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Purpose: To compare the energetic contribution and pacing in 2000- and 1500-m maximal rowing-ergometer performances. Methods: On separate visits (>48 h apart, random order), 18 trained junior (16.7 [0.4] y) male rowers completed 3 trials: a 7 × 4-minute graded exercise test, a 2000-m time trial (TT₂₀₀₀), and a 1500-m TT (TT₁₅₀₀). Respiratory gases were continuously measured throughout each trial. The submaximal power-to-oxygen-consumption relationship from the graded exercise test was used to determine the accumulated oxygen deficit for each TT. Differences in mean power output (MPO), relative anaerobic contribution, percentage of peak oxygen uptake, pacing index, maximum heart rate, rating of perceived exertion, and blood lactate concentration were assessed using linear mixed modeling. Results: Compared to TT₂₀₀₀ (324 [24] W), MPO was 5.2% (3.3%) higher in TT₁₅₀₀ (341 [29 W]; P < .001, ${\eta }_{\mathrm{p}}^2=.70$ ). There was a 4.9% (3.3%) increase (P < .001, ${\eta }_{\mathrm{p}}^2=.71$ ) in anaerobic contribution from 17.3% (3.3%) (TT₂₀₀₀) to 22.2% (4.3%) (TT₁₅₀₀). Compared to TT₁₅₀₀, maximum heart rate, rating of perceived exertion, and blood lactate concentration were all greater (P < .05) in TT₂₀₀₀. The pacing index was not different between trials. Percentage increase in MPO from TT₂₀₀₀ to TT₁₅₀₀ was negatively associated with pacing variance in TT₁₅₀₀ (R ² = .269, P = .027). Conclusions: Maximal ergometer performance over 1500 m requires a significantly greater anaerobic contribution compared with 2000 m. Junior male athletes adopt a consistent pacing strategy across both distances. However, those who experienced greater percentage increases in MPO over the shorter test adopted a more even pacing strategy. To prepare for 1500-m performance, greater emphasis should be placed on developing capacity for work in the severe domain and completing race simulations with a more even pacing strategy.

Objectives: This study sought to report the reliability (intrasession) values of initial maximum push-rim propulsion (IMPRP) and sprint performance in elite wheelchair basketball (WB) players and to assess the involvement of strength in sprint capacity. Methods: Fifteen Spanish international WB male players participated in this study. The maximum single wheelchair push from a stationary position (IMPRP) and the sprint performance (ie, 3, 5, and 12 m) of WB players were measured in this study. Results: IMPRP mechanical outputs V, V _max, P, Rel. P, F, and Rel. F variables presented high reliability values (intraclass correlation coefficient [ICC] ≥ .92; coefficient of variation [CV] ≤ 8.04 ± 7.37; standard error of measurement [SEM] ≤ 29.92), but the maximum strength variables P_max, Rel. P_max, F _max, and Rel. F _max (ICC ≥ .63; CV ≤ 13.19 ± 16.63; SEM ≤ 203.76) showed lower ICC values and by contrast higher CV and SEM values. The most substantial correlations were identified between maximum IMPRP values (ie, V, V _max, P, Rel. P, F, and Rel. F) and sprint performance in 3 m (r ±  confidence limits ≥ −0.74 ± 0.22, very large; R ² ≥ .55), 5 m (r ±  confidence limits ≥ −0.72 ± 0.24, very large; R ² ≥ .51), and 12 m (r ±  confidence limits ≥ −0.67 ± 0.27, large; R ² ≥ .44). Conclusions: The IMPRP test and sprint tests (3, 5, and 12 m) are practical and reliable for measuring strength and speed in WB players. In addition, there were large to very large associations among strength variables (ie, P, Rel. P, F, and Rel. F) and all sprint variables. This could indicate a need to implement specific strength exercises in WB players to improve sprint capacity.

Purpose: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices’ sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. Methods: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100–2000 Hz) and signal length (100–300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. Results: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: −.02 to −.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: −.05 to −.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively). Conclusions: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.

Purpose: To examine the effects of a neuromuscular training program combining plyometric exercises with acceleration, deceleration, and change-of-direction drills conducted on sand or hard surfaces on the fitness qualities of young male tennis players. Methods: Thirty-one young male players were allocated to a training group performing 12 training sessions on sand or hard surfaces, during a 6-week period. Tests included linear sprint (10-m acceleration with 5-m split times), change of direction (modified 5-0-5 test), vertical jumps (countermovement jump and the 10/5 repeated-jump test), isometric hip abduction and adduction strength, and dynamic balance (Y-balance test). Perceived training loads and muscle soreness were assessed during the intervention. Results: Both training strategies were similarly effective in improving the analyzed fitness components. Group × time interaction effects were noticed, with countermovement jump (P = .032), repeated-jump test (P = .029), and reactive strength index (P = .008) favoring hard surfaces and 5-m sprint (P = .009), dynamic balance (P < .05), adduction strength (P < .05), and abduction strength (P < .001) indices favoring sand. Furthermore, the sand group promoted greater perceived training loads and muscle soreness (P < .05) than the hard group across the intervention period. Conclusion: Neuromuscular training strategies characterized by a relatively low volume (∼35 min), conducted on sand or hard surfaces, promoted similar improvements in the fitness qualities of young tennis players, with selected surface-interaction effects. Training on sand can cause transiently higher training loads and persistently higher muscle soreness, suggesting the need for an adequate familiarization period.

Purpose: To investigate the effect of personalized sweat sodium replacement on drinking behavior, sodium and water balance, and thermophysiological responses during and after ultraendurance running in hot conditions. Methods: Nine participants (7 male, 2 female) completed two 5-hour treadmill runs (60% maximum oxygen uptake, 30°C ambient temperature), in a double-blind randomized crossover design, consuming sodium chloride (SODIUM) capsules to replace 100% of previously assessed losses or placebo (PLACEBO). Fluid was consumed ad libitum. Results: No effect of SODIUM was observed for ad libitum fluid intake or net fluid balance (P > .05). Plasma sodium concentration increased in both trials, but to a greater extent in SODIUM at 2.5 hours (mean [SD]: 4 [4] mmol·L⁻¹ vs 1 [5] mmol·L⁻¹; P < .05) and postexercise (4 [3] mmol·L⁻¹ vs 1 [5] mmol·L⁻¹; P < .05). Plasma volume change was not different between trials (P > .05) but was strongly correlated with sodium balance in SODIUM (r = .880, P < .01). No effect of sodium replacement was observed for heart rate, rectal temperature, thermal comfort, perceived exertion, or physiological strain index. During the 24 hours postexercise, ad libitum fluid intake was greater following SODIUM (2541 [711] mL vs 1998 [727] mL; P = .04), as was urinary sodium excretion (NaCl: 66 [35] mmol, Pl: 21 [12] mmol; P < .01). Conclusions: Personalized sweat sodium replacement during ultraendurance running in hot conditions, with ad libitum fluid intake, exacerbated the rise in plasma sodium concentration compared to no sodium replacement but did not substantially influence overall body-water balance or thermophysiological strain. A large sodium deficit incurred during exercise leads to substantial renal sodium conservation postexercise.

Purpose: To investigate performance-determining variables of an on-snow sprint cross-country skiing competition and the evolvement in their relationship with performance as the competition progresses from the individual time trial (TT) to the final. Methods: Sixteen national-level male junior skiers (mean [SD] age, 18.6 [0.8] y; peak oxygen uptake [VO₂peak], 67.6 [5.5] mL·min⁻¹·kg⁻¹) performed a simulated sprint competition (1.3 km) in the skating style, comprising a TT followed by 3 finals (quarterfinals, semifinals, and final) completed by all skiers. In addition, submaximal and incremental roller-ski treadmill tests, on-snow maximal speed tests, and strength/power tests were performed. Results: VO₂peak and peak treadmill speed during incremental testing and relative heart rate, rating of perceived exertion, blood lactate concentrations, and gross efficiency during submaximal testing were all significantly correlated with performance in the TT and subsequent finals (mean [range] r values: .67 [.53–.86], all P < .05). Relative VO₂peak and submaximal relative heart rate and blood lactate concentration were more strongly correlated with performance in the semifinals and final compared with the TT (r values: .74 [.60–.83] vs 0.55 [.51–.60], all P < .05). Maximal speed in uphill and flat terrain was significantly correlated with performance in the TT and subsequent finals (r values: .63 [.38–.70], all P < .05), while strength/power tests did not correlate significantly with sprint performance. Conclusions: VO₂peak and high-speed abilities were the most important determinants of sprint cross-country skiing performance, with an increased importance of VO₂peak as the competition format progressed toward the final.

Purpose: This study determined the evolution of performance and pacing for each winner of the men’s Olympic 1500-m running track final from 1924 to 2020. Methods: Data were obtained from publicly available sources. When official splits were unavailable, times from sources such as YouTube were included and interpolated from video records. Final times, lap splits, and position in the peloton were included. The data are presented relative to 0 to 400 m, 400 to 800 m, 800 to 1200 m, and 1200 to 1500 m. Critical speed and D′ were calculated using athletes’ season’s best times. Results: Performance improved ∼25 seconds from 1924 to 2020, with most improvement (∼19 s) occurring in the first 10 finals. However, only 2 performances were world records, and only one runner won the event twice. Pacing evolved from a fast start–slow middle–fast finish pattern (reverse J-shaped) to a slower start with steady acceleration in the second half (J-shaped). The coefficient of variation for lap speeds ranged from 1.4% to 15.3%, consistent with a highly tactical pacing pattern. With few exceptions, the eventual winners were near the front throughout, although rarely in the leading position. There is evidence of a general increase in both critical speed and D′ that parallels performance. Conclusions: An evolution in the pacing pattern occurred across several “eras” in the history of Olympic 1500-m racing, consistent with better trained athletes and improved technology. There has been a consistent tactical approach of following opponents until the latter stages, and athletes should develop tactical flexibility, related to their critical speed and D′, in planning prerace strategy.