Reliability and Validity of Predicted Performance in the Severe-Intensity Domain From the 3-Minute All-Out Running Test.

PURPOSE: The aim of this study was to analyze the reliability and validity of the predicted distance-time relationship in the severe-intensity domain from a 3-minute all-out running test (3MT). METHODS: Twelve runners performed two 3MTs (test #1 and test #2) on an outdoor 400-m track after familiarization. Eighteen-hertz Global Positioning System data were used to estimate critical speed (CS) and distance covered above CS (D'). Time to cover 1200 and 3600 m (T1200 and T3600, respectively) was predicted using CS and D' estimates from each 3MT. Eight runners performed 2 time trials in a single visit to assess real T1200 and T3600. Intraclass correlation coefficients (ICCs) and standard errors of measurement were calculated for reliability analysis. RESULTS: Good to excellent reliability was found for CS, T1200, and T3600 estimates from 3MT (ICC > .95, standard error of measurement between 1.3% and 2.2%), and poor reliability was found for D' (ICC = .55, standard error of measurement = 27%). Predictions from 3MT were significantly correlated to actual T1200 (r = .87 and .85 for test #1 and test #2, respectively) and T3600 (r = .91 and .82 for test #1 and test #2, respectively). The calculation of error prediction showed a systematic error between predicted and real T3600 (6.4% and 7.8% for test #1 and test #2, respectively, P < .01) contrary to T1200 (P > .1). Random error was between 4.4% and 6.1% for both distances. CONCLUSIONS: Despite low reliability of D', 3MT yielded a reliable predicted distance-time relationship allowing repeated measures to evidence change with training adaptation. However, caution should be taken with prediction of performance potential of a single individual because of substantial random error and significant underestimation of T3600.

Cardiorespiratory and Neuromuscular Improvements Plateau after 2 wk of Sprint Interval Training in Sedentary Individuals.

INTRODUCTION: Previous studies ranging from 2 to 12 wk of sprint interval training (SIT) have reported improvements in maximal oxygen uptake (V̇O 2max ) and neuromuscular function in sedentary populations. However, whether the time course of the changes in these variables correlates with greater training volumes is unclear. METHODS: Thirteen sedentary participants performed three all-out training weekly sessions involving 15-s sprints interspersed with 2 min of recovery on a cycle ergometer. The 6-wk training program was composed of three identical blocks of 2 wk in which training volume was increased from 10 to 14 repetitions over the first four sessions and reduced to 8 in the last session. The power output and the heart rate (HR) were monitored during the sessions. The V̇O 2max , the power-force-velocity profile, and the isometric force were assessed every 2 wk from baseline. RESULTS: A significant increase in V̇O 2max was observed from the second week plateauing thereafter despite four additional weeks of training. The dynamic force production increased from the second week, and the speed production decreased by the end of the protocol. The isometric force and the maximal power output from the power-force-velocity profile did not change. Importantly, the time spent at high percentages of the maximal HR during the training sessions was lower in the second and third training block compared with the first. CONCLUSIONS: SIT resulted in an effective approach for rapidly increasing V̇O 2max , and no change in the isometric force was found; cycling-specific neuromuscular adaptations were observed from the second week of training. SIT may be useful in the short term, but further improvement of overall physical fitness might need other training modalities like endurance and/or resistance training.

Neuromuscular and autonomic function is fully recovered within 24 h following a sprint interval training session.

BACKGROUND: Recovery is a key factor to promote adaptations and enhance performance. Sprint Interval Training (SIT) is known to be an effective approach to improve overall physical function and health. Although a 2-day rest period is given between SIT sessions, the time-course of recovery after SIT is unknown. PURPOSE: The aim of this study was to determine whether the neuromuscular and autonomic nervous systems would be impaired 24 and 48 h after an SIT session. METHODS: Twenty-five healthy subjects performed an 8 × 15 s all-out session on a braked cycle ergometer with 2 min of rest between repetitions. Isometric maximal voluntary contraction (iMVC) and evoked forces to electrical nerve stimulation during iMVC and at rest were used to assess muscle contractile properties and voluntary activation before (Pre), 1 (Post24h), and 2 (Post48h) days after the session. Two maximal 7 s sprints with two different loads were performed at those same time-points to evaluate the maximal theoretical force (F0), velocity (V0) and maximal power (Pmax) production during a dynamic exercise. Additionally, nocturnal heart rate variability (HRV) was assessed the previous and the three subsequent nights to the exercise bout. RESULTS: No significant impairments were observed for the iMVC or for the force evoked by electrical stimulation 1 day after the session. Similarly, F0, V0, and Pmax were unchanged at Post24h and Post48h. Furthermore, HRV did not reveal any temporal or frequential significant difference the nights following SIT compared to Pre. CONCLUSION: The results of this study show a full recovery of neuromuscular and autonomic functions a day after an all-out SIT session.

Changes in power-force-velocity profile induced by 2 weeks of sprint interval training.

BACKGROUND: This study aimed to determine the effects of a running sprint interval training protocol (R-SIT) on the sprint acceleration mechanical properties and jump performance. Eleven young male basketball players performed 6 R-SIT sessions for 2 weeks. METHODS: Each session consisted of 30-second running bouts repeated 4 to 7 times interspersed by 4 minutes of recovery. Performance was assessed from the individual power-force-velocity profiles (PVFP) over a 20-m sprint and from a countermovement jump at baseline (PRE) and after two weeks of training (POST). RESULTS: Sprint time decreased by 2% over the first 5 and 10 meters (P<0.01) while no significant changes in the time at 20 meters (-0.8%, P=0.09) nor in maximal velocity (-1%, P=0.31) were detected. The average PFVP showed an increase in theoretical maximal force and power output of 5 and 4%, respectively (P<0.05), with no change in theoretical maximal speed (P=0.26). Jump height and power also increased after training (5 and 3% respectively, P<0.01). Players improved their maximal sprint distance covered during the 30-second bouts and became more fatigue-resistant to long sprint events. CONCLUSIONS: Six sessions of R-SIT helped to enhance short sprint times, acceleration and power output.

Validity and Accuracy of Impulse-Response Models for Modeling and Predicting Training Effects on Performance of Swimmers.

PURPOSE: The aim of this study was to compare the suitability of models for practical applications in training planning. METHODS: We tested six impulse-response models, including Banister's model (Model Ba), a variable dose-response model (Model Bu), and indirect-response models differing in the way they account or not for the effect of previous training on the ability to respond effectively to a given session. Data from 11 swimmers were collected during 61 wk across two competitive seasons. Daily training load was calculated from the number of pool-kilometers and dry land workout equivalents, weighted according to intensity. Performance was determined from 50-m trials done during training sessions twice a week. Models were ranked on the base of Aikaike's information criterion along with measures of goodness of fit. RESULTS: Models Ba and Bu gave the greatest Akaike weights, 0.339 ± 0.254 and 0.360 ± 0.296, respectively. Their estimates were used to determine the evolution of performance over time after a training session and the optimal characteristics of taper. The data of the first 20 wk were used to train these two models and predict performance for the after 8 wk (validation data set 1) and for the following season (validation data set 2). The mean absolute percentage error between real and predicted performance using Model Ba was 2.02% ± 0.65% and 2.69% ± 1.23% for validation data sets 1 and 2, respectively, and 2.17% ± 0.65% and 2.56% ± 0.79% with Model Bu. CONCLUSIONS: The findings showed that although the two top-ranked models gave relevant approximations of the relationship between training and performance, their ability to predict future performance from past data was not satisfactory for individual training planning.

Exercise Frequency Determines Heart Rate Variability Gains in Older People: A Meta-Analysis and Meta-Regression.

BACKGROUND: Previous studies have suggested that exercise training improves cardiac autonomic drive in young and middle-aged adults. In this study, we discuss the benefits for the elderly. OBJECTIVES: We aimed to establish whether exercise still increases heart rate variability (HRV) beyond the age of 60 years, and to identify which training factors influence HRV gains in this population. METHODS: Interventional controlled and non-controlled studies were selected from the PubMed, Ovid, Cochrane and Google Scholar databases. Only interventional endurance training protocols involving healthy subjects aged 60 years and over, and measuring at least one heart rate global or parasympathetic index, such as the standard deviation of the normal-to-normal intervals (SDNN), total frequency power (Ptot), root mean square of successive differences between adjacent NN intervals (RMSSD), or high frequency power (HF) before and after the training intervention, were included. HRV parameters were pooled separately from short-term and 24 h recordings for analysis. Risks of bias were assessed using the Methodological Index for Non-Randomized Studies and the Cochrane risk of bias tool. A random-effects model was used to determine effect sizes (Hedges' g) for changes, and heterogeneity was assessed using Q and I statistics. RESULTS: Twelve studies, seven of which included a control group, including 218 and 111 subjects, respectively (mean age 69.0 ± 3.2 and 68.6 ± 2.5), were selected for meta-analysis. Including the 12 studies demonstrated homogeneous significant effect sizes for short-term (ST)-SDNN and 24 h-SDNN, with effect sizes of 0.366 (95% CI 0.185-547) and 0.442 (95% CI 0.144-0.740), respectively. Controlled study analysis demonstrated homogeneous significant effect sizes for 24 h-SDNN with g = 0.721 (95% CI 0.184-1.257), and 24 h-Ptot with g = 0.731 (95% CI 0.195-1.267). Meta-regression analyses revealed positive relationships between ST-SDNN effect sizes and training frequency ([Formula: see text] = 0.000; [Formula: see text] = 0.000; p = 0.0462). CONCLUSION: This meta-analysis demonstrates a positive effect of endurance-type exercise on autonomic regulation in older adults. However, the selected studies expressed some risks of bias. We conclude that chronic endurance exercise leads to HRV improvements in a linear frequency-response relationship, encouraging the promotion of high-frequency training programmes in older adults.

Dynamic Force Production Capacities Between Coronary Artery Disease Patients vs. Healthy Participants on a Cycle Ergometer.

BACKGROUND: The force-velocity-power (FVP) profile is used to describe dynamic force production capacities, which is of great interest in training high performance athletes. However, FVP may serve a new additional tool for cardiac rehabilitation (CR) of coronary artery disease (CAD) patients. The aim of this study was to compare the FVP profile between two populations: CAD patients vs. healthy participants (HP). METHODS: Twenty-four CAD patients (55.8 ± 7.1 y) and 24 HP (52.4 ± 14.8 y) performed two sprints of 8 s on a Monark cycle ergometer with a resistance corresponding to 0.4 N/kg × body mass for men and 0.3 N/kg × body mass for women. The theoretical maximal force (F 0) and velocity (V 0), the slope of the force-velocity relationship (S fv) and the maximal mechanical power output (P max) were determined. RESULTS: The P max (CAD: 6.86 ± 2.26 W.kg-1 vs. HP: 9.78 ± 4.08 W.kg-1, p = 0.003), V 0 (CAD: 5.10 ± 0.82 m.s-1 vs. HP: 5.79 ± 0.97 m.s-1, p = 0.010), and F 0 (CAD: 1.35 ± 0.38 N.kg-1 vs. HP: 1.65 ± 0.51 N.kg-1, p = 0.039) were significantly higher in HP than in CAD. No significant difference appeared in Sfv (CAD: -0.27 ± 0.07 N.kg-1.m.s-1 vs. HS: -0.28 ± 0.07 N.kg-1.m.s-1, p = 0.541). CONCLUSION: The lower maximal power in CAD patients was related to both a lower V 0 and F 0. Physical inactivity, sedentary time and high cardiovascular disease (CVD) risk may explain this difference of force production at both high and low velocities between the two groups.

Regulation of Akt-mTOR, ubiquitin-proteasome and autophagy-lysosome pathways in locomotor and respiratory muscles during experimental sepsis in mice.

Sepsis induced loss of muscle mass and function contributes to promote physical inactivity and disability in patients. In this experimental study, mice were sacrificed 1, 4, or 7 days after cecal ligation and puncture (CLP) or sham surgery. When compared with diaphragm, locomotor muscles were more prone to sepsis-induced muscle mass loss. This could be attributed to a greater activation of ubiquitin-proteasome system and an increased myostatin expression. Thus, this study strongly suggests that the contractile activity pattern of diaphragm muscle confers resistance to atrophy compared to the locomotor gastrocnemius muscle. These data also suggest that a strategy aimed at preventing the activation of catabolic pathways and preserving spontaneous activity would be of interest for the treatment of patients with sepsis-induced neuromyopathy.

From an indirect response pharmacodynamic model towards a secondary signal model of dose-response relationship between exercise training and physical performance.

The aim of this study was to test the suitability of using indirect responses for modeling the effects of physical training on performance. We formulated four different models assuming that increase in performance results of the transformation of a signal secondary to the primary stimulus which is the training dose. The models were designed to be used with experimental data with daily training amounts ascribed to input and performance measured at several dates ascribed to output. The models were tested using data obtained from six subjects who trained on a cycle ergometer over a 15-week period. The data fit for each subject was good for all of the models. Goodness-of-fit and consistency of parameter estimates favored the model that took into account the inhibition of production of training effect. This model produced an inverted-U shape graphic when plotting daily training dose against performance because of the effect of one training session on the cumulated effects of previous sessions. In conclusion, using secondary signal-dependent response provided a framework helpful for modeling training effect which could enhance the quantitative methods used to analyze how best to dose physical activity for athletic performance or healthy living.

Modeling the responses to resistance training in an animal experiment study.

The aim of the present study was to test whether systems models of training effects on performance in athletes can be used to explore the responses to resistance training in rats. 11 Wistar Han rats (277 ± 15 g) underwent 4 weeks of resistance training consisting in climbing a ladder with progressive loads. Training amount and performance were computed from total work and mean power during each training session. Three systems models relating performance to cumulated training bouts have been tested: (i) with a single component for adaptation to training, (ii) with two components to distinguish the adaptation and fatigue produced by exercise bouts, and (iii) with an additional component to account for training-related changes in exercise-induced fatigue. Model parameters were fitted using a mixed-effects modeling approach. The model with two components was found to be the most suitable to analyze the training responses (R(2) = 0.53; P < 0.001). In conclusion, the accuracy in quantifying training loads and performance in a rodent experiment makes it possible to model the responses to resistance training. This modeling in rodents could be used in future studies in combination with biological tools for enhancing our understanding of the adaptive processes that occur during physical training.

Modeling of performance and ANS activity for predicting future responses to training.

PURPOSE: Our aim was to assess whether we can predict satisfactorily performance in swimming and high frequency power (HF power) of heart rate variability from the responses to previous training. We have tested predictions using the model of Banister and the variable dose-response model. METHODS: Data came from ten swimmers followed during 30 weeks of training with performance and HF power measured each week. The first 15-week training period was used to estimate the parameters of each model for both performance and HF power. Both were then predicted in response to the training done during the second 15-week training period. The bias and precision were estimated from the mean and SD of the difference between prediction and actual value expressed as a percentage of performance or HF power at the first week. RESULTS: With the variable-dose response model, the bias for performance prediction was -0.24 ± 0.06 and the precision 0.69 ± 0.24% (mean ± between-subject SD). For HF power, the bias was 0 ± 21 and the precision 22 ± 8%. When HF power was transformed into performance using a quadratic relation in each swimmer established from the first 15-week period, the bias was 0.18 ± 0.74 and the precision 0.80 ± 0.30%. No clear trend in the error was observed during the second period. CONCLUSIONS: This study showed that the modeling of training effects on performance allowed accurate performance prediction supporting its relevance to control and predict week after week the responses to future training.

Perceived training intensity and performance changes quantification in judo.

The objective of this study was to determine the methods of quantification for training and performance, which would be the most appropriate for modeling the responses to long-term training in cadet and junior judo athletes. For this, 10 young male judo athletes (15.9 ± 1.3 years, 64.9 ± 10.3 kg, and 170.8 ± 5.4 cm) competing at a regional/state level volunteered to take part in this study. Data were collected during a 2-year training period (i.e., 702 days) from January 2011 to December 2012. Their mean training volume was 6.52 ± 0.43 hours per week during the preparatory periods and 4.75 ± 0.49 hours per week during the competitive periods. They followed a training program prescribed by the same coach. The training load (TL) was quantified through the session rating of perceived exertion (RPE) and expressed in arbitrary unit (a.u.). Performance was quantified from 5 parameters and divided into 2 categories: performance in competition and performance in training. The evaluation of performance in competition was based on the number of points per level. Performance in training was assessed through 4 different tests. A physical test battery consisting of a standing long jump, 2 judo-specific tests that were the maximal number of dynamic chin-up holding the judogi, and the Special Judo Fitness Test was used. System modeling for describing training adaptations consisted of mathematically relating the TL of the training sessions (system input) to the change in performance (system output). The quality of the fit between TL and performance was similar, whether the TL was computed directly from RPE (R = 0.55 ± 0.18) or from the session RPE (R = 0.56 ± 0.18) and was significant in 8 athletes over 10, excluding the standing jump from the computation of the TL, leading to a simplest method. Thus, this study represents a first attempt to model TL effects on judo-specific performance and has shown that the best relationships between amounts of training and changes in performance were obtained when training amounts were quantified simply from RPE.

Modelling training response in elite female gymnasts and optimal strategies of overload training and taper.

The aim of the study is the modelling of training responses with a variable dose-response model in a sport discipline that requires highly complex coordination. We propose a method to optimise the training programme plan using the potential maximal performance gain associated with overload and tapering periods. Data from five female elite gymnasts were collected over a 3-month training period. The relationship between training amounts and performance was then assessed with a non-linear model. The optimal magnitude of training load reduction and its duration were investigated with and without an overload period using simulation procedures based on individual responses to training. The correlation between actual and modelled performances was significant (R² = 0.81 ± 0.02, P < 0.01). The standard error was 2.7%. Simulations revealed that taper preceded by an overload period allows a higher performance to be achieved compared to an absence of overload period (106.3 ± 0.3% vs. 105.1 ± 0.3%). With respect to the pre-taper load, the model predicts that optimal load reductions during taper were 48.4 ± 0.7% and 42.5 ± 1.0% for overloading and non-overloading strategies, respectively. Moreover, optimal durations of the taper period were 34 ± 0.5 days and 22 ± 0.5 days for overloading and non-overloading strategies, respectively. In conclusion, the study showed that the variable dose-response model describes precisely the training response in gymnasts.

A mixed-effects model of the dynamic response of muscle gene transcript expression to endurance exercise.

Altered expression of a broad range of gene transcripts after exercise reflects the specific adjustment of skeletal muscle makeup to endurance training. Towards a quantitative understanding of this molecular regulation, we aimed to build a mixed-effects model of the dynamics of co-related transcript responses to exercise. It was built on the assumption that transcript levels after exercise varied because of changes in the balance between transcript synthesis and degradation. It was applied to microarray data of 231 gene transcripts in vastus lateralis muscle of six subjects 1, 8 and 24 h after endurance exercise and 6-week training on a stationary bicycle. Cluster analysis was used to select groups of transcripts having highest co-correlation of their expression (r > 0.70): Group 1 comprised 45 transcripts including factors defining the oxidative and contractile phenotype and Group 2 included 39 transcripts mainly defined by factors found at the cell periphery and the extracellular space. Data from six subjects were pooled to filter experimental noise. The model fitted satisfactorily the responses of Group 1 (r (2) = 0.62 before and 0.85 after training, P < 0.001) and Group 2 (r (2) = 0.75 and 0.79, P < 0.001). Predicted variation in transcription rate induced by exercise yielded a difference in amplitude and time-to-peak response of gene transcripts between the two groups before training and with training in Group 2. The findings illustrate that a mixed-effects model of transcript responses to exercise is suitable to explore the regulation of muscle plasticity by training at the transcriptional level and indicate critical experiments needed to consolidate model parameters empirically.

A model for the training effects in swimming demonstrates a strong relationship between parasympathetic activity, performance and index of fatigue.

Competitive swimming as a physical activity results in changes to the activity level of the autonomic nervous system (ANS). However, the precise relationship between ANS activity, fatigue and sports performance remains contentious. To address this problem and build a model to support a consistent relationship, data were gathered from national and regional swimmers during two 30 consecutive-week training periods. Nocturnal ANS activity was measured weekly and quantified through wavelet transform analysis of the recorded heart rate variability. Performance was then measured through a subsequent morning 400 meters freestyle time-trial. A model was proposed where indices of fatigue were computed using Banister's two antagonistic component model of fatigue and adaptation applied to both the ANS activity and the performance. This demonstrated that a logarithmic relationship existed between performance and ANS activity for each subject. There was a high degree of model fit between the measured and calculated performance (R(2)=0.84±0.14,p<0.01) and the measured and calculated High Frequency (HF) power of the ANS activity (R(2)=0.79±0.07, p<0.01). During the taper periods, improvements in measured performance and measured HF were strongly related. In the model, variations in performance were related to significant reductions in the level of 'Negative Influences' rather than increases in 'Positive Influences'. Furthermore, the delay needed to return to the initial performance level was highly correlated to the delay required to return to the initial HF power level (p<0.01). The delay required to reach peak performance was highly correlated to the delay required to reach the maximal level of HF power (p=0.02). Building the ANS/performance identity of a subject, including the time to peak HF, may help predict the maximal performance that could be obtained at a given time.

A comparison of modelling procedures used to estimate the power-exhaustion time relationship.

This study aimed to test the consistency of using the power required to elicit maximal oxygen uptake during incremental test (P (t)) to demarcate the range of power intensity in the modelling of the power-exhaustion time relationship. Different mathematical procedures were tested using data from ten subjects exercising on a cycle ergometer. After the determination of P (t) and the power at the ventilatory threshold, the subjects did six tests at constant power to exhaustion within 2-15 min. Estimates were obtained from a segmented model using two distinct equations of the anaerobic contribution to power below and above P (t), respectively. This model fit the overall data with a better adequacy than the simple hyperbolic model (standard error of 29.2 +/- 25.2 vs. 42.3 +/- 25.2 s). The power asymptotes were 225.7 +/- 27.3 W from the segmented model, 226.2 +/- 27.3 and 283.3 +/- 20.5 W from the simple model applied to data below and above P (t), respectively. The estimates from the segmented model were strongly correlated with their analogues from the simple model applied only to data below P (t) (R = 1.00 for power asymptote and curvature coefficient). They were not correlated with their analogues from the simple model applied only to data above P (t). These discrepancies between modelling procedures could arise from the method used to determine P (t) and the oversimplification of the oxygen uptake kinetics. These limitations could lead the segmented model to an overestimation of the anaerobic contribution which was around 15% of total energy expended at P (t).

Computer simulations assessing the potential performance benefit of a final increase in training during pre-event taper.

A nonlinear model of training responses was utilized to test whether a 2-phase taper could be more effective than a traditional linear taper. Simulations were conducted using model parameters previously determined in 6 nonathletes trained on a cycle ergometer (non-ATH) and 7 elite swimmers trained in sport-specific conditions (ATH). Linear and 2-phase tapers were compared after a 28-day overload period at 120% of normal training. The 2-phase taper was assumed to be identical to the optimal linear taper, except for the final 3 days during which the training load was varied to maximize the final performance. The optimal linear taper was characterized by a mean training reduction by 32 +/- 6% during 35 +/- 6 days in non-ATH and by 49 +/- 18% during 33 +/- 16 days in ATH. The last 3 days of the 2-phase taper were characterized by a significant increase in training load by 23 +/- 18% in non-ATH and 29 +/- 42% in ATH (p < 0.005). The optimal taper characteristics were not statistically different between non-ATH and ATH. The maximal performance reached with the 2-phase taper was higher by 0.04 +/- 0.02% in non-ATH and 0.01 +/- 0.01% in ATH than with the optimal linear taper (p < 0.001). Positive and negative influences of training on performance were estimated as indicators of adaptation and fatigue, respectively. The negative influence was completely removed during both tapers, whereas the positive influence was slightly further enhanced during the 2-phase pattern. In conclusion, simulations showed that a 20 to 30% increase in training at the end of the taper, as compared to a prolonged reduction in training, allowed additional adaptations without compromising the removal of fatigue.

A comparison of lactate indices during ramp exercise using modelling techniques and conventional methods.

The aim of this study was to compare the lactate indices provided by single- and double-breakpoint models with lactate thresholds obtained with conventional methods. Arterial samples for the determination of lactate concentrations were drawn from eight participants at rest and every minute during a ramp test (15 W x min(-1)) on a cycle ergometer. Lactate thresholds were determined from a blood lactate concentration equal to 4 mM (LT(4)), from an increase of 1 mM above the resting level (Delta1 mM), and from indirect methods using ventilatory parameters. Other indices were computed from the modelling of the lactate curve using an exponential function (LSI), a polynomial function (Dmax), a semi-log model (SLog), a parabola plus delay model (Mod P), and a two-breakpoint model (Mod M). Mod P and Mod M showed poor agreement with the other methods. LT(4), Dmax, LSI, and respiratory exchange ratio equal to 1 were correlated with each other (0.81