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In sport, there is extensive evidence that supports the benefits associated with a mastery climate. However, limited studies have explored how physical tools could be used to promote mastery climates in youth sport contexts. Using an action research approach, we sought to understand the benefits and drawbacks of applying tools grounded in goal setting to promote a mastery environment: (a) an “arm sleeve” to be worn by athletes during training and matches and (b) a “reflection sheet” for use pre- and posttraining/-matches. These tools were implemented for a 3-week period with a U13 academy team (18 players and two coaches). Based on observation notes, focus groups, and one-on-one interviews, the analysis showed that the arm sleeves were helpful reminders for process goals, whereas the coaches had abandoned the use of reflection sheets due to lack of time. The benefits and drawbacks of the tools are discussed while pedagogical and practical implications are considered.

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.