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Purpose: To investigate the indirect measurement of 1-repetition-maximum (1RM) free-weight half-squat in high-level sprinters using the load–velocity relationship. Methods: Half-squat load and velocity data from 11 elite sprinters were collected in 2 separate testing sessions. Approximately 24 hours prior to the first testing session, sprinters completed a fatiguing high-intensity training session consisting of running intervals, staircase exercises, and body-weight exercises. Prior to the second testing session, sprinters had rested at least 48 hours. Two different prediction models (multiple-point method, 2-point method) were used to estimate 1RM based on the load and either mean or peak concentric velocity data of submaximal lifts (40%–90% 1RM). The criterion validity of all methods was examined through intraclass correlation coefficients, coefficient of variation (CV%), Bland–Altman plots, and the SEM. Results: None of the estimations were significantly different from the actual 1RM. The multiple-point method showed higher intraclass correlation coefficients (.91 to .97), with CVs from 3.6% to 11.7% and SEMs from 5.4% to 10.6%. The 2-point method showed slightly lower intraclass correlation coefficients (.76 to .95), with CVs 1.4% to 17.5% and SEMs from 9.8% to 26.1%. Bland–Altman plots revealed a mean random bias in estimation of 1RM for both methods (mean and peak velocity) ranging from 1.06 to 13.79 kg. Conclusion: Velocity-based methods can be used to roughly estimate 1RM in elite sprinters in the rested and fatigued conditions. However, all methods showed variations that limit their applicability for accurate load prescription for individual athletes.

Background: Laboratory assessment of maximal oxygen uptake ( ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ ) is physically and mentally draining for the athlete and requires expensive laboratory equipment. Indirect measurement of ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ could provide a practical alternative to laboratory testing. Purpose: To examine the relationship between the maximal power output (MPO) in an individualized 7 × 2-minute incremental test (INCR-test) and ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ and to develop a regression equation to predict ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ from MPO in female rowers. Methods: Twenty female club and Olympic rowers (development group) performed the INCR-test on a Concept2 rowing ergometer to determine ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ and MPO. A linear regression analysis was used to develop a prediction of ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ from MPO. Cross-validation analysis of the prediction equation was performed using an independent sample of 10 female rowers (validation group). Results: A high correlation coefficient (r = .94) was found between MPO and ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ . The following prediction equation was developed: ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ (mL·min⁻¹) = 9.58 × MPO (W) + 958. No difference was found between the mean predicted ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ in the INCR-test (3480 mL·min⁻¹) and the measured ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ (3530 mL·min⁻¹). The standard error of estimate was 162 mL·min⁻¹, and the percentage standard error of estimate was 4.6%. The prediction model only including MPO, determined during the INCR-test, explained 89% of the variability in ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ . Conclusion: The INCR-test is a practical and accessible alternative to laboratory testing of ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ .

Purpose: To determine whether competitive performance, as defined by International Biathlon Union (IBU) and International Ski Federation (FIS) points in biathlon and cross-country (XC) skiing, respectively, can be projected using a combination of anthropometric and physiological metrics. Shooting accuracy was also included in the biathlon models. Methods: Data were analyzed using multivariate methods from 45 (23 female and 22 male) biathletes and 202 (86 female and 116 male) XC skiers who were all members of senior national teams, national development teams, or ski-university or high school invite-only programs (age range: 16–36 y). Anthropometric and physiological characteristics were assessed via dual-energy X-ray absorptiometry and incremental roller-ski treadmill tests, respectively. Shooting accuracy was assessed via an outdoor standardized testing protocol. Results: Valid projective models were identified for female biathletes’ IBU points (R ² = .80/Q ² = .65) and female XC skiers’ FIS distance (R ² = .81/Q ² = .74) and sprint (R ² = .81/Q ² = .70) points. No valid models were identified for the men. The most important variables for the projection of IBU points were shooting accuracy, speeds at blood lactate concentrations of 4 and 2 mmol·L⁻¹, peak aerobic power, and lean mass. The most important variables for the projection of FIS distance and sprint points were speeds at blood lactate concentrations of 4 and 2 mmol·L⁻¹ and peak aerobic power. Conclusions: This study highlights the relative importance of specific anthropometric, physiological, and shooting-accuracy metrics in female biathletes and XC skiers. The data can help to identify the specific metrics that should be targeted when monitoring athletes’ progression and designing training plans.

Purpose: This study investigated the effects of 4 weeks of repeated sprint training (RST) versus repeated high-intensity technique training (RTT) on the physiological responses (ie, blood lactate), mean and peak heart rate, rating of perceived exertion, technical–tactical performance, and time–motion variables during simulated taekwondo combats. Methods: Twenty-four taekwondo athletes (18 male and 6 female; age: 16 [1] y) were randomly and equally assigned to RST (10 × 35-m running sprints interspersed by 10-s rest) or RTT (10 × 6-s bandal-tchagui kicking executions interspersed by 10-s rest) groups in addition to their regular training. Both groups performed simulated combats before and after training. Results: Delta lactate and peak heart rate were attenuated following training (P < .001 and P = .03, respectively), with no differences identified between RTT and RST conditions. Rating of perceived exertion decreased after training only in the RTT (P = .002). Time fighting and preparatory activities increased following training (P < .001), with higher values observed following RTT than RST (P < .001). Nonpreparatory time decreased after training (P < .001), with more pronounced reductions observed following RTT when compared to RST (P < .001). The number of single attacks decreased only following RST (P < .001), whereas combined attacks increased only after RTT training (P < .001). Conclusions: Similar adjustments in the physiological responses to combat were observed following 4 weeks of either RST or RTT, but RTT elicited more favorable perceptual responses and combat-related performance. This highlights the importance of specificity of training and its effective transfer to combat.

Purpose: The aim of this study was to assess the interaction of kinematic, kinetic, and energetic variables as speed predictors in adolescent swimmers in the front-crawl stroke. Design: Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed. Methods: The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed. Results: The final model identified time (estimate = −0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = −0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = −0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers. Conclusions: Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.

This research provides a review of seated shot put alongside new data from the Tokyo 2020 Paralympic Games with the aim to understand the latest trends in equipment within a recently established rule set and how key equipment variables may impact performance for athletes in different classifications. First, a review of the literature found that the throwing pole is a key equipment aid that is not well understood, in part due to limitations in testing design. New data from the 2020 Paralympic Games showed inconsistent trends for the use of the throwing pole among athletes, particularly in transitionary classes (F33–34 and F54–55). A two-way analysis of variance found a main effect of classification on performance (p < .001), as well as an interaction effect between pole use and classification on performance (p < .05). Notably, pole users are seen to perform better than non–pole users in Class F32 (p < .05).

Purpose: The purpose of the current investigation was to retrospectively assess possible differences in physiological performance characteristics between junior cyclists signing a contract with an under-23 (U23) development team versus those failing to sign such a contract. Methods: Twenty-five male junior cyclists (age: 18.1 [0.7] y, stature: 181.9 [6.0] cm, body mass: 69.1 [7.9] kg, peak oxygen uptake: 71.3 [6.2] mL·min⁻¹·kg⁻¹) were assigned to this investigation. Between September and October of the last year in the junior category, each cyclist performed a ramp incremental exercise test to determine certain physiological performance characteristics. Subsequently, participants were divided in 2 groups: (1) those signing a contract with a U23 development team (JUNIOR_U23) and (2) those failing to sign such a contract (JUNIOR_NON-U23). Unpaired t tests were used to assess possible between-groups differences in physiological performance characteristics. The level of statistical significance was set at P < .05 two tailed. Results: No significant between-groups differences in submaximal (ie, gas exchange threshold, respiratory compensation point) and maximal physiological performance characteristics (ie, peak work rate, peak oxygen uptake) expressed in absolute values (ie, L·min⁻¹, W) were observed (P > .05). However, significant between-groups differences were observed when physiological performance characteristics were expressed relative to the cyclists’ body weights (P < .05). Conclusions: The current investigation showed that junior cyclists stepping up to a U23 development team might be retrospectively differentiated from junior cyclists not stepping up based on certain physiological performance characteristics, which might inform practitioners and/or federations working with young cyclists during the long-term athletic development process.