Upper body power as a determinant of classical cross-country ski performance

Alsobrook, Nathan G.; Heil, Daniel P.

doi:10.1007/s00421-008-0943-z

Cited by 40 publications

(37 citation statements)

References 29 publications

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“…As both aerobic (Ingjer, 1991;Rusko, 1987) and upper body power (Alsobrook & Heil, 2009; Mahood, Kenefik, Kertzer., & Quinn, 2001; Nesser, Chen, Serfass., & Gaskill, 2004) correlate well with cross-country skiing performance, periodical assessment of these variables may indicate a skier's current performance capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Several upper body sprint and endurance tests using customised ski-ergometer protocols reportedly predict double poling performance on snow ( . However, these protocols often use a modified Concept II rowing ergometer to replicate the double poling technique (Holmberg & Nilsson, 2008;Alsobrook & Heil, 2009). To our knowledge, no studies to date have evaluated an incremental double poling protocol using the now commercially available Concept II upright ski-ergometer.…”

Section: Introductionmentioning

confidence: 99%

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Validity and reliability of an incremental double poling protocol in cross-country skiers

Govus¹,

Marsland²,

Martin³

et al. 2015

jhse

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Govus, A., Marsland, F., Martin, D., & Chapman, D. (2015). Validity and reliability of an incremental double poling protocol in cross-country skiers. J. Hum. Sport Exerc., 10(3), pp.827-834. This study determined the validity and reliability of an incremental double poling protocol performed on a Concept II ski-ergometer and validated this against an existing treadmill ski-striding protocol. Ten well-trained male cross-country skiers (age: 19 ± 1.4 y; height: 182 ± 72 cm; body mass: 76.0 ± 10.8 kg, whole body VO2Peak: 5.2 ± 1.0 L.min -1 ; upper body VO2Peak: 4.6 ± 1.0 L.min -1 ; upper body:lower body ratio: 87.2 ± 5.6%) performed four VO2Peak tests; one treadmill ski-striding test and three double poling ski-ergometer tests over five days. Test-retest reliability of the ski-ergometer protocol was determined for maximal oxygen consumption (VO2peak). The ski-ergometer test showed excellent reliability for VO2Peak (L.min -1 ) (coefficient of variation [CV] = 1.9%, 95% confidence limit [95% CL] [1.2, 4.7]; r = 1.00, [0.96, 1.00]) and UBPPeak (W) (CV = 1.4%, [0.9, 3.4]; r = 1.00, [0.97, 1.00]). Very strong correlations existed between the ski-ergometer and the ski-striding protocol for VO2Peak (r = 0.95, [0.76, 0.99]). The upper body ski-ergometer test provided valid and reliable measurements of ski-specific upper body aerobic power in well-trained male cross-country skiers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validity and reliability of an incremental double poling protocol in cross-country skiers

Govus¹,

Marsland²,

Martin³

et al. 2015

jhse

View full text Add to dashboard Cite

show abstract

“…Studies have indicated that elite cross-country skiers have highest values in endurance athletes [5,6]. The relevance of VO2max, time to exhaustion, lactate thresholds, upper body power output, and peak upper body oxygen consumption for cross-country skiers has been also confirmed by some studies showing a relationship between these test variables and competitive performance capacity of cross-country skiing [7,8].…”

Section: Introductionmentioning

confidence: 58%

Roller-Ski Aerobic High-Intensity Interval Training Improves the VO2max and Anaerobic Power in Cross-Country Skiers

Ateş

Çetin

2017

Int J Appl Exerc Physiol

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ABSTRACT:The purpose of this study was to investigate the 8-week of roller-ski aerobic highintensity interval training on aerobic and anaerobic power in cross-country skiers. 10 male (age, 18.3±2.1 years; height, 171.3±4.12 cm; weight, 61.4±6.28 kg) and 8 female (age, 16.1±0.3 years; height, 158.3±6.47 cm; weight, 49.3±0.7 kg) junior cross-country skiers completed the study. All skiers performed 2x2-km all-out uphill intervals with roller-skis, 3 times a week, in addition to their traditional training program. Measurements included maximal oxygen consumption (VO2max), anaerobic power, and also for 2-km time-trial performance. All values were listed as pre-to post-test mean (±SD), significant level, and percentage changes (%). Pre-to post-testing changes in VO2max, anaerobic power, and also 2-km time-trial performance were significantly higher during all post-test trials in all groups (p<0.005). As a result, we suggest that the skiers should integrate the roller-ski aerobic high-intensity interval uphill models in their training programs for improving performance.

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“…Before exercising a single 60 seconds test, subjects relaxed for an additional 5 minutes. After that, subjects performed a single 60 seconds test during which the aim was to achieve the maximum average power output over the entire 60 seconds when starting from the last stop (Alsobrook & Heil, 2009).…”

Section: Dataset Generationmentioning

confidence: 99%

“…Moreover, there is no standardization in measurement protocol of UBP. Consequently, it may be advantageous to predict rather than measure UBP using actual training data previously collected by tests on ergometers (Alsobrook & Heil, 2009). …”

Section: Introductionmentioning

confidence: 99%

On the Application of Artificial Intelligence and Feature Selection in Sports Science Education and Research: A Case Study

Akay

Özçiloğlu

Heil

2017

PROSOC

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In sports science education and research, the use of artificial intelligence methods along with feature selection algorithms can be of great help for developing prediction models where experimental studies based on measurements are not feasible. In this paper, we present a case study in regards to how sports science can benefit from the use of artificial intelligence methods combined with a feature selection algorithm. More specifically, the purpose of our study is to develop prediction models for upper body power (UBP), which is one of the most important factors affecting the performance of cross-country skiers during races. The dataset, which includes 75 subjects, was obtained from the College of Education, Health and Development of Montana State University. Multilayer Perceptron (MLP) and Single Decision Tree (SDT) along with the minimum-redundancy maximum-relevance (mRMR) feature selection algorithm were used to produce prediction models for predicting the 10-second UBP (UBP 10 ) and 60-second UBP (UBP 60 ). The predictor variables in the dataset are protocol, gender, age, body mass index (BMI), maximum oxygen uptake (VO 2 max), maximum heart rate (HRmax), time and heart rate at lactate threshold (HRLT) whereas UBP 10 and UBP 60 are the target variables. Based on the ranking scores of predictor variables assigned by the mRMR, 16 different prediction models have been developed. By using 10-fold cross-validation, the efficiency of the prediction models has been calculated with their multiple correlation coefficients (R's) and standard error of estimates (SEE's). The results show that using less amount of predictor variables than the full set of predictor variables can be useful for prediction of UBP 10 and UBP 60 with comparable error rates. The model consisting of the predictor variables gender, BMI, VO 2 max, HRLT and time yields the lowest SEE's for prediction of UBP 10 , while the model including the predictor variables gender, age, BMI and VO 2 max gives the lowest SEE's for prediction of UBP 60 , whichever regression method is used. Using these two models instead of the full set of predictor variables yields up to 4.95% and 6.83% decrement rates in SEE's for MLP and SDT based UBP prediction models, respectively.

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Upper body power as a determinant of classical cross-country ski performance

Cited by 40 publications

References 29 publications

Validity and reliability of an incremental double poling protocol in cross-country skiers

Validity and reliability of an incremental double poling protocol in cross-country skiers

Roller-Ski Aerobic High-Intensity Interval Training Improves the VO2max and Anaerobic Power in Cross-Country Skiers

On the Application of Artificial Intelligence and Feature Selection in Sports Science Education and Research: A Case Study

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