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.