Dataset Information

Pacing, Exercise Intensity, and Technique by Performance Level in Long-Distance Cross-Country Skiing.

ABSTRACT: Introduction:Long-distance cross-country skiing (XCS) has gained increased popularity within the past decades. However, research about long-distance XCS is limited; therefore, the aim of this study was to analyze the intensity distribution, technique application, and pacing strategies during long-distance XCS racing. Methods:Heart rate (HR) and section skiing speeds of 9 elite (ranked 1-100) and 10 amateur skiers (ranked 101-1,500) during the 90-km Vasaloppet race were collected. In addition, during the first uphill, the first 1,000 skiers were video-recorded to analyze the applied skiing strategy (e.g. grip-waxed skis versus exclusive double poling). Results:Mean race intensity was 82% of maximal HR and was not different between performance groups even though elite skiers skied ?15% faster than amateurs. There was an interaction effect of section × group with a pronounced decrease in HR in amateurs compared with more even pacing in elite skiers (0.13 vs. 0.04% decrease/km) and skiing at higher percentage in the high-intensity zones in elite compared with amateurs (46 vs. 24%). Ninety-eight percent of the top 100 skiers and 59% of the first 1,000 skiers used exclusively double poling. Conclusion:Elite and amateur skiers ski at comparable mean race exercise intensity, but they have clear differences in skiing speed. The difference in the pacing profiles between elite and amateur skiers (more even vs. distinct positive pacing) demonstrate the greater capacity of the former with respect to physiological capacity and highlights that amateurs seem to start too fast according to their capacities. The exclusive application of the double poling technique is no longer a phenomenon of elite skiers but is widely used among the top 1,000 ranked skiers.

SUBMITTER: Stoggl TL

PROVIDER: S-EPMC7033470 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pacing, Exercise Intensity, and Technique by Performance Level in Long-Distance Cross-Country Skiing.

Stöggl Thomas L TL Hertlein Markus M Brunauer Richard R Welde Boye B Andersson Erik P EP Swarén Mikael M

Frontiers in physiology 20200214

<h4>Introduction</h4>Long-distance cross-country skiing (XCS) has gained increased popularity within the past decades. However, research about long-distance XCS is limited; therefore, the aim of this study was to analyze the intensity distribution, technique application, and pacing strategies during long-distance XCS racing.<h4>Methods</h4>Heart rate (HR) and section skiing speeds of 9 elite (ranked 1-100) and 10 amateur skiers (ranked 101-1,500) during the 90-km Vasaloppet race were collected. ...[more]

PMID: 32116742

Similar Datasets

Project description:Cross-country skiing is a popular Olympic winter sport, which is also used extensively as a recreational activity. While cross-country skiing primarily is regarded as a demanding endurance activity it is also technically challenging, as it contains two main styles (classical and skating) and many sub-techniques within these styles. To further understand the physiological demands and technical challenges of cross-country skiing it is imperative to identify sub-techniques and basic motion features during training and competitions. Therefore, this paper presents features for identification and assessment of the basic motion patterns used during classical-style cross-country skiing. The main motivation for this work is to contribute to the development of a more detailed platform for comparing and communicating results from technique analysis methods, to prevent unambiguous definitions and to allow more precise discussions and quality assessments of an athlete's technical ability. To achieve this, our paper proposes formal motion components and classical style technique definitions as well as sub-technique classifiers. This structure is general and can be used directly for other cyclic activities with clearly defined and distinguishable sub-techniques, such as the skating style in cross country skiing. The motion component features suggested in our approach are arm synchronization, leg kick, leg kick direction, leg kick rotation, foot/ski orientation and energy like measures of the arm, and leg motion. By direct measurement, estimation, and the combination of these components, the traditional sub-techniques of diagonal stride, double poling, double poling kick, herringbone, as well as turning techniques can be identified. By assuming that the proposed definitions of the classical XC skiing sub-techniques are accepted, the presented classifier is proven to map measures from the motion component definitions to a unique representation of the sub-techniques. This formalization and structure may be used on new motion components, measurement principles, and classifiers, and therefore provides a framework for comparing different methodologies. Pilot data from a group of high-level cross-country skiers employing inertial measurement sensors placed on the athlete's arms and skis are used to demonstrate the approach. The results show how detailed sub-technique information can be coupled with physical, track, and environmental data to analyze the effects of specific motion patterns, to develop useful debriefing tools for coaches and athletes in training and competition settings, and to explore new research hypotheses.

Project description:The purposes of our study were to investigate the physiological and biomechanical responses to low-intensity (LI) and high-intensity (HI) roller ski skating on varying terrain and compare these responses between training intensities. Nine elite male skiers performed treadmill roller skiing consisting of two 21 min sessions (7 × 3 min laps) at LI and HI with the same set inclines and intensity-dependent speeds (LI/HI: distance: 5.8/7.5 km, average speed: 16.7/21.3 km/h). Physiological and biomechanical variables were measured continuously, and each movement cycle and sub-technique employed were detected and classified with a machine learning model. Both the LI and HI sessions induced large terrain-dependent fluctuations (relative to the maximal levels) in heart rate (HR, 17.7 vs. 12.2%-points), oxygen uptake ( V.O2 , 33.0 vs. 31.7%-points), and muscle oxygen saturation in the triceps brachii (23.9 vs. 33.4%-points) and vastus lateralis (12.6 vs. 24.3%-points). A sub-technique dependency in relative power contribution from poles and skis exhibited a time-dependent shift from Lap 1 to Lap 7 toward gradually more ski power (6.6 vs. 7.8%-points, both p < 0.01). The terrain-dependent fluctuations did not differ between LI and HI for V.O2 (p = 0.50), whereas HR fluctuated less (p < 0.01) and displayed a time-dependent increase from Lap 2 to Lap 7 (7.8%-points, p > 0.01) during HI. Oxygen saturation shifted 2.4% points more for legs than arms from LI to HI (p > 0.05) and regarding sub-technique, 14.7% points more G3 on behalf of G2 was employed on the steepest uphill during HI (p < 0.05). Within all sub-techniques, cycle length increased two to three times more than cycle rate from LI to HI in the same terrains, while the corresponding poling time decreased more than ski contact time (all p > 0.05). In sum, both LI and HI cross-country (XC) skiing on varying terrain induce large terrain-dependent physiological and biomechanical fluctuations, similar to the patterns found during XC skiing competitions. The primary differences between training intensities were the time-dependent increase in HR, reduced relative oxygen saturation in the legs compared to the arms, and greater use of G3 on steep uphill terrain during HI training, whereas sub-technique selection, cycle rate, and pole vs. ski power distribution were similar across intensities on flat and moderately uphill terrain.

Dataset Information

Pacing, Exercise Intensity, and Technique by Performance Level in Long-Distance Cross-Country Skiing.

Publications

Pacing, Exercise Intensity, and Technique by Performance Level in Long-Distance Cross-Country Skiing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets