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Performance-Determining Variables of a Simulated Sprint Cross-Country Skiing Competition

Rune Kjøsen Talsnes, Jan-Magnus Brattebø, Tore Berdal, Trine Seeberg, Knut Skovereng, Thomas Losnegard, Jan Kocbach, and Øyvind Sandbakk

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.

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Volume 18 (2023): Issue 11 (Nov 2023)

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Evolution of 1500-m Olympic Running Performance

Carl Foster, Brian Hanley, Renato Barroso, Daniel Boullosa, Arturo Casado, Thomas Haugen, Florentina J. Hettinga, Andrew M. Jones, Andrew Renfree, Philip Skiba, Alan St Clair Gibson, Christian Thiel, and Jos J. de Koning

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.

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A Comparison of Critical Speed and Critical Power in Runners Using Stryd Running Power

Cody R. van Rassel, Kate M. Sales, Oluwatimilehin O. Ajayi, Koki Nagai, and Martin J. MacInnis

Purpose: Although running traditionally relies on critical speed (CS) as an indicator of critical intensity, portable inertial measurement units offer a potential solution for estimating running mechanical power to assess critical power (CP) in runners. The purpose of this study was to determine whether CS and CP differ when assessed using the Stryd device, a portable inertial measurement unit, and if 2 running bouts are sufficient to determine CS and CP. Methods: On an outdoor running track, 10 trained runners ( V ˙ O 2 max , 59.0 [4.2] mL·kg−1·min−1) performed 3 running time trials (TT) between 1200 and 4400 m on separate days. CS and CP were derived from 2-parameter hyperbolic speed–time and power–time models, respectively, using 2 (CS2TT and CP2TT) and 3 (CS3TT and CP3TT) TTs. Subsequently, runners performed constant-intensity running for 800 m at their calculated CS3TT and CP3TT. Results: Running at the calculated CS3TT speed (3.88 [0.44] m·s−1) elicited an average Stryd running power (271 [28] W) not different from the calculated CP3TT (270 [28]; P = .940; d = 0.02), with excellent agreement between the 2 values (intraclass correlation coefficient = .980). The CS2TT (3.97 [0.42] m·s−1) was not higher than CS3TT (3.89 [0.44] m·s−1; P = .178; d = 0.46); however, CP2TT (278 [29] W) was greater than CP3TT (P = .041; d = 0.75). Conclusion: The running intensities at CS and CP were similar, supporting the use of running power (Stryd) as a metric of aerobic fitness and exercise prescription, and 2 trials provided a reasonable, albeit higher, estimate of CS and CP.

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Physiological Responses and Swimming-Performance Changes Induced by Altering the Sequence of Training Sets

Ioannis S. Nikitakis, Gregory C. Bogdanis, Giorgos P. Paradisis, and Argyris G. Toubekis

Purpose: Interval-training sets may be applied in a different sequence within a swimming training session. The aim of this study was to investigate the effect of different set sequences on performance and physiological responses in a training session. Methods: Twelve highly trained male swimmers performed 4 sessions in randomized order. Each session included a different combination of 2 training sets: set A–set C, set C–set A, set B–set C, or set C–set B. Set A consisted of 8 × 200 m at speed corresponding to lactate threshold (30-s recovery), set B included 8 × 100 m at maximum aerobic speed (30-s recovery), and set C included 4 × 50-m all-out swimming (2-min recovery). Performance and physiological responses (lactate concentration, pH, base excess, bicarbonate, heart rate, and heart-rate variability) were measured. Results: Performance in each set was similar between sessions irrespective of set sequence. Blood lactate, heart rate, and acid–base responses during set C were similar in all sessions, but blood lactate was higher in sets A and B during C–A and C–B sessions (P = .01). The overall blood lactate and acid–base response was higher in C–A and C–B sessions compared with A–C and B–C sessions, respectively (P = .01). Heart-rate variability in each set, separately as well as the overall session effect, did not differ and was thus independent to the set sequence applied. Conclusions: Training sessions including all-out swimming as a first set increase the magnitude of metabolic responses to the subsequent aerobic-dominated training set.

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Load–Velocity Profile and Active Drag in Young Female Swimmers: An Age-Group Comparison

Christina Wettengl, Rebecca Karlsson, Bjørn H. Olstad, and Tomohiro Gonjo

Purpose: The present study aimed to establish differences in load–velocity profiling, active drag (AD), and drag coefficient (Cd) between 3 age groups of female swimmers. Methods: Thirty-three swimmers (11, 13, or 16 y old) were recruited. The individual load–velocity profile was determined for the 4 competitive swimming strokes. The maximal velocity (V0), maximal load (L0), L0 normalized to the body mass, AD, and Cd were compared between the groups. A 2-way analysis of variance and correlation analysis were conducted. Results: Compared with their younger counterparts, 16-year-old swimmers generally had larger V0, L0, and AD, which was particularly evident when comparing them with 11-year-old swimmers (P ≤ .052). The exception was breaststroke, where no differences were observed in L0 and AD and Cd was smaller in the 16-year-old group than the 11-year-old group (P = .03). There was a negative correlation between Cd and V0 for all groups in backstroke (P ≤ .038) and for the 11-year-old group and 13-year-old group in breaststroke (P ≤ .022) and front crawl (P ≤ .010). For the 16-year-old group, large correlations with V0 were observed for L0, L0 normalized to the body mass, and AD (P ≤ .010) in breaststroke and for L0 and AD with V0 in front crawl (P ≤ .042). In butterfly, large negative correlations with V0 were observed in the 13-year-old group for all parameters (P ≤ .027). Conclusions: Greater propulsive force is likely the factor that differentiates the oldest age group from the younger groups, except for breaststroke, where a lower Cd (implying a better technique) is evident in the oldest group.

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Thermal Sensation After the 10-km Open-Water Swimming in Cool Water Depends on the Skin’s Thermal Sensitivity Rather Than Core Temperature

Tomomi Fujimoto, Yuiko Matsuura, Yasuhiro Baba, and Reira Hara

Purpose: To assess the core temperature fluctuations during 10-km open-water swimming (OWS) in cool water and the relationship between thermal sensation (TS) after 10-km OWS, core temperature, and local skin thermal sensitivity. Methods: Nine highly trained OWS swimmers (4 female; age 22 [3] y) completed a single 10-km trial in cool water (22.3 °C) wearing swimsuits for OWS. During the trial, core temperature was continuously recorded via ingestible temperature sensors, and TS after trial was also measured. Then, local skin warm/cool sensitivity was measured in the forearm. Results: All swimmers completed the 10-km OWS. Mean swimming speed for males and females were 1.39 (1.37–1.42 m/s) and 1.33 m/s (1.29–1.38 m/s), respectively. Core temperature increased in 8 out of 9 swimmers during 10-km OWS (P = .047), with an average increase of 0.8 °C. TS after 10-km OWS varied among swimmers. There were no correlations between post-OWS TS and post-OWS core temperature (P = .9333), whereas there was a negative correlation between post-OWS TS and local skin cool sensitivity (P = .0056). Conclusion: These results suggest that core temperature in elite swimmers might not decrease during 10-km OWS in the cool water temperature of official OWS. In addition, individual differences in TS after 10-km OWS may be related to skin cool sensitivity rather than core temperature.

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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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Performance Management in Elite Football: A Teamwork Modeling Approach

Joao Marques and Karim Chamari

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The Force–Velocity Profiling Concept for Sprint Running Is a Dead End

Gertjan Ettema

Purpose: In this commentary, I present arguments against the use of the force–velocity profiling concept in design and adaptations of training programs targeting sprinting. The purpose of this commentary is to make sports practitioners more aware of the rationale behind the concept and explain why it does not work. Rationale: Force–velocity profiling is a mathematical way to present the velocity development during sprint behavior. Some details of this behavior may be accentuated by transforming it to other variables, but it does not add any new information about sprint performance. Thus, contrary to what is often claimed, the force–velocity profile does not represent maximal capacities (ability of force and velocity generation) of the athlete. It is claimed that through force–velocity profiling one may identify the optimal ratio of force and velocity capacities. Furthermore, proponents of the force–velocity profiling concept suggest that through directed training force and velocity capacities can be altered (inversely dependent) to obtain this optimal ratio, without changing the capacity to express power. Fundamentally, this idea is unfounded and implausible. Conclusion: At best, force–velocity profiling may be able to identify between-athletes differences. However, these can be more easily deduced directly from performance time traces.