Competitive Performance Prediction of Elite Alpine Skiers1317167/FULLTEXT03.pdf · Competitive...

Competitive Performance

Prediction of Elite Alpine Skiers

Robert Nilsson

Department of Community Medicine and Rehabilitation

Section of Sports Medicine

Umeå 2019

Responsible publisher under Swedish Law: The Dean of the Medical Faculty This work is protected by the Swedish Copyright Legislation (Act 1960:729) Dissertation for PhD ISBN: 978-91-7855-079-1 ISSN: 0346-6612 Electronic version available at: http://umu.diva-portal.org/ Printed by: CityPrint i Norr AB Umeå, Sweden 2019

I dedicate this dissertation to my parents, who have always

believed in me and through their love and support made the

completion of this dissertation possible.

i

Table of Contents

Abstract ............................................................................................ iii

Abbreviations .................................................................................... v

Sammanfattning på svenska ............................................................ vi

List of publications ......................................................................... viii

Introduction ....................................................................................... 1

Background ....................................................................................... 3 Competitive alpine skiing .................................................................................................3

Slalom .........................................................................................................................3 Giant Slalom ...............................................................................................................3 How competitive performance is quantified ........................................................... 4

The competitive season and structuring of on-snow training ....................................... 4 On-snow training ...................................................................................................... 4

Characteristics of alpine skiers ........................................................................................ 5 The career of elite alpine skiers ................................................................................. 5 Body composition and skiing performance .............................................................. 5

Forces, muscular activity and movement speed during skiing ...................................... 6 Forces ......................................................................................................................... 6 Muscular activity ...................................................................................................... 6 Isometric and eccentric muscle strength .................................................................. 7 Movement speed ......................................................................................................... 7

Aerobic and anaerobic capacity ...................................................................................... 8 Balance ............................................................................................................................. 9 Core strength and core stability ..................................................................................... 10 Flexibility ........................................................................................................................ 10 Physiological testing in alpine skiing ............................................................................. 11 Summary ......................................................................................................................... 16

The rationale for the thesis .............................................................. 17

Aims ................................................................................................. 18 Specific Aims ................................................................................................................... 18

Materials and Methods .................................................................... 19 Study design .................................................................................................................... 19 Participants .................................................................................................................... 20 Testing procedures ......................................................................................................... 21

One Repetition Maximum tests ............................................................................... 21 Hand Grip Strength ................................................................................................ 22 Pull-ups .................................................................................................................... 22 Brutal Bench ............................................................................................................ 23 Change of Direction Speed ...................................................................................... 23 20m Sprint test ........................................................................................................ 23 Jump tests ................................................................................................................ 23 Force-Velocity Bike Test .......................................................................................... 24

ii

Y Balance Test™ ...................................................................................................... 24 Lateral Box Jump 90-s ............................................................................................ 25 500 meters Rowing Ergometer Test ...................................................................... 25 Peak Oxygen Uptake ............................................................................................... 25 Anthropometric tests ............................................................................................... 26

Fédération Internationale de Ski points ....................................................................... 26 Statistics .......................................................................................................................... 27

Bivariate analysis ................................................................................................... 28 Multivariate analysis .............................................................................................. 28

Ethical statement ........................................................................................................... 29

Results ............................................................................................ 30 Paper I ............................................................................................................................ 30 Paper II ........................................................................................................................... 31 Paper III ......................................................................................................................... 33

Discussion ....................................................................................... 34 Why is there a lack of predictive power on a group level? ........................................... 34 How important is the physiological dimension? .......................................................... 36 Is there an assumption of validity? ............................................................................... 38 Validity and reliability of physiological tests in alpine skiing ...................................... 38 Methodological considerations ..................................................................................... 40

Other dimensions .................................................................................................... 40 Menstrual cycle effects ............................................................................................. 41 Other predictive modeling procedures ................................................................... 41 More tests specifically designed for alpine skiers .................................................. 41 Fédération Internationale de Ski points ................................................................. 41

The novel approach of this thesis .................................................................................. 42 Application in sports ..................................................................................................... 42

Conclusion ...................................................................................... 43

Acknowledgments ........................................................................... 44

References ...................................................................................... 46

iii

Abstract

Introduction Competitive alpine skiing is a multifaceted and challenging sport

that places high demands on its practitioners. To achieve competitive success,

alpine skiers must master a variety of physical, technical, mental and social skills.

Among the physiological qualities that have been described as necessary for elite

performance in alpine skiing are qualities such as muscular strength and

endurance, power, aerobic and anaerobic capacity, balance, core strength and

core stability as well as flexibility. Testing these qualities can therefore be

important, not only in order to optimize physiological training but ultimately to

maximize the sport-specific performance of the individual athlete. Although

several individual tests have been shown to correlate with skiing performance,

the combined importance of a selection of tests, referred to as "test batteries", has

rarely been investigated. As a consequence, these test batteries risk misleading

both athletes and coaches when planning and implementing preparatory

training. Also, by using results from these tests, prediction of future performance

is difficult. To generate useful results, a test protocol must be valid and reliable.

Without taking this into account, significant resources seem to be invested in

non-relevant tests each year, wasting important resources for clubs and

federations as well as valuable time for athletes and coaches. Therefore, the

development of valid test protocols is essential for all sports. 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

iv

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

OPLS 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 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.

v

Abbreviations

1RM One Repetition Maximum

BMI Body Mass Index

C Combined

CMJ Countermovement Jump

CMJa Countermovement Jump with arm-swing

DXA Dual-Energy X-ray Absorptiometry

DH Downhill

FIS Fédération Internationale de Ski

GS Giant Slalom

GRF Ground Reaction Force

MVDA Multivariate Data Analysis

OPLS Orthogonal Projections to Latent Structures

PCA Principal Component Analysis

SC Super Combined

SIMCA Soft Independent Modeling of Class Analogy

SL Slalom

SG Super Giant Slalom

SOC Swedish Olympic Committee

V̇O2max Maximal Oxygen Uptake

V̇O2peak Peak Oxygen Uptake

vi

Sammanfattning på svenska

Introduktion Tävlingsmässig alpin skidåkning är en mångfacetterad och

utmanande idrott som ställer höga krav på dess utövare. För att uppnå

tävlingsmässig framgång måste alpin skidåkare uppvisa prov på en rad olika

fysiologiska, tekniska, mentala och sociala färdigheter. Bland de fysiologiska

egenskaper som har beskrivits som viktiga för prestationsförmågan i alpin

skidåkning återfinns kvalitéer som exempelvis muskelstyrka och uthållighet,

power, aerob och anaerob kapacitet, balans, bålstyrka och bålstabilitet samt

flexibilitet. Att testa dessa kvalitéer kan därför vara viktigt, inte bara för att

optimera fysiologisk träning utan också för att maximera idrottarens

grenspecifika prestationsförmåga. Även om enskilda fysiologiska tester har visat

sig korrelera med prestationsförmåga på skidor har betydelsen av kombinationer

av tester (ofta benämnda som "testbatterier") sällan undersökts. På grund av

detta riskerar dessa testbatterier att vilseleda både idrottare och tränare vid

planering och tillämpning av förberedande träning. Dessutom är möjligheten att

predicera idrottsspecifik prestationsförmåga med hjälp av testresultaten låg. För

att generera praktiskt användbara resultat bör testprotokoll ha hög validitet och

reliabilitet. Trots detta tycks ändå betydande resurser satsas på icke relevanta

tester varje år, något som slösar med viktiga ekonomiska medel för klubbar och

förbund likväl som värdefull tid för idrottare och tränare. Av denna anledning är

identifieringen av valida och reliabla testprotokoll viktigt för alla idrotter. Det

övergripande syftet med denna doktorsavhandling var att identifiera fysiologiska

och antropometriska variabler valida för prediktion av tävlingsmässig

prestationsförmåga i alpin skidåkning (indikerad av FIS-punkter).

Metod Studie I-III som ingår i denna doktorsavhandling följde en experimentell,

hypotesgenererande design som inkluderade alpin skidåkare på både junior och

senior elitnivå. I samtliga studier korrelerades fysiologiska och antropometriska

testresultat (X-variabler) med FIS-punkter (Y-variabler) i syfte att undersöka

deras prediktiva förmåga för tävlingsmässig prestanda i alpin skidåkning.

Betydelsen av inkluderade testresultat undersöktes med hjälp av bivariat och

multivariat dataanalys.

Resultat Resultaten av Studie I visar att inkluderade aeroba testresultat, varken

enskilt eller i kombination med antropometriska variabler, kunde predicera

tävlingsmässig prestationsförmåga hos vare sig manliga eller kvinnliga alpin

skidåkare på junior elitnivå. Tillämpningen av principalkomponentanalys visade

att manliga och kvinnliga junior alpin skidåkare kan separeras med hjälp av deras

fysiologiska testresultat men att ingen av de undersökta variablerna var

diskriminerande för idrottsspecifik prestationsförmåga. Resultaten av Studie II

visar att de inkluderade testresultaten från testbatteriet Fysprofilen inte kunde

vii

predicera tävlingsmässig prestationsförmåga hos kvinnliga alpin skidåkare på

senior elitnivå. Tillämpningen av principalkomponentanalys visade att det

föreligger en hög korrelation mellan fysiologiska testresultat och

korresponderande Fysprofilen poäng, något som indikerar att de kan användas

som alternativ för varandra. Mann-Whitney U testet var inte signifikant för vare

sig SL eller för GS, något som tyder på att Fysprofilen poäng (summerat som

Fysprofilen Index) och tävlingsmässig prestationsförmåga (indikerat av FIS-

punkter) är oberoende av varandra. Resultaten av Studie III visar att det

applicerade testbatteriet inte genererade multivariata modeller med hög

prediktiv förmåga för vare sig SL eller GS när betydelsen av inkluderade variabler

analyserades på gruppnivå. När data analyserades på individuell nivå kunde

emellertid valida modeller med hög predikativ förmåga för både SL och GS

genereras. En jämförelseanalys mellan individuella OPLS modeller visar att den

relativa betydelsen av olika fysiologiska delkvalitéer varierar mellan olika

utövare.

Slutsats Vid tillämpning av tester på alpin skidåkare bör en holistisk ansats

övervägas. Detta eftersom tävlingsmässig prestationsförmåga i alpin skidåkning

är ett resultat av ett flertal interagerande dimensioner. Innan fysiologiska tester

appliceras bör validiteten och reliabiliteten hos testprotokollen fastställas. Att

administrera tester som inte uppfyller dessa kriterier kommer sannolikt inte bara

slösa med viktiga resurser för klubbar och skidförbund utan riskerar även att

vilseleda tränare och idrottare vid planering och implementering av förberedande

träning.

viii

List of publications

This doctoral thesis is based on the following original articles. They will be referred to by their roman numerals. Paper I Nilsson R, Lindberg A-S, Theos A, Ferguson RA, Malm C (2018).

Aerobic Variables for Prediction of Alpine Skiing Performance – A

Novel Approach. SMIO 2(4): E105–E112

Paper II Nilsson R, Theos A, Lindberg A-S, Ferguson RA, Malm C. Lack

of Predictive Power in Commonly Used Tests for Performance in

Alpine Skiing. Submitted

Paper III Nilsson R, Theos A, Lindberg A-S, Ferguson RA, Malm C.

Individual Profiling for Prediction of Competitive Performance in

Alpine Skiing. In manuscript

The scientific work in this thesis is reprinted with permission from the original

publisher.

1

Introduction

Competitive alpine skiing is a multifaceted and challenging sport that places high

demands on its practitioners [134,149]. To achieve competitive success, alpine

skiers must master a variety of physical, technical, mental and social skills [46].

During skiing, athletes may be subjected to immense physiological strains

[78,113] and must, therefore, be able to display a broad spectrum of physiological

qualities. Elite performance in alpine skiing is suggested as a result of a number

of interacting qualities [7,41] including muscular strength and endurance, power,

aerobic and anaerobic capacity, balance, core strength and core stability as well

as flexibility [4,66,97,108]. Testing these qualities can therefore be important, not

only in order to optimize physiological training but ultimately to maximize the

sport-specific performance of the individual athlete [46,66]. An overview of

influencing dimensions is exemplified in Figure 1.

Figure 1. Overview of dimensions that affect competitive performance in alpine skiing.

An overview of dimensions that potentially affect competitive performance in alpine skiing (including potential

unknown dimensions). The proportion of each dimension has not yet been determined. This doctoral thesis focuses

on the physical dimension (physiological performance and anthropometric variables).

Over the years, several different testing protocols in alpine skiing have been used

[66], many of them with the intention of evaluating the physical status of athletes

before and after the racing season. These protocols usually include general

evaluations of strength, power, mobility, and endurance, but some countries also

use physiological tests specifically designed for alpine skiers [46,66,109].

Although several individual tests have been shown to correlate with skiing

performance (Table 1), the combined importance of a selection of tests, referred

to as "test batteries", has rarely been investigated. As a consequence, these test

batteries risk misleading both athletes and coaches when planning and

Physical

Equipment

?

?

2

implementing preparatory training. Also, by using results from these tests,

prediction of future performance is difficult.

To be useful, a test battery intended for a specific type of athlete must be valid

and reliable [96]. Still, substantial efforts seem to be spent on non-relevant tests

each year, wasting both financial resources as well as valuable time for athletes,

coaches, and training advisors alike. Therefore, the development of valid test

protocols is essential for all sports. This thesis focuses on alpine skiing, with the

overall aim to identify physiological and anthropometric variables valid for

prediction of competitive performance (indicated by FIS points). Through a

multivariate statistical approach and validation of models, the results and

analytical methods presented in this thesis can hopefully contribute to the future

development of more relevant testing procedures for alpine skiing and other

sports as well.

3

Background

Competitive alpine skiing

Competitions for elite alpine skiers are organized by the Fédération

Internationale de Ski (FIS) and include FIS World Championship, FIS World Cup

and Continental Cups. Alpine skiing has been an Olympic sport since its debut in

the Winter Olympic Games 1936 [144] and now consists of six different

disciplines including Slalom (SL), Giant Slalom (GS), Super Giant Slalom (SG),

Downhill (DH), Combined (C) (or Super Combined (SC)) and the Team event

[145]. The disciplines are divided into different categories, of which SL and GS

are referred to as the technical disciplines, and SG and DH are referred to as the

speed disciplines. Each of these disciplines differs from each other, mainly by

different turning radius, speed, the length of the course and the vertical distance

between the gates. FIS regulates the standards of each discipline which differs

based on age and gender [37]. As the focus of this thesis is on SL and GS, other

disciplines are not described in detail.

Slalom

Slalom (and GS) consist of two consecutive runs conducted on the same slope but

with two different courses where the skier with the fastest combined time wins.

Slalom is the shortest event, lasting 45-60 s and are usually conducted in

relatively steep paths with speeds ranging from 20-60 km·h-1 [7,41]. In SL, a race

course is composed of a series of gates by alternating pairs of red and blue poles

[37]. For the Winter Olympic Games, FIS World Championships and FIS World

Cup, an SL course must have a vertical drop of between 140-220 m and be

comprised by approximately 55-75 gates for men and between 40-60 gates for

women. Each gate should be located at a relatively close distance to each other

(4-13 m), forcing skiers to conduct quick and tight, short radius turns. Unlike the

other disciplines, each gate in SL consists of just one pole, which allows skiers to

follow a tight race line by cross-blocking the gates with their hands or repress

them with the shin guards. By using this technique, athletes can ski close to, or

even crossing the gates, in order to limit the intended skiing path [75].

Giant Slalom

Giant Slalom events are often conducted in relatively steep and undulating terrain

in courses covering the entire width of the slope. A GS run usually lasts between

60-90 s [7] with speeds between 60-90 km·h-1 [66]. In GS, SG, and DH, each gate

consists of four slalom poles and two gate panels [37]. As for SL, the course setting

for GS and SG is regulated via the vertical drop distance of the course [44].

According to the FIS regulations [37], a GS course must have a vertical drop of

4

250-450 m (up to 400 m for women) and direction changes equal to 11-15% of

the altitude change for the race course.

How competitive performance is quantified

Competitive performance in alpine skiing is quantified by the FIS point scoring

system and is used to regulate starting positions in all disciplines. Fédération

Internationale de Ski points are calculated according to the alpine formula and

are organized so that the best skiers in the world in each discipline have 0 (zero)

points and that those placed 30 have 6 points [38]. In brief, FIS points is

calculated according to the following formula: P = ((F * Tx) / To) - F where P =

FIS points, Tx = time for qualified competitor in seconds, To = time of the winner

in seconds and F = factor for each discipline (which is announced annually by

FIS) [38]. The FIS points lists are adjusted several times each year and apply to

all athletes participating in FIS alpine skiing events.

The competitive season and structuring of on-snow training

The FIS official competition calendar for alpine skiing starts on July 1 and extends

until June 30 the following year [39]. For most elite skiers, the competitive season

starts in October/November and ends in the middle of March [46]. During a

transitional period lasting from the end of the competition season until mid-

April, many athletes continue their on-snow training before transitioning to

physical preparation training that lasts from May until the end of July [42]. From

August until the competition season starts, the focus is on on-snow training which

is alternated with recovery periods and physical training aimed at maintaining

the general physical fitness and increasing the sport-specific performance

[42,46].

On-snow training

Elite alpine skiers usually train and compete for 130-150 days during a

competitive season [46]. For skiers who specialize in the technical disciplines,

approximately 120-140 of these are training days, and the remaining are

competition days. For speed specialists, the number of training and competition

events amounts to approximately 100-120 days in one year [46]. Usually, training

sessions are conducted in the morning due to favorable snow conditions and low

temperatures. A typical training session lasts between 2-4 hours which include

recovery between training runs and lift transports [66,123].

During ski training, athletes usually perform between 1-5 warm-up runs, one

inspection run of the training course and between 3-12 training runs depending

on the discipline [46,66]. A SL training session typically consists of 2-12 runs and

up to 700 turns in total. During a GS training, skiers usually perform up to 12

5

runs and a total of up to 600 directional changes while a SG training often

consists of 2-8 runs and up to 300 turns depending on the number of runs and

the length of the course. Like SG, a DH training usually consists of up to 8 runs

and, depending on the training venue, between 100-280 directional changes in

total [46].

Characteristics of alpine skiers

The career of elite alpine skiers

The career of professional alpine skiers seems to get longer. Throughout 40 years

(from 1967-71 to 2009-13), the average age of medalists during the major alpine

skiing competitions (FIS World Championships and the Winter Olympic Games)

was extended by about five years (from 20.7 to 25.8 years for women and from

24.3 to 28.7 years for men) [118]. Speculative, the reasons for this development

may be due to, e.g., improved training methods (on-snow training and

preparatory training), development of surgical procedures and rehabilitation

strategies in case of injuries and an increased opportunity to make a living on the

sport (thanks to a larger amount of prize and sponsor money).

Body composition and skiing performance

Body composition is an important determinant of performance in many sports

[89]. However, the requirements in different sports are often unique and require

specific body compositions for them to be advantageous. While endurance

athletes such as triathletes [49] and distance runners [79] usually benefit from

relatively low body mass and low body fat percentage, basketball players

generally have the advantage of being tall [141] and a having a larger amount of

lean body mass. In alpine skiing, anthropometric variables such as body fat

percentage [3,8,55,100], body weight [3,8,19,55,97,152,154], body stature [3,64],

lean body mass [55,97], leg length [127] and somatotype [32,149,152] have all

been suggested to affect the competitive success of athletes. For example, one

study [64] examined the difference in anthropometric variables between skiers,

based on their placing during one so-called Olympic cycle (including the Winter

Olympic Games, FIS World Cup, and FIS World Championships between the

years 2006 and 2010). While the researchers did not find any statistical difference

between skiers ranked top 30 in any of the investigated disciplines, they observed

a significant difference in stature and BW when comparing top 10 between the

disciplines SL, GS and C among those who placed over the top 30. As such, these

results indicate that it may be more beneficial to be shorter and lighter in

disciplines such as SL [55,64], while longer and heavier skiers are likely to have

an advantage in disciplines such as DH [55,64,97]. These results also suggest that

6

differences in body composition [64] or somatotype [32] can potentially

discriminate between athletes at different levels.

During the years, successful alpine skiers have seemingly become taller and

heavier [97,100,154]. In a classical study by Eriksson, Ekholm, Hulten, et al. [33],

the researchers found that skiers who were part of the Swedish national team

between 1965-1975 had an increase in both body weight and body stature, from

64 to 76 kg and 168 to 178 cm, respectively. These results were later supported by

interventions showing that the average body weight among U.S national female

alpine skiers had increased from ~58 [55] to 66 kg [116] and that the body weight

in the Swedish men's national alpine team had increased to 81 kg on average [12].

However, based on more recent studies [52,67,91,154], and despite some

exceptions [97], this development seems to have stagnated.

An explanation for the previous development has been proposed as a result of the

change in ski technique and the introduction of breakaway poles [155]. As the

poles could be passed through instead of being forced around them, this likely

favored skiers with higher body weight [155]. Although changes to alpine skiing

have been made since then, e.g., the introduction of the carving ski [62], none of

which has seemingly meant a fundamental change in the requirements of athletes

body size or body composition. An exception to this is speed discipline specialists,

where a larger amount of upper body muscle mass [97] or of being heavier in

general probably is beneficial [47,136,152].

Forces, muscular activity and movement speed during skiing

Forces

There is a number of different forces acting on the athletes during a run. Among

the most important of these are gravity, ski-snow friction, air drag, centripetal

and centrifugal force [56,130]. Forces generated during alpine skiing vary

depending on discipline [45,47] and are affected by factors such as speed, turning

radius and body weight of the athlete [42,59]. Studies conducted during skiing as

well as mathematical calculations indicate that skiers may be subjected to ground

reaction forces (GRF) of 2-4 times the BW in both SL and GS [78,113,135] and

that peak GRF can reach 3 times of the athletes BW during a regular SG and DH

run [45,48].

Muscular activity

The ability to withstand these forces while maintaining balance and control is

crucial for the competitive success of an alpine skier [66] and requires a

significant contribution and coordination of muscle structures in the trunk and

7

lower extremities [59,66,136]. Monitoring of electromyographic (EMG) activity

shows that muscle structures such as the tibialis anterior, erector spinae, gluteus

maximus, hamstrings, and quadriceps muscle groups are highly activated during

different turning phases [59,74,136] and that several antagonists muscles help to

stabilize the knee and hip joints during the completion of a turn [59]. Studies also

show that the regimen for muscular activity ranges between 50-280% of

maximum voluntary isometric contraction (MVC) in investigated disciplines

[59,60], indicating that large forces occur and that high levels of isometric and

eccentric muscle strength could be essential to maintain performance [42].

Isometric and eccentric muscle strength

Not surprisingly, elite alpine skiers demonstrate significant leg strength during

both isometric [55,73] and eccentric muscle actions [1,12,143]. When compared

to other athletes, alpine skiers tend to have superior strength during low angular

velocities and static muscle contractions [55,73,148]. However, as the angular

velocities increases, this difference diminishes, suggesting a specific

neuromuscular adaptation to loading patterns associated with skiing [148].

Studies conducted on elite and sub-elite skiers also indicate that more successful

skiers tend to have higher maximum torque output and smaller hamstring-to-

quadriceps (HQ) ratio compared to those competing at lower levels [1,19].

However, when scaled for body mass, differences in isometric and concentric

strength between these practitioners seem to be alleviated [1]. As such, eccentric

muscle strength has been proposed as a predictor of performance in alpine skiing

[12,143].

Movement speed

The skiing turn itself can be divided into various phases that typically include the

initiation, turning, completion and the transition phase [66]. Each of these

phases differs from each other in terms of physical and technical requirements

[66] and can, therefore, be categorized based on visual analysis or interpretation

of EMG data [59]. The execution of a complete turning cycle takes about 1.4 – 4.1

s (depending on discipline) [11] and usually requires a shift from isometric-

eccentric muscle work at the beginning of the turn to a concentric muscle action

at the completion and transition to the next [66]. During such transitions, the

average joint angle values of the hip, the inner leg and the outer leg ranges

between 81-129°, 101±16°, and 114±26° respectively [118]. In addition, contrary

to the notion that alpine skiing is an explosive sport, research shows that the joint

angular velocity at SL, GS, and SG usually are below 200° s-1 [59,74], which is

significantly slower compared to those of martial arts athletes and professional

sprinters who can reach 1000° s-1 [13,43].

8

Aerobic and anaerobic capacity

The relative importance of aerobic versus anaerobic capacity for alpine skiing

performance has long been a matter of debate [4,16,55,73,90,97,106,127,132,155].

For many years, high aerobic capacity was considered critical for the competitive

success of skiers [106,132], probably due to the high V̇O2max test results [73]

recorded on dominant skiing star Ingemar Stenmark [42,90]. While some

scientific findings support this assertion [55,97,127,159], others have failed to

establish aerobic capacity as a discriminating physiological variable between

skiers at different performance levels [19,154]. Instead, several researchers have

reported a correlation between anaerobic test results and skiing performance

[5,19,93,131,155,161,162].

Because a majority of alpine skiing racing events last between 45-120 s [42], both

the aerobic and the anaerobic energy systems will be utilized [42,66]. Studies

show that the average energy requirement during high-intensity skiing varies

between 80-200% of maximal oxygen uptake (V̇O2max) depending on discipline

[122,142,151]. When differentiating the total energy supply, calculations indicate

that the aerobic energy system accounts for 30-54% of the energy required during

a SL or GS run [122,151]. Studies also suggest that differences in ski technique

[21,121], mechanical and metabolic stress [41], as well as the overall skill of

athletes, affect the relative contribution of the energy systems [123,151]. As such,

these results further emphasize the intermediary relationship between the

aerobic and anaerobic abilities of alpine skiers compared to pronounced

endurance athletes [42].

Studies on alpine skiers show that the average V̇O2max values range from ~ 53 to

68 ml·min·kg-1 for males and ~ 46 to 57 mL·kg-1·min-1 for female athletes

[4,55,73,97,108,129,151]. Although many of these skiers exhibit relatively high

aerobic test values, even when compared to elite cyclists and distance runners

[54], high V̇O2max values are considered more a result of endurance training

adaptations than a representation of real demands of the sport [72].

The consensus of published studies seems to be that both aerobic and anaerobic

capacity is essential for maximum performance in alpine skiing. Although many

researchers suggest that skiers should focus on training aimed at increasing their

anaerobic capacity, the energy systems relative importance for racing

performance on a group level has not yet been established. While most scientific

findings indicate that aerobic capacity is not a limiting physiological factor for

competitive performance, complementary aerobic endurance training can still be

valuable as aerobic energy metabolism contributes up to ~50% of the energy

demands during a run. In addition, a high aerobic capacity will likely allow

athletes to tolerate higher blood lactate levels [80], faster recovery between

9

training runs and help to maintain competitive performance over an extended

period of time [97].

Balance

Todays' alpine skiing is highly demanding and therefore requires athletes to

display a wide range of spatial and proprioceptive abilities [111]. As skiing events

are often performed in challenging and ever-changing environments, athletes

must continuously compensate for high internal and external forces in order to

maintain balance and postural control [160]. As a result, a good balance has been

proposed as imperative for both competitive and recreational skiers [85,160]. In

sports such as basketball and soccer, an excellent balance can be essential in order

to prevent injuries [18]. However, if this also applies to alpine skiing, or if the

balance is important for the sport-specific performance of alpine skiers, remains

to be proven [65,102].

Biomechanical studies suggest that a poor balance can adversely affect skiing

performance [40,56], primarily by initiating compensatory body movements that

ultimately leads to less than optimal skiing technique and lower force production

[102]. While these results support the general perception of superior balance for

alpine skiers, others have shown that elite skiers often perform comparatively or,

in some cases, worse at both static and dynamic balance tests when compared to

less skilled counterparts [99]. These contradictory results may be due to

unspecific testing protocols (tests conducted in a laboratory environment) or that

the balance tests used were not challenging enough to distinguish between skiers

at different levels [102,140].

When balancing tests on experienced skiers are performed in skiing boots, the

balance focus of these athletes seems to be shifted from the ankles to more hip

involvement [98]. Also, experienced skiers seem to have similar balance

performance with and without skiing boots [98], suggesting sport-specific

balance abilities that cannot be tested using standard balance testing procedures

[102]. When more specific balance tests for alpine skiers were applied, they

proved reliable and sensitive, even for top-level skiers [102].

Collectively, testing the balance of alpine skiers in a laboratory environment is

complicated because of the complex demands of the sport. Skiing usually takes

place in a squatting position, which directs the balance towards the hip, while

common balance tests drive towards testing the ankles. During skiing, athletes

are subject to considerable internal and external forces, requiring constant

adjustment of the balance on the skis, which is hard to replicate in a laboratory

setting. Hence, to distinguish between competitive and recreational skiers,

balance tests must be both specific and highly challenging.

10

Core strength and core stability

Core strength and core stability are common topics in the field of sports

performance research [57,112,157]. Although the terms are often used as

synonyms, they are in reality fundamentally different [36,57]. Whereas core

strength generally refers to the ability of the trunk muscles to generate contractile

force [36], core stability can be defined as the ability of the spinal stabilization

system to maintain the lumbopelvic region in a physiologically neutral position

during static and dynamic conditions [101]. Today, core strength and core

stability training is an integral part of many sports conditioning programs [24,70]

and is generally considered important also for alpine skiers [51,58,66,97].

Studies of core strength and core stability on alpine skiers have mainly included

tests of isometric back flexion and extension strength [105,110]. While some of

these studies have indicated that core training may be necessary for injury

prevention [84,110], no one has yet been able to establish core strength or core

stability as important physiological qualities for competitive performance [58].

However, these results are not surprising since alpine skiing is a highly dynamic

sport [58,66] that does not require a significant contribution from muscle

structures such as external obliques and rectus abdominis [59] (which indirectly

suggests a lack of specific testing procedures) [57]. Furthermore, as for other

sports, the importance of core strength and core stability for alpine skiing

performance can be difficult to determine because core training is practically

never performed in isolation and therefore cannot be established as the

performance-enhancing factor [112].

Despite the lack of substantial scientific evidence supporting core strength and

core stability as essential for competitive performance, alpine skiers (especially

those with limited on-snow training time) may still benefit from specific core

training as the core muscles help to maintain balance and control during skiing

[58,66,160]. However, even though specific core training is also suggested to have

other benefits (such as injury prevention) [84,110], alpine skiers should probably

focus mostly on free-weight exercises such as squats and deadlift as these seem

to entail higher core muscle activation and greater overall improvement of

athletic performance [10].

Flexibility

Flexibility in an anatomical context refers to the range of motion (ROM) in joints

and is reflected by the ability of an individual to move without any

musculoskeletal limitations [76]. In alpine skiing, sufficient flexibility

presumably allows athletes to adopt an efficient skiing position without undue

strain on stabilizing structures [42]. In a study by Song [127], junior skiers were

found to have higher overall flexibility in investigated structures compared to age-

11

matched controls. While some have suggested flexibility as an essential quality

for alpine skiers [42], studies have yet to find a difference between practitioners

across performance levels [4,19].

Physiological testing in alpine skiing

Physiological testing in alpine skiing is common [66,109], yet the practical

usefulness can be questioned, which is discussed throughout this thesis. One

crucial aspect to understand is the complexity of testing physiological capacities.

Often a wide range of different tests are required to evaluate physiological

qualities, potentially affecting performance. In addition, many tests are

correlated (e.g., intercorrelated) and therefore, in an overall assessment of

physiological performance, only one representative test for each capacity may be

needed (Figure 2). This can be referred to as a reduction of dimensions, and in

statistical terms deemed principal component analysis (PCA), which we will

return to later.

Although elite athletes often perform better on certain physiological tests when

compared to their less skilled counterparts, these findings are rarely an adequate

measure of true sport-specific performance.

Figure 2. Correlation between Countermovement Jump and Squat Jump.

A significant positive correlation was observed between Countermovement Jump and Jump Squat (R2 = 0.90, p =

<.001). This correlation indicates that as Countermovement Jump increases, Squat Jump tends to increase. Thus,

only one of the tests are needed, or useful, in an overall evaluation of lower-body power.

A selection of studies that have investigated the correlation of physiological and

anthropometric variables to competitive performance in alpine skiing can be

28

30

32

34

36

38

40

42

44

46

26 28 30 32 34 36 38 40 42 44

CM

J (

cm

)

SJ (cm)

12

found in Table 1-2. As this thesis focuses on variables that affect skiing

performance, only studies that met the following criteria were selected:

• Physiological performance and anthropometric variables correlated to

sport-specific performance (points/ranking or time trials)

• Non-descriptive data (e.g., comparisons between skiers on different

performance levels)

In addition, studies were selected based on the following criteria:

• Subjects ≥ 14 years

• Peer-reviewed

• More than one publication per variable

The importance of the physiological variable was assessed as Yes (Y) and No (N):

• For P rs ’s rr ff (in the following referred to as

P rs ’s rr ); Y if variables were described as important,

correlated and/or significant by the authors

• Correlation values outside the range r = ±0.5 were considered important

• For Multiple linear regression; Y if the variable was included in the model

Table 1. Physiological performance variables correlated to sport-specific performance (points/ranking or time trials).

Reference Sex (n) Level/Age Outcome Statistics Important

Y/N (notes)

Lateral Box Jump 90-s

Andersen, Montgomery and Turcotte [5]

M (33) National+

Club Time trail

P rs ’s correlation

Y

Klika and Malina [77] M (21) Junior +

National USSA

ranking

Multiple linear

regression

Y

F (17) Y

von Duvillard and Knowles [162]

M (12) Junior

USSA ranking


N (SL) Y (GS)

N (SG) Y (DH)

F (14)

Aerobic Capacity

Haymes and Dickinson [55]

M (12) Elite FIS ranking


N

F (13) Y (DH)

Neumayr, Hoertnagl, Pfister, et al. [97]


Simple linear

regression

Y (Speed group, 1998)

F (20) N

Miura and Miura [94]

Junior elite M&F

(31) Junior + National

SAJ (Japan) Points


Y (SL) Y (GS)

High School

elite M&F (41)

Y SL) Y (GS)

13

Song [127] M (9) Junior Time trail P rs ’s

correlation Y (DH N (GS)

Hexagonal test


M (33) National+

Club Time trail


Y


National

USSA ranking

Multiple linear

regression

N

F (17) N

Vertical Jump


M (33) National+

Club Time trail


Y

Emeterio and Gonzalez-Badillo [32]

M (16) Junior

Spanish FEDI

ranking


Y

F (12) N


M (12) Elite

USSA ranking


Y (GS)

F (13) N

Klika and Malina [77]

M (21) Junior +

National USSA

ranking

Multiple linear

regression

N

F (17)

Y


M&F (26) National USSA

ranking P rs ’s

correlation

N (SL) Y (GS) N (SG) Y (DH)

Repeated Vertical Jumps


M (16) Junior

Spanish FEDI

ranking


Y (average cm)

F (12) N

Patterson, Platzer and Raschner [103]

F (10) Elite FIS ranking P rs ’s

correlation Y (season 4)

Wingate Bicycle test


M (33) National+

Club Time trail


Y (mean power)

Bacharach and von Duvillard [7]

M (10) National

USSA ranking


Y (SL, 90-s) Y (GS, 90-s)

F (8) Y (SL, 30 and 90-s) Y (GS, 30 and 90-s)


M (16) Junior

Spanish FEDI

ranking


Y (mean power)

F (12) N

Song [127] M (9) Junior Time trail P rs ’s

correlation Y (DH, mean power)

N (GS)


M&F (26) Junior USSA

Ranking P rs ’s

correlation

N (SL) Y (GS) N (SG) Y (DH)

M = Male; F = Female; Junior = age 14-17; Club = Skiers at club level; National = Skiers at national level; Elite = Skiers at international level

14

Table 2. Anthropometric variables correlated to sport-specific performance (points/ranking or time trials).

Reference Sex (n) Level/Age Outcome Statistics Important

Y/N (notes)

Body fat percentage

Aerenhouts [3] M (58)

Elite FIS ranking P rs ’s

correlation N

F (26) N


M (12) Elite

USSA ranking

Pears ’s correlation

Y (SL and DH)

F (13) Y (DH)


Junior elite M&F


SAJ (Japan) Points


N (SL) N (GS)

High School

elite M&F (41)

N (SL) N (GS)

Miura [93] M (14)

National + Elite

FIS ranking P rs ’s

correlation

Y (SL) Y (GS)

F (10) N (SL) N (GS)



Simple linear

regression

N

F (20) N

Body weight



correlation

Y

F (26) N



Simple linear

regression

N

F (20) N


M (12) Elite

USSA ranking


Y (SL)

F (13) N


National USSA

ranking

Multiple linear

regression

Y

F (17) Y


Junior elite M&F


SAJ (Japan) Points


N (SL) Y (GS)

High School

elite M&F (41)

Y (SL) Y (GS)

Miura [93] M (14)

National + Elite


correlation

N (SL) N (GS)

F (10) N (SL) N (GS)

Vermeulen, Clijsen, Fässler, et al. [152]



Y (DH)

F (24) Y (DH)

Body Mass Index



correlation Y

F (26) N


M (16) Junior

Spanish FEDI

ranking


-

F (15) -



Simple linear

regression

N

F (20) N




N F (24) N

Body stature

15



correlation

Y (SL) N (DH)

F (26) N (SL and DH)



Simple linear

regression

N

F (20) N


National USSA

ranking

Multiple linear

regression

N

F (17) N


Junior elite M&F


SAJ (Japan) Points


Y (SL) Y (GS)

High School

elite M&F (41)

N (SL) N (GS)

Miura [93] M (14)

National + Elite


correlation

N (SL) N (GS)

F (10) N (SL) Y (GS)




N F (24) N

Lean body mass


M (16) Junior

Spanish FEDI

ranking


Y (calculated muscle mass)

F (15)

N


M (12) Elite

USSA ranking


Y (SL)

F (13) N


Junior elite M&F


SAJ (Japan) Points


N (SL) N (GS)

High School

elite M&F (41)

N (SL) N (GS)

Miura [93] M (14)

National + Elite


correlation

N (SL) N (GS)

F (10) N (SL) N (GS)

Somatotype



correlation N

F (26) N


M (16) Junior

Spanish FEDI

ranking


-

F (15) -




N

F (24) N

Skinfold


M (16) Junior

Spanish FEDI

ranking

Pears ’s correlation

-

F (15) -


National USSA

ranking

Multiple linear

regression

Y

F (17) N




N

F (24) Y (DH) M = Male; F = Female; Junior = age 14-17; Club = Skiers at club level; National = Skiers at national level; Elite = Skiers at international level

16

Summary

In summary, competitive events in alpine skiing are organized and regulated by

FIS. Competitions for elite alpine skiers include; FIS World Championships, FIS

World Cups, Continental Cups, and the Winter Olympic Games. Disciplines in

alpine skiing all differ from one another in terms of turning radius, the length of

the course and the vertical distance between the gates. The competitive season

for elite athletes is generally in progress from October/November until mid-

March. Elite alpine skiers usually train and compete for 130-150 days in a year.

On-snow training is often conducted during the morning and often consists of

between 3-12 runs depending on the discipline. During skiing, athletes are

subjected to immense forces, causing significant contribution from, and

coordination of, several interacting muscle structures. Alpine skiing requires a

large number of isometric and eccentric muscle contractions. In contrast to a

common belief, alpine skiing cannot be characterized as an explosive sport.

The career of professional alpine skiers appears to get longer. Alpine skiers also

seem to have become heavier and taller, which may be due to changes in skiing

technique and the induction of breakaway poles. Both the aerobic and the

anaerobic energy systems are utilized during a run, but the relative importance of

each energy system for alpine skiing performance is still a matter of debate. While

balance, core strength, core stability, and flexibility have been proposed as

important physiological qualities for competitive performance, current scientific

findings do not support these assertions. Physiological tests can be important for

governing organizations as well as for individual athletes. However, to be useful

for world-class competitors, a physiological test battery should be validated

against sport-specific performance. To date, only a few studies have investigated

the correlation between a combination of physiological test results and skiing

performance in alpine skiing (indicated by points/ranking or time trials).

17

The rationale for the thesis

The notion that specific physiological qualities are necessary for alpine skiing

performance seems to lack solid scientific evidence. While several studies have

found a correlation between individual physiological test results and

points/ranking or time trails, few have examined the predictive power of a

combination of test results (Table 1-2). Thus, the optimal combination and the

true importance of physiological test results remain to be determined. To our

knowledge, no previous studies have approached this issue using multivariate

data analysis (MVDA). Hence, the scientific rationale for this doctoral thesis was

to identify physiological and anthropometric variables valid for prediction of

competitive performance in alpine skiing using a multivariate statistical

approach.

18

Aims

The overall aim of this doctoral thesis was to identify physiological and

anthropometric variables valid for prediction of competitive performance

(indicated by FIS points).

Specific Aims

Paper I to investigate the predictive power of aerobic test results and

anthropometric variables for competitive performance (indicated

by FIS points) of junior elite male and female alpine skiers.

Paper II to investigate the predictive power of the national test battery of

the Swedish Olympic Committee (Fysprofilen) and

anthropometric variables for competitive performance (indicated

by FIS points) of senior elite female alpine skiers.

Paper III to investigate the predictive power of a novel physiological test

battery, or by individual physiological profiling, for competitive

performance (indicated by FIS points) of senior elite female alpine

skiers.

19

Materials and Methods

Study design

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 (Figure 3). The importance of the included tests was

examined using bivariate and multivariate data analysis. Selection of tests was

based on the following:

• Paper I - The importance of aerobic capacity for alpine skiing

performance has long been a matter of debate. Whether disagreements

are due to inadequate predictive power of aerobic capacity for skiing

performance, or the use of less than optimal statistical methods is

unclear. For the prediction of competitive performance of junior elite

alpine skiers, we applied multivariate data analysis on commonly used

aerobic test results.

• Paper II - Lack of predictive power using aerobic test results in Paper I,

led us to the assumption that this was due to the inclusion of results

recorded during only one physiological performance test. Therefore, we

chose to analyze a commonly used test battery in Sweden (Fysprofilen)

for prediction of competitive performance of senior elite female alpine

skiers.

• Paper III - As a more complex test battery (Fysprofilen) could not predict

alpine skiing performance, we chose to apply a novel test battery on

senior elite female alpine skiers. The new test battery was based on

previous research [5,15,32,88,127,162] and physiological reasoning

regarding the performance demands in competitive alpine skiing.

20

Figure 3. Overall study design and project stages. The "X" marked boxes list physiological and anthropometric tests

that were carried out during each study. The "Y" marked boxes list outcome variables that were used as an indication

of competitive performance in each study. The arrow indicates the order in which the studies were conducted.

Participants

Physiological test results from two sample groups were included in this thesis.

The participants included in Paper I consist of a total of twenty-three (n = 23)

junior elite male and female alpine skiers (Table 3), and the participants included

in Paper II-III consist of a total of fourteen (n = 12-14) senior elite female alpine

skiers (all members of the Swedish national alpine ski team, which was the

inclusion criteria) (Table 3). Participating athletes were tested multiple times

each season dependent on training and racing schedule as well as federation

decisions. Prior to each test occasion, all participants underwent a brief medical

examination and completed a health questionnaire. Exclusion criteria for

participation during each occasion were indicated forms of health status (e.g.,

disease or injury) or use of medication that could potentially adversely affect their

health or performance. After being informed of the risks and possible discomforts

that could occur during testing, all participants provided their written consent for

participation, as stated in the corresponding ethical approval form.

Paper I P r P r

r s r s s

Anthropometric testsBody stature Body mass



Body composition

Physiological performance tests1RM Back squat

1RM Bench press1RM Cleans

Hand Grip StrengthPull-ups

Brutal BenchHarre's test

20m Sprint testSquat Jump

Countermovement JumpCountermovement Jump with arm-swing

Peak Oxygen Uptake

Physiological performance testsHand Grip StrengthIllinois Agility test

Countermovement JumpForce-Velocity Bike Test

Y Balance s ™Lateral Box Jump 90-s

500m Rowing Ergometer TestPeak Oxygen Uptake

s

Project stages

X X

Y

X

Competitive performanceFIS points in SL and GS

Competitive performanceFIS points in SL and GS

21

Table 3. Descriptive values of participants included in Paper I-III

Level Paper Sex (age/n) Body mass (kg) Body stature (cm)

Junior elite Paper I

M (age 17, n = 10) 75.4 ± 5.3 178 ± 5

M (age 16, n = 13) 69.3 ± 5.5 178 ± 5

F (age 17, n = 6) 68.1 ± 3.7 170 ± 6

F (age 16, n = 10) 69.7 ± 3.4 172 ± 4

Senior elite Paper II F (n = 14) 67.4 ± 3.6 171 ± 5

Paper III F (n = 12) 67.9 ± 3.7 172 ± 5

M = Male; F = Female; Junior elite= Skiers at junior elite level; Senior elite = Skiers at senior elite level. Data as Mean ± SD.

Testing procedures

Physiological tests were conducted at the Section of Sports Medicine, Umeå

University, Sweden (Paper I-III) and Bosön (the development center for the

Swedish Sports Confederation), Lidingö, Sweden (Paper II). For Paper I,

physiological testing was conducted during the summer break (June-July) and

the autumn term (October-December). For Paper II-III, all physiological testing

was performed either immediately after the previous (April-May) and prior to the

upcoming competitive season (August-October). All physiological performance

tests included in Paper II were performed according to guidelines described in

the testing procedures for Fysprofilen [138]. In brief, Fysprofilen is a

physiological test battery developed by the Swedish Olympic Committee and

consists (for alpine skiing) of thirteen different performance tests.

Participants were instructed to avoid strenuous physical activities and adhere to

the same routines regarding, e.g., sleep, water, and nutrition intake during the 24

hours before each test occasion. Prior to any physiological performance testing,

participants performed a ~10-15 minutes warm-up protocol consisting of low-

intensity jogging or cycling, lateral strides, jumps, sprints, and dynamic

stretching exercises. Before each specific test, a self-regulated exercise-specific

warm-up was also performed. These warm-ups were carried out with low to

moderate intensity (e.g., before rowing 500m) or at low to moderate load (e.g.,

before back squats). Verbal encouragement was provided during all physiological

performance testing to encourage participants to perform at their maximum

potential. All performance tests were carried out with maximum effort or to

volitional exhaustion. Physiological test results were assigned as X-variables in

the statistical analyses.

One Repetition Maximum tests

The assessment of One Repetition Maximum (1RM) performance (tested with

Back squats, Bench press and Cleans) was conducted using factory calibrated

competition weights and barbells from Eleiko (Eleiko Sport, Halmstad, Sweden)

22

(Paper II). All 1RM performance tests were carried out using an incremental

loading protocol (suggested load increase was (set x reps/percent of 1RM): 1 x

8/50%, 1 x 6/60%, 1 x 5/70%, 1 x 3/80 %, 1 x 2/90%, 1 x 1/100, 1 x 1/102%).

Attempts were conducted until failure or voluntary discontinuation of more

attempts on higher loads. The recovery time between attempts was standardized

≥ 5 m s.

A Back squats attempt was approved when the participants descended, with the

top of their femur parallel to the floor, and back up to the starting position. A

Bench press attempt was approved when the participants descended the barbell,

from fully extended arms, down to the chest and back up to the starting position

without bouncing the barbell on the sternum or by lifting the back of the bench.

A Cleans attempt was approved when participants managed to clean the barbell

in one explosive motion from the initial starting position, up to the anterior

deltoids and catch it in an upright position. The best performance (kg) for each

1RM test was recorded. The 1RM tests for back squats [26,124,150], bench press

[115,124], and cleans [26,35] are considered safe and reliable tests (ICC ≥ 0.91)

for assessing strength in a number of different populations.

Hand Grip Strength

Maximal isometric hand grip strength was tested unilaterally using calibrated

hand grip dynamometers (T.K.K. 5401 GRIP D (Takei Scientific Instruments Co.,

Ltd., Niigata, Japan) (Paper II) and Jamar Hydraulic Hand Grip Dynamometer

(Patterson Medical, Warrenville, IL, USA) (Paper III)). A Hand Grip Strength test

was approved when the participants squeezed the dynamometer with as much

force as possible for 3 seconds. The best performance (kg) out of three attempts

on each hand was recorded. The Hand Grip Strength test is considered a valid

and reliable method (r ≥ 0.9 r r s r s and ICC = 0.86–0.97,

respectively) for monitoring hand grip strength of athletes [30].

Pull-ups

The Pull-ups test was conducted using a regular pull-up bar with a pronated hand

grip position slightly wider than shoulder width (Paper II). A Pull-ups repetition

was approved when the participants pulled themselves up until the chin was

above the bar, without kipping of the body or legs or by changing the hand grip.

The total number (n) of approved repetitions was recorded. The Pull-ups test is

considered a reliable method (ICC = 0.96-0.99) for assessing upper extremity

pulling strength [29].

23

Brutal Bench

The Brutal Bench test was conducted using a regular brutal bench (Paper II). A

Brutal Bench repetition was approved when the participants managed to get up

from the starting position, mark with their elbows against their knees and down

back again without bouncing against the backrest in the bottom position. The

total number (n) of approved repetitions was recorded. To our knowledge, the

Brutal Bench test has not yet been validated.

Change of Direction Speed

The assessment of Change of Direction Speed (CODS) performance (tested with

H rr ’s s s g y s ( )) w s s g v

timing systems (PF MuscleLab MA4020e (Ergotest Innovation AS, Porsgrunn,

Norway) [137] and IVAR Jump and Sprint system (Spin Test, Tallinn, Estonia)

[23]). All CODS tests were performed three times with ≥3 minutes of rest between

each attempt.

H rr ’s s w s f w g r r s s r Hoyek,

Champely, Collet, et al. [63] (Paper II). A Harre's test attempt was approved when

the participants completed the course as fast as possible without touching the

center cone or the hurdles. The IAT test was conducted following the procedures

described in Lacy [82] (Paper III). An IAT test attempt was approved when the

participants completed the course as fast as possible without touching the

markers or by taking a shortcut. The best performance (s) for each CODS test was

recorded. r k w g , H rr ’s s s y been validated. The IAT

is considered a reliable test (ICC = 0.85–0.98) for the assessment of CODS

performance [53].

20m Sprint test

The assessment of 20m Sprint performance was conducted using the same timing

equipment as for the CODS tests (Paper II). A 20m Sprint attempt was approved

when the participants completed 20 meters as fast as possible. The best

performance (s) was recorded. The 20m Sprint test is considered a reliable test

(ICC = 0.91) for the assessment of sprint performance [95].

Jump tests

The assessment of jump performance (tested with Squat Jump (SJ),

Countermovement Jump (CMJ) and Countermovement Jump with arm-swing

(CMJa)) was conducted using validated infrared jump mat systems (PF

MuscleLab MA4020e (Ergotest Innovation AS, Porsgrunn, Norway) and IVAR

Jump and Sprint system (Spin Test, Tallinn, Estonia)). All jump tests were

24

performed as single jumps three times with ≥3 minutes of rest between each

attempt.

The SJ test was conducted from a squatting position (at 90° knee angle) with the

hands firmly placed on the hips (Paper II). A SJ test attempt was approved when

participants, on the test leader's command and without countermovement,

jumped as high as possible and landed with normal knee flexion in the same

position as the take-off. The Countermovement Jump tests were conducted from

an upright position: the CMJ with the hands firmly placed on the hips and with

countermovement (Paper II-III), the CMJa with countermovement and arm-

swing (Paper II). A Countermovement Jump test attempt was approved when

participants, on the test leader's command, jumped as high as possible and

landed with normal knee flexion in the same position as the take-off. The best

performance (cm) for each jump test was recorded. The SJ and the CMJ are both

considered reliable tests (ICC = 0.91-0.93) for the assessment of jumping

performance [95].

Force-Velocity Bike Test

The Force-Velocity Bike Test (FVBT) was conducted from a standing start against

a breaking weight equivalent to 2, 4, 6, 8, 10 and 12% of participants body weight

using a friction-loaded Monark 894E Peak Bike (Monark Exercise, Varberg,

Sweden) (Paper III). s r s w r r m z r r w ≥5

minutes of rest between each attempt. Peak power in absolute measurement (W)

(PP), peak power relative to body weight (W·kg-1) (RPP) and time to peak power

(TTPP) for each sprint were recorded using Monark ATS Software (Monark

Exercise, Varberg, Sweden). The best performance (PP, RPP, and TTPP) for 10

seconds during all sprints were used in the statistical analysis. The FVBT is

considered a reliable test (ICC = 0.94-0.98) for the assessment of power in the

lower extremities [68].

Y Balance Test™

The assessment of balance performance was conducted following the procedures

described in Plisky, Gorman, Butler, et al. [107] using a s K ™

(FunctionalMovement, Danville, VA, USA) (Paper III). A s ™ (YBT)

attempt was approved when the participants, on the test leader's command,

managed to reach out as far as possible and back without losing their balance or

by lifting their heels from the center platform or by releasing their hands from

their hips. The best performance (cm) on each foot in each of the three directions

(anterior, posteromedial and posterolateral) was recorded. The YBT is considered

a reliable test (ICC = 0.85-0.93) for the assessment of balance performance [126].

25

Lateral Box Jump 90-s

The Lateral Box Jump 90-s (LBJ90) test was conducted following the procedures

described in von Duvillard and Knowles [162] using a wood-frame box with the

dimensions of 40 cm high x 60 cm long x 50 cm wide (Paper III). A LBJ90

repetition was approved when the participants, starting from the top of the box,

performed a lateral jump with both feet down on one side and then back up to the

top of the box. The total number (n) of approved repetitions (back and forth over

the box) for 90 seconds was recorded. The LBJ90 is considered a reliable test for

the assessment of jump strength endurance of both male (ICC = 0.90) and female

(ICC = 0.87) athletes [110].

500 meters Rowing Ergometer Test

The 500 meters Rowing Ergometer Test (500mRET) was conducted following the

procedures described in Lindberg, Oksa, Gavhed, et al. [88] using a Concept2

Model D Indoor Rower (Concept2, Morrisville, VT, USA) (Paper III). The test was

performed from a standing start using the highest resistance (spiral damper

setting at 10). The time (s) to complete 500 meters, peak power (W) (PP) and

mean power (W) (MP) was recorded. The 500mRET is considered a reliable test

for the assessment of rowing performance (ICC = 0.99-1.0 to 0.80-0.98 for power

output and overall rowing time, respectively) [128].

Peak Oxygen Uptake

The Peak Oxygen Uptake (V̇O2peak) tests was conducted using either a

programmable cycle ergometer (Monark 839E, Monark Exercise, Varberg,

Sweden) (Paper I-III) or a running treadmill (Rodby RL2500E, Rodby

Innovation, Hagby, Sweden) (Paper II-III). During both protocols, pulmonary

gas exchange was measured continuously using a calibrated Oxycon Jeager Pro

(mixing chamber mode setting) (CareFusion, Hoechberg, Germany). The highest

mean value for VO2 for 30 seconds was considered maximum and recorded as

V̇O2peak in both absolute rate (L·min-1) (Paper I) and the relative rate (mL·kg-

1·min-1) (Paper I-III). Heart rate (HR) was monitored using a Polar Electro S610i

(Polar Electro Oy, Kempele, Finland) (Paper I-III). Fingertip blood lactate

samples were collected during the last 30 seconds on each load (3-minute stages)

and analyzed using a YSI 1500 Sport Lactate Analyzer (YSI Life Sciences, Yellow

Springs, OH, USA) (Paper I).

For Paper III, V̇O2peak was also estimated using the following prediction formula:

V̇O2peak (mL·kg-1·min-1) = 124.165 + (-0.748 * 500mRET (s)).

26

Anthropometric tests

For assessment of anthropometric variables, participants were instructed to wear

only light clothing (e.g., t-shirt, sports bra, shorts, and tights). Body mass was

measured to the nearest 0.1 kilograms using a standard digital weight scale

(Soehle weights, Leifheit AG, Nassau, Germany) (Paper I-III). Body stature was

measured to the nearest 0.1 centimeters using a wall-mounted body length meter

(Fosamax stadiometer, Merck & Co. Inc., New Jersey, USA) (Paper I-III). Body

mass index (BMI) (Paper I) was calculated using the following formula: BMI =

body mass (kg) · stature (m)2. Body composition was measured using a phantom

calibrated Dual-energy x-ray absorptiometry (DXA) Lunar iDXA scanner (GE

Medical Systems Lunar, Madison, WI, USA; Encore Version 14.10.022) (Paper

III). Due to a large number of variables recorded during a DXA scan, PCA was

used to reduce the number of dimensions in the dataset. After visual inspection

of the PCA plot, the following variables were selected as they appeared relevant:

the total amount of fat mass (g), lean body mass (g), tissue mass (g), bone mass

and body fat in percentage (%). Dual-energy x-ray absorptiometry is considered

a valid method for the assessment of body composition in a variety of populations

[2,14,119]. Anthropometric data were assigned as X-variables in the statistical

analyses.

Fédération Internationale de Ski points

Fédération Internationale de Ski points used in this thesis were extracted from

FIS point score lists published in December (6th list) and in April (11th list) each

year (Paper I-III). The lists were selected because (1) they were published during

the ongoing competition season, (2) results from the first competition in both

disciplines were included and, (3) they represent a change in points/ranking data

over time (Table 4). The points lists were obtained from the FIS official website

at https://www.fis-ski.com/DB/alpine-skiing/fis-points-lists.html. Fédération

Internationale de Ski points were assigned as Y-variables in the statistical

analyses.

27

Table 4. Spearman’s correlation matrix (values as Spearman's rs) between FIS points lists in SL and GS for Swedish female skiers published during the season 2013/2014.

Discipline List 1 2 3 4 5 6 7 8 9 10 11 12

SL

2 1.00 -

3 1.00 1.00 -

4 1.00 1.00 1.00 -

5 1.00 1.00 1.00 1.00 -

6 1.00 1.00 1.00 1.00 1.00 -

7 0.98 0.98 0.98 0.98 0.98 0.98 -

8 0.97 0.97 0.97 0.97 0.97 0.97 1.00 -

9 0.97 0.97 0.97 0.97 0.97 0.97 1.00 1.00 -

10 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 -

11 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 1.00 -

12 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 1.00 1.00 -

13 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 1.00 1.00 1.00

GS

2 1.00 -

3 1.00 1.00 -

4 0.99 0.99 0.99 -

5 0.95 0.95 0.95 0.96 -

6 0.94 0.94 0.94 0.95 0.99 -

7 0.94 0.94 0.94 0.95 0.99 1.00 -

8 0.83 0.83 0.83 0.85 0.95 0.96 0.96 -

9 0.83 0.83 0.83 0.85 0.95 0.96 0.96 1.00 -

10 0.83 0.83 0.83 0.85 0.94 0.95 0.95 0.96 0.96 -

11 0.74 0.74 0.74 0.76 0.85 0.86 0.86 0.85 0.85 0.89 -

12 0.74 0.74 0.74 0.76 0.85 0.86 0.86 0.85 0.85 0.89 1.00 -

13 0.74 0.74 0.74 0.76 0.85 0.86 0.86 0.85 0.85 0.89 1.00 1.00

Correlated lists contained only skiers who were listed as active. The critical values are 0.58, 0.71, and 0.82 for significance levels .05, .01, and .001 respectively. Lists published during other seasons may correlate between each other differently.

Statistics

Statistical calculations were conducted using JMP 13.1-14.0 (SAS Institute Inc,

Cary, NC, USA) and SIMCA 14.0-16.0 (Sartorius Stedim Data Analytics AB,

Umeå, Sweden).

For Paper I-II, Shapiro-Wilk goodness of fit tests investigated data distributions

of physiological performance tests and anthropometric variables. Data were

considered normally distributed if p > 0.05. For Paper III, distribution was

assessed using SIMCA's built-in tests for normality (skewness). The following

variables had skewed distributions: Pull-ups (W = 0.94, p = 0.004) (Paper II),

Stature (cm), Hand Grip Strength R (kg), YBT Posteromedial H (cm) and YBT

Posterolateral L (cm) (Paper III). Variables that were skewed (based on an alpha

of 0.05) were log10 transformed to normality. After transformation, all variables

exhibited a normal distribution.

28

Bivariate analysis

A Spearman’s rank correlation analysis was conducted in order to examine the

relationship between physiological test results and FIS points in both SL and GS

(Paper I). Cohen's standard was used to evaluate the strength of the relationship:

levels of effect size between 0.10 and 0.29 were classified as small, levels of effect

size between 0.30 and 0.49 were classified as moderate, and levels of effect size

above 0.50 was classified as large. Spearman’s rank correlation is a

nonparametric test that estimates how well the relationship between two

variables can be described in the form of a monotonic function [27].

A Mann-Whitney U two-sample rank-sum test was performed in order to

investigate whether there were significant differences in physiological

performance (indicated by Fysprofilen Index) between the levels of ranking

(Lowest and Highest) in both SL and GS (Paper II). The Mann-Whitney U two-

sample rank-sum test is a nonparametric alternative to the independent samples

t-test but does not share the same assumptions of normal distributions [27].

Multivariate analysis

Principal component analysis was conducted in order to identify representative

body composition variables (Paper III) and to detect trends and outliers in the

data (Paper I-III). Principal component analysis is a statistical method used to

identify trends and patterns as well as for visualizing and describing similarities

and differences between variables in a multivariate dataset [34].

Orthogonal projections to latent structures (OPLS) was performed in order to

examine regression and prediction of competitive performance (indicated by FIS

points) from physiological test results (Paper I-III) (Figure 4). R2 (goodness of

fit) and Q2 (goodness of prediction) measures were calculated for the overall

assessment of all models. R2 and Q2 >0.60 (less than ≤0.20 difference between

R2 and Q2) were considered valid [86] (Paper I). DmodX and H g’s 2 range

plots were used to visually access the statistical significance of fitted models

(Paper II-III). Shared and Unique Structures (SUS)-plots were generated in

order to compare individual OPLS SL and GS models (Paper III). Cross-

validation by permutation was used to visually assess the validity of each OPLS

model (Paper I-III). In brief, when conducting a permutation test, class labels are

permuted and assigned in a random order to different observations [153]. With

incorrectly assigned class labels, a new model is then calculated. The assumption

when conducting a permutation test is that permuted models should not be able

to predict data well. Because all new models are based on random assignment of

class labels, there should be no difference between them [153]. Thus, an original

model with lower predictive power than a certain number of permuted models is

probably not valid. All data were scaled according to Univariate (UV) scaling

29

(Paper I-III). Orthogonal projections to latent structures is a robust statistical

method used to generate models that describe the relationship between sets of

variables in a multivariate dataset [34].

Figure 4. A simplified description of the OPLS procedure. R2 is a measurement of goodness of fit. The procedure is

used to estimate the relationship between one dependent variable (Y) and one or more independent variable (X). Q2

is a measurement of goodness of prediction. Q2 is calculated via cross-validation as follows: the dataset is divided

into seven parts, where 1/7th of the data is removed each cycle in a systematic order. The remaining 6/7th of the data

is then used to predict a new model. This procedure is repeated until a complete set of new models has been

predicted. Thereafter, cumulated data is compared with the original model for the calculation of Q2. The arrows

indicate the theoretical order of the procedures.

Ethical statement

The ethical committee for Northern Sweden at Umeå University granted ethical

permissions for Paper I (Dnr 2011-236-31M) and Paper II-III (Dnr 2016-260-

31M) and the studies were conducted according to the guidelines expressed in the

Declaration of Helsinki – Ethical Principles for Medical Research Involving

Human Subjects 2008 [146]. As strenuous exercise and maximal performance

are part of the daily routines of elite athletes, included procedures, and all

competitive results are openly published no ethical issues are visualized.

Regression

X2

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X4

-

-

-

-

-

-

-

X5

-

-

-

-

-

-

-

6/7th

Pr

2

x 7

Pr Original datavs

2

Predicted data

f s

30

Results

Paper I

This study investigated the predictive power of aerobic test results and

anthropometric data for competitive performance (indicated by FIS points) of

junior elite alpine skiers. The study included physiological test results from

twenty-three (n = 23) junior elite male and female alpine skiers. While principal

component analysis indicated that male and female skiers could be separated

based on their aerobic test results, none of the included variables were of

significant importance for performance. The best multivariate prediction models

reached R2 = 0.51 to 0.86 and Q2 = −0. 3 0. 8. Although several significant

regression models could be observed, none of these met the criteria for valid

models (R2 >0.8, Q2 >0.5, s g f y “y s” f r 2 cross-validation)

(Table 2 in Paper I). The lack of validity of these models was confirmed via cross-

validation by permutation. Also, an important finding in this study was that FIS

points demonstrate a non-normal distribution when treated as continuous data.

As such, this finding may be an explanation for inconsistent results among

previous studies that have examined the importance of aerobic test results for

alpine skiing performance (Table 1).

The lack of performance predictive power of included physiological test results in

this study is exemplified in Figure 5-6.

Figure 5. Correlations between physiological test results and FIS points in SL and GS for thirteen

junior elite male alpine skiers age 16. A) Orthogonal projections to latent structures visualize correlations

between variables (X = 16, Y = 2, n = 13). Physiological test results and FIS points (labeled as rank) located in the

same or opposite part of the loading plot are correlated. The horizontal axis displays the X- and Y- loadings of the

predictive component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal component. For

example, a high value (max = 1) means that the component is aligned with the original variable, a value close to zero

X-

an

d Y

-lo

ad

ing

sfo

r t

he

fo

r f

irs

tY

co

mp

on

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FIS SL ranking

R2VY

Q2VY

A B

31

shows that it has no influence. A low value (min = –1) indicates the opposite influence. B) The X/Y overview plot

shows the cumulated R2 and Q2 values for the model. The plot indicates a low predictive power (R2 = 0.25 to 0.45,

Q2 = − 0.50 to – 0. 27).

Figure 6. Correlations between physiological test results and FIS points in SL and GS for ten junior

elite female alpine skiers age 16. A) Orthogonal projections to latent structures visualize correlations between

variables (X = 16, Y = 2, n = 10). Physiological test results and FIS points (labeled as rank) located in the same or

opposite part of the loading plot are correlated. The horizontal axis displays the X- and Y- loadings of the predictive

component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal component. For example, a high

value (max = 1) means that the component is aligned with the original variable, a value close to zero shows that it

has no influence. A low value (min = –1) indicates the opposite influence. B) The X/Y overview plot shows the

cumulated R2 and Q2 values for the model. The plot indicates a low predictive power (R2 = 0.65 to 0. 71, Q2 = − 0.25

to – 0.24).

Paper II

This study investigated the predictive power of the physiological test battery

Fysprofilen for competitive performance (indicated by FIS points) of senior elite

female alpine skiers. Fysprofilen is the official test battery of the Swedish Olympic

Committee and consists of thirteen different tests divided into four main

categories: strength, power, aerobic and anaerobic. The study included

physiological test results from fourteen (n = 14) senior elite female alpine skiers.

Principal component analysis demonstrated 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 for either SL (U = 99.5, z = -0.07, p = 0.95) or GS (U = 80,

z = -0.83, p = 0.41), indicating that Fysprofilen score points (summarized as

Fysprofilen Index) and competitive performance (indicated by FIS points) are

independent. Generated OPLS models for SL and GS reached R2 = 0.27 to 0.43

and Q2 =− 0.8 to - 0.17, indicating low predictive power. Cross-validation by

permutation confirmed the lack of validity of these models.

R2VYQ2VY

X

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sfo

r t

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tY

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X- and Y-loadings for the predictive components

32

The lack of performance predictive power of included physiological test results in

this study is exemplified in Figure 7-8.

Figure 7. Correlations between physiological test results and FIS points in SL for fourteen senior

elite female alpine skiers between the years 2012 and 2014. A) Orthogonal projections to latent structures

visualize correlations between variables (X = 15, Y = 1, n = 14). Physiological test results and FIS points (labeled as

rank) located in the same or opposite part of the loading plot are correlated. The horizontal axis displays the X- and

Y- loadings of the predictive component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal

component. For example, a high value (max = 1) means that the component is aligned with the original variable, a

value close to zero shows that it has no influence. A low value (min = –1) indicates the opposite influence. B) The

X/Y overview plot shows the cumulated R2 and Q2 value for the model. The plot indicates a low predictive power (R2

= 0.43, Q2 = − 0.17).

Figure 8. Correlations between physiological test results and FIS points in GS for fourteen senior

elite female alpine skiers between the years 2012 and 2014. A) Orthogonal projections to latent structures

visualize correlations between variables (X = 15, Y = 1, n = 14). Physiological test results and FIS points (labeled as

rank) located in the same or opposite part of the loading plot are correlated. The horizontal axis displays the X- and

A B

XY

X-

an

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sfo

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A BR2VY

Q2VY

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R2/Q

2v

alu

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33

Y- loadings of the predictive component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal

component. For example, a high value (max = 1) means that the component is aligned with the original variable, a

value close to zero shows that it has no influence. A low value (min = –1) indicates the opposite influence. B) The

X/Y overview plot shows the cumulated R2 and Q2 value for the model. The plot indicates a low predictive power (R2

= 0.39, Q2 = − 0.17).

Paper III

This study investigated the predictive power of a novel physiological test battery

for competitive performance (indicated by FIS points) of senior elite female

alpine skiers. The test battery was based on previous research and designed to

cover a wide range of physiological and anthropometric qualities (e.g., strength,

endurance, balance, and body composition). The study included physiological

test results from twelve (n = 12) senior elite female alpine skiers. When data were

analyzed on a group level, the best OPLS models for SL and GS reached (R2 = 0.39

to 0.40, Q2 = 0.15 to 0.21), indicating low predictive power. In contrast to Paper

II, however, the included physiological test results in this study generated valid

models (as confirmed by cross-validation by permutation). Importantly, when

data were analyzed on an individual level, valid models with high predictive

power were generated (R2 = 0.88 to 0.99 and Q2 = 0.64 to 0.96). In order to

exemplify the unique individual profiles of equally top-ranked skiers, individual

OPLS models for SL and GS were generated, and subsequently compared with

the SUS plot analysis.

34

Discussion

The overall aim of this doctoral thesis was to identify physiological and

anthropometric variables valid for prediction of competitive performance in

alpine skiing. The main result of Paper I was that included aerobic test results,

neither alone nor in combination with anthropometric variables, could predict

competitive performance of junior elite male and female alpine skiers. The main

result of Paper II was that included physiological test results could not predict

competitive performance of senior elite female alpine skiers. Even when

converted into separate score points or as a summarizing test score (Fysprofilen

Index), the included tests failed to discriminate between skiers on an individual

level. These results were confirmed in Paper III, where included physiological

test results failed to generate OPLS models with high predictive power (R2 and Q2

>0.6) for competitive performance of senior elite female alpine skiers on a group

level. In contrast, when data were analyzed on an individual level, valid models

with high predictive power could be generated. Collectively, findings indicate that

none of the investigated physiological performance tests are predictive for

competitive performance on a group level. Rather, ranking data and physiological

test results must be collected over time and analyzed using individual profiling.

Why is there a lack of predictive power on a group level?

Competitive performance of alpine skiers is affected by a number of interacting

factors [134,149], including the mental, social, technical and physiological

dimensions [46]. As this thesis focus on physiology, other dimensions were not

directly investigated, but are to some extent incorporated in the discussion. The

significance of particular dimension varies between individuals, as exemplified

by the SUS-plots of physiological variables in Paper III. Individual variations, and

the lack of comprehensive testing of all dimensions, partially explain the lack of

predictive power when analyzing data on a group level.

Another explanation for the lack of significant models may be due to the lack of a

reliable outcome variable, as validity and reliability interact [6,31]. As stated by

Currell and Jeukendrup [31], "A protocol can be reliable, but not valid, while a

valid protocol must be reliable". These issues are discussed in more detail below.

The reliability of the outcome variable (Y) affects the validity of the test protocol

(X). In this thesis, FIS points were used as the outcome variable, an indication of

competitive performance. As extensively discussed in Paper II, the ranking

system has been criticized for not being fair (primarily because of how points are

calculated) and encouraging an opportunistic behavior [92].

35

However, the main problem with the use of FIS points as an outcome variable is

that individual differences in ranking points over time (Figure 9) are not

exclusively due to changes in physiological performance (concluded in Paper I-

III and exemplified in Figure 10). Factors such as starting order, participating

competitors and previous race results also affect the ranking position. Hence, FIS

points are probably not at any given time a reliable measure of physical

performance. Consequently, no matter the reliability and validity in

measurements of physical performance tests, prediction of competitive

performance cannot be performed with high accuracy (closeness to the truth) and

precision (closeness between repeated measurements). Thus, when data is

analyzed on a group level, individual changes in terms of ranking will inevitably

result in models with low predictive power. While other performance outcomes

than FIS points could have been used (e.g., time trails during training and

individual competition results), none of these represent true long-term

competitive performance. Despite its apparent shortcomings, FIS points are

currently the most reliable and valid indications of competitive performance over

multiple competition seasons.

Figure 9. Overview of the change of FIS points in GS between the season 2011/2012 until the season

2016/2017 for all participants included in Paper III. Data were extracted from the 11th FIS points list

published each season. An upward trend indicates an improvement in FIS points while a downward trend indicates

a deterioration. For ethical reasons and in order to protect each participants identity, the y-axis scale has been

removed.

Season2011/2012

Season2012/2013

Season2013/2014

Season2014/2015

Season2015/2016

Season2016/2017

Skier A Skier B Skier C Skier D Skier E Skier F

Skider G Skier H Skier I Skier J Skier K Skier L

36

Figure 10. Example of the individual change of FIS points in GS and physiological test results

between the season 2011/2012 until the season 2016-2017. The plot is an example of the change of

physiological performance and FIS points in GS for one of the participants included in Paper II-III. Ranking data

was extracted from the 11th FIS point list published each season. Included physiological test results have been scaled

proportionally to FIS points for an illustrative purpose. An upward trend indicates an improvement in performance

while a downward trend indicates a deterioration. For ethical reasons and in order to protect the participant's

identity, the y-axis scales have been removed.

How important is the physiological dimension?

As shown in Figure 1, the physiological dimension is just one of several interacting

dimensions affecting competitive performance in alpine skiing. As this thesis only

investigated the importance of physiological test variables, the proportion of

physiology in relation to athletic performance cannot be determined. High

physiological performance is important in almost every sport [9]. The ability to

generate high neuromuscular strength and power [54,133], high oxygen uptake

[54], superior coordination and change of direction ability [20] are just some of

the physiological qualities that often characterize top-level athletes. In alpine

skiing, physiological qualities such as muscle strength, aerobic and anaerobic

capacity, balance, core strength and core stability have all been proposed as

prerequisites for elite performance [4,66,97,108]. These arguments have been

supported by numerous studies [3,5,32,55,93,94,97,103,127,152,162], all finding

significant correlations between physiological test variables and skiing

performance.

As indicated by our results, these claims may not be valid. First and foremost, it

can be concluded that competitive alpine skiing does not seem to require

physiological qualities that can be classified as extreme in terms of human

performance. If this were the case, some of the participants included in this thesis

Season2011/2012

Season2012/2013

Season2013/2014

Season2014/2015

Season2015/2016

Season2016/2017

FIS points GS Body mass (kg) Hand grip (kg)

CMJ (cm)

37

would probably not belong to the absolute elite. Rather, our results show that

athletes with significantly different physiological capacities can reach comparable

elite level performance. In other words, one weakness can be compensated for by

another strength. Also, in agreement with previous studies [19,99,102,140,154],

our findings suggest that differences in competitive performance between groups

of skiers cannot be explained by physiological test results alone. For example, in

Paper I junior elite female skiers displayed similar peak V̇O2 values as their

higher-ranked senior counterparts, included in Paper II-III. Some studies have

identified differences in physiological performance between skiers at different

competitive levels [32,114]. However, this is no proof of causality; physiological

characteristics may not be related to, or the reason for, the difference in ranking.

Furthermore, as discussed briefly in Paper II-III, there may be an issue with the

choice of statistical method in the abovementioned correlation studies as almost

all of these have applied P rs ’s rr to ranking data. For example, as

demonstrated in Paper I, FIS points is an ordinal variable based on a sigmoid

function which, if assigned as continuous data, does not exhibit a normal

distribution. However, as P rs ’s rr assumes constant variance and

linearity [25], it is unable to detect monotonic relationships in data. That is,

applying Pears ’s rr to this type of data may not be optimal. Another

concern with published studies is the lack of cross-validation as this technique

could have been used to assess the validity and reliability of observed

correlations. As such, the results in some previous studies may be due to chance,

which to a certain extent could also explain the inconsistent results reported in

different publications.

The question one must ask is then; how important is the physiological dimension

for alpine skiing performance (Figure 1)? As this thesis has shown, the question

will probably never be fully answered as the relative importance of each

influencing dimension may vary on an individual level. What can be stated,

however, is that one or a few physiological test variables will not discriminate

between skiers once they have reached a certain level of performance. Skiers with

similar physiological characteristics can reach different competitive performance

levels, and vice versa. Alternatively, physiological variables not measured by us,

or other research groups, are the ones of importance. Regardless, there is an

imminent risk that the use of inappropriate statistical methods may have misled

research focus and that the importance of certain physiological qualities for skiing

performance may be overemphasized.

Despite extensive physiological testing, included test results could not generate

models valid for prediction of competitive performance. Rather, our results

indicate that the importance of the physiological dimension is likely due to

variations on an individual level. Thus, it can be concluded that the relative

38

importance of all influencing dimensions must be considered when investigating

the sport-specific performance of athletes (Figure 1).

Is there an assumption of validity?

With the previous section in mind, another interesting question is, therefore, why

the physiological dimension seems to get so much attention from coaches and ski

federations alike? One explanation is probably that physiological test results are

often easier for coaches to understand in comparison to results provided by tests

of other essential qualities (e.g., mental "performance"). For example, the results

of a jump test performed on an infrared jump mat are likely easier to interpret

and evaluate compared to the assessment and evaluation of reaction time and

decision making in the virtual reality [117]. Furthermore, assessment of social

skills can be an overwhelming task as this includes measurements of verbal and

non-verbal interactions, as well as communications with others in a specific

context. Most likely more difficult to comprehend than results from a body mass

assessment using a regular body weight scale [81]. Content validity [83] may also

(incorrectly) be assumed [109] due to the fact that people are generally inclined

to accept arguments that support their knowledge and beliefs. To understand the

world around them, people tend to simplify complex problems [50]. Because

performance in alpine skiing can be difficult to quantify, coaches and leaders may

be convinced that selected parts of the physiological dimension are crucial for

competitive performance [109]. It is, therefore, reasonable to believe that some

of these coaches and leaders may seek evidence to support their presumptions.

Another plausible explanation may be that many coaches and leaders assume

scientifically reported results to be valid and reliable, as this is the very core tasks

of science [120]. However, in the field of alpine skiing research, it seems almost

as if the scientific community itself may have contributed to creating some

misconceptions regarding the importance of certain physiological qualities for

skiing performance. This, in turn, seems to have created a form of chain reaction

where coaches and leaders trust what researchers say, and vice versa [109]. The

importance of applying correct scientific methods, study designs and statistical

analyses when reporting results emphasize the responsibility researchers have

towards society [120].

Validity and reliability of physiological tests in alpine skiing

During the last decade, physiological testing of athletes has become an essential

part of high-performance sports [139]. Due to the often dynamic and complex

nature of sport [22,31], a controlled simulation and estimation of athletic

performance can provide competitive advantages [17]. Among the many benefits

of conducting physiological tests is the opportunity to establish baseline values

that provide athletes with an insight into their current physiological status (data

39

useful when conducting comparative follow-up tests) [28]. Physiological tests can

also provide incentives for athletes to try to beat previous test records, providing

motivation in their preparatory training [158]. Testing athletes' physiological

capacity is important also when designing exercise programs for maximized

training efficiency by addressing correct physiological qualities [22]. Finally, test

results are used by coaches and managers as selection criteria for recruitment to

teams and participation in competition.

To generate useful results within a competitive context, a performance test

protocol must be valid and reliable to the performance in question [31]. For the

athletes themselves, the validity and reliability of the testing protocol are critical

as they presumably perform physiological tests with the intention of identifying

strengths and weaknesses in relation to their sport. In sports performance

research, different types of validity and reliability can be applied [31]. In general,

validity refers to which degree a test protocol or instrument measures what they

intend to measure [61] whereas reliability refers to the extent to which they

provide consistent results [6]. For physiological test protocols, validity can be

divided into three main categories: logical validity, construct validity and

criterion validity [31]. Logical validity can be defined as a subjective assessment

of whether the test measures what it intends to measure [147]. Construct validity

refers to the degree to which the test protocol measures the theoretical construct

or the characteristics that are examined (e.g., a comparison between two groups

in terms of physiological performance) [147]. Lastly, criterion validity indicates

to which degree a test protocol correlates with a present or a prospective external

criterion [31,147]. Hence, for alpine skiing, criterion validity refers to the extent

to which of a test protocol can simulate or predict sport-specific performance.

Based on our results, however, it is unlikely that a performance test protocol for

alpine skiers, applied on a group level, will ever achieve high logical, construct or

criterion validity for several reasons. In the case of logical validity, high validity

applies only if the logical argument is true [147]. As discussed above, several

previous studies have found a significant correlation between physiological test

results and points or ranking [3,32,55,77,93,94,97,103,127,152,162]. If these

results had high logical validity, this would have meant that, for example, athletes

with the highest V̇O2 value should also be the fastest skiers. However, as our

results show, it is relatively unlikely that this argument would be true.

To exemplify the issue with construct validity, an example with basketball players

follows. As in other sports, an assessment of the physiological characteristics of

alpine skiers may be necessary in order to recognize trend patterns in a specific

population, establish normative data, and provide educational content to coaches

and others involved in the development of young athletes [22,139]. While this

certainly can be important on an organizational level, our results indicate that

40

descriptive and normative data often are of little use to the very elite athlete. For

example, in a sport like basketball, athletes have a competitive advantage of being

tall [104]. For a professional basketball team, this is probably vital information

when they are about to add new players to their roster. For shorter basketball

players, this can be valuable information because they know that they must "train

themselves longer" to compete on the same terms as taller players. For NBA

players, however, this is probably not important information because they are

already playing at the highest level in their sport, where body size is no longer a

discriminating factor in terms of performance [104]. Although the basketball-

case may be an absurd example, our results indicate that this analogy most likely

also applies to elite alpine skiers, where physiological performance comparisons

with lower ranked athletes do not help top athletes improve their sport-specific

performance. This is especially evident when it comes to V̇O2peak, where the senior

elite athletes in Paper II-III exhibited similar values as the junior elite skiers in

Paper I.

Finally, in terms of criterion validity, skiing performance does not seem to be

predictable on a group level, at least not using physiological test results alone. As

already discussed, this can be due to the fact that several different dimensions

(Figure 1) influence competitive skiing performance [46,134,149] and that the

relative importance of these dimensions may vary on an individual level. Most

likely, this is also due to the lack of a reliable outcome variable [6,31] as FIS points

have a considerable within-group variation between seasons (Figure 9). Because

of these considerations, it is highly unlikely that a physiological test protocol

intended for alpine skiers, applied on a group level, will ever achieve high

criterion validity (i.e., high cross-validated Q2) for true racing performance. As

such, all these arguments also emphasize the need for a new approach when it

comes to assessing and evaluating the physiological performance of elite athletes.

Methodological considerations

Other dimensions

Because this thesis has focused on the physiological dimension, the importance

of other influencing dimensions for alpine skiing performance has not been

investigated. Given the fact that competitive performance in alpine skiing is

affected by dimensions other than just the physical (such as equipment, skiing

technique, and mental performance), the relative importance of these dimensions

could also be important to investigate. By including other dimensions, the

predictive power for competitive performance on a group level would maybe have

been improved.

41

Menstrual cycle effects

The menstrual cycle phases and fluctuations of hormones may affect trainability

[156] and physiological performance in women [69]. This may, in turn, explain

part of the individual variations in physiological performance between different

test occasions (Figure 10). As physical testing occurred in random orders for

several years, systematic errors in data were avoided. Implementation of tests

based on the menstrual cycle may prove difficult, as the cycle does not always

start on the same day and vary in length [69]. Regardless, it was beyond our

control when tests were carried out.

Other predictive modeling procedures

Besides using only PCA and OPLS, it would also be interesting to investigate the

predictive power of physiological test results for competitive performance using

other predictive modeling procedures. While we are confident that the analytical

techniques used in this thesis are useful in this type of context [87], other

predictive methods could have been used to ensure the validity of our results.

More tests specifically designed for alpine skiers

This thesis could also be criticized for not including sufficiently challenging and

specifically designed tests for alpine skiers. Examples of these could be a more

demanding balance test performed in ski boots, a test for the assessment of

isometric and eccentric muscle strength as well as specific core strength and core

stability tests. While this would have been desirable, it could be argued that valid

tests for assessing the sport-specific performance of alpine skiers do not exist.

Instead of examining the validity of numerous individual tests for skiing

performance, we focused on investigating the predictive power of already

validated tests or those previously used in research on alpine skiers. In addition,

as the end goal of this doctoral thesis was to provide practically useful results, we

mainly chose to include physiological performance tests that can be performed

without access to advanced laboratory equipment.

Fédération Internationale de Ski points

As mentioned, one of the limitations of this thesis was also the use of FIS points

as an indication of competitive performance. While other skiing performance

metrics could have been used, FIS points still seem to be the best indication for

actual competitive performance over several competition seasons. Still, a more

reliable measure of performance had been desirable, which can hopefully be

determined in future research.

42

The novel approach of this thesis

Elite performance in any sport is multi-dimensional [71]. Consequently, data

analysis must follow the same trait. Traditionally, variables are analyzed one by

one, using bivariate statistical methods [125]. Finding correlations between

capacities and performance may not correspond to high predictive power. That

is, a correlated variable may or may not be essential for competitive performance.

This thesis explores, for the first time in an elite athletic setting, a novel approach

of multivariate analysis proven useful among firefighters [86]. By doing so, both

correlation and importance of each included physiological testing result are

evaluated. A move from evaluating test results on a group level to individual

profiling is suggested. Also, other dimensions presented in Figure 1 must be

incorporated in future research in order to cover the complex aspects of athletic

performance.

Application in sports

Although no valid physiological tests for competitive performance were

identified, the results of this thesis can hopefully encourage both athletes and

coaches involved in alpine skiing to continue with the implementation of

physiological tests. However, as our results show, these tests should be

implemented over time with the intention of identifying important physiological

qualities for sport-specific performance on an individual level. A test battery

intended for alpine skiers should include valid and reliable tests aimed at

assessing physiological performance in general (e.g., strength, endurance,

mobility and balance) and providing easy-to-understand and practically useful

results to training advisors and athletes alike. This in order to provide with a

comprehensive individual physiological profile that can be correlated with sport-

specific performance (e.g., FIS points) while simultaneously providing with

results that can be implemented (and be useful) in everyday training.

Furthermore, the results and analytical procedures presented in this thesis can

also be useful in sports such as ski cross, snowboard and cross-country skiing

where competitive performance is quantified using a similar ranking system such

as the one used in alpine skiing. Besides the similarities between the ranking

systems, it is also likely that the competitive performance of these athletes, as for

alpine skiers, is affected by several interacting dimensions where, for instance,

the relative importance of physiological performance varies on an individual

level.

43

Conclusion

A more holistic approach to testing of elite athletes must be considered, as elite

performance is multi-dimensional. Before implementing extensive testing of

athletes, validity and reliability of protocols applied must be determined. Failing

to meet these criteria may have extensive consequence, such as a potential waste

of the limited resources for athletes, coaches, clubs, and ski federations as well as

misguided training.

44

Acknowledgments

During my years as a Strength and Conditioning coach at Ski Team Sweden

Alpine and as a PhD student, I have met several fantastic people who have all

inspired me and, in some way, contributed to the completion of this work. I would

like to express my deepest gratitude to the following persons:

My supervisor Christer Malm for everything that you have done for me.

Without you I would not have been where I am today and thanks to you, I have

gained a more profound knowledge, not only in the field of sports medicine but

also about myself. You are an extraordinary researcher but an even better person.

Thank you for sharing your expertise with me. I will be grateful for this forever.

My co-supervisor Apostolos Theos because you have always had time to listen

and because you have always been there for me. You are a kind-hearted person

who always tries to do the best for everyone. Thank you for sharing your

knowledge with me and because you have taught me some of the Greek ways of

handling different situations in life.

My co-supervisor Ann-Sofie Lindberg because you have contributed structure

to this project. You are an incredibly skilled researcher and without you, the

completion of this project would have been so much more difficult. Thank you for

sharing your knowledge with me and for being such a nice and caring person.

My co-supervisor Richard Ferguson for your invaluable contribution to the

completion of this project. Thanks to your involvement and expertise, I have

always felt confident that I am on the right track. Thank you for being a part of

this project and for sharing your knowledge with me.

The former Sports Director at Ski Team Sweden Alpine, Anders Sundqvist, for

believing in me and for the opportunity to start this project. I will always be

grateful for everything that you have done for me. I am also grateful to survive the

colossal ice hockey tackle that you handed out to me during the 2015 World

Championship in Vail, USA.

My mentor and friend Tom Pietilä, who have supported and helped me ever

since I started my studies at the Section for Sports Medicine. Thanks for all the

advice and because you have continued to motivate and encourage me during

periods when it sometimes felt tough.

The personnel at the Section for Sports Medicine: Håkan Alfredson, Kajsa

Gilenstam, Lars Berglund and Lisbeth Wikström-Frisén for your

45

encouraging words and invaluable inputs during this work, Michael Svensson

for sharing your knowledge with me and for your encouraging words, Lars

Göran Fjellborg for your encouraging words and because you have adjusted my

back now and then, Daniel Jansson, for rewarding discussions and because

being an arch-enemy during important games between Sweden and Finland in all

major international ice hockey tournaments, Ewa Johansson, Britt-Marie

Eliasson and Jonas Lorentzon for help with all the administrative and

technical work, Ji-Guo Yu for encouraging words.

The personnel at the Sports Medicine Laboratory at Umeå University: Roger

Andersson, Lennart Burlin, Mikael Therell and Joel Sjölander for

invaluable help during the data collection and all the pleasant conversations in

between. Without your help, this project could not have been completed.

My former PhD student colleagues, Andreas Hult, Jonas Johansson, and

Tommy Henriksson because you have shown me th “ s” and

thereby inspired me to continue to work hard.

My friends and former colleagues at Ski Team Sweden Alpine, Anders Nilsson,

Sara Königsson and Mikael Junglind, for all the pleasant and rewarding

conversations. Thank you for believing in me.

The former head of the Swedish female alpine skiing national team, Fredrik

Steinwall, for giving me the opportunity to work with such an incredible group

of formidable athletes and colleagues.

All the fantastic athletes I have had the privilege to work with. You are the biggest

reason why this project was started. You all have been a huge inspiration during

all these years. Thanks for all the wonderful memories, I will carry them with me

for the rest of my life.

In addition, I would like to express my deepest gratitude to my family:

My sister Josefine Nilsson and her wonderful family for their love and support.

You are all fantastic and I wish that we could meet each other more often.

My fiancé Ida Johansson because you have always been there for me. Without

you, I would never have been able to complete this project. Now I finally have

some more time to help you with all your crazy ideas. I love you.

Finally, my parents Bo Nilsson and Birgitta Nilsson who have always

supported and believed in me. I could not have wished for better parents than

you. I am proud to be your son. I love you.

46

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