Effects of Sprint versus High-Intensity Aerobic Interval Training on Cross-Country Mountain Biking Performance: A Randomized Controlled Trial

Reliability of Time to Exhaustion Above the Power Output at VO2peak in Trained Mountain Bikers

To our knowledge, no study has investigated the reliability of the time to exhaustion (TTE) test during constant-load trials in Olympic distance cross-country mountain bike (XCO-MTB) athletes. Thus, the aim was to analyze the reliability of the TTE test at intensities above peak oxygen uptake (VO2peak) in trained XCO-MTB athletes. Fifteen male XCO-MTB athletes (mean ± SD: age 31.5 ± 6.6 years, stature 174.0 ± 5.4 cm, body mass 67.2 ± 5.1 kg, VO2peak 64.5 ± 4.7 mL·kg-1·min-1) completed 2 TTE tests on the cycle ergometer with 4 different intensities above the maximal work rate in the incremental test (Wmax) (105%, 120%, 130%, and 140% of Wmax). There was moderate reliability between TTE tests at 105% (intraclass correlation coefficient (ICC) = 0.81, p ≤ 0.001; coefficient of variation (CV) = 9.1%; standard error of measurement (SEM) = 18.3%), and 120% (ICC = 0.88, p ≤ 0.001; CV = 6.6%; SEM = 9.3%) Wmax. For intensities of 130% (ICC = 0.53, p = 0.018; CV = 9.2%; SEM = 15.8%) and 140% (ICC = 0.56, p = 0.012; CV = 12.2%; SEM = 13.5%) Wmax, the reliability results proved to be questionable. In addition, no significant differences were found between the 2 TTE tests in all intensities (p > 0.05). Caution should be taken when assessing TTE above VO2peak or when using it as a performance indicator, given its moderate to questionable reliability.

Current Perspectives of Cross-Country Mountain Biking: Physiological and Mechanical Aspects, Evolution of Bikes, Accidents and Injuries

Mountain biking (MTB) is a cycling modality performed on a variety of unpaved terrain. Although the cross-country Olympic race is the most popular cross-country (XC) format, other XC events have gained increased attention. XC-MTB has repeatedly modified its rules and race format. Moreover, bikes have been modified throughout the years in order to improve riding performance. Therefore, the aim of this review was to present the most relevant studies and discuss the main results on the XC-MTB. Limited evidence on the topic suggests that the XC-MTB events present a variation in exercise intensity, demanding cardiovascular fitness and high power output. Nonetheless, these responses and demands seem to change according to each event. The characteristics of the cyclists differ according to the performance level, suggesting that these parameters may be important to achieve superior performance in XC-MTB. Moreover, factors such as pacing and ability to perform technical sections of the circuit might influence general performance. Bicycles equipped with front and rear suspension (i.e., full suspension) and 29″ wheels have been shown to be effective on the XC circuit. Lastly, strategies such as protective equipment, bike fit, resistance training and accident prevention measures can reduce the severity and the number of injuries.

Effect of Two Different Training Interventions on Cycling Performance in Mountain Bike Cross-Country Olympic Athletes

To improve performance in endurance sports, it is important to include both high-intensity and low-intensity training, but there is neither a universally accepted practice nor clear scientific evidence that allows reliable statements about the predominance of a specific training method. This randomized controlled trial compared the effects of a polarized training model (POL) to a low-intensity training model (LIT) on physiological parameters and mountain bike cross-country Olympic (XCO) race performance in eighteen competitive XCO athletes (17.9 ± 3.6 years). The superiority of one of the two methods could not be shown in this study. The results did not show statistically significant differences between POL and LIT, as both interventions led to slight improvements. However, a small tendency toward better effects for POL was seen for cycling power output during the race (4.4% vs. –2.2%), at the 4 mmol/L (6.1% vs. 2.8%) and individual anaerobic lactate threshold (5.1% vs. 2.3%), and for maximal aerobic performance (4.4% vs. 2.6%), but not for maximal efforts lasting 10 to 300 s. Despite the lack of significant superiority in this and some other studies, many athletes and coaches prefer POL because it produces at least equivalent effects and requires less training time.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

Exercise Intensity and Pacing Pattern During a Cross-Country Olympic Mountain Bike Race

Objective: To examine the power profiles and pacing patterns in relation to critical power (CP) and maximal aerobic power (MAP) output during a cross-country Olympic (XCO) mountain bike race.

Methods: Five male and two female national competitive XCO cyclists completed a UCI Cat. 1 XCO race. The races were 19 km and 23 km and contained five (female) and six (male) laps, respectively. Power output (PO) during the race was measured with the cyclists’ personal power meters. On two laboratory tests using their own bikes and power meters, CP and work capacity above CP (W') were calculated using three time trials of 12, 7, and 3 min, while MAP was established based on a 3-step submaximal test and the maximal oxygen uptake from the 7-min time trial.

Results: Mean PO over the race duration (96 ± 7 min) corresponded to 76 ± 9% of CP and 63 ± 4% of MAP. 40 ± 8% of race time was spent with PO > CP, and the mean duration and magnitude of the bouts >CP was ~8 s and ~120% of CP. From the first to last lap, time >CP and accumulated W' per lap decreased with 9 ± 6% and 45 ± 17%, respectively. For single >CP bouts, mean magnitude and mean W' expended decreased by 25 ± 8% and 38 ± 15% from the first to the last lap, respectively. Number and duration of bouts did not change significantly between laps.

Conclusion: The highly variable pacing pattern in XCO implies the need for rapid changes in metabolic power output, as a result of numerous separate short-lived >CP actions which decrease in magnitude in later laps, but with little lap-to-lap variation in number and duration.

Acute Effects of Work Rest Interval Duration of 3 HIIT Protocols on Cycling Power in Trained Young Adults

High-Intensity Interval Training (HIIT) is described as a succession of short duration and maximum or near-maximum intensity efforts, alternated by recovery periods during which exercise continues at a lower intensity (active recovery) or is interrupted (passive recovery). Our objective was to evaluate the acute responses of three HIIT protocols of different work/rest interval times over the total time of the session, with self-selectable load and up to exhaustion, “all out”.The sample was composed of 22 male participants (n = 22) between 19 and 24 years old. The HIIT protocol consisted of one of the three HIIT protocols, of 30, 60 and 90 s density ratio 1:1 and with passive rest, with a total exercise duration of 10 min. The test was performed in a cycloergometer set in workload mode independent of the pedaling frequency. The comparison of the three HIIT protocols shows that the duration of the work/rest intervals, starting from 30 s of work, in the cycloergometer, there are no significant differences in the levels of lactate concentration in the blood, nor in the heart rate, since a similar amount is obtained in the three protocols. The percentage of maximum power developed reached in each HIIT protocol is related to the duration of the working intervals.

Programming Interval Training to Optimize Time-Trial Performance: A Systematic Review and Meta-Analysis

BACKGROUND

Interval training has become an essential component of endurance training programs because it can facilitate a substantial improvement in endurance sport performance. Two forms of interval training that are commonly used to improve endurance sport performance are high-intensity interval training (HIIT) and sprint interval training (SIT). Despite extensive research, there is no consensus concerning the optimal method to manipulate the interval training programming variables to maximize endurance performance for differing individuals.

OBJECTIVE

The objective of this manuscript was to perform a systematic review and meta-analysis of interval training studies to determine the influence that individual characteristics and training variables have on time-trial (TT) performance.

DATA SOURCES

SPORTDiscus and Medline with Full Text were explored to conduct a systematic literature search.

STUDY SELECTION

The following criteria were used to select studies appropriate for the review: 1. the studies were prospective in nature; 2. included individuals between the ages of 18 and 65 years; 3. included an interval training (HIIT or SIT) program at least 2 weeks in duration; 4. included a TT test that required participants to complete a set distance; 5. and programmed HIIT by power or velocity.

RESULTS

Twenty-nine studies met the inclusion criteria for the quantitative analysis with a total of 67 separate groups. The participants included males (n = 400) and females (n = 91) with a mean group age of 25 (range 19-45) years and mean [Formula: see text] of 52 (range 32-70) mL·kg^-1·min^-1. The training status of the participants comprised of inactive (n = 75), active (n = 146) and trained (n = 258) individuals. Training status played a significant role in improvements in TT performance with trained individuals only seeing improvements of approximately 2% whereas individuals of lower training status demonstrated improvements as high as 6%. The change in TT performance with HIIT depended on the duration but not the intensity of the interval work-bout. There was a dose-response relationship with the number of HIIT sessions, training weeks and total work with changes in TT performance. However, the dose-response was not present with SIT.

CONCLUSION

Optimization of interval training programs to produce TT performance improvements should be done according to training status. Our analysis suggests that increasing interval training dose beyond minimal requirements may not augment the training response. In addition, optimal dosing differs between high intensity and sprint interval programs.

Effect of High-Intensity Interval Training Versus Sprint Interval Training on Time-Trial Performance: A Systematic Review and Meta-analysis

BACKGROUND

Two forms of interval training commonly discussed in the literature are high-intensity interval training (HIIT) and sprint interval training (SIT). HIIT consists of repeated bouts of exercise that occur at a power output or velocity between the second ventilatory threshold and maximal oxygen consumption (VO_2max). SIT is performed at a power output or velocity above those associated with VO_2max.

OBJECTIVE

The primary objective of this study is to systematically review published randomized and pair-matched trials to determine which mode of interval training, HIIT versus SIT, leads to a greater improvement in TT performance in active and trained individuals. The second objective of this review is to perform a subgroup analysis to determine if there is a distinction between HIIT programs that differ in work-bout duration.

DATA SOURCES

SPORTDiscus (1800-present) and Medline with Full Text (1946-present) were used to conduct a systematic literature search.

STUDY SELECTION

Studies were selected for the review if they met the following criteria: (1) individuals (males and females) who were considered at least moderately trained (~ 3-h per week of activity) as specified by the authors of the included studies; (2) between the ages of 18 and 45 years; (3) randomized or pair-matched trials that included a HIIT and a SIT group; (4) provided detailed information about the interval training program; (5) were at least 2 weeks in duration; (6) included a TT test that required participants to complete a set distance.

RESULTS

A total of 6 articles met the inclusion criteria for the subjective and objective analysis. The pooled analysis was based on a random-effects model. There was no difference in the change in TT performance when comparing all HIIT versus SIT (0.9%; 90% CI - 1.2-1.9%, p = 0.18). However, subgroup analysis based on duration of work interval indicated a 2% greater improvement in TT performance following long-HIIT (≥ 4 min) when compared to SIT. There was no difference in change in VO_2max/_peak oxygen consumption (VO_2peak) between groups. There was a moderate effect (ES = 0.70) in favor of HIIT over SIT in maximal aerobic power (MAP) or maximal aerobic velocity (MAV).

CONCLUSION

The results of the meta-analysis indicate that long-HIIT may be the optimal form of interval training to augment TT performance. Additional research that directly compares HIIT exercise differing in work-bout duration would strengthen these results and provide further insight into the mechanisms behind the observed benefits of long-HIIT.

Interval running with self-selected recovery: Physiology, performance, and perception

This study (1) compared the physiological responses and performance during a high-intensity interval training (HIIT) session incorporating externally regulated (ER) and self-selected (SS) recovery periods and (2) examined the psychophysiological cues underpinning SS recovery durations. Following an incremental maximal exercise test to determine maximal aerobic speed (MAS), 14 recreationally active males completed 2 HIIT sessions on a non-motorised treadmill. Participants performed 12 × 30 s running intervals at a target intensity of 105% MAS interspersed with 30 s (ER) or SS recovery periods. During SS, participants were instructed to provide themselves with sufficient recovery to complete all 12 efforts at the required intensity. A semi-structured interview was undertaken following the completion of SS. Mean recovery duration was longer during SS (51 ± 15 s) compared to ER (30 ± 0 s; p < .001; d = 1.46 ± 0.46). Between-interval heart rate recovery was higher (SS: 19 ± 9 b min^-1; ER: 8 ± 5 b min^-1; p < .001; d = 1.43 ± 0.43) and absolute time ≥90% maximal heart rate (HR_max) was lower (SS: 335 ± 193 s; ER: 433 ± 147 s; p = .075; d = 0.52 ± 0.39) during SS compared to ER. Relative time ≥105% MAS was greater during SS (90 ± 6%) compared to ER (74 ± 20%; p < .01; d = 0.87 ± 0.40). Different sources of afferent information underpinned decision-making during SS. The extended durations of recovery during SS resulted in a reduced time ≥90% HR_max but enhanced time ≥105% MAS, compared with ER exercise. Differences in the afferent cue utilisation of participants likely explain the large levels of inter-individual variability observed.

Pacing Strategy During Simulated Mountain Bike Racing

Cross-country mountain biking (XCO) is a popular high-intensity endurance cycling event, but XCO pacing strategy has not been fully examined. This study examined the pacing strategies of different XCO athletes during a laboratory-simulated XCO performance test. Brazilian cyclists classified as performance-cohort level 3 performed an XCO race simulation. The simulation consisted of four 10-km laps with a gradient of 0-10%. No group-vs-time interaction was found in lap time (P = .169), absolute (P = .719) and relative (P = .607) power output, ratings of perceived exertion (P = .182), or heart rate (P = .125). There was a time main effect, as athletes decreased power output by 0.3 W/kg throughout the XCO simulation, thereby resulting in a 1.6-min decrement per lap. The power output corresponding to the onset of blood lactate accumulation adequately represented the mean power of the first lap. These results showed that 2 groups of cyclists with different training status adopted similar pacing strategies during an XCO race simulation, as they both used a fast-starting pacing strategy followed by positive pacing that resulted in a linear decrease in power output at every lap.

Physiological and anthropometric characteristics of top-level youth cross-country cyclists

In the literature there is a lack of data about the development of top level athletes in cross-country mountain biking (XCO). The purpose of this study was to analyze anthropometric and physiological characteristics of some of the best XCO bikers aged between 13 and 16. The study involved 45 bikers (26 males and 19 females) belonging to a youth national team. The evaluations, consisting of anthropometric measures, incremental cycling tests (VO_2max, PPO, P@RCP), and 30 s Wingate Tests (PMax, PMean), were conducted over a lapse of 4 years. Our findings showed in bikers, already at young age, a specific athletic profile advantageous for XCO performance. At the age of 16, just before entering the junior category and competing at international level, male and female bikers showed physiological values normalized to the body mass comparable to those reported in literature for high level athletes (VO_2max>70 and >60 ml/kg/min, PPO >6.5 and >5.5 W/kg, respectively in males and females). The production of high power-to-weight ratios and high peaks of anaerobic power attests the presence of highly developed aerobic and anaerobic systems in young XCO cyclists reflecting the high physiological demand of this sport.

On-field prediction vs monitoring of aerobic capacity markers using submaximal lactate and heart rate measures

This study aimed to validate the use of a single blood lactate concentration measurement taken following a 5-minute running bout at 10 km·h^-1 (BLC₁₀ ) and the speed associated with 90% of maximal heart rate (S₉₀ ) to predict and monitor fixed blood lactate concentration (FBLC) thresholds in athletes. Three complementary studies were undertaken. Study I: A cross-sectional study examining the associations of BLC₁₀ and S₉₀ with running speeds at FBLC of 3 (S3mM) and 4 mmol·L^-1 (S4mM) in 100 athletes. Study II: A cross-validation study assessing the predictive capacity of BLC₁₀ and S₉₀ to estimate FBLC thresholds in real practice. Study III: A longitudinal study examining whether training-induced changes in FBLC thresholds could be monitored using BLC₁₀ and S₉₀ in 80 athletes tested before and after an intensified training period. Study I: BLC₁₀ (r=-.87 to -.89) and S₉₀ (r=.73-.79) were very largely (P<.001) related to FBLC thresholds. Study II: Predictive models yielded robust correlations between estimated and measured FBLC thresholds (r=.75-.91; P<.001). The limits of agreements, however, revealed that prediction of FBLC thresholds could be biased up to 9%-15%. Study III: BLC₁₀ was very largely related to training-induced changes in FBLC thresholds (r=-.72 to -.76; P<.001). Increases in S₉₀ were associated with improvements in FBLC thresholds, but decreases in S₉₀ led to unclear changes in FBLC thresholds. This study supports the use of BLC₁₀ as a simple, low-cost, non-fatiguing, and time-efficient functional variable to monitor, but not predict, FBLC thresholds in athletes. The present results also question the use of S₉₀ to detect declines in endurance performance.