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The Validity of Perceptual Recovery Status on Monitoring Recovery During a High-Intensity Back-Squat Session

Nicholas A. Buoncristiani, Grant Malone, Whitley J. Stone, Scott Arnett, Mark A. Schafer, and Danilo V. Tolusso

Adaptations to resistance training and subsequent performance can be undermined by inadequate interset recovery. Methods typically used to monitor recovery were developed for longitudinal use, making them time-inefficient within singular exercise bouts. If valid, perceptual recovery status (PRS) may be used as an efficient and inexpensive assessment tool to monitor individual recovery. Purpose: The aim of this study was to assess the validity of PRS on monitoring recovery during a high-intensity back-squat session. Methods: Ten healthy men participated in the 2-session study (separated by at least 48 h). Session 1 included anthropometrics, PRS familiarization, and a 1-repetition-maximum back squat. Session 2 included a high-intensity protocol (5 sets of 5 repetitions; 5-min interset recovery; 85% of 1-repetition maximum). PRS was obtained before the first set and during the last 30 seconds of each 5-minute recovery; rating of perceived exertion (RPE) was also collected. A linear position transducer collected mean barbell velocity (MBV). Repeated-measures correlations assessed the common intraindividual relationships of PRS scores to intraset MBV and RPE, respectively. Results: A very large, positive correlation appeared between PRS and MBV (r [95% CI] = .778 [.613 to .878]; P < .0001). A large, negative correlation emerged between PRS and RPE (r [95% CI] = −.549 [−.737 to −.282]; P < .001). Conclusions: Results indicate that PRS can be a means for practitioners to monitor individualized recovery. PRS tracked well with RPE, strengthening its utility in a practitioner-based setting. Findings provide insight into the practicality of PRS for recovery monitoring. It could be used alongside other measures (eg, MBV and countermovement jump) to individually program and maintain performance.

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Analysis of Sprint Ski Mountaineering Performance

Alessandro Fornasiero, Simone Fornoni, Alexa Callovini, Beatrice Todesco, Aldo Savoldelli, Federico Schena, Hans-Christer Holmberg, Barbara Pellegrini, and Lorenzo Bortolan

Ski mountaineering sprint competitions are short individual races involving 3 uphill sections (U), 3 transitions (T), and a final descent. To date, relatively little is known about this novel Olympic discipline, and here we examined (1) the contribution of the time spent on U, T, and final descent to overall finishing time and (2) the potential relationships with final ranking. During the different rounds of 2 International Ski Mountaineering Federation World Cup sprint competitions, male and female ski mountaineers were video recorded. Correlation and multiple linear regression analyses were used to investigate the impact of U, T, and final descent on the best overall finishing time. Linear-mixed model analysis was applied to explore potential interactions between section times, rounds, and final ranking. Overall, U (r = .90–.97) and T (r = .57–.89) were closely correlated with the best overall finishing time (all P < .05). U explained approximately 80% to 90% of the variation in the best finishing time for both sexes, with U + T explaining approximately 95% to 98% of this variation. In each successive round, the ski mountaineers eliminated were all slower on U than the Top 3 (all P < .05). The fastest skiers increased their performance on U in the later rounds of the competitions, while those eliminated showed a tendency toward a decrease. Our findings reveal that world-class sprint ski mountaineers conduct transitions optimally and perform effectively uphill. Training for such competitions should aim to improve short supramaximal uphill performance (∼1.5–2.5 min), ensuring that this does not decline with multiple efforts. These insights into ski mountaineering sprint performance are of considerable value in connection with training for the 2026 Winter Olympics.

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A Methodological Comparison of Protocols and Analytical Techniques to Assess the Outcome Measures of Submaximal Fitness Tests

Tzlil Shushan, Ric Lovell, Shaun J. McLaren, Steve Barrett, Martin Buchheit, Tannath J. Scott, and Dean Norris

Background: Submaximal fitness test (SMFT) outcome measures are frequently collected with a wide array of technologies and methodological approaches. Purpose: To examine the test–retest reliability of various SMFT outcome measures derived from different protocols and analytical techniques. Methods: Twenty-six semiprofessional adult soccer players performed 3 SMFT protocols, including 2 continuous (3 min, 11 and 12.8 km·h−1) and 1 intermittent (4 × 50 m, 18 km·h−1) twice, each separated by 7 days. Heart-rate (HR) indices (exercise HR, HR recovery) and scapula-mounted (PlayerLoad vector magnitude) and foot-mounted (flight time and contact time, stride length) microelectrical mechanical system–derived variables were collected using different time frames and analytical approaches adopted in the literature and practice. Absolute reliability was quantified as the group mean difference, typical error of measurement, also expressed as the coefficient of variation (where appropriate) and standardized units (ie, d). Intraclass correlation coefficient was used to quantify relative reliability. Results: The highest degrees of reliability were evident for exercise HR (typical error: 1.0%–1.6% points), the vertical component of PlayerLoad (expressed in arbitrary units; coefficient of variation: 5.5%–7.0%), and contact time (coefficient of variation: 1.5%–3.0%). These estimates were not influenced by SMFT protocol or analytical approach. All other measures displayed poorer reliability and/or were different between protocols and analytical methods. Conclusions: SMFT protocols impact the test–retest reliability of various outcome measures; however, exercise HR, vertical PlayerLoad, and contact time (derived from foot-mounted micro-electrical mechanical systems) appear to have stable measurement properties to assist the assessment of aerobic capacity and lower-limb neuromuscular status, respectively.

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Erratum. Match Running Performance in Australian Football Is Related to Muscle Fiber Typology

International Journal of Sports Physiology and Performance

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Specialize Early and Select Late: Performance Trajectories of World-Class Finalists and International- and National-Class Swimmers

Dennis-Peter Born, Glenn Björklund, Jenny Lorentzen, Thomas Stöggl, and Michael Romann

Purpose: To investigate performance progression from early-junior to peak performance age and compare variety in race distances and swimming strokes between swimmers of various performance levels. Methods: Using a longitudinal data analysis and between-groups comparisons 306,165 annual best times of male swimmers (N = 3897) were used to establish a ranking based on annual best times at peak performance age. Individual performance trajectories were retrospectively analyzed to compare distance and stroke variety. Performances of world-class finalists and international- and national-class swimmers (swimming points: 886 [30], 793 [28], and 698 [28], respectively) were compared across 5 age groups—13–14, 15–16, 17–18, 19–20, and 21+ years—using a 2-way analysis of variance with repeated measures. Results: World-class finalists are not significantly faster than international-class swimmers up to the 17- to 18-year age group (F 2|774 = 65, P < .001, η p 2 = .14 ) but specialize in short- or long-distance races at a younger age. World-class breaststroke finalists show faster breaststroke times compared to their performance in other swimming strokes from an early age (P < .05), while world-class freestyle and individual medley finalists show less significant differences to their performance in other swimming strokes. Conclusions: While federation officials should aim for late talent selection, that is, not before the 17- to 18-year age group, coaches should aim to identify swimmers’ preferred race distances early on. However, the required stroke variety seems to be specific for each swimming stroke. Breaststroke swimmers could aim for early and strong specialization, while freestyle and individual medley swimmers could maintain large and very large stroke variety, respectively.

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Volume 18 (2023): Issue 12 (Dec 2023)

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Shifting the Energy Toward Los Angeles: Comparing the Energetic Contribution and Pacing Approach Between 2000- and 1500-m Maximal Ergometer Rowing

Daniel J. Astridge, Peter Peeling, Paul S.R. Goods, Olivier Girard, Sophie P. Watts, Myles C. Dennis, and Martyn J. Binnie

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, η p 2 = .70 ). There was a 4.9% (3.3%) increase (P < .001, η 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.

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Bringing on the Next Generation of Sport Scientists: The Benefits of Work-Integrated Learning

David B. Pyne

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Initial Maximum Push-Rim Propulsion and Sprint Performance in Elite Men’s Wheelchair Basketball

Aitor Iturricastillo, Jordi Sanchez-Grau, Gerard Carmona, Adrián García-Fresneda, and Javier Yanci

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 Pmax, Rel. Pmax, 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.

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Optimizing Wearable Device and Testing Parameters to Monitor Running-Stride Long-Range Correlations for Fatigue Management in Field Settings

Joel T. Fuller, Dominic Thewlis, Jodie A. Wills, Jonathan D. Buckley, John B. Arnold, Eoin Doyle, Tim L.A. Doyle, and Clint R. Bellenger

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.