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Introduction: Predicting competitive alpine skiing performance using conventional statistical methods has proven challenging. Many studies assessing the relationship between physiological performance and skiing outcomes have employed statistical methods of questionable validity. Furthermore, the reliance on Fédération Internationale de Ski (FIS) points as a performance outcome variable presents additional limitations due to its potential unreliability in reflecting short-term, sport-specific performance. These factors complicate the selection of appropriate tests and the accurate prediction of competitive outcomes.

Method: This study aimed to evaluate the predictive power of a generalized physiological test battery for alpine skiing performance, as measured by FIS points, utilizing multivariable data analysis (MVDA). Physiological test results from a total of twelve (n = 12) world-class female skiers were included in the analysis.

Results: The result on goodness of regression (R2) and goodness of prediction (Q2) in this study indicate that valid Orthogonal Projection to Latent Structures (OPLS) models for both Slalom and Giant Slalom can be generated (R2 = 0.39 to 0.40, Q2 = 0.21 to 0.15), but also that competition performance still cannot be predicted at a group level (low Q2). In contrast, higher predictive power of competitive performance was achieved on an individual level using the same data (R2 = 0.88 to 0.99 and Q2 = 0.64 to 0.96).

Discussion: The findings of this investigation indicate that the selected tests employed in this study exhibit limited generalizability for the assessment of elite alpine skiers, as the predictive value of specific physiological parameters on competitive performance appears to be highly athlete-dependent.

BACKGROUND: Early, objective prognostication after aneurysmal subarachnoid haemorrhage (aSAH) is difficult. A biochemical marker would be desirable. Correlation has been found between levels of the protein S100 beta (S100B) and outcome after aSAH. Timing and clinical usefulness are under investigation.

METHODS: Eighty-nine patients admitted within 48 h of aSAH were included. Modified ranking scale (mRS), EuroQoL health-related quality of life measure (EQ-5D_index) and EuroQoL visual analogue scale (EQ-VAS) values were evaluated after 1 year. S100B was measured in blood samples collected at admission and up to day 10.

RESULTS: S100B correlated significantly with EQ-5D_index and mRS, but not EQ-VAS at 1 year after aSAH. A receiver operating characteristic analysis for peak S100B values (area under the curve 0.898, 95% confidence interval 0.828-0.968, p < 0.0001), with a cutoff of 0.4 μg/l, yielded 95.3% specificity and 68% sensitivity for predicting unfavourable outcome. Dichotomized S100B (> 0.4 μg/l vs ≤ 0.4 μg/l), age and Hunt and Hess grading scale score (HH) were associated with unfavourable mRS outcome in univariate logistic regression analysis. Dichotomized S100B was the only variable independently correlated with unfavourable mRS outcome in a multivariate logistic regression analysis.

CONCLUSIONS: For the first time, S100B was shown to correlate with mRS and health-related quality of life at 1 year after aSAH. Peak S100B can be used as a prognostic factor for unfavourable outcome measured as dichotomized mRS after aSAH. A peak value cutoff of 0.4 μg/l is suggested. Ethical approval no: 2013/366-31, 4th of February 2014.

Competitive alpine skiing is a complex sport that requires high physical and technical competence. Testing the physical status of athletes may be important to increase their ability to achieve elite sport-specific performance. This study aimed to investigate the predictive power of the national test battery of the Swedish Olympic Committee (Fysprofilen) and anthropometric variables in the prediction of competitive performance of elite alpine skiers, indicated by Fédération Internationale de Ski points. Data from fourteen Swedish elite female alpine skiers were analyzed using bivariate and multivariate statistical methods. Physiological test results and anthropometric data could not generate significant bivariate or multivariate models for prediction of competitive performance. Multivariate regression (R2) and prediction (Q2) models for Fédération Internationale de Ski Slalom and Giant Slalom rank reached R2=0.27 to 0.43, Q2=+− 0.8 to−0.17, indicating no valid models. The overall interpretation of these and previous findings are that future test batteries must be validated before implemented, and that test results should be treated with caution when it comes to prediction of future competitive results. Applying tests that are not validated against competitive performance risk misleading coaches and training advisors who aim to increase the sports-specific performance of the individual athlete.

Introduction: The overall aim of this doctoral thesis was to identify physiological and anthropometric variables valid for prediction of competitive performance in alpine skiing (indicated by FIS points).

Method: Paper I-III in this doctoral thesis followed an experimental, hypothesis-generating design which included both junior and senior elite alpine skiers. In all papers, physiological and anthropometric test results (X-variables) were correlated with FIS points (Y-variables) in order to investigate the predictive power of physiological and anthropometric variables for competitive performance in alpine skiing. The significance of the included test results was examined using bivariate and multivariate data analysis.

Results: The results of Paper I show that included aerobic test results, neither alone nor in combination with anthropometric variables, could predict competitive performance of junior elite alpine skiers. Principal component analysis shows that male and female junior alpine skiers could be separated based on test results but that none of the included tests were important for sport-specific performance. The best multivariate models reached R² = 0.51 to 0.86 and Q² = -0.73 to 0.18. While several significant regression models could be observed, none of these met the criteria for valid models. The lack of predictive power of observed prediction models was confirmed by cross-validation. The results of Paper II show that included physiological test results from the test battery Fysprofilen could not predict competitive performance of senior elite female alpine skiers. Principal component analysis shows that there is a high correlation between individual physiological test results and their corresponding Fysprofilen score points, indicating that they can be used interchangeably. The Mann-Whitney U test was not significant neither for SL nor for GS. This suggests that Fysprofilen score points (summarized as Fysprofilen Index) and competitive performance (indicated by FIS points) are independent. The best multivariate models for SL and GS reached R² = 0.27 to 0.43 and Q² = - 0.8 to - 0.17, indicating low predictive power for competitive performance (as confirmed by cross-validation). The results of Paper III show that included physiological test results from a novel test battery could not predict competitive performance of senior elite female alpine skiers on a group level. When data were analyzed on a group level, the best models for SL and GS reached R² = 0.39 to 0.40, Q² = 0.15 to 0.21, indicating low predictive power. In contrast, when data were analyzed on an individual level, valid models with high predictive power (R² = 0.88 to 0.99 and Q² = 0.64 to 0.96) were generated. A comparative analysis between individual multivariate models shows that the relative importance of different physiological qualities for athletic performance varies between skiers.

Conclusion: When applying tests on alpine skiers, a holistic approach should be considered. This because competitive performance in alpine skiing is the result of a number of interacting dimensions. Before applying physiological tests, the validity and reliability of the test protocols must also be determined. Administering tests that do not meet these criteria will probably waste not only important resources for clubs and ski federations but also risk misleading coaches and athletes when planning and implementing preparatory training.

The aim of this study was to investigate the predictive power of aerobic test results and anthropometric variables on FIS-ranking of junior elite alpine skiers. Results from twenty-three male and female adolescent elite alpine skiers from two seasons were included in the multivariate statistical models. Physical work capacity was determined by V̇O2peak, blood lactate concentration ([HLa]b), and heart rate (HR) during ergometer cycling. Anthropometric variables were body stature, body weight and calculated BMI. No significant correlation between competitive performance and aerobic work capacity or anthropometric data was observed neither in male nor female adolescent skiers. Pre-season physical tests and anthropometric data could therefore not predict end-season FIS-ranking. The best regression (R2) and prediction (Q2) models of FIS slalom (SL) and giant slalom (GS) rank reached R2=0.51 to 0.86, Q2=−0.73 to 0.18, indicating no valid models. This study could not establish V̇O2peak and other included variables as predictors of competitive performance. When combining results from commonly used tests for alpine skiers, and applying multivariate statistical models, investigated tests seems of limited used for athletes, coaches, and ski federations. Performance-specific pre-season tests must be developed and validated for prediction of performance and guidance of exercise training.