et al., 2014). In this respect, additional research is needed to gain a deeper understanding of the determinants of training response in order to achieve optimal training results. ...
... The interpretation of "response" or "non-response" can also be considered within an individual and highly depends on the variable of interest. For example, an athlete may experience an improvement in VO 2max (i.e., response) through aerobic HIIT wiht long intervals, while e.g., threshold performance remains unchanged (i.e., non-response) (Mann et al., 2014). ...
There are various categorization models of high-intensity interval training (HIIT) in the literature that need to be more consistent in definition, terminology, and concept completeness. In this review, we present a training goal-oriented categorization model of HIIT, aiming to find the best possible consensus among the various defined types of HIIT. This categorization concludes with six different types of HIIT derived from the literature, based on the interaction of interval duration, interval intensity and interval:recovery ratio. We discuss the science behind the defined types of HIIT and shed light on the possible effects of the various types of HIIT on aerobic, anaerobic, and neuromuscular systems and possible transfer effects into competition performance. We highlight various research gaps, discrepancies in findings and not yet proved know-how based on a lack of randomized controlled training studies, especially in well-trained to elite athlete cohorts. Our HIIT “toolbox” approach is designed to guide goal-oriented training. It is intended to lay the groundwork for future systematic reviews and serves as foundation for meta-analyses.
... On the other hand, some participants (n = 5 [13.5%]) were resistant to the training stimuli, which has been reported to occur in numerous other studies Petrella et al. 2008;Ahtiainen et al. 2016;Stec et al. 2016;Damas et al. 2019;Lixandrão et al. 2024). While non-responsiveness can be broadly affected by genetic factors and nutritional status (Mann et al. 2014), it is possible that the training stimuli provided by both overload progression models used in the present study were insufficient to activate muscle hypertrophy-related molecular mechanisms . Although it is unknown whether these individuals would benefit from different RT stimuli, Lixandrão et al. (2024) recently reported that increased volume mitigated non-responsiveness in older adults. ...
Purpose
We aimed to compare individual hypertrophic responses to resistance training in which overload progressed either by adjusting the load (LOADProg) or by increasing the number of repetitions (REPSProg). Furthermore, we investigated whether greater responsiveness to one protocol was associated with chronic changes in myonuclei and satellite cells, proteolysis and extracellular matrix (ECM) remodeling biomarkers.
Methods
Thirty-seven untrained participants had their legs randomized into LOADProg and REPSProg and underwent 10 weeks of training. Muscle cross-sectional area (mCSA) ultrasound and muscle biopsies were performed pre- and post-training. Based on mCSA changes between protocols, we applied a criterion of 2 typical errors (5.7%) to create 4 clusters.
Results
Twelve participants (~ 34%) showed greater mCSA increases after REPSProg (14.2 ± 7.6%) than LOADProg (3.4 ± 8.7%, p = 0.004). Seven participants (~ 19%) responded better to LOADProg (21.5 ± 7.5% vs. 12 ± 7.5%, p = 0.041). Thirteen participants (~ 35%) showed no differences between protocols (p = 0.852). Five participants were nonresponders (mCSA changes smaller than the 5.7% threshold) for both protocols. There were no significant differences (p > 0.05) in myonuclear content, proteolysis, or ECM remodeling markers within any of the clusters. However, for those who responded better to REPSProg, this protocol promoted greater satellite cell changes (108.6 ± 77.0%) than LOADProg (48.9 ± 63.1%, p = 0.015).
Conclusion
Our findings suggest that overload progression models may influence individual responsiveness to RT-induced muscle hypertrophy. Additionally, progression through increased repetitions was associated with a chronic addition of satellite cells. However, responsiveness was not explained by chronic changes in myonuclei, proteolysis or ECM remodeling biomarkers.
Trial registration
This study is registered in the Brazilian Registry of Clinical Trials (RBR-57v9mrb).
... These findings have significant implications for personal health management, suggesting the need for paradigm shifts toward consistency-prioritized rather than volume-maximized exercise approaches, with individualized optimization of training parameters based on personal response patterns rather than population averages [105]. From a public health perspective, the results highlight the potential inadequacy of current physical activity guidelines that emphasize weekly volume accumulation without sufficient attention to temporal distribution, suggesting potential refinements incorporating regularity metrics and individualized intensity recommendations [106]. ...
This study developed a bidirectional LSTM neural network framework for analyzing long-term exercise patterns and predicting multidimensional health outcomes from wearable sensor data. The model demonstrated 92.7% accuracy in recognizing diverse exercise modalities while characterizing temporal exercise dynamics over a 12-month period. Seven distinct exercise pattern clusters were identified, with differential effects across physical fitness, physiological function, body composition, and psychological wellbeing dimensions. Exercise consistency and temporal distribution emerged as stronger predictors of health improvements than traditional volume metrics. The predictive framework enabled personalized exercise prescription optimization, demonstrating potential improvements of 21–35% in health outcomes compared to standardized approaches. These findings suggest the need for paradigm shifts toward consistency-prioritized rather than volume-maximized exercise approaches, with individualized optimization based on personal response patterns rather than population averages.
... In addition, complex traits that constitute an essential aspect of physical performance, such as endurance, muscular strength, power, speed, and agility, have been reported to require a very specific genetic combination [5,9,10]. The corresponding matches in DNA sequences have a positive effect on adaptation to physiological changes in metabolism caused by athletic stress and the development of peak performance [11,12]. ...
Background: The present investigation aims to elucidate the physical fitness attributes inherent in male football players about the Angiotensinogen (AGT) rs699 and Interleukin-6 (IL-6) rs1800795 gene polymorphisms. Methods: Twenty-two male football players, aged 18 to 35 years, voluntarily enrolled in the study conducted within the North Macedonian Super League. Genomic DNA was extracted from oral epithelial cells. Genotyping procedures were then executed using real-time polymerase chain reaction (RT-PCR). All participants were actively involved in an intensive training program six days a week throughout the six-week pre-season preparation phase. The male football players underwent physical assessments both before and after the training program. Statistical analysis involved the use of the Paired-Sample t-Test to discern differences between the pre-test and post-test measurements of the male football players. Results: When stratifying the outcomes according to the IL-6 genotype and AGT genotype variables, statistically significant differences were not observed in Squat Jump (SJ), 5 m sprint, 30 m sprint, Counter Movement Jump (CMJ), Drop Jump (DJ) evaluations, and body fat percentage (p > 0.05). In contrast, statistically significant differences were observed in the Yo-Yo Intermittent Recovery Test Level 2 (Yo-Yo IRT 2), 10 m sprint, and One Repetition Maximum (1RM) bench press variables (Yo-Yo IRT 2: CC and CT p = 0.005, <0.001; 10 m sprint: CT p = 0.024; and 1RM bench press: CC, CT and TT p < 0.001, <0.001, 0.045, respectively). Significant differences were also identified in the Yo-Yo IRT 2, 10 m sprint, and 1RM bench press measurements (Yo-Yo IRT 2: CC, CG and GG p = 0.002, 0.021, 0.001; 10 m sprint: CC and GG p = 0.020, 0.028; and 1RM bench press: CC, GG p = 0.001, 0.001, respectively). Conclusions: In summary, the AGT rs699 and IL-6 rs1800795 gene polymorphisms may play a role in the adaptations induced by training in male football players.
... Commonly referred to as velocity-based training (VBT), 1 aim of using (mean) velocity data is to automatically regulate (i.e., "autoregulate") the intensity (typically defined as a 1-repetition maximum [%1RM] (35)) and volume (sets multiplied by reps (11)) of resistance training based on the current preparedness of the trainee (109). The motivation behind this use case is that various factors such as fatigue (both within (25) and between (76) sessions), diet (36), whether an individual is a "high-" or "low-responder" to a training program (63), muscle fiber topology (60), the focus of attention (112), and athletes' psychoemotional perspectives of training (56) can all uniquely influence an individual's response from an imposed training demand. As a result, ensuring the volume and intensity of resistance training imposes neither too much nor too little training stress can be challenging in the absence of any additional context or data. ...
Hirsch, SM, Chapman, CJ, Singh, H, Baker, DG, and Frost, DM. A critical appraisal of using barbell velocity data to regulate training. J Strength Cond Res 39(3): 360-372, 2025-Practitioners must balance numerous training variables to ensure they do not impose too much nor too little training stress on their athlete. As an athlete's capacity can fluctuate based on their preparedness for training, the intended vs. actual training intensity in a fixed training program may not coincide. Similarly, the training set volume that an athlete should be exposed to may fluctuate depending on their current state. A discrepancy between intended vs. actual training intensity and volume could negatively impact subsequent training adaptations. Thus, researchers and practitioners have advocated for "autoregulation," whereby the volume and intensity of training are automatically adjusted based on the athlete's preparedness. One proposed method of autoregulating resistance training is by using barbell velocity data. However, it is unclear whether, and under which contexts, these data are appropriate for regulating resistance training. Therefore, the purpose of this literature review was to critically examine the current research on using barbell velocity data to regulate resistance training intensity and volume. After examining the relevant literature, it is the authors' belief that the current data do not support using velocity data to precisely regulate resistance training intensity. However, it is the authors' belief that the current literature does suggest that researchers and practitioners can leverage these data to regulate other aspects of resistance training, such as athlete motivation, autonomy, and focus of attention, which could also impact the resulting adaptations from training. Overall, more research is required to better understand how researchers and practitioners should use velocity data to guide training.
... Acute fatigue in indoor court-based team sports water content post-exercise [182][183][184]. The declines in CMJ height following 1-hour are consistent with a previous review [39] and may be linked to the induction of exercise induced muscle damage due the high eccentric load of indoor court sports [9,11,169]. ...
Fatigue in team sports has been widely researched, with a number of systematic reviews summarising the acute (i.e., within 48-hours) response in outdoor sports. However, the fatigue response to indoor court-based sports is likely to differ to outdoor sports due to smaller playing fields, harder surfaces, and greater match frequencies, thus should be considered separately to outdoor sports. Therefore, this study aimed to conduct a systematic review on acute fatigue in indoor court-based team-sport, identify methods and markers used to measure acute fatigue, and describe acute fatigue responses. A systematic search of the electronic databases (PubMed, SPORTDiscus, MEDLINE and CINHAL) was conducted from earliest record to June 2023. Included studies investigated either a physical, technical, perceptual, or physiological response taken before and after training, match, or tournament play. One-hundred and eight studies were included, measuring 142 markers of fatigue. Large variability in methods, fatigue markers and timeline of measurements were present. Cortisol (n = 43), creatine kinase (n = 28), countermovement jump (n = 26) and testosterone (n = 23) were the most frequently examined fatigue markers. Creatine kinase displayed the most consistent trend, increasing 10–204% at 24-hours across sports. There is large variability across studies in the methods and markers used to determine acute fatigue responses in indoor court-based team sports. Future researchers should focus on markers that display high reliability and transfer to practice. The robustness of studies may be increased by ensuring appropriate methods and timescale of fatigue marker measurement are used. Further research is required to determine which combination of markers best describes a fatigue response.
... The fact that two people can respond differently to the same physiological stimulus is well known and has been demonstrated in clinical trials, for example, with drugs and exercise interventions (Ross et al., 2019;Smith & Rawlins, 2013). This is also the case with astronauts when they perform the exercise countermeasures on board the ISS, as indicated, for example, in the performance and phenotypic adaptations of astronauts after an in-flight high intensity training programme (English et al., 2020), or in molecular adaptations to the classic exercise routines on the ISS (Blottner et al., 2023 and 'responders' (Mann et al., 2014). Once this is established, the mechanisms influencing the differential response (i.e. ...
The current understanding of crew health maintenance is founded upon decades of physiological research conducted in terrestrial spaceflight analogues and in low Earth orbit, particularly on the International Space Station. However, as we progress towards the Lunar Gateway and interplanetary missions, it is imperative that the tools employed to maintain crew health are redefined, including the utilisation of exercise countermeasures. The successful implementation of exercise countermeasures for deep space missions must address a number of challenges, including those posed by new environments with elevated levels of cosmic radiation and solar particle events, extended mission durations and constrained space availability. In this Topical Review, the authors address points that are important (and sometimes critical), but often ignored, in order to define future exercise countermeasures for long‐duration space missions. Multi‐organ countermeasures, countermeasure enjoyment, time‐dependent load variability, the relationship between nutrition and the success of exercise countermeasures, and the individual variability in response to a given countermeasure are presented and discussed. The aim of this article is to raise awareness of important aspects that can profoundly influence the efficacy of exercise countermeasures, thereby affecting the health of the crew and the success of the mission during prolonged spaceflight.
... The results of this study Communicated by Michael I Lindinger. continue to be interpreted as evidence of interindividual variability in exercise response (Dalleck et al. 2016;Mann et al. 2014;Meyler et al. 2021;Weatherwax et al. 2019). This interpretation persists despite the contentionforwarded by Batterham in 2015 (Atkinson andBatterham 2015) -that observed response variability may reflect individual variability in exercise response, but also likely (or perhaps only) random error (summarized in ). ...
Purpose
(1) To determine if the blood lactate concentration ([BLa]) response is a repeatable individual trait, and (2) To examine whether threshold-based prescription (THR) reduces interindividual variability in [BLa] response compared to traditional (maximally anchored) exercise prescription (TRAD).
Method
A crossover within-participant repeated measures design was used to assess [BLa] during the TRAD and THR exercise in 17 participants (9 M/8F). Participants initially undertook an incremental test to exhaustion to determine peak work rate (WRpeak), a lactate threshold (LT) test and a critical power (CP) test. All baseline tests were repeated twice. Participants then completed 6 15-min bouts of continuous cycling at 65%WRpeak (TRAD; 3 bouts) and 80% of the difference (Δ80) between LT and CP (THR; 3 bouts). [BLa] response was measured at 10 and 15 min of exercise.
Results
Across individuals, there was a wide range in [BLa] response, but within individual responses were repeatable. [BLa] ranges and mean individual 90% confidence interval width (CIw) were as follows: TRAD@10 min = 2.1–9.7 mmol, CIw = 0.5 mmol, THR@10 min = 3.4–9.3 mmol, CIw = 0.6 mmol, TRAD@15 min = 2.2–9.9 mmol, CIw = 0.6 mmol, THR@15 min = 3.6–12.3 mmol, CIw = 0.7 mmol. Levene’s tests revealed no significant differences in the variability of [BLa] response between TRAD and THR at 10 min (F = 0.523, p = 0.475) or 15 min (F = 0.351, p = 0.558) of exercise.
Conclusion
Our results demonstrate that true interindividual variability in the [BLa] response to exercise exists, but failed to confirm that variability in [BLa] response is reduced with the use of THR.
... With respect to confounding factors, participants' dietary, sleep, and alcohol habits were not monitored over the course of the ~8-weeks. As protein intake, sleep quality/quantity, and alcohol consumption can impact adaptations to exercise (Mann et al. 2014), participants' lifestyles may have been suboptimal for muscle hypertrophy and strength development. Additionally, it is important to note the study was conducted with young, healthy, untrained individuals, where training effects are more robust (Lopez et al. 2021). ...
As a novel, low-velocity resistance exercise method, eccentric quasi-isometric resistance exercise (EQI-RE) results in greater time under tension than traditional isotonic resistance exercise (TRD-RE) and is surmised to increase muscle mass and strength. However, females may be more fatigue resistant than males when performing acute EQI-RE, which could lead to long-term differences in time under tension and resistance exercise volume. At present, studies have yet to compare muscle hypertrophy or strength improvements following TRD-RE and EQI-RE training, and whether sex-differences exist in these outcomes. Twenty-two (n = 13 females) untrained individuals completed ~8-weeks of effort matched unilateral TRD-RE and EQI-RE of the elbow flexors. Muscle thickness and estimated one-repetition maximum (E-1RM) were evaluated before and after training. TRD-RE produced significantly larger relative increases in muscle thickness (6.7% ± 3.9% vs. 4.0 ± 3.3%, p =.004) and E-1RM (19.6 ± 8.5% vs. 12.8 ± 6.2%, p = .001) than EQI-RE. Although females accrued greater resistance exercise volume than males across the TRD-RE and EQI-RE training, there were no relative sex-differences in muscle thickness or E-1RM improvements (p > .25). Sex-differences in fatiguability may therefore manifest in differences in resistance exercise volume between males and females after 8-weeks of TRD-RE and EQI-RE of the elbow flexors, but this does not lead to relative differences in muscle thickness or E-1RM improvements. Although EQI-RE did produce significant increases, TRD-RE of the elbow flexors appears more effective at increasing muscle thickness and E-1RM.
... For example, recent work (Jodoin et al., 2023b) suggests that females may lose their fatiguability advantage over males when sustained isometric muscle actions are preceded by maximal eccentric muscle actions, which is associated with a significant increase in female antagonist activation. It should be noted that the EQI-RE data in the current study, as well as in previous studies (Henderson et al., 2023;Oranchuk et al., 2020Oranchuk et al., , 2021, has been highly variable, however, this is not unexpected in resistance exercise studies of untrained individuals (Mann et al., 2014), and thus individual responses to EQI-RE may vary greatly. ...
Traditional isotonic resistance exercise (TRD-RE) improves muscle mass, strength, and overall health. However, TRD-RE may be impractical or unfeasible in injury or sport specific situations. Compared to TRD-RE, eccentric quasi-isometric resistance exercise (EQI-RE) is a low-velocity resistance exercise modality suggested to acutely produce similar and/or greater time under tension, motor unit recruitment, and antagonist co-activation. With limited investigations or comparisons to other forms of resistance exercise, however, evidence is lacking. As differences between males and females exist in time under tension and motor unit behaviour in other resistance exercise contexts, the current study explored sex-differences in time under tension and surface electromyography (sEMG) across 2 sets of TRD-RE and EQI-RE. Twenty-seven (n = 13 females) participants performed unilateral TRD-RE and EQI-RE of the elbow flexors while sEMG was collected from the biceps and triceps brachii. Several main and interaction effects of resistance exercise type, set, and sex were present for time under tension, linear envelope peak (LEpeak), absolute (iEMGabs) and relative (iEMG%) integrated sEMG, with set 1 typically having higher sEMG values than set 2, and EQI-RE having greater time under tension than TRD-RE. Notably, females produced significantly more time under tension, iEMGabs, iEMG%, and co-activation than males during EQI-RE, while males experienced a more significant set-to-set reduction in time under tension and LEpeak during TRD-RE. Overall, TRD-RE may result in quicker voluntary excitation and subsequent fatigue of motor units compared to EQI-RE, while females may accrue more resistance exercise volume than males when performing EQI-RE. Theoretically, these effects could lead to long-term sex-differences in strength and hypertrophy outcomes between males and females, TRD-RE and EQI-RE.
... Regarding changes in functional capacity, a lower proportion of women reached the MICD in the SPP group (30%), despite their high compliance with the intervention (training sessions and intensity), compared to the SSPP group (55%). The response to exercise stimuli is multifactorial and related to training parameters (intensity, frequency, duration, and modality) and non-training parameters, such as individual characteristics (genetics, age, baseline capacity in a sedentary state, etc.), and other behaviors or environmental factors (diet, sleep, other habitual physical activities, etc.) [59][60][61][62]. Considering that women in the SPP group had lower baseline physical activity, a higher volume of exercise (i.e., more weeks of training or a higher weekly training frequency) was maybe required for some of them to reach the minimum clinically important difference. ...
Patients with endometrial neoplasia (EN) often have multiple comorbidities and a higher surgical risk. Prehabilitation programs (PPs) combine various interventions to improve preoperative conditions and reduce impairment due to surgical stress. We conducted a pragmatic pilot study to evaluate the acceptability and feasibility of a trimodal telehealth PP (exercise, nutrition, and psychological support) for EN patients. The participants could select their exercise group: (1) a supervised PP (SPP), group sessions 3×/week; (2) a semi-supervised PP (SSPP), group session 1×/week, training alone 2×/week; or (3) a physical activity counseling session (PACS). Out of the 150 EN patients awaiting surgery screened during the 18 months of the study recruitment, 66% (99/150) were eligible, and 40% consented to participate (SPP, n = 13; SSPP, n = 17; PACS, n = 9). The overall dropout was low (13%; 5/39), with no significant differences across groups. No serious adverse events occurred. We observed a positive impact on different outcomes across the different groups, such as in the Functional Assessment of Cancer Therapy quality of life score (SPP; delta = 6.1 [CI: 0.9; 12.6]) and functional capacity measured using the 30″ sit-to-stand test (PACS delta = 2.4 [CI: 1.2; 3.6]). The same-day hospital leave was high in the SSPP group (54.5%). Our pilot telehealth PP seems to be safe, feasible, and well accepted and may procure clinical and patient-centered gains that need to be confirmed in a larger trial.
... A control period was used due to the inter-individual variability of homeostatic stress responses during exercise. Mann, Lamberts, and Lambert [25] shown variabilities of ~ 50% in VO 2max and ~ 36% in HR during a 50 W cycle. Further, Havenith, Luttikholt and Vrijkotte [14] shown 1.5 °C rectal temperature and 1006 g sweat loss individual differences during a 60W 60-min cycle in 35 °C and 80% RH. Hence, the participants served as their own controls to minimise between-participant variability. ...
Purpose
Standard heat acclimation (HA) protocols (low-moderate intensity over a continuous 7–14 days) restore performance and thermoregulation but lack specificity and practicality for intermittent sports athletes. This study compared different pedal resistances in a 3-week intermittent sprint-based HA protocol.
Methods
Fourteen physically active adults were assigned to a sprint pedal resistance training group (TG): 0.075 kg/kg (7.5TG, 6 males, 1 female) or 0.085 kg/kg (8.5TG, 5 males, 2 females). Participants completed baseline incremental time to exhaustion test (TTE), continued with own training for 3 weeks before post-control TTE, then carried out 6 × 15 s cycle sprints with 30 s recovery followed by 30 min low intensity cycling thrice weekly for 3 weeks before completing post-HA TTE test. Testing and HA were completed at 38 °C and 30% relative humidity.
Results
Both groups improved TTE from baseline to post-HA (7.5TG: 9.6% ± 10.8%, 8.5TG: 7.4% ± 3.1%) and post-control to post-HA (7.5TG: 11.0% ± 11.7%, 8.5TG: 6.7% ± 3.9%). Maximal power improved from baseline to post-HA (7.5TG: 293 ± 40 W vs. 321 ± 46 W, 8.5TG: 318 ± 90 W vs. 339 ± 96 W), while only 7.5TG improved maximal power from post-control to post-HA (289 ± 42 W vs. 321 ± 46 W). From baseline to post-HA and post-control to post-HA, only 7.5TG increased time till maximum skin temperature (460 ± 76 s vs. 509 ± 75 s, 461 ± 72 s vs. 509 ± 75 s, respectively) and minimum core-skin gradient (461 ± 71 s vs. 510 ± 74 s, 455 ± 75 s vs. 510 ± 74 s, respectively), while exercising core temperature remained unchanged in both groups. Both groups increased sweat rate (7.5TG: 7.0 ± 3.4 mg/cm ² /min vs. 9.6 ± 4.1 mg/cm ² /min, 8.5TG: 5.7 ± 3.6 mg/cm ² /min vs. 8.3 ± 4.3 mg/cm ² /min). Only 7.5TG delayed the onset of blood lactate accumulation from baseline to post-HA (259 ± 126 s vs. 354 ± 86 s).
Conclusion
Intermittent sprint-based HA improves TTE performance and sweat rate while a lighter sprint pedal resistance offers, greater thermal adaptation and fatigue tolerance.
... The current study demonstrated how significant short-term increases in the training load can be tolerable for recreational runners. It is known that several factors besides the training itself, such as baseline fitness, nutrition status, and mental stressors, can contribute to the recovery-adaptation processes and interindividual variability in training tolerances [57]. Therefore, objective recovery markers can help confirm that the training load is not excessive at a given moment, despite possibly impaired subjective recovery. ...
Previous studies on the effects of intensified training on sleep quality/quantity have been somewhat contradictory. Moreover, recreational athletes often track various sleep metrics, and those metrics’ actual connections to training adaptations are unknown. This study explored the effects of intensified training on sleep and nightly recovery along with their associations with training adaptations. A total of 24 participants (10 females) performed a 3-week baseline training period (BL), a 2-week overload period (OL), and a 1-week recovery period (REC), which were followed by test days (T1–T3). The endurance performance was assessed with a 3000 m running test. Throughout all of the periods, the nightly recovery information was monitored with a wrist-worn wearable, including sleep quantity and quality, heart rate (HR) and HR variability (HRV), and proprietary parameters combining several parameters and scaling the results individually. In addition, the perceived strain and muscle soreness were evaluated daily. The 3000 m running performance improved from T1 to T2 (−1.2 ± 1.7%, p = 0.006) and from T1 to T3 (−1.7 ± 1.2%, p = 0.002). The perceived strain and muscle soreness increased (p < 0.001) from the final week of the BL to the final week of the OL, but the subjective sleep quality and nightly recovery metrics remained unchanged. The OL average of the proprietary parameter, autonomic nervous system charge (“ANS charge”, combining the HR, HRV, and breathing rate), as well as the change in the sleep HR and HRV from the BL to the OL, were associated (p < 0.05) with a change in the 3000 m running time. In conclusion, the subjective recovery metrics were impaired by intensified training, while the sleep and nightly recovery metrics showed no consistent changes. However, there were substantial interindividual differences in nightly recovery, which were also associated with the training adaptations. Therefore, monitoring nightly recovery can help in recognizing individual responses to training and assist in optimizing training prescriptions.
... Notably, these performance decrements can last up to 72-96 hours (33,65) or even longer depending on the training stimulus and can negatively impact performance (54). Although fatigue is a crucial mediator of adaptive responses, insufficient recovery may lead to acute performance reductions (39,52), chronic maladaptations (41), or even overtraining (12). ...
Grammenou, M, Kendall, KL, Wilson, CJ, Porter, T, Laws, SM, and Haff, GG. Effect of fitness level on time course of recovery after acute strength and high-intensity interval training. J Strength Cond Res XX(X): 000–000, 2024—The aim was to investigate time course of recovery after acute bouts of strength (STR) and high-intensity interval training (HIIT). A secondary goal was to assess the influence of total fitness score (TFS), composed of handgrip strength and maximum aerobic power on recovery. Twenty-eight resistance-trained individuals completed 8 testing sessions within a 14- to 17-day period. Subjects performed a testing battery comprising isometric midthigh pull (IMTP), countermovement jump (CMJ), and a modified Wingate test (WINmod) at baseline, immediately after exercise, as well as at 6 and 24 hours after the training sessions. A one-way ANOVA was performed to examine time changes after the training sessions. Subjects were then grouped based on their TFS in high, medium, and low groups. To examine the influence of TFS on time course of recovery, we performed a linear mixed-effects model. Statistical significance was set at p < 0.05. Both training sessions resulted in a significant reduction in peak force (PF) that persisted for up to 6 ( p < 0.05) and 24 hours ( p < 0.001). The STR session showed immediate and 24-hour postexercise declines in jump height and reactive strength index modified (RSImod) compared with baseline. The low TFS group exhibited a significant RSImod reduction immediately after HIIT ( p < 0.001), compared with the medium TFS group ( p = 0.0002). In the STR session, the high TFS group displayed an increased eccentric displacement during CMJ 24 hours after exercise compared with baseline ( p = 0.033). Overall, subjects with high TFS may be able to recover CMJ performance at the same rate as other TFS groups, despite performing more work.
... However, RT effects do not follow a simple causal stimulus-response relationship between mechanical input and physiological adaptation, but are subject to a complex network of effect modificators [16,41]. Thus, RT effects may vary between individuals and leads to the classification of individuals into distinct response-categories [1,3,10,12,17,25]. In contrast to medical oncology, in which response refers to the efficacy of tumor treatment in reducing tumor size or severity [27] in RT research, the term is used more ambiguously and could refer to several RT-related outcomes of interest, such as the one repetition maximum (1RM), the cross sectional area of a particular muscle [17], or the performance in a functional test [10]. ...
Purpose
In resistance training (RT), the change in volume-load from training sessions (TS) to TS is an indicator of training progress. Resulting growth trajectories are likely to differ between individuals. Understanding this variation is important for exercise planning in general, but even more for clinical populations. We investigated this variation in breast cancer patients undergoing treatment.
Methods
Data of 69 patients from two randomized controlled trails were investigated. They conducted a 12-week RT program. We fitted a quadratic Bayesian regression model to the baseline standardized volume-load over the course of the intervention. We allowed all parameters to vary both between exercises and between individuals.
Results
We observed a positive linear component of 0.093 (95% uncertainty interval (UI) 0.058 to 0.120) and a negative quadratic component of − 0.002 (95% UI -0.008 to 0.001) for the mean trajectory of the change in volume-load. For the different exercises, we observed a dispersion for both the linear (0.043, 95% UI 0.018 to 0.082) and the quadratic component (0.002, 95% UI < 0.001 to 0.004). Variation between individual appears to be approximately four times larger. We also observed between-exercise variation within individuals. Extrapolation of the regression model indicates training progression stagnates after 20.6 TS (95% UI 14.8 to 44.4).
Conclusion
There is substantial variation in RT response between breast cancer patients undergoing tumor therapy and in-between exercises. The non-linear trajectory indicates that training progression will eventually plateau, demanding periodization and timely modification.
Trial registration
BEATE Study: NCT01106820, Date: April 20, 2010; BEST Study: NCT01468766, Date: November 9, 2011.
... More importantly, studies typically report responses to diverse exercise interventions as average group values, assuming these values reflect individual responses (Arazi et al., 2017;Lee et al., 2020;Boullosa et al., 2022;Mann et al., 2014). Recently, Sheykhlouvand and Gharaat (2024) stated that to clarify the adaptations related to training the calculating of inter-individual variability in team players should be consider. ...
The study aimed to evaluate the effects of varying frequencies (1 vs. 2 vs. 3) of short sprint interval training (sSIT) on young male soccer players' physical performance and physiological parameters. Forty young male soccer players were randomly assigned to four experimental groups engaging in 36 trials sSIT for a duration of 6 weeks as follows: once weekly (1sSIT = 4 sets of 9 × 5 sec all-out runs), twice weekly (2sSIT = 2 sets of 9 × 5 sec all-out runs), and three times weekly (3sSIT = 2 sets of 6 × 5 sec all-out runs), or an active control group which continued their soccer practice routines. Before and after the 6-week training period, physical performance (countermovement vertical jump, 20-m sprint, Illinois change of direction, Yo-Yo intermittent recovery level 1 [Yo-Yo IR 1] and kicking distance) and physiological parameters (cardiorespiratory fitness, peak and average power output) were evaluated. All sSIT groups demonstrated significant (p < 0.01) and small to very large training effects (i.e., effect size) on measured parameters. More importantly, a comparison of inter-individual variability in the adaptive changes revealed that the 3sSIT group results in lower residuals in changes in cardiorespiratory fitness and anaerobic power, coupled with lower coefficient of variations in the mean group changes and perceived exertion throughout the training period. The findings indicate that incorporating one, two, or three weekly sessions of sSIT into routine soccer training can lead to similar enhancements in soccer players' physiological and performance adaptations. More importantly, higher training frequencies result in more homogenized adaptations among team members by reducing inter-individual variability in the magnitude of the adaptive responses.
... detailed studies have been conducted on the consistent basic effect of genetics on adaptation to exercise (33). Genetics is known to have an effect on both exercise performance (16) and adaptation (64). Studies of genetics are generally focused on the effects of single nucleotide polymorphisms (SNP's) and a combination of these polymorphisms (65). ...
... First, the frequency and duration of the SSE program may not have been sufficient to produce substantial changes in some biomarkers when compared to the control group. Individual differences in response to exercise could also have influenced the results, as biological variability can affect how participants respond to the intervention [68]. Another important factor to consider is the composition of the control group in the study by Cha et al. [58]. ...
Background: The aim of this systematic review is to analyze the effects of Square Stepping Exercise (SSE) on physical and cognitive function in older people, including its effects on biomarkers, body composition and mental health, focusing only on research that assessed the efficacy of SSE-based interventions. Methods: PubMed, Web of Science, Scopus and Cochrane databases were searched from June 2006 to June 2024 according to the PRISMA guidelines. The main search terms used were related to “older people” and “square-stepping exercise”. Controlled trials that included at least one intervention group focused on SSE were included. Participants had to be healthy, without physical or cognitive impairment, and the studies published in English or Spanish. The methodological quality of the selected research was assessed using the Physiotherapy Evidence Database (PEDro). Results: Twelve studies were selected from a total of 444 original records, with a total sample size of 577 participants. The health parameters of the participants were homogeneous, with ages ranging from 60 to 80 years. Significant gains were reported in certain physical function assessments, including balance, lower body strength and power, gait speed and flexibility. There were also significant findings in cognitive function, particularly in general cognitive status, focused attention, response time, basic task performance, and executive function. In addition, SSE can improve metrics such as body composition, brain-derived neurotrophic factor (BDNF), and mental health characteristics. Conclusions: SSE has the potential to significantly improve physical function, cognitive performance and body composition, as well as provide mental health benefits and have variable effects on biomarkers and cardiovascular health.
... Bir başka önemli nokta, sürdürülebilir fizyolojik adaptasyonların, sporcuların yalnızca fizyolojik performansını değil, aynı zamanda genel sağlık ve iyilik hallerini de olumlu yönde etkilemesidir. Düzenli antrenmanlar sonucunda elde edilen adaptasyonlar, kardiyovasküler sağlığı iyileştirebilir, kemik yoğunluğunu artırabilir ve bir hızlanma sağlayabilir (Mann, Lamberts, & Lambert, 2013). Bununla birlikte, bu kazanımların sürdürülebilir olması, sporcuların dinlenme süreçlerini, beslenme alışkanlıklarını ve antrenman yoğunluklarını dikkatlice yönetmeleriyle mümkündür. ...
Antrenman bilimi, sporcuların performansını artırmak ve uzun vadeli başarı sağlamak için sürekli gelişen bir alan olmuştur. Ancak günümüzün dinamik spor dünyasında, sadece kısa vadeli performans artışı değil, uzun süreli sürdürülebilir bir gelişim de giderek daha fazla önem kazanmaktadır. Sürdürülebilirlik kavramı, antrenman bilimi içinde, hem sporcuların fiziksel ve zihinsel sağlığını korumayı hem de spor kaynaklarını ve çevresel etkiyi optimize etmeyi içerir. Bu bağlamda, sürdürülebilir bir antrenman yönetimi, sporcuların uzun yıllar boyunca en üst düzeyde performans gösterebilmesi ve sporun gelecek nesiller için aynı etkiyi devam ettirebilmesi açısından kritik bir rol oynamaktadır. Antrenman bilimi alanında sürdürülebilirlik, üç temel bileşen etrafında şekillenir: fizyolojik, psikolojik ve çevresel. Fizyolojik sürdürülebilirlik, sporcuların aşırı antrenman, sakatlık ve yorgunluk gibi sorunlarla karşılaşmadan gelişimlerini sürdürebilmelerini sağlamayı hedefler. Psikolojik sürdürülebilirlik ise sporcuların zihinsel dayanıklılığını artırarak, motivasyonlarının uzun süre devam etmesine katkı sağlar. Çevresel sürdürülebilirlik ise antrenman süreçlerinde kullanılan malzeme, ekipman ve tesislerin çevre dostu olmasını ve antrenmanların doğaya minimum zarar vermesini içerir. Bu noktada, nitel araştırmalar, sürdürülebilir antrenman uygulamalarının anlaşılması ve geliştirilmesi açısından önemli bir yöntemdir. Niteliksel araştırma yöntemleri, sporcuların bireysel deneyimlerini, antrenörlerin stratejilerini ve uzun vadeli başarı planlarını daha derinlemesine anlamamıza olanak tanır. Mülakatlar, vaka incelemeleri ve katılımcı gözlem gibi nitel yöntemler, sürdürülebilir antrenman programlarının nasıl yapılandırılması gerektiği konusunda değerli bilgiler sunar. Örneğin, nitel araştırmalar yoluyla sporcuların antrenman süreçlerinde karşılaştıkları zorluklar, motivasyon kayıpları ya da sürdürülebilir bir başarı için hangi stratejilerin daha etkili olduğu gibi sorulara yanıt bulmak mümkündür. Aynı şekilde, antrenörlerin sürdürülebilirlik konusundaki bakış açıları, bu alandaki politikaların ve uygulamaların iyileştirilmesine yardımcı olabilir. Bu kitap, antrenman bilimi alanında sürdürülebilirlik konusunu derinlemesine ele almakta ve bu süreçte nitel araştırmaların nasıl bir katkı sağladığını irdelemektedir. Kitap boyunca, antrenmanların sürdürülebilirliğini artırmak için kullanılabilecek stratejilere ve bu stratejilerin uygulanabilirliğini destekleyen nitel araştırma bulgularına yer verilecektir. Sporcu sağlığı ve performansının sürdürülebilirliği üzerine odaklanan bu çalışma hem akademisyenler hem de pratikte çalışan antrenörler için değerli bir kaynak olmayı hedeflemektedir. Ayrıca bu kitap Sürdürülebilir Spor ve Niteliksel Araştırmalar Serimizin üçüncü kitabını oluşturmaktadır. Alan yazına bilimsel olarak büyük anlamlar katacak bir araştırma kitabı olması temennisiyle.
... Given the current scientific focus on individual responses to (standardized) training (e.g., high vs. low responding) and sexspecific differences in physiological training adaptation (10)(11)(12)(13), it is of particular interest to investigate the individual response to intense WB-EMS training since knowledge is still limited. Additionally, identifying markers that may indicate unfavorable outcomes is crucial. ...
Purpose
This brief report aimed to characterize inter-individual training responses following a single session of high-intense whole-body electromyostimulation (WB-EMS) using markers of muscle damage over a period of 72 h.
Methods
Twelve healthy individuals (5 men, 7 women; 32.0 ± 7 years) participated in a single 20-minute high-intensity WB-EMS training session. Markers of muscle damage, creatine kinase (CK) and myoglobin (Mb), were assessed before and immediately after training, as well as at 1.5, 3, 24, 48 and 72 h post-exercise. Lactate levels were determined pre- and post-exercise.
Results
Overall, WB-EMS induced significant CK elevations, peaking at 72 h (18.358 ± 21.380 U/L; p < 0.01), and correlating Mb levels peaking at 48 h (1.509 ± 1.394 ng/dl, p < 0.01). Despite significant inter-individual variability in CK levels, both slow (SR) and fast responders (FR) were identified. FR showed significant increases in CK at all time points post WB-EMS (p < 0.05), whereas CK in SR significantly elevated after 48 h. Post-WB-EMS lactate concentration was identified to predict peak CK and Mb levels (r ≥ 0.65, both p < 0.05).
Conclusion
High-intensity WB-EMS has the potential to induce severe muscle damage, as indicated by elevated levels of CK and Mb. We identified two distinct groups of individuals, SR and FR, indicating variability in response to WB-EMS. Furthermore, we suggest that individual responses to WB-EMS can be predicted based on post-WB-EMS lactate concentration.
... Alternatively, individual patterns of response may result from measurement variability obscuring our ability to accurately classify individual response. Importantly, instrumentation error (e.g., error associated with a metabolic cart), day-to-day biological variations (e.g., variations in external physical activity prior to an experimental trial) and within-subject variability (e.g., chronic changes to participant diet) can impact an individual's observed response (reviewed in (29)). Although within-subject variability may influence different outcomes equally (e.g., VO2peak and TTF) -it is possible that some outcomes may be differentially impacted (e.g., mCRF vs sCRF). ...
The purpose of the current study was to test the hypothesis that individual response classification for surrogate markers of cardiorespiratory fitness (CRF) will agree with response classification for VO2peak. Surrogate markers of CRF were time to fatigue on treadmill test (TTF), time trial performance (3kTT), resting heart rate (RHR), submaximal heart rate (SubmaxHR), and submaximal ratings of perceived exertion (SubmaxRPE). Twenty-five participants were randomized into a high-intensity interval training (HIIT: n = 14) group or non-exercise control group (CTL: n = 11). Training consisted of four weeks of high-intensity interval training (HIIT) – 4x4 minute intervals at 90–95% HRmax 3 times per week. We observed poor agreement between response classification for VO2peak and surrogate markers (agreement < 60% for all outcomes). Although surrogate markers and VO2peak correlated at the pre- and post-intervention time points, change scores for VO2peak were not correlated with changes in surrogate markers of CRF. Interestingly, a significant relationship (r² = 0.36, p = 0.02) was observed when comparing improvements in estimated training performance (VO2) and change in VO2peak. Contrary to our hypothesis, we observed poor classification agreement and non-significant correlations for changes scores of VO2peak and surrogate markers of CRF. Our results suggest that individuals concerned with their VO2peak response seek direct measurements of VO2.
... Moreover, modifiable personal factors (e.g., nutrition, training status) and environmental conditions (e.g., heat [37], local and systemic hypoxia [38]) can change, leading to different psychophysiological responses to the identical exercise stimulus in the same individual. Given that the interplay of the characteristics of the exercise, the contextual factors, and the resulting acute response to the exercise determine chronic adaptations and, thus, the training outcome, it is recommended to use internal load markers in conjunction with external load measures to monitor and control the training process [23,[39][40][41][42]. In addition, specific contextual factors should also be considered, especially if the training method used implies a deliberate change in one or more of these factors (e.g., hypoxia [43] or heat conditioning [44]). ...
Physical exercise induces acute psychophysiological responses leading to chronic adaptations when the exercise stimulus is applied repeatedly, at sufficient time periods, and with appropriate magnitude. To maximize long-term training adaptations, it is crucial to control and manipulate the external load and the resulting psychophysiological strain. Therefore, scientists have developed a theoretical framework that distinguishes between the physical work performed during exercise (i.e., external load/intensity) and indicators of the body's psychophysiological response (i.e., internal load/intensity). However, the application of blood flow restriction (BFR) during exercise with low external loads/intensities (e.g., ≤ 30% of the one-repetition-maximum, ≤ 50% of maximum oxygen uptake) can induce physiological and perceptual responses, which are commonly associated with high external loads/intensities. This current opinion aimed to emphasize the mismatch between external and internal load/intensity when BFR is applied during exercise. In this regard, there is evidence that BFR can be used to manipulate both external load/intensity (by reducing total work when exercise is performed to exhaustion) and internal load/intensity (by leading to higher physiological and perceptual responses compared to exercise performed with the same external load/intensity without BFR). Furthermore, it is proposed to consider BFR as an additional exercise determinant, given that the amount of BFR pressure can determine not only the internal but also external load/intensity. Finally, terminological recommendations for the use of the proposed terms in the scientific context and for practitioners are given, which should be considered when designing, reporting, discussing, and presenting BFR studies, exercise, and/or training programs.
... Indeed, research results based on population samples are not generalizable and representative of individual changes 38À40 While some participants within an experimental sample may show substantial beneficial changes after an intervention, others may encounter adverse effects and others may show no response at all. 40,41 However, the published literature tends to overestimate the precision of group statistical estimates and the generalizability of conclusions to individuals. 42 Exercise recommendations for health should be informed by individual needs, rather than arising from entire population averages. ...
... This variability in cardiorespiratory fitness responses is consistent with findings by Williams et al. [13], who reported that high-volume HIIT elicited a responders rate of 31% when accounting for both the technical error of measurement (coefficient of variation of 5.6%) and the minimal clinically important difference (3.5 mL/kg/min). Following these findings, it is important to consider that individual responses to exercise can vary based on a multitude of factors, including genetics/heredity, training status, the homeostatic stress of each training session, habitual physical activity, sleep, and nutrition [39]. Along these lines, further exploration of how different exercise protocols align with individual responses is warranted. ...
Background
This study aimed to investigate the effectiveness of a 6‐month home‐based high‐intensity interval training (HIIT) intervention to improve peak oxygen consumption (V̇O2peak) and lactate threshold (LT) in older adults.
Methods
Two hundred thirty‐three healthy older adults (60–84 years; 54% females) were randomly assigned to either 6‐month, thrice‐weekly home‐based HIIT (once‐weekly circuit training and twice‐weekly interval training) or a passive control group. Exercise sessions were monitored using a Polar watch and a logbook for objective and subjective data, respectively, and guided by a personal coach. The outcomes were assessed using a modified Balke protocol combining V̇O2peak and LT measures. General linear regression models assessed between‐group differences in change and within‐group changes for each outcome.
Results
There was a significant between‐group difference in the pre‐to‐post change in V̇O2peak (difference: 1.8 [1.2; 2.3] mL/kg/min; exercise: +1.4 [1.0; 1.7] mL/kg/min [~5%]; control: −0.4 [−0.8; −0.0] mL/kg/min [approximately −1.5%]; effect size [ES]: 0.35). Compared with controls, the exercise group had lower blood lactate concentration (−0.7 [−0.9; −0.4] mmol/L, ES: 0.61), % of peak heart rate (−4.4 [−5.7; −3.0], ES: 0.64), and % of V̇O2peak (−4.5 [−6.1; −2.9], ES: 0.60) at the intensity corresponding to preintervention LT and achieved a higher treadmill stage (% incline) at LT (0.6 [0.3; 0.8]; ES: 0.47), following the intervention.
Conclusion
This study highlights the effectiveness of a home‐based HIIT intervention as an accessible and equipment‐minimal strategy to induce clinically meaningful improvements in cardiorespiratory fitness in older adults. Over 6 months, the exercise group showed larger improvements in all outcomes compared with the control group. Notably, the LT outcome exhibited a more pronounced magnitude of change than V̇O2peak.
... This study aimed to investigate the associations between different subjective indicators and measures used in research practice to identify NFOR/OT, which may exhibit greater responsiveness to the stress induced by training compared to objective measures (Saw, Main, & Gastin, 2016). Since there is substantial interindividual variability in response to overload training (Bellinger, 2020;Mann et al., 2014), we focused on the intra-individual level and examined the covariance of variables in a longitudinal perspective. The intensive longitudinal part focused exclusively on highly variable characteristics, which were monitored on a small sample of athletes at 14-day intervals (L26 study). ...
This study explores the relationships among indicators of overtraining in adolescent athletes. The research employed widely-accepted tools for subjective overtraining indicator detection, as identified through a systematic review. Two groups of athletes were observed, comprising 13 athletes with bi-weekly assessments and 66 athletes with assessments every 3 months over a one-year period. The study analyzed relationships between variables using repeated measures correlations. A correlation matrix was subjected to principal component analysis. Three fundamental groups of indicators, reflecting negative emotionality (accompanied by sleep problems), self-concept characteristics, and core symptoms of overtraining syndrome captured through subjective sport fitness and vigor. This research emphasizes the intrinsic link between emotional and physical aspects in the lives of elite athletes, shedding light on the complex interplay of overtraining indicators in the context of adolescent developmental period.
... Given that the homeostatic stress to an exercise session is different between people [25], then this would impact on recovery duration selected and result in greater variability in the outcome measure if a separate control group were utilised. Therefore, a self-controlled design was used where participants were asked to continue with their own training for 3 weeks after baseline testing and retested before undertaking the training intervention. ...
Purpose
Recovery within and between rounds is crucial to combat sports performance. We sought to determine whether sprint interval training (SIT) improves recovery dynamics and aerobic performance.
Methods
Eleven male kickboxing athletes (26 ± 5 years; body mass index 25 ± 3 kg/m ² ) were recruited. Participants were tested three times for VO 2peak /time to exhaustion and critical power; baseline, 3 weeks control, 3 weeks of SIT (8 × 10 s lower body sprints followed by a maximum of 10 min recovery before completing 8 × 10 s upper body sprints). During SIT session 1 and 9 continuous gas analysis was performed.
Results
There was a significant reduction in recovery time between lower and upper body sprints with training (session 1: 441 ± 150 s; session 9: 268 ± 10 s; P < 0.01; d = 2.77) and change in oxygen off-kinetics amplitude (session1: 3.0 ± 0.7 L/min, session 9: 3.6 ± 1.0 L/min; P < 0.05; d = − 1.77), VO 2 end (session 1: 0.59 ± 0.19 L/min, session 9: 0.81 ± 0.21 L/min; P < 0.05, d = − 0.90), time constant (session 1: 81 ± 21 s; session 9: 60 ± 11 s; P < 0.05; d = 1.03). Following training there was a significant improvement in critical power ( P < 0.05; η ² p = 0.72) time to exhaustion ( P < 0.05; η ² p = 0.30) but not VO 2peak ( P > 0.05).
Conclusion
SIT improves recovery time associated and aerobic performance associated with improved oxygen off-kinetics. Therefore, training needs to focus on improving oxygen off-kinetics to enhance combat performance.
... Finally, more studies are needed to be able to determine if there is a specificity of nonresponse to each type of exercise (T. N. Mann et al., 2014), in this case to OS. Despite the exposed limitations, we believe that, understanding our study as an intervention proposal, we can provide some practical implications for its applicability in training: ...
Introduction. Maximum running speed (MRS) has a significant impact on sports performance. Among the different training methods, overspeed (OS) - or assisted speed - stands out for its specificity. However, there is a scarcity of scientific evidence to support its effectiveness, and the methodologies used to generate OS conditions lack standardization. Motorized devices offer characteristics that can potentially improve this situation. Objectives. 1) to determine the current situation of the acute effects of OS conditions using towing systems; 2) to analyze the acute effects of different OS loads using a motorized towing system; 3) to propose a standardization method for OS loads based on the percentage of body weight; 4) to analyze the effects of an OS training program on MRS in young athletes within an ecological approach to training. Methodology. The first objective was addressed through a systematic review with meta-analysis following the PRISMA methodology. The second objective involved analyzing the acute effects of three different OS loads on young athletes using a motorized towing system. The third objective focused on establishing theoretical optimal training loads based on individual effects and standardizing them as a percentage relative to the athletes' body weight. Finally, the fourth objective entailed implementing an OS intervention in young athletes and analyzing the post-training effects on MRS. Results. The systematic review with meta-analysis revealed that acute increases in MRS (d: 1.54; 95% CI: 0.94 – 2.14; p < 0.001) were primarily attributed to an increase in step length (d: 0.92; 95% CI: 0.57 – 1.28; p < 0.001) and flight time (d: 0.28; 95% CI: 0.09 – 0.48; p = 0.004), as well as a decrease in contact time (d: -0.57; 95% CI: -0.77 – -0.37; p < 0.001). However, the mechanisms underlying these changes could not be determined. The analysis of different OS loads demonstrated that the theoretical optimal loads, relative to body weight, ranged from 3.47 ± 0.68% to 6.94 ± 1.35%, resulting in speeds of 102.91 ± 2.91% and 104.88 ± 3.01% of MRS, respectively. The OS intervention led to non-significant increases (p < 0.05) in MRS across the sample, although with a large effect size (d: 0.89; 95% CI: -0.10 – 1.82), indicating individual differences among the subjects studied. Conclusions. Towing systems have been shown to acutely increase MRS in athletes. However, the specific mechanical, physiological and molecular mechanisms underlying these improvements remain to be determined. It is essential to identify the training load that produces individual increases in MRS while minimizing interference with the natural sprinting pattern. Therefore, it is recommended to standardize the control and expression of these training loads based on the percentage of body weight. However, this field of study requires further methodological development, and it is not yet possible to argue that OS training is beneficial for all athletes to increase their MRS. In order to confirm this, more studies need to be conducted, and results should be standardized, with a focus on exploring the mechanical, physiological, and molecular variables associated with the potential mechanisms of change.
... However, it is critical to acknowledge that not just the sensitivity to a certain (external) training load is important. Even more relevant would be the specificity of the marker to distinguish excessive load from sustainable load for an individual, as the homeostatic stress induced by a certain training load and ability to recover is highly variable between individuals (Mann et al., 2014). ...
The purpose of this study was firstly to examine the sensitivity of heart rate (HR)‐based and subjective monitoring markers to intensified endurance training; and secondly, to investigate the validity of these markers to distinguish individuals in different fatigue states. A total of 24 recreational runners performed a 3‐week baseline period, a 2‐week overload period, and a 1‐week recovery period. Performance was assessed before and after each period with a 3000m running test. Recovery was monitored with daily orthostatic tests, nocturnal HR recordings, questionnaires, and exercise data. The participants were divided into subgroups (overreached/OR, n = 8; responders/RESP, n = 12) based on the changes in performance and subjective recovery. The responses to the second week of the overload period were compared between the subgroups. RESP improved their baseline 3000 m time (p < 0.001) after the overload period (−2.5 ± 1.0%), and the change differed (p < 0.001) from OR (0.6 ± 1.2%). The changes in nocturnal HR (OR 3.2 ± 3.1%; RESP −2.8 ± 3.7%, p = 0.002) and HR variability (OR −0.7 ± 1.8%; RESP 2.1 ± 1.6%, p = 0.011) differed between the subgroups. In addition, the decrease in subjective readiness to train (p = 0.009) and increase in soreness of the legs (p = 0.04) were greater in OR compared to RESP. Nocturnal HR, readiness to train, and exercise‐derived HR‐running power index had ≥85% positive and negative predictive values in the discrimination between OR and RESP individuals. In conclusion, exercise tolerance can vary substantially in recreational runners. The results supported the usefulness of nocturnal HR and subjective recovery assessments in recognizing fatigue states.
... Notwithstanding, understanding the connections between personality traits and movement holds potential public health implications. Indeed, tailoring physical interventions through suitable exercises and instructions could mitigate non-adherence [45] and variability in responses [46] to a running training program in the context of a modern sedentary lifestyle. The disparities in running biomechanics associated with sensing and intuition personality traits might result in distinct injury locations or different underlying causes for a given injury. ...
Delving into the complexities of embodied cognition unveils the intertwined influence of mind, body, and environment. The connection of physical activity with cognition sparks a hypothesis linking motion and personality traits. Hence, this study explored whether personality traits could be linked to biomechanical variables characterizing running forms. To do so, 80 runners completed three randomized 50-m running-trials at 3.3, 4.2, and 5m/s during which their running biomechanics [ground contact time (tc), flight time (tf), duty factor (DF), step frequency (SF), leg stiffness (kleg), maximal vertical ground reaction force (Fmax), and maximal leg compression of the spring during stance (ΔL)] was evaluated. In addition, participants’ personality traits were assessed through the Myers-Briggs Type Indicator (MBTI) test. The MBTI classifies personality traits into one of two possible categories along four axes: extraversion-introversion; sensing-intuition; thinking-feeling; and judging-perceiving. This exploratory study offers compelling evidence that personality traits, specifically sensing and intuition, are associated with distinct running biomechanics. Individuals classified as sensing demonstrated a more grounded running style characterized by prolonged tc, shorter tf, higher DF, and greater ΔL compared to intuition individuals (p≤0.02). Conversely, intuition runners exhibited a more dynamic and elastic running style with a shorter tc and higher kleg than their sensing counterparts (p≤0.02). Post-hoc tests revealed a significant difference in tc between intuition and sensing runners at all speeds (p≤0.02). According to the definition of each category provided by the MBTI, sensing individuals tend to focus on concrete facts and physical realities while intuition individuals emphasize abstract concepts and patterns of information. These results suggest that runners with sensing and intuition personality traits differ in their ability to use their lower limb structures as springs. Intuition runners appeared to rely more in the stretch-shortening cycle to energetically optimize their running style while sensing runners seemed to optimize running economy by promoting more forward progression than vertical oscillations. This study underscores the intriguing interplay between personality traits of individuals and their preferred movement patterns.
Athletes must maintain their physical and mental condition to sustain performance and prevent mental health decline. Given the variability in individual responses to training, tailored conditioning support is crucial. Additionally, multiple factors interact during athlete conditioning, necessitating an analytical approach that effectively captures these complex dynamic relationships. However, previous research and practice have primarily focused on the variability of individual variables and unilateral relationships between group-averaged independent and dependent variables, paying limited attention to interactions among variables within individuals. The psychological network approach, which models the interactions among mental health symptoms to optimize individualized treatments, has recently gained prominence in clinical psychology. Given the significant individual differences in physical and psychological responses and interactions among conditioning variables, this approach may also offer a valuable framework for athlete conditioning. Therefore, this study outlines the psychological network approach and then explores its potential for individualized conditioning strategies. Additionally, building on existing research and practices related to athlete conditioning supports the psychological network approach. We also consider future challenges and research directions for practical applications. In conclusion, despite some challenges, the psychological network approach may provide individualized strategies to optimize athlete performance and mental health.
Objectives: Single-case experimental designs (SCEDs) provide a robust way to observe adaptations to training in highly specific populations. Furthermore, they provide unique insights into inter-participant variance in responses to interventions, which traditional randomized control trials cannot obtain. However, there is limited sports science literature that has applied this methodology to assess the effectiveness of training interventions. Thus, the aim of this study was to determine the individual and combined changes to reactive strength following a 6-week strength and plyometric training intervention. Methods: A non-concurrent multiple baseline SCED was used, where four participants completed weekly 10/5 repeated jump (RJ), drop jump (DJ), and loaded squat jumps during a 5–7-week baseline phase and a 6-week intervention phase. The intervention consisted of traditional resistance and plyometric training. Results: The results found inter-participant variance in changes to reactive strength, with some individuals having significant improvements whilst others declined. The combined results found that during the intervention, the reactive strength index (RSI) of the RJ significantly decreased (baseline: 2.15 vs. intervention: 2.0) whilst no change in DJ occurred. This led to a significant increase in the reactive quality ratio (RQR) (baseline: 1.02 vs. intervention: 1.08). Conclusions: These findings highlight the importance of considering individual responses to training reactive strength rather than cohort observations alone, and the SCED is a viable methodology to achieve this. Practitioners should consider exercise selection, maximum strength levels and responsiveness of individuals when prescribing plyometric exercise to improve high and low amplitude reactive strength qualities.
Background
Hypoxic conditioning has emerged as a promising intervention for enhancing physiological adaptations. This systematic review and meta-analysis of randomized controlled trials aims to investigate the efficacy of hypoxic conditioning on physical fitness measures in aging populations.
Methods
The Embase, PubMed, Cochrane Library, and Web of Science were searched from inception to November 2024 (Prospero registration: CRD42023474570). The Cochrane Evaluation Tool and Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework were used for risk of bias assessment and evidence certainty evaluation. Mean differences (MD) and standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated using the Review Manager software. Subgroup analysis was performed to explore possible associations between the study characteristics and the effectiveness of the intervention.
Results
A total of 13 randomized controlled trials (RCTs) with 368 subjects were included in the meta-analysis. High certainty evidence found hypoxic conditioning (HC) significantly improved peak oxygen uptake (VO 2 peak) (SMD = 0.31, 95% CI [0.01–0.61]; P < 0.05), while very low to moderate certainty evidence shown that hypoxic conditioning (HC) have not induced greater changes on functional outcomes (SMD = −0.21, 95% CI [−0.66–0.24]; P > 0.05), muscle strength (SMD = −0.19, 95% CI [−0.63–0.26]; P > 0.05), maximal power output (SMD = 0.29, 95% CI [−0.17–0.76]; P > 0.05), VO 2 max (SMD = −0.39, 95% CI [−1.12–1.90]; P > 0.05), and exercise workload (MD = −10.07, 95% CI [−34.95–14.80]; P > 0.05).
Conclusion
This study suggests that hypoxia conditioning has a greater effect on enhancing VO 2 peak compared to equivalent normoxic training in the middle-aged and older population. More high-quality RCTs are needed in the future to explore the optimal oxygen concentration and exercise intensity during hypoxia conditioning.
Background and Objective
There has been a substantial increase in the number of studies demonstrating improvements in walking capacity in people with chronic stroke following moderate-to-high intensity walking exercise interventions. Yet, there is significant variability in response to these interventions. This is likely due to the heterogeneity in this population and the variability in the exercise dose parameters actually attained within these walking interventions. Exercise prescription can be optimized by understanding how individual variables impact walking exercise dose. This study leveraged a large, clinical dataset to classify people with chronic stroke into homogeneous groups (called classes) and compare classes on the walking exercise dose achieved in a walking intervention.
Methods
One hundred sixty-nine people with chronic (>6-Months) stroke completed clinical evaluations and a 12-week high-intensity treadmill intervention. Baseline measures of walking capacity, physical health, and psychosocial factors were used in a latent variable mixture model to assess if latent, homogeneous classes existed within the dataset. Objective criteria determined the optimal number of classes, which were compared to the walking exercise dose attained across the intervention.
Results
Four homogeneous classes were distinguished by differences in baseline walking capacity, steps-per-day, comorbidity burden, and balance self-efficacy. Despite clear “clinical profiles” of people with chronic stroke, these classes did not differ on the walking exercise dose attained.
Discussion and Conclusions
Prior literature and clinical intuition suggest individuals with lower baseline walking capacity, physical health, and self-efficacy are less likely to tolerate high-intensity exercise, however our results demonstrate this is not true for people with chronic stroke.
Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A524.
Purpose
This study investigates the intra- and inter-individual time courses of physiological adaptation to high-intensity interval training (HIIT), comparing single and duplicate pre-to-post testing with session-by-session analysis to more accurately identify “genuine” adaptations.
Methods
Seventeen participants (nine men) engaged in repeated 4x4 min HIIT sessions (2 times/week) until a meaningful change in the primary outcome i.e. relative peak oxygen uptake (VO 2peak ) was observed.
Results
Mixed-effects model analysis revealed a significant improvement for VO 2peak for both session-by-session (estimate: 0.18, p < 0.01, d = 0.11) analysis and duplicate pre-to-post analysis (estimate: 3.97, p < 0.01, ηp ² = 0.36). Session-by-session analysis revealed significant variability in physiological responses, with a low coefficient of variation (CV) for VO 2peak (3.49% + 1.96) and estimated maximum stroke volume (SV max ) (3.07% ± 1.92) and, indicating their reliability for detecting small changes. With a CV of 22.14% ± 13.80 submaximal blood lactate ([BLa] submax ) was the least reliable parameter. With session-by-session analysis VO 2peak was the only parameter displaying 100% positive responders after 9.5 ± 3.8 sessions. Additionally, session-by-session analysis revealed lower proportions of participants with positive adaptations for submaximal VO 2 and SV max , but higher proportions for submaximal respiratory exchange ratio and rating of perceived exertion compared with pre-to-post analysis.
Conclusions
This study highlights the value of longitudinal assessments for understanding the variability and dynamics of training adaptations. By addressing the limitations of pre-to-post evaluations, the findings emphasize the importance of frequent monitoring to accurately capture individual responses, thereby advancing strategies for optimizing exercise interventions across diverse populations.
Background and aims
Physical exercise (PE) has been suggested as a potential complementary tool for substance use disorder (SUD) recovery. However, its potential benefits for the brain and cognitive functions are relatively less explored, even though cognitive functions play a key role in the recovery process. Here, we aim to (1) compile results from studies that examined the effects of PE on brain and/or cognitive functions in individuals with SUD, and (2) provide recommendations for future research and practitioners.
Methods
We searched for articles that investigated either the acute or chronic effects of PE on brain markers and/or cognitive functions in individuals diagnosed with SUD. We then provided recommendations for future research studies based on limitations of the current literature, as well as instructions to practitioners about how to set up a PE program aiming to help the recovery process.
Results
We found 9 studies examining the acute effects of PE and 14 investigating the impact of chronic PE. Most of them (∼70%) were from China and had methamphetamine users (∼61%) as their sample. Several limitations in the literature were found, including the lack of baseline physical activity levels, lack of studies on other populations, and lack of studies examining other exercise modalities (e.g., resistance training).
Conclusion
Recommendations include the use of affect and perceived effort scales, expanding the studies to include behavioral economic variables (e.g., delay discounting and demand), exploring self-selected intensity exercises to increase adherence rates, and taking into consideration individual exercise type preference (e.g., running, swimming, lifting).
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.
We studied the effects of aerobic exercise training and detraining in humans on post-exercise vagal reactivation. Ten healthy
untrained men trained for 8weeks using a cycle ergometer [70% of initial maximal oxygen uptake (
) for 1h, 3–4days·week–1] and then did not exercise for the next 4weeks. Post-exercise vagal reactivation was evaluated as the time constant of the
beat-by-beat decrease in heart rate during the 30s (t30) immediately following 4min exercise at 80% of ventilatory threshold (VT). The
and the oxygen uptake at VT had significantly increased after the 8weeks training programme (P<0.0001, P<0.001, respectively). The t30 had shortened after training, and values after 4weeks and 8weeks of training were significantly shorter than the initial
t30 (P<0.05, P<0.01, respectively). The change in the t30 after 8weeks of training closely and inversely correlated with the initial t30 (r=–0.965, P<0.0001). The reduced t30 was prolonged significantly after 2weeks of detraining, and had returned almost to the baseline level after a further 2weeks
of detraining. These results suggest that aerobic exercise training of moderate intensity accelerates post-exercise vagal
reactivation, but that the accelerated function regresses within a few weeks of detraining.
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 (VO2peak) 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 VO2peak (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.
Skeletal muscle displays remarkable plasticity, enabling substantial adaptive modifications in its metabolic potential and functional characteristics in response to external stimuli such as mechanical loading and nutrient availability. Contraction-induced adaptations are determined largely by the mode of exercise and the volume, intensity, and frequency of the training stimulus. However, evidence is accumulating that nutrient availability serves as a potent modulator of many acute responses and chronic adaptations to both endurance and resistance exercise. Changes in macronutrient intake rapidly alter the concentration of blood-borne substrates and hormones, causing marked perturbations in the storage profile of skeletal muscle and other insulin-sensitive tissues. In turn, muscle energy status exerts profound effects on resting fuel metabolism and patterns of fuel utilization during exercise as well as acute regulatory processes underlying gene expression and cell signaling. As such, these nutrient-exercise interactions have the potential to activate or inhibit many biochemical pathways with putative roles in training adaptation. This review provides a contemporary perspective of our understanding of the molecular and cellular events that take place in skeletal muscle in response to both endurance and resistance exercise commenced after acute and/or chronic alterations in nutrient availability (carbohydrate, fat, protein, and several antioxidants). Emphasis is on the results of human studies and how nutrient provision (or lack thereof) interacts with specific contractile stimulus to modulate many of the acute responses to exercise, thereby potentially promoting or inhibiting subsequent training adaptation.
The purpose of this investigation was to assess mixed-muscle fractional synthesis rate (FSR) and the expression of genes involved in skeletal muscle remodeling after aerobic exercise in the fasted and fed states. Eight recreationally active males (25 ± 1 yr; Vo(2 max): 52 ± 2 ml·kg(-1)·min(-1)) performed 60-min of cycle ergometry at 72 ± 1% Vo(2 max) on two occasions in a counter-balanced design. Subjects ingested a noncaloric placebo (EX-FAST) or a beverage containing (per kg body wt): 5 kcal, 0.83 g carbohydrate, 0.37 g protein, and 0.03 g fat (EX-FED) immediately and 1 h after exercise. FSR was assessed at rest and following exercise with the use of a l-[ring (2)H(5)]-phenylalanine infusion combined with muscle biopsies at 2 and 6 h postexercise. mRNA expression was assessed at 2 and 6 h postexercise via real-time RT-PCR. FSR was higher (P < 0.05) after exercise in both EX-FAST (0.112 ± 0.010%·h(-1)) and EX-FED (0.129 ± 0.014%·h(-1)) compared with rest (0.071 ± 0.005%·h(-1)). Feeding attenuated the mRNA expression (P < 0.05) of proteolytic factors MuRF-1 (6 h) and calpain-2 (2 and 6 h) postexercise but did not alter FOXO3A, calpain-1, caspase3, or myostatin mRNA expression compared with EX-FAST. Myogenic regulatory factor (MRF4) mRNA was also attenuated (P < 0.05) at 2 and 6 h postexercise in EX-FED compared with EX-FAST. These data demonstrate that a nonexhaustive bout of aerobic exercise stimulates skeletal muscle FSR in the fasted state and that feeding does not measurably enhance FSR between 2 and 6 h after aerobic exercise. Additionally, postexercise nutrient intake attenuates the expression of factors involved in the ubiquitin-proteosome and Ca(2+)-dependent protein degradation pathways. These data provide insight into the role of feeding on muscle protein metabolism during recovery from aerobic exercise.
To test the utility of HR variability (HRV) in daily exercise prescription in moderately active (approximately two exercises per week) men and women.
A total of 21 men and 32 women were divided into standard training (ST: males = 7 and females = 7), HRV-guided training (HRV-I: males = 7 and females = 7; HRV-II: females = 10), and control (males = 7 and females = 8) groups. The 8-wk aerobic training period included 40-min exercises at moderate and vigorous intensities (70% and 85% of maximal HR). The ST group was instructed to perform two or more sessions at moderate and three or more sessions at vigorous intensity weekly. HRV-I and HRV-II groups trained on the basis of changes in HRV, measured every morning. In the HRV-I group, an increase or no change in HRV resulted in vigorous-intensity training on that day. Moderate-intensity exercise or rest was prescribed if HRV had decreased. The HRV-II group performed a vigorous-intensity exercise only when HRV had increased. Peak oxygen consumption (VO2peak) and maximal workload (Loadmax) were measured by a maximal bicycle ergometer test before and after the intervention.
The changes in VO2peak did not differ between the training groups either in men or in women. In men, the change in Loadmax was higher in the HRV-I group than in the ST group (30 +/- 8 vs 18 +/- 10 W, P = 0.033). In women, no differences were found in the changes in Loadmax between the training groups (18 +/- 10, 15 +/- 11, and 18 +/- 5 W for ST, HRV-I, and HRV-II, respectively). The HRV-II group performed fewer vigorous-intensity exercises than the ST and HRV-I groups (1.8 +/- 0.3 vs 2.8 +/- 0.6 and 3.3 +/- 0.2 times per week, respectively, P < 0.01 for both).
HRV measurements are beneficial in exercise training prescription in moderately active men and women. Women benefit from HRV guidance by achieving significant improvement in fitness with a lower training load.
There is considerable inter-individual variability in adaptations to endurance training. We hypothesised that those individuals with a low local leg-muscle peak aerobic capacity relative to their whole-body maximal aerobic capacity would experience greater muscle training adaptations compared to those with a relatively high . 53 untrained young women completed one-leg cycling to measure and two-leg cycling to measure . The one-leg was expressed as a ratio of the two-leg (Ratio
1:2). Magnetic resonance imaging was used to indicate quadriceps muscle volume. Measurements were taken before and after completion of 6 weeks of supervised endurance training. There was large inter-individual variability in the pre-training Ratio
1:2 and large variability in the magnitude of training adaptations. The pre-training Ratio
1:2 was not related to training-induced changes in (P = 0.441) but was inversely correlated with changes in one-leg and muscle volume (P < 0.05). No relationship was found between the training-induced changes in two-leg and one-leg (r = 0.21; P = 0.129). It is concluded that the local leg-muscle aerobic capacity and Ratio
1:2 vary from person to person and this influences the extent of muscle adaptations following standardised endurance training. These results help to explain why muscle adaptations vary between people and suggest that setting the training stimulus at a fixed percentage of might not be a good way to standardise the training stimulus to the leg muscles of different people.
The aims of the present study were to (1) assess relationships between running performance and parasympathetic function both at rest and following exercise, and (2) examine changes in heart rate (HR)-derived indices throughout an 8-week period training program in runners. In 14 moderately trained runners (36 +/- 7 years), resting vagal-related HR variability (HRV) indices were measured daily, while exercise HR and post-exercise HR recovery (HRR) and HRV indices were measured fortnightly. Maximal aerobic speed (MAS) and 10 km running performance were assessed before and after the training intervention. Correlations (r > 0.60, P < 0.01) were observed between changes in vagal-related indices and changes in MAS and 10 km running time. Exercise HR decreased progressively during the training period (P < 0.01). In the 11 subjects who lowered their 10 km running time >0.5% (responders), resting vagal-related indices showed a progressively increasing trend (time effect P = 0.03) and qualitative indications of possibly and likely higher values during week 7 [+7% (90% CI -3.7;17.0)] and week 9 [+10% (90% CI -1.5;23)] compared with pre-training values, respectively. Post-exercise HRV showed similar changes, despite less pronounced between-group differences. HRR showed a relatively early possible decrease at week 3 [-20% (90% CI -42;10)], with only slight reductions near the end of the program. The results illustrate the potential of resting, exercise and post-exercise HR measurements for both assessing and predicting the impact of aerobic training on endurance running performance.
Purpose:
The purpose of this study was to assess research aimed at measuring performance enhancements that affect success of individual elite athletes in competitive events.
Analysis:
Simulations show that the smallest worthwhile enhancement of performance for an athlete in an international event is 0.7-0.4 of the typical within-athlete random variation in performance between events. Using change in performance in events as the outcome measure in a crossover study, researchers could delimit such enhancements with a sample of 16-65 athletes, or with 65-260 in a fully controlled study. Sample size for a study using a valid laboratory or field test is proportional to the square of the within-athlete variation in performance in the test relative to the event; estimates of these variations are therefore crucial and should be determined by repeated-measures analysis of data from reliability studies for the test and event. Enhancements in test and event may differ when factors that affect performance differ between test and event; overall effects of these factors can be determined with a validity study that combines reliability data for test and event. A test should be used only if it is valid, more reliable than the event, allows estimation of performance enhancement in the event, and if the subjects replicate their usual training and dietary practices for the study; otherwise the event itself provides the only dependable estimate of performance enhancement. Publication of enhancement as a percent change with confidence limits along with an analysis for individual differences will make the study more applicable to athletes. Outcomes can be generalized only to athletes with abilities and practices represented in the study.
Conclusion:
estimates of enhancement of performance in laboratory or field tests in most previous studies may not apply to elite athletes in competitive events.
BOUCHARD, C, A. S. LEON, D. C. RAO, J. S. SKINNER, J. H. WlLMORE, and J. GAGNON. Aims, design, and measurement protocol, Med. Sci. Sports Exerc., Vol 27, No. 5, pp. 721-729, 1995. The HERITAGE family study (HEalth, RIsk factors, exercise Training And GEnetics) will document the role of the genotype in the cardiovascular, metabolic, and hormonal responses to aerobic exercise training. A consortium of five universities in the United States and Canada are involved in carrying out the study. A total of 90 Caucasian families and 40 African-American families with both parents and three or more biological adult offspring are being recruited, tested, exercise-trained in the laboratory with the same program for 20 wk, and re-tested. Oxygen uptake, respiratory exchange ratio, blood pressure, heart rale, cardiac output, blood lactatc, glucose, and free-fatty acids are measured during exercise, and maximal oxygen uptake is determined before and after training. Plasma lipids, lipoproteins and apoproteins, glucose and insulin response to an intravenous glucose load, plasma sex steroids and glucocorticoids, and body fat and fat distribution are assessed. Dietary and activity habits and other life style components are assessed by questionnaires, prior to, during, and after training. A variety of genetic analyses will be undertaken, including heritability studies and major gene effects, for each phenotype and its response to regular exercise. Cell lines arc established, and DNA sequence variation at a variety of molecular markers will be determined for association and linkage studies.
(C)1995The American College of Sports Medicine
Objective:
To study the protective role of lower resting heart rate (RHR) in cardiovascular disease (CVD) and all-cause mortality.
Patients and methods:
Patients (n=53,322) who received a baseline medical examination between January 1, 1974, and December 31, 2002, were recruited from the Cooper Clinic, Dallas, Texas. They completed a medical questionnaire and underwent clinical evaluation. Patients with CVD or cancer or who had less than 1 year of mortality follow-up were excluded from the study. Relative risks and 95% CIs for all-cause and CVD mortality across RHR categories were estimated using Cox proportional hazards models.
Results:
Highest cardiorespiratory fitness with lower mortality was found in individuals with an RHR of less than 60 beats/min. Similarly, patients with a higher RHR (≥80 beats/min) were at greater risk for CVD and all-cause mortality compared with an RHR of less than 60 beats/min. This analysis was followed by stratification of the data by hypertension, where hypertensive individuals with high RHRs (≥80 beats/min) were found to be at greater risk for CVD and all-cause mortality compared with those with hypertension and lower RHRs (<60 beats/min). In addition, unfit individuals with high RHRs had the greatest risk of CVD and all-cause mortality. The unfit with low RHR group had a similar risk for CVD and all-cause mortality as the fit with high RHR group.
Conclusion:
Lower cardiorespiratory fitness levels and higher RHRs are linked to greater CVD and all-cause mortality.
Objective: To study the protective role of lower resting heart rate (RHR) in cardiovascular disease (CVD)
and all-cause mortality.
Patients and Methods: Patients (n¼53,322) who received a baseline medical examination between
January 1, 1974, and December 31, 2002, were recruited from the Cooper Clinic, Dallas, Texas. They
completed a medical questionnaire and underwent clinical evaluation. Patients with CVD or cancer or who
had less than 1 year of mortality follow-up were excluded from the study. Relative risks and 95% CIs for
all-cause and CVD mortality across RHR categories were estimated using Cox proportional hazards
models.
Results: Highest cardiorespiratory fitness with lower mortality was found in individuals with an RHR of
less than 60 beats/min. Similarly, patients with a higher RHR (�80 beats/min) were at greater risk for CVD
and all-cause mortality compared with an RHR of less than 60 beats/min. This analysis was followed by
stratification of the data by hypertension, where hypertensive individuals with high RHRs (�80 beats/min)
were found to be at greater risk for CVD and all-cause mortality compared with those with hypertension
and lower RHRs (<60 beats/min). In addition, unfit individuals with high RHRs had the greatest risk of
CVD and all-cause mortality. The unfit with low RHR group had a similar risk for CVD and all-cause
mortality as the fit with high RHR group.
Conclusion: Lower cardiorespiratory fitness levels and higher RHRs are linked to greater CVD and
all-cause mortality.
Fiercer competition between athletes and a wider knowledge of optimal training regimens dramatically influence current training methods. A single training bout per day was previously considered sufficient, whereas today athletes regularly train twice a day or more. Consequently, the number of athletes who are overtraining and have insufficient rest is increasing.
Positive overtraining can be regarded as a natural process when the end result is adaptation and improved performance; the supercompensation principle — which includes the breakdown process (training) followed by the recovery process (rest) — is well known in sports. However, negative overtraining, causing maladaptation and other negative consequences such as staleness, can occur.
Physiological, psychological, biochemical and immunological symptoms must be considered, both independently and together, to fully understand the ’staleness’ syndrome. However, psychological testing may reveal early-warning signs more readily than the various physiological or immunological markers.
The time frame of training and recovery is also important since the consequences of negative overtraining comprise an overtraining-response continuum from short to long term effects. An athlete failing to recover within 72 hours has presumably negatively overtrained and is in an overreached state. For an elite athlete to refrain from training for >72 hours is extremely undesirable, highlighting the importance of a carefully monitored recovery process.
There are many methods used to measure the training process but few with which to match the recovery process against it. One such framework for this is referred to as the total quality recovery (TQR) process. By using a TQR scale, structured around the scale developed for ratings of perceived exertion (RPE), the recovery process can be monitored and matched against the breakdown (training) process (TQR versus RPE). The TQR scale emphasises both the athlete’s perception of recovery and the importance of active measures to improve the recovery process. Furthermore, directing attention to psychophysiological cues serves the same purpose as in RPE, i.e. increasing self-awareness.
This article reviews and conceptualises the whole overtraining process. In doing so, it (i) aims to differentiate between the types of stress affecting an athlete’s performance; (ii) identifies factors influencing an athlete’s ability to adapt to physical training; (iii) structures the recovery process. The TQR method to facilitate monitoring of the recovery process is then suggested and a conceptual model that incorporates all of the important parameters for performance gain (adaptation) and loss (maladaptation).
Often exercise intensities are defined as percentages of maximal oxygen uptake ((V) over dot O-2max) or heart rate (HRmax). Purpose: The purpose of this investigation was to test the applicability of these criteria in comparison with the individual anaerobic threshold. Methods: One progressive cycling test to exhaustion (initial stage 100 W. increment 50 W every 3 min) was analyzed in a group of 36 male cyclists and triathletes (24.9 +/- 5.5 yr; 71.6 +/- 5.7 kg; W. increment 50 W every 3 min) was analyzed in a group of 36 male cyclists and triathletes (24.9 +/- 5.5 yr; 71.6 +/- 5.7 kg; (V) over dot O-2max; 62.2 +/- 5.0 mL.min(-1).kg(-1); individual anaerobic threshold = IAT: 3.64 +/- 0.41 W.kg(-1); HRmax: 188 +/- 8 min). Power output and lactate concentrations for 60 and 75% of (V) over dot O-2max as well as for 70 and 85% of HRmax were related to the IAT. Results: There was no significant difference between the mean value of WT (261 +/- 34 W, 2.92 +/- 0.65 mmol.L-1), 75% of (V) over dot O-2max (257 +/- 24 W, 2.84 +/- 0.92 mmol.L-1), and 85% of HRmax (259 +/- 30 W, 2.98 +/- 0.87 mmol.L-1). However, the percentages of the IAT ranged between 86 and 118% for 75% (V) over dot O-2max and 87 and 116% for 85% HRmax (corresponding lactate concentrations: 1.41-4.57 mmol.L-1 and 1.25-4.93 mmol.L-1, respectively). The mean values at 60% of (V) over dot O-2max (198 +/- 19 W, 1.55 +/- 0.67 mmol.L-1) and 70% of HRmax (180 +/- 27 W, 1.45 +/- 0.57 mmol.L-1) differed significantly (P < 0.0001) from the WT and represented a wide range of intensities (66-91% and 53-85% of the IAT, 0.70-3.16 and 0.70-2.91 mmol.L-1, respectively). Conclusions: In a moderately to highly endurance-trained group, the percentages of (V) over dot O-2max and HRmax vary considerably in relation to the IAT. As most physiological responses to exercise are intensity dependent, reliance on these parameters alone without considering the IAT is not sufficient.
Exercise prescribed according to relative intensity is a routine feature in the exercise science literature and is intended to produce an approximately equivalent exercise stress in individuals with different absolute exercise capacities. The traditional approach has been to prescribe exercise intensity as a percentage of maximal oxygen uptake (VO2max) or maximum heart rate (HRmax) and these methods remain common in the literature. However, exercise intensity prescribed at a %VO2max or %HRmax does not necessarily place individuals at an equivalent intensity above resting levels. Furthermore, some individuals may be above and others below metabolic thresholds such as the aerobic threshold (AerT) or anaerobic threshold (AnT) at the same %VO2max or %HRmax. For these reasons, some authors have recommended that exercise intensity be prescribed relative to oxygen consumption reserve (VO2R), heart rate reserve (HRR), the AerT, or the AnT rather than relative to VO2max or HRmax. The aim of this review was to compare the physiological and practical implications of using each of these methods of relative exercise intensity prescription for research trials or training sessions. It is well established that an exercise bout at a fixed %VO2max or %HRmax may produce interindividual variation in blood lactate accumulation and a similar effect has been shown when relating exercise intensity to VO2R or HRR. Although individual variation in other markers of metabolic stress have seldom been reported, it is assumed that these responses would be similarly heterogeneous at a %VO2max, %HRmax, %VO2R, or %HRR of moderate-to-high intensity. In contrast, exercise prescribed relative to the AerT or AnT would be expected to produce less individual variation in metabolic responses and less individual variation in time to exhaustion at a constant exercise intensity. Furthermore, it would be expected that training prescribed relative to the AerT or AnT would provide a more homogenous training stimulus than training prescribed as a %VO2max. However, many of these theoretical advantages of threshold-related exercise prescription have yet to be directly demonstrated. On a practical level, the use of threshold-related exercise prescription has distinct disadvantages compared to the use of %VO2max or %HRmax. Thresholds determined from single incremental tests cannot be assumed to be accurate in all individuals without verification trials. Verification trials would involve two or three additional laboratory visits and would add considerably to the testing burden on both the participant and researcher. Threshold determination and verification would also involve blood lactate sampling, which is aversive to some participants and has a number of intrinsic and extrinsic sources of variation. Threshold measurements also tend to show higher day-to-day variation than VO2max or HRmax. In summary, each method of prescribing relative exercise intensity has both advantages and disadvantages when both theoretical and practical considerations are taken into account. It follows that the most appropriate method of relative exercise intensity prescription may vary with factors such as exercise intensity, number of participants, and participant characteristics. Considering a method's limitations as well as advantages and increased reporting of individual exercise responses will facilitate accurate interpretation of findings and help to identify areas for further study.
HAMEL, P., J.-A. SIMONEAU, G. LORTIE, M. R. BOULAY, and C. BOUCHARD. Heredity and muscle adaptation to endurance training. Med. Sci. Sports Exerc., Vol. 18, No. 6, pp. 690-696, 1986. To determine whether sensitivity of muscle characteristics and aerobic performances to endurance training was genotype-dependent, 6 pairs of monozygotic (MZ) twins, 21 +/- 4 yr of age (mean +/- SD), took part in a 15-wk ergocycle endurance training program. Tests were performed before and after 7 and 15 weeks of training. A biopsy of the vastus laterulis muscle was obtained for the determination of fiber type composition and activities of creatine kinase, hexokinase, phosphofructokinasc, lactate dehydrogenase, malate dehydrogenase, 3-hydroxyacyl CoA dehydrogenase, and oxoglutarate dehydrogenase. Maximal oxygen uptake was measured with a progressive maximal ergocycle test, while endurance performance was determined as the total work output during a 90-min maximal ergocycle test. Results indicated that maximal oxygen uptake.kg-1 and endurance performance.kg-1 increased significantly (14 and 31%, respectively) with training, and intra-pair resemblance (intra-class) in response to 15 wk of training ranged from 0.65 to 0.83. Hexokinase (31%), phosphofructokinase (37%), lactate dehydrogenase (21%), malate dehydrogenase (31%), and 3-hydroxyacyl CoA dehydrogenase (60%) were significantly increased with training whereas no mean change in fiber-type proportions, oxoglutarate dehydrogenase and creatine kinase activities and the phosphofructokinase/oxoglutarate dehydrogenase ratio was observed. Similarity within twin pairs in the response to enzyme activities was mainly detected in the second half of the training program. The present results confirm, therefore, that both maximal oxygen uptake and endurance performance responses to training are largely genotype-dependent. Similarity within twin pairs in the response to enzyme activities was low in the first 7 wk of endurance training, but upon exposure to an additional 8 wk of training, muscle enzyme adaptation became generally more closely associated with the genotype.
(C)1986The American College of Sports Medicine
Physical exercise positively influences epigenetic mechanisms and improves health, several issues remain unclear concerning the links between physical exercise and epigenetics. There is growing concern about the negative influence of excessive and persistent physical exercise on health. How an individual physically adapts to the prevailing environmental conditions might influence epigenetic mechanisms and modulate gene expression. In this manuscript, we put forward the idea that physical exercise, especially long-term repetitive strenuous exercise, positively affects health, reduces the aging process and/or decreases the incidence of cancer through induced stress and epigenetic mechanisms. We propose herein that stress may stimulate genetic adaptations through epigenetics that, in turn, modulate the link between the environment, human lifestyle factors and genes.
The concept of individual differences in the response to exercise training or trainability was defined three decades ago. In a series of experimental studies with pairs of monozygotic twins, evidence was found in support of a strong genotype dependency of the ability to respond to regular exercise. In the HERITAGE Family Study, it was observed that the heritability of the maximal oxygen uptake response to 20 weeks of standardized exercise training reached 47% after adjustment for age, sex, baseline maximal oxygen uptake and baseline body mass and composition. Candidate gene studies have not yielded as many validated gene targets and variants as originally anticipated. Genome-wide explorations have generated more convincing predictors of maximal oxygen uptake trainability. A genomic predictor score based on the number of favourable alleles carried at 21 single nucleotide polymorphisms appears to be able to identify low and high training response classes that differ by at least threefold. Combining transcriptomic and genomic technologies has also yielded highly promising results concerning the ability to predict trainability among sedentary people.
This study investigates the familial resemblance of maximal oxygen uptake (VO2max) based on data from 86 nuclear families of Caucasian descent participating in the HERITAGE Family Study. In the current study, VO2max was measured twice on a cycle ergometer in 429 sedentary individuals (170 parents and 259 of their offspring), aged between 16 and 65 yr. The VO2max was adjusted by regression procedures for the effects of 1) age and sex; 2) age, sex, and body mass; and 3) age, sex, body mass, fat mass, and fat-free mass, as determined by underwater weighing. Evidence for significant familial resemblance was observed for each of the three VO2max phenotypes. Spouse, sibling, and parent-offspring correlations were significant, suggesting that both genetic and environmental factors contribute to the familial resemblance for VO2max. Maximal heritability estimates were at least 50%, a value inflated to an undetermined degree by nongenetic factors. The hypothesis of maternal inheritance, with the father's contribution being environmental, was also found to fit the data with estimates of maternal heritability, potentially associated in part with mitochondrial inheritance, reaching about 30%. These results suggest that genetic and nongenetic factors as well as maternal influences contribute to the familial aggregation of VO2max in sedentary individuals.
Skeletal muscles improve their oxidative fatty acid and glucose metabolism following endurance training, but the magnitude of response varies considerably from person to person. In 20 untrained young women we examined interindividual variability in training responses of metabolic enzymes following 6 weeks of endurance training, sufficient to increase maximal oxygen uptake by 10 ± 8% (mean ± SD). Training led to increases in mitochondrial enzymes [succinate dehydrogenase (SDH; 47 ± 78%), cytochrome c oxidase (52 ± 70%) and ATP synthase (63 ± 69%)] and proteins involved in fatty acid metabolism [3-hydroxyacyl CoA dehydrogenase (69 ± 92%) and fatty acid transporter CD36 (86 ± 31%)]. Increases in enzymes of glucose metabolism [phosphofructokinase (29 ± 94%) and glucose transporter 4 (18 ± 65%)] were not significant. There was no relationship between changes in maximal oxygen uptake and the changes in the metabolic proteins. Considerable interindividual variability was seen in the magnitude of responses. The response of each enzyme was proportional to the change in SDH; individuals with a large increase in SDH also showed high gains in all other enzymes, and vice versa. Peroxisome proliferator-activated receptor γ coactivator 1α protein content increased after training, but was not correlated with changes in the metabolic proteins. In conclusion, the results revealed co-ordinated adaptation of several metabolic enzymes following endurance training, despite differences between people in the magnitude of response. Differences between individuals in the magnitude of response might reflect the influence of environmental and genetic factors that govern training adaptations.
MicroRNAs (miRNA), small noncoding RNA molecules, may regulate protein synthesis, while resistance exercise training (RT) is an efficient strategy for stimulating muscle protein synthesis in vivo. However, RT increases muscle mass, with a very wide range of effectiveness in humans. We therefore determined the expression level of 21 abundant miRNAs to determine whether variation in these miRNAs was able to explain the variation in RT-induced gains in muscle mass. Vastus lateralis biopsies were obtained from the top and bottom ∼20% of responders from 56 young men who undertook a 5 day/wk RT program for 12 wk. Training-induced muscle mass gain was determined by dual-energy X-ray absorptiometry, and fiber size was evaluated by histochemistry. The expression level of each miRNA was quantified using TaqMan-based quantitative PCR, with the analysis carried out in a blinded manner. Gene ontology and target gene profiling were used to predict the potential biological implications. Of the 21 mature miRNAs examined, 17 were stable during RT in both groups. However, miR-378, miR-29a, miR-26a, and miR-451 were differentially expressed between low and high responders. miR-378, miR-29a, and miR-26a were downregulated in low responders and unchanged in high responders, while miR-451 was upregulated only in low responders. Interestingly, the training-induced change in miR-378 abundance was positively correlated with muscle mass gains in vivo. Gene ontology analysis of the target gene list of miR-378, miR-29a, miR-26a, and miR-451, from the weighted cumulative context ranking methodology, indicated that miRNA changes in the low responders may be compensatory, reflecting a failure to "activate" growth and remodeling genes. We report, for the first time, that RT-induced hypertrophy in human skeletal muscle is associated with selected changes in miRNA abundance. Our analysis indicates that miRNAs may play a role in the phenotypic change and pronounced intergroup variation in the RT response.
When human skeletal muscle is exposed to exercise training, the outcomes, in terms of physiological adaptation, are unpredictable. The significance of this fact has long been underappreciated, and only recently has progress been made in identifying some of the molecular bases for the heterogeneous response to exercise training. It is not only of great medical importance that some individuals do not substantially physiologically adapt to exercise training, but the study of the heterogeneity itself provides a powerful opportunity to dissect out the genetic and environmental factors that limit adaptation, directly in humans. In the following review I will discuss new developments linking genetic and transcript abundance variability to an individual's potential to improve their aerobic capacity or endurance performance or induce muscle hypertrophy. I will also comment on the idea that certain gene networks may be associated with muscle "adaptability" regardless the stimulus provided.
Literature examining the effects of aerobic exercise training on excess postexercise oxygen consumption (EPOC) is sparse. In this study, 9 male participants (19-32 yr) trained (EX) for 12 wk, and 10 in a control group (CON) maintained normal activity. VO(2max), rectal temperature (T(re)), epinephrine, norepinephrine, free fatty acids (FFA), insulin, glucose, blood lactate (BLA), and EPOC were measured before (PRE) and after (POST) the intervention. EPOC at PRE was measured for 120 min after 30 min of treadmill running at 70% VO(2max). EX completed 2 EPOC trials at POST, i.e., at the same absolute (ABS) and relative (REL) intensity; 1 EPOC test for CON served as both the ABS and REL trial because no significant change in VO(2max) was noted. During the ABS trial, total EPOC decreased significantly (p < .01) from PRE (39.4 ± 3.6 kcal) to POST (31.7 ± 2.2 kcal). T(re), epinephrine, insulin, glucose, and BLA at end-exercise or during recovery were significantly lower and FFA significantly higher after training. Training did not significantly affect EPOC during the REL trial; however, epinephrine was significantly lower, and norepinephrine and FFA, significantly higher, at endexercise after training. Results indicate that EPOC varies as a function of relative rather than absolute metabolic stress and that training improves the efficiency of metabolic regulation during recovery from exercise. Mechanisms for the decreased magnitude of EPOC in the ABS trial include decreases in BLA, T(re), and perhaps epinephrine-mediated hepatic glucose production and insulin-mediated glucose uptake.
A combination of endurance and strength training is generally used to seek further health benefits or enhanced physical performance in older adults compared with either of the training modes alone. The mean change within a training group, however, may conceal a wide range of individual differences in the responses. The purpose, therefore, was to examine the individual trainability of aerobic capacity and maximal strength, when endurance and strength training are performed separately or concurrently.
For this study, 175 previously untrained volunteers, 89 men and 86 women between the ages of 40 and 67 yr, completed a 21-wk period of either strength training (S) twice a week, endurance training (E) twice a week, combined training (ES) four times per week, or served as controls. Training adaptations were quantified as peak oxygen uptake (VO2peak) in a bicycle ergometer test to exhaustion and maximal isometric bilateral leg extension force (MVC) in a dynamometer.
A large range in training responses, similar to endurance or strength training alone, was also observed with combined endurance and strength training in both ΔVO2peak (from -8% to 42%) and ΔMVC (from -12% to 87%). There were no significant correlations between the training responses in VO2peak and MVC in the E, S, or especially in the ES group, suggesting that the same subjects did not systematically increase both aerobic capacity and maximal strength.
The goal of combined endurance and strength training--increasing both aerobic capacity and maximal strength simultaneously--was only achieved by some of the older subjects. New means are needed to personalize endurance, strength, and especially combined endurance and strength training programs for optimal individual adaptations.
Lacking responses to endurance training (ET) have been observed for several variables. However, detailed analyses of individuals' responses are scarce. To learn more about the variability of ET adaptations, patterns of response were analyzed for each subject in a 1-year ET study. Eighteen participants [42 ± 5 years, body mass index: 24 ± 3 kg/m(2), maximal oxygen uptake (VO(2max) ): 38 ± 5 mL/min/kg] completed a 1-year jogging/walking program on 3 days/week, 45 min/session at 60% heart rate (HR) reserve. VO(2max), resting HR (rHR), exercise HR (eHR) and individual anaerobic threshold (IAT) were determined by treadmill and cycling ergometry respectively. Intraindividual coefficients of variation were extracted from the literature to distinguish random changes from training responses. Eight participants showed improvements in all variables. In 10 participants, one or two variables did not improve (VO(2max), rHR, eHR and IAT remained unchanged in four, four, three and one cases, respectively). At least one variable improved in each subject. Data indicate that ET adaptations might be detected in each individual using multiple variables of different adaptation levels and intensity domains. Nonresponse seems to occur frequently and might affect all variables. Further studies should investigate whether nonresponders improve with altered training. Furthermore, associations between patterns of nonresponse and health benefits from ET are worth considering.
A low maximal oxygen consumption (VO2max) is a strong risk factor for premature mortality. Supervised endurance exercise training increases VO2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts VO2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous VO2max response. Two independent preintervention RNA expression data sets were generated (n=41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in VO2max ("predictor" genes). The HERITAGE Family Study (n=473) was used for genotyping. We discovered a 29-RNA signature that predicted VO2max training response on a continuous scale; these genes contained approximately 6 new single-nucleotide polymorphisms associated with gains in VO2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., "reciprocal" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in VO2max, corresponding to approximately 50% of the estimated genetic variance for VO2max. In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. VO2max responses to endurance training can be predicted by measuring a approximately 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
Elite athletes often undertake multiple altitude exposures within and between training years in an attempt to improve sea level performance.
To quantify the reproducibility of responses to live high/train low (LHTL) altitude exposure in the same group of athletes.
Sixteen highly trained runners with maximal aerobic power (VO2max) of 73.1 +/- 4.6 and 64.4 +/- 3.2 mL x kg(-1) x min(-1) (mean +/- SD) for males and females, respectively, completed 2 x 3-wk blocks of simulated LHTL (14 h x d(-1), 3000 m) or resided near sea level (600 m) in a controlled study design. Changes in the 4.5-km time trial performance and physiological measures including VO2max, running economy and hemoglobin mass (Hb(mass)) were assessed.
Time trial performance showed small and variable changes after each 3-wk altitude block in both the LHTL (mean [+/-90% confidence limits]: -1.4% [+/-1.1%] and 0.7% [+/-1.3%]) and the control (0.5% [+/-1.5%] and -0.7% [+/-0.8%]) groups. The LHTL group demonstrated reproducible improvements in VO2max (2.1% [+/-2.1%] and 2.1% [+/-3.9%]) and Hb(mass) (2.8% [+/-2.1%] and 2.7% [+/-1.8%]) after each 3-wk block. Compared with those in the control group, the runners in the LHTL group were substantially faster after the first 3-wk block (LHTL - control = -1.9% [+/-1.8%]) and had substantially higher Hb(mass) after the second 3-wk block (4.2% [+/-2.1%]). There was no substantial difference in the change in mean VO2max between the groups after the first (1.2% [+/-3.3%]) or second 3-wk block (1.4% [+/-4.6%]).
Three-week LHTL altitude exposure can induce reproducible mean improvements in VO2max and Hb(mass) in highly trained runners, but changes in time trial performance seem to be more variable. Competitive performance is dependent not only on improvements in physiological capacities that underpin performance but also on a complex interaction of many factors including fitness, fatigue, and motivation.