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The classical work by Robert C. Hickson showed in 1980 that the addition of a resistance-training protocol to a predominantly aerobic program could lead to impaired leg-strength adaptations in comparison with a resistance-only training regimen. This interference phenomenon was later highlighted in many reports, including a meta-analysis. However, it seems that the interference effect has not been consistently reported, probably because of the complex interactions between training variables and methodological issues. On the other side of the medal, Dr Hickson et al subsequently (1986) reported that a strength-training mesocycle could be beneficial for endurance performance in running and cycling. In recent meta-analyses and review articles, it was demonstrated that such a training strategy could improve middle- and long-distance performance in many disciplines (running, cycling, cross-country skiing, and swimming). Notably, it appears that improvements in the energy cost of locomotion could be associated with these performance enhancements. Despite these benefits, it was also reported that strength training could represent a detrimental stimulus for endurance performance if an inappropriate training plan has been prepared. Taken together, these observations suggest that coaches and athletes should be careful when concurrent training seems imperative to meet the complex physiological requirements of their sport. This brief review presents a practical appraisal of concurrent training for sports performance. In addition, recommendations are provided so that practitioners can adapt their interventions based on the training objectives.

Purpose: To assess the effect of a rugby-specific high-intensity interval-training (HIIT_Rugby) protocol on the repeated high-intensity-effort ability of young elite rugby union players and to verify the influence of 2 preconditioning sequences composed either of physical contacts (ie, tackles) or of additional runs on the magnitude of improvement. Method: Fourteen players (19 [1] y; 183.5 [8.6] cm; 95.6 [15.6] kg) underwent an HIIT_Rugby protocol, consisting of 7 supervised training sessions over 4 weeks, each session including 3 or 4 sets of 1 to 2 minutes with 1-minute recovery. Prior to HIIT_Rugby training, players underwent a preconditioning contact sequence or a preconditioning running sequence, to assess their influence on subsequent interval-training sessions. Results: The overall group showed a moderate improvement in total sprint time, sprints ≥90% of the best, and 20-m sprint (−3.91% [2.68%], P = .0002; 74.6% [123.7%], P = .012; −3.22% [3.13%], P = .003, respectively) and a large improvement in percentage decrement (−23.1% [20.5%], P = .005) following the 4-week training block. Relative improvements were similar between groups in total sprint time, 20-m sprint, and perceived difficulty, but the preconditioning running-sequence group exhibited a larger magnitude of gains in percentage decrement (−28.6% [20.2%] vs −17.6% [20.7%]; effect size = −1.01 vs −0.73). Conclusion: An HIIT_Rugby training block was effective to improve repeated high-intensity-effort ability. A preconditioning contact sequence prior to HIIT_Rugby can reduce subsequent long-interval running activity, which may attenuate the improvement of repeated high-intensity-effort indices related to the aerobic system.

Purpose: The accuracy of heart rate measured with a wrist photoplethysmography monitor can be influenced by the tightening of the wristband, movement of arms, or kinetics of the signal (eg, steady-state exercise vs on- and off-transients). To test these hypotheses, photoplethysmographic and electrocardiographic (ECG) signals were compared. Methods: Thirty participants (50% female) randomly performed two 13′ sequences (3′ rest, 5′ submaximal-intensity exercise, and 5′ passive recovery) on a motorized treadmill and a bicycle ergometer. Heart rate was measured concomitantly with a 10-lead ECG, a chest-strap monitor, and 2 wrist photoplethysmography monitors (Polar Unite) with different tightening (free vs imposed at the maximum tolerable). Results: The level of association (r) and coefficient of variation (CV; ie, the error of measurement) of the Polar Unite versus the 10-lead ECG is affected by the tightness of the wristband (normal vs high; r = .83 and .96, CV = 16.1 and 8.1% for the treadmill, respectively; r = .71 and .97, CV = 20.3% and 6.2% for the bicycle, respectively) by the phase of the signal (transition vs steady state; r = .90 and .97, CV = 9.0% and 7.6% for the treadmill, respectively; r = .93 and .99, CV = 7.5% and 3.1% for the bicycle, respectively) and movement of arms (treadmill vs bicycle; r = .90 and .93, CV = 9.0% and 7.5% during the transition phase, respectively; r = .97 and .99, CV = 7.6% and 3.1% during the steady-state phase, respectively). Conclusion: The accuracy of heart rate measured with a wrist photoplethysmography monitor is affected by the tightness of the wristband and the phase of the signal. A high tightening is required when high accuracy is expected.

Purpose: To assess the effects of a short-term taper on the ability to perform repeated high-intensity efforts, depending on players’ fatigue level following an intensive training block. Method: After a 3-day off-season camp, 13 players followed the same 3-week preseason training block followed by a 7-day exponential taper. Performance was assessed by a repeated high-intensity effort test before and after the taper. Total sprint time, percentage of decrement, and the number of sprints equal to or higher than 90% of the best sprint were retained for analysis. Players were a posteriori classified in normal training or acute fatigue groups based on their readiness to perform prior to the taper, assessed through the magnitude of difference in psychological (Profile of Mood State Questionnaire), cardiovascular (submaximal constant-duration cycling), and neuromuscular (countermovement jump) tests between the preintensive and postintensive training blocks. Results: Training load declined by 55% (9%) during the taper (P = .001, g = −2.54). The overall group showed a small improvement in total sprint time (−3.40% [3.90%], P = .04, g = −0.39) following the taper. Relative changes tended to be higher in the acute fatigue compared with the normal training group (−5.07% [4.52%] vs −1.45% [1.88%], respectively; P = .08; d = 1.01). No taper-induced improvement was observed in percentage of decrement or number of sprints equal to or higher than 90% of the best sprint. Conclusion: A 7-day taper consisting of 55% training load reduction improved repeated high-intensity effort performance in young elite rugby union players. Pretaper level of fatigue seems to be a key determinant in the taper supercompensation process, as acutely fatigued players at the end of the intensive training block tended to benefit more from the taper.

Purpose: To investigate the relationship between physical fitness and repeated high-intensity effort (RHIE) ability in elite rugby union players, depending on playing position. Method: Thirty-nine players underwent a fitness testing battery composed of a body composition assessment, upper-body strength (1-repetition maximum bench press and 1-repetition maximum bench row), lower-body strength (6-repetition maximum back squat), and power (countermovement jump, countermovement jump with arms, and 20-m sprint), as well as aerobic fitness (Bronco test) and RHIE tests over a 1-week period. Pearson linear correlations were used to quantify relationships between fitness tests and the RHIE performance outcomes (total sprint time [TST] and percentage decrement [%D]). Thereafter, a stepwise multiple regression model was used to verify the influence of physical fitness measures on RHIE ability. Results: TST was strongly to very strongly associated to body fat (BF, r = .82, P < .01), the 20-m sprint (r = .86, P < .01), countermovement jump (r = −.72, P < .01), and Bronco test (r = .90, P < .01). These fitness outcomes were related to %D, with moderate to strong associations (.82 > ∣r∣ > .54, P < .01). By playing position, similar associations were observed in forwards, but RHIE ability was only related to the 20-m sprint in backs (r = .53, P < .05). The RHIE performance model equations were TST = 13.69 + 0.01 × BF + 0.08 × Bronco + 10.20 × 20 m and %D = −14.34 + 0.11 × BF +0.18 × Bronco − 9.92 × 20 m. These models explain 88.8% and 68.2% of the variance, respectively. Conclusion: Body composition, lower-body power, and aerobic fitness were highly related with RHIE ability. However, backs expressed a different profile than forwards, suggesting that further research with larger sample sizes is needed to better understand the fitness determinants of backs’ RHIE ability.

Research shows that negative or threatening emotional stimuli can foster movement velocity and force. However, less is known about how evaluative threat may influence movement parameters in endurance exercise. Based on social self-preservation theory, the authors predicted that evaluative threat would facilitate effort expenditure in physical exercise. In an exploratory study, 27 young men completed a bogus intelligence test and received either low-intelligence-quotient feedback (evaluative threat) or no feedback (control). Next, they were asked to pedal on a stationary bicycle for 30 min at a constant cadence. After 10 min (calibration period), the cadence display was hidden. Findings show that participants under evaluative threat increased cadence more than control participants during the subsequent 20-min critical period. These findings underline the potential importance of unrelated evaluative threat on physical performance.

Purpose: To assess the net effects of strength training on middle- and long-distance performance through a meta-analysis of the available literature. Methods: Three databases were searched, from which 28 of 554 potential studies met all inclusion criteria. Standardized mean differences (SMDs) were calculated and weighted by the inverse of variance to calculate an overall effect and its 95% confidence interval (CI). Subgroup analyses were conducted to determine whether the strength-training intensity, duration, and frequency and population performance level, age, sex, and sport were outcomes that might influence the magnitude of the effect. Results: The implementation of a strength-training mesocycle in running, cycling, cross-country skiing, and swimming was associated with moderate improvements in middle- and long-distance performance (net SMD [95%CI] = 0.52 [0.33–0.70]). These results were associated with improvements in the energy cost of locomotion (0.65 [0.32–0.98]), maximal force (0.99 [0.80–1.18]), and maximal power (0.50 [0.34–0.67]). Maximal-force training led to greater improvements than other intensities. Subgroup analyses also revealed that beneficial effects on performance were consistent irrespective of the athletes’ level. Conclusion: Taken together, these results provide a framework that supports the implementation of strength training in addition to traditional sport-specific training to improve middle- and long-distance performance, mainly through improvements in the energy cost of locomotion, maximal power, and maximal strength.

The purpose of this study was to assess the effects of exercise intensity, age, and fitness levels on executive and nonexecutive cognitive tasks during exercise. Participants completed a computerized modified-Stroop task (including denomination, inhibition, and switching conditions) while pedaling on a cycle ergometer at 40%, 60%, and 80% of peak power output (PPO). We showed that a bout of moderate-intensity (60% PPO) to high-intensity (80% PPO) exercise was associated with deleterious performance in the executive component of the computerized modified-Stroop task (i.e., switching condition), especially in lower-fit individuals (p < .01). Age did not have an effect on the relationship between acute cardiovascular exercise and cognition. Acute exercise can momentarily impair executive control equivalently in younger and older adults, but individual’s fitness level moderates this relation.

Physiological and psychological demands during training and competition generate fatigue and reduce an athlete’s sport-specific performance capacity. The magnitude of this decrement depends on several characteristics of the exercise stimulus (eg, type, duration, and intensity), as well as on individual characteristics (eg, fitness, profile, and fatigue resistance). As such, the time required to fully recover is proportional to the level of fatigue, and the consequences of exercise-induced fatigue are manifold. Whatever the purpose of the ensuing exercise session (ie, training or competition), it is crucial to understand the importance of optimizing the period between exercise bouts in order to speed up the regenerative processes and facilitate recovery or set the next stimulus at the optimal time point. This implies having a fairly precise understanding of the fatigue mechanisms that contribute to the performance decrement. Failing to respect an athlete’s recovery needs may lead to an excessive accumulation of fatigue and potentially “nonfunctional overreaching” or to maladaptive training. Although research in this area recently increased, considerations regarding the specific time frames for different physiological mechanisms in relation to exercise-induced fatigue are still missing. Furthermore, recommendations on the timing and dosing of recovery based on these time frames are limited. Therefore, the aim of this article is to describe time courses of recovery in relation to the exercise type and on different physiological levels. This summary supports coaches, athletes, and scientists in their decision-making process by considering the relationship of exercise type, physiology, and recovery.