Master’s thesis in Sport science
Comparison of Pacing Strategies between Sprint and Individual biathlon competitions: Evaluation of Speed and Heart Rate Profiles
submitted to the Faculty of Psychology and Sports Science Leopold-Franzens University, Innsbruck
submitted by
Daniel Langegger, BSc.
Supervisor Univ.-Prof. DDr. Martin Burtscher
2nd Supervisor Univ.-Prof. Dr. Øyvind Sandbakk, PhD
Trondheim, 16.2.2019
ABSTRACT
The presented thesis aimed to investigate pacing profiles in two different types of biathlon races, the sprint and the individual discipline. Biathlon competition events in a 10-km sprint and 20-km individual discipline event were analyzed during 2 consecutive days by an integrated Heart Rate (HR) monitor and Global Positioning System (GPS). Nine elite male -1 biathletes (HRpeak 197 ± 7 beats min ), who regularly competed on National Championship races, were tested in the cross-country skating technique. Only comparable sections of the race were analyzed between disciplines and sprint laps 1-3 were equated with the starting lap, middle race lap and final lap in individual discipline, respectiviely, individual lap 1, 3 and 5. For results, speed profiles were viewed in relation to exercise intensity monitored by HR and further designated to a predefined pacing strategy. Results for pacing revealed positive pacing in the sprint competition, whilst reverse J-shaped pacing was found in the individual event. Sprint results demonstrated higher average speed in uphill and downhill sections (+ ∼4%, p <.001), respectively, in uphill sections (+ ∼10%, p <.001) throughout the race. HR profiles in individual were higher in both, uphill and downhill sections in the starting lap, and revealed higher HR profiles in sprint in the final lap (p <.001). HR showed a progressive lap-to-lap increase from the first to the other laps in sprint (p <.001), whilst a variable HR profile was found in individual discipline displaying an increase in HR only from lap 1 to lap 2 and lap 4 to lap 5 (p <.01). In sprint competition, speed and HR profiles revealed a gradual increase in exercise intensity throughout the race, whereas a variable profile of exercise intensity after a fast start strategy was found in individual competition. The selection of pacing seems to be related to distinguishing impact of shooting on overall performance, consequently resulting in different intensity during XC skiing. Sprint races reflect a strategy close to “all-out”, while pacing in individual appears to be more carefully to avoid possible negative impact on shooting misses. Indications reveal that biathletes’ exercise intensity is selected in order to balance out the physiological load with the risk of failed shooting and the duration of physical activity.
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SUMMARY IN GERMAN
Die vorliegende Arbeit diente der Untersuchung von Pacingstrategien in 2 unterschiedlichen Wettkampfdisziplinen des Biathlonsports, der Sprint- und der Individualdistanz. Die Wettkämpfe der 10-km Sprint- und 20-km Individualdisziplin wurden an 2 aufeinanderfolgenen Tagen anhand eines mobilen Herzfrequenzmessers und eines Global Positioning Systems (GPS) analysiert. -1 Neun männliche Biathlete (Herzfrequenz(HF)peak 197 ± 7 beats min ), welche regelmäßig bei nationalen Meisterschaften teilnahmen, wurden in der Skatingtechnik des Skilanglaufs getestet. Ausschließlich miteinander vergleichbare Abschnitte des Rennen wurden für die Analysen ausgewählt und die Runden 1-3 im Sprint mit der Startrunde (Runde 1), dem Mittelabschnitt (Runde 3) und der finalen Runde (Runde 3) des Individualbewerbs verglichen. Zur näheren Interpretation wurden die Geschwindigkeitsprofile in Relation zur Intensität während der Belastung, ermittelt durch die Messung der HF, betrachtet und einer im Vorfeld definierten Pacingstrategie zugeordnet. Es zeigte sich ein positives Pacingmuster im Sprintwettkampf, während ein ‘reverse J-shaped’-Pacing im Individualwettkampf ersichtlich war. Die Resultate ergaben höhere Durchschnittsgeschwindigkeiten in der Sprintdistanz über Rennabschnitte mit Anstiegen und Neigungen (+ ∼4%, p <.001) sowie in Rennabschnitten in welchen ausschließlich Steigungen untersucht wurden (+ ∼10%, p <.001). Profile der HF in der Individualdistanz zeigten höhere Durchschnittswerte in den untersuchten Rennabschnitten zu Beginn des Rennens, und höhere HF im Sprint in der letzten Runde des Wettkampfs (p <.001). Bei den HF konnte ein sukzessiver Anstieg der Belastungsintensität von der ersten bis zur letzten Runde in der Sprintdistanz beobachtet werden (p <.001), während die HF-Profile im Individualrennen ein variables Profil aufwiesen und ein Anstieg der HF lediglich von Runde 1 zu Runde 2 sowie von Runde 4 zu Runde 5 beobachtet wurde. Im Sprintwettkampf wurde durch die Berücksichtigung von Profilen der Geschwindigkeiten und HF auf einen graduellen Anstieg der Belasungsintensität über die gesamte Renndistanz geschlossen, während sich nach einem schnellen Start mit stark ansteigender HF im Individualwettkampf ein variables Profil der Belastungsintensität mit einem ‘reverse J-shaped’-Pacing zeigte. Die Auswahl der Pacingstrategie im Biathlon scheint in Verbindung mit Unterschieden hinsichtlich des Einflusses des Schießens auf das Gesamtresultat zu stehen, was die Intensität beim Langlaufen maßgeblich zu beeinflussen scheint. Im Sprintwettkampf zeigt sich die Strategie einer “all-out” Intensität, während das Pacingprofil im Individualbewerb mit größerer Vorsicht gewählt wurde. Die Intensität der Biathleten scheint somit je nach Disziplin insofern gewählt, sodass die physiologische Leistungsfähigkeit mit Faktoren wie der Dauer der Belastung und den negativen Folgen durch Schiessfehler harmonisiert wird.
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ABBREVIATIONS
bpm Beats per minute d Cohens’s d (effect size) F Fisher–Snedecor distribution HI High intensity HR Heart rate
HRpeak Peak heart rate
HRmax Maximal heart rate IBU International Biathlon Union GPS Global Positioning System MANOVA Multivariate analysis of variance NSD Norwegian Centre for Research Data NL National level p Level of significance r Correlation coefficient SD Standard deviation WC World cup
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DANKSAGUNG
Dieses Projekt wäre ohne des damaligen Insistierens von Thomas Stöggl, meine örtlichen Überlegungen hinsichtlich meines 1.Erasmusaufenthalts nochmals ausgiebig zu überdenken, mit an Sicherheit grenzender Wahrscheinlichkeit nie entstanden. Hierfür, nämlich dass du mich damals in den skandinavischen Norden anstatt auf die iberische Halbinsel entsendet hast, und für zahlreiche Denkansätze, möchte ich dir herzlich danken, Tom. Im Zuge der Anfertigung der Masterarbeit war ein weiterer Mann von herausragender Bedeutung, der mich vom 1.Tag an im Olympiazentrum in Grånasen, Norwegen, exzellent betreute: Øyvind Sandbakk. Lieber Øyvind, du hast es in herausragender Art und Weise verstanden durch wenige Worte, kurze und präzise zusammengetragene Gedanken, mich strukturell über den gesamten Zeitraum hinweg so sicher und ausbalanciert zu begleiten, sodass ich die Arbeit wohl selbst in norwegischer Sprache problemlos bewältigt hätte. Takk skal du ha! Einen weiteren Löwenanteil, diese Arbeit in sichere Gewässer zu leiten, nimmst aber du ein, Martin Burtscher. Martin, mir ist durchaus bewusst, dass du in deiner gewohnt bescheidenen Art und Weise dein Zutun lediglich minimal beziffern würdest, aber in dieser Hinsicht muss ich dir leider (oder Gott sei Dank) widersprechen. Du warst stets da, wenn ich in irgendeiner Hinsicht einen Rat brauchte, was für einen Professor im emeritierten Zustand nun wirklich keine Selbstverständlichkeit darstellt. Schon alleine deine Reaktion auf meine damals leicht skeptische Frage, ob du es dir denn selbst im Leben nach der Universität vorstellen könntest mich bei dieser Arbeit zu betreuen, sprach Bände: mit einem Lächeln hast du mir ohne den leisesten Ansatz von Zweifel sofort erwidert, dass dies für dich absolut kein Problem darstellt und du mich gerne betreuen wirst. Vielen, vielen lieben Dank für all die zahlreichen Mails, Gespräche, dein Beistand in motivational schwierigen Zeiten sowie der souveränen Abwicklung aller organisatorischen Dinge, die im Rahmen eines Auslandsaufenthalts wirklich keine Kleinigkeit darstellten.
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Contents
INTRODUCTION 8 METHODS 14 Subjects 14 Self-Perception 15 Overall Design 15 Race profile analyses 16 Instruments and materials 17 Statistical analyses 18
RESULTS 18
DISCUSSION 31 Speed Profiles/Pacing 31 Heart-Rate Profiles 34
CONCLUSIONS 36
LIMITATIONS 37
REFERENCES 37
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Introduction
Over the last 250 years, biathlon has developed from a classic military sport practiced by the Scandinavian countries to a worldwide, high-performance sport incorporated into the International Biathlon Union (IBU). The Winter Olympic sport biathlon contains intensive loops of cross-country skiing (XC) while using the skating technique interrupted by either 2 or 4 shot series of rifle shooting in prone or standing position. Biathletes have to carry their 3.5-kg rifle on their back while XC skiing, only taking the weapon off to fire five shots at the shooting range. Two of the main disciplines in biathlon are sprint- and individual distance races, which differ substantially from each other in regard to their competition structure. Whereas sprint events have a total distance of 10-km, containing 3 loops of XC skiing suspended by one shooting series in prone- and one shooting series in standing position, the individual races double the amount by including a total distance of 20 km. In detail, the individual race contains five loops interrupted by two shot series in prone and two shot series in standing position, which have to be performed alternately starting with prone shooting. Additionally, a major difference between sprint- and individual races lies in the penalisation of missed targets: Whilst misses in sprint events are sanctioned with a penalty circuit of 150- meters (m), corresponding to approximately 25 seconds of time loss, a missed shot in individual events automatically results in adding a one-minute penalty to the athletes’ final time. Although research in biathlon race track analyses is scarce, existing studies revealed considerable divergences in speed level between the disciplines. At this, total race distance appears to be a distinguishing factor since former research in biathlon revealed considerable divergences in speed level between sprint and individual events. Examinations amongst Top 30 athletes showed average skiing speeds in World Cup, World Championship and Olympic Game sprint events of 7 m/s or slightly higher in contrast to individual races displaying average skiing speeds of approximately 6 m/s (Pustovrh et al., 1995; Cholewa et al., 2005). Findings from Luchsinger further revealed, that approximately 60% of the total race time is explained by XC skiing speed (Luchsinger et al. 2017). By looking at the demands and characteristics in biathlon, a review from Laaksonen et al. outlined the unique start-and-stop nature of the sport. Because of this, periods of HI (high- intensity) skiing are seperated by short intervals of recovery in which shooting is performed. Researchers further emphasized the requirement of effective delivery of oxygen and excellent
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skiing skills in biathlon by stating that a high lactate threshold and gross mechanical efficiency, in combination with a well developed aerobic capacity are essential in order to superior skiing performance (Laaksonen et al. 2018). Such evidence may be of exceptional importance since it reflects the high cardiovascular, metabolic, and technical demands in biathlon, both during XC skiing and when performing on the shooting range. It therefore seems worthwhile to investigate the effects of physical exercise on such factors in order to evaluate potential differences with regard to sprint and individual discipline. Findings from Hoffman et al. in biathlon research, showed a decrease in shooting performance after high intensity exercise on a bicycle ergometry. Even though Hoffman et al. illustrated that exercise intensity had only a minimal effect on shooting accuracy and precision for prone shooting, their findings demonstrated that shooting in the standing position was affected substantially (Hoffman et al., 1992). Based on these facts, Groslambert et al. further hypothesized that cognitive functions may have been impaired after intensive exercise (Groslambert et al. 1995). These results are in accordance with research from Grebot et al. who identified the aspect of combining intense exercise with a shooting task of precision rifle marksmanship as sufficient in order to test cognitive functioning (Grebot et al. 2003). Despite large increases in HR and rate of perceived exertion, examinations from Gallicchio et al. with biathletes on high national level demonstrated a constancy in rest-level shooting accuracy after HI blocks of
3-min at 90% of HRmax (Maximal heart rate). Since a decrease in monitoring capacity during testing was simultaneously found, Gallicchio et al. concluded that compensatory strategies through neural efficiency might have been adopted to the shooting process in order to cope with the increasing demands under intense workload (Gallichhio et al., 2016). Although possible assumptions about mechanism have been stated, it is yet not well established to which extent the cardiovascular system under HI exercise can be challenged, in order to deliver a successful shooting result. This seems to be especially relevant in biathlon competition, where distances in men events differ remarkably from sprint races having the shortest XC skiing courses on the one hand and individual events on the other. Consequently, the potential risk of missed shots because of physical stress could be crucial, when looking at the different demands for shooting, thus resulting in greater penalties in individual races. Former research underlined this by placing the shooting component in individual races more or less as important as the skiing time, whereas efficiency in shooting in sprint events was given a significantly lower influence on the final result (Pustovrh et al. 1995; Cholewa et al. 2005). In addition, former investigations in biathlon standing shooting demonstrated alterations after skiing exercise, thus resulting in significantly decreased postural control
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(Groslambert et al 1998; Groslambert et al. 1999) and negatively affected stability of hold (Hoffman et al. 1992; Ihalainen et al. 2018). The aforementioned findings might be particularly relevant in individual discipline, where a long-lasting exercise load of 20-km must be completed. This can be especially challenging, since such a high cardiovascular load may be crucial when biathletes need to fulfill their standing shooting attempts after 2, respectively, 4 completed laps of XC skiing. Furthermore, these deductions relate to findings from Laaksonen et al. stating a relationship between the complex task of aiming in shooting and postural and rifle stability (Laaksonen et al., 2018). In addition, research in military research is in agreement with the critical reflection with regard to the standing shooting position by naming an influence of upper body exercise on shooting performance (Evans et al., 2003). By looking at available race track results in biathlon competition, data on physiological parameters in sprint or individual races is very limited. In one of the few studies Hoffman and Street evaluated HR responses in competitions with similar distances to sprint and individual events. In both disciplines, skiing results revealed high intensities of approximately 90% of athletes’ HRmax and on average 85% in sprint as well as 87% in individual races when approaching the firing lane. According to results from Hoffman and Street, neither a type of discipline had a distinguishing influence on the average HR when skiers reached the firing range (Hoffman and Street, 1992). Research from Zinner et al. demonstrated only marginally differences in the percentage of HRmax compared to the data presented by Hoffman and Street. Their results based on HR data of elite male biathletes during five consecutive World Cup seasons revealed average values in skiing of 91.4 % in sprint and 92.4 % in individual competitions (Zinner et al. 2015). Although general analyses in biathlon were made to investigate HR on average and in sections close to the shooting range, there is currently no evidence on HR responses considering differences in terrain structure. Evaluations of physiological parameters on varying terrain might be useful, since former research in XC skiing emphasized that 50% of the racing time is spent on the uphill sections where individual performance varies widely (Sandbakk and Holmberg, 2014). Previous research including track analyses from Bolger et al. found a delay in athletes’ HR response, consequently displaying the increased intensity during the uphill sections in the beginning of the following flat or downhill section. Researchers analyzed WC XC skiers in international and classical competitions and it was further concluded, that average HR did not show significance with regard to the specific terrain section of the slope (Bolger et al. 2015). In accordance with that, results from
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Mognoni et al. revealed no indications toward higher HR in men and female XC skiers during uphill runs in comparison to the downhill sections. Even though a lack of significance was stated, they mentioned that in some cases HR during downhill skiing were somewhat higher than in the subsequent flat skiing section, further referring this behavior to payment of an oxygen debt. Mognoni et al. tested competitors in classical and skating distance races by summping up that HR values varied as a function of the slope and of repetitions on the same part of the track (Mognoni et al. 2001). Sprint and individual are both events in which biathletes race against the clock individually in order to finish the course in the shortest possible time. Such races are also known as interval start races and contrary to direct ‘head to head’ competition in which skiers are forced to compete against each other following a mass start (Formenti et al., 2014), the absence of direct confrontation permits athletes to accurately model and prepare their performance strategy individually (Foster and Snyder, 1993, Formenti et al., 2015). Moreover, former research outlined that team strategies and tactics do not play a role in interval start races, resulting in greater importance of pacing strategies during exercise (Stickland et al, 2004). A major distinguishing factor when looking at sprint and individual events might be an athletes’ ‘pacing strategy’ in regard to the specific event. Whilst biathletes in sprint competition have to complete a total distance of 10-km, athletes in individual races need to ski as much as double of the amount, consequently resulting in the selection of different pacing. Moreover, athletes racing in individual competition need to shoot twice as much as in sprint competition, accompanied by a more time-consuming penalization system in individual discipline, which may also affect the chosen pacing strategy. Here, investigations from Skorski and Abbiss defined a pacing strategy as the ability to appropriately distribute energy during athletic competition, in order to prevent premature fatigue prior to the completion of the event (Skorski and Abbiss, 2017). Moreover, Abbiss and Laursen described 6 different pacing strategies associated with endurance performance in their review by naming negative, all-out, positive, even parabolic-shaped, and variable pacing. As for parabolic-shaped-pacing, a further subdivision was created leading to 3 subcategories: U-shaped, J-shaped, and reverse J-shaped pacing (Abbiss and Laursen, 2008) (Figure 1).
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Figure 1 ⎢Illustration of pacing strategies in the style of Stöggl et al. (2018).
Although a decent number of research projects examined pacing in sports like cycling (de Koning et al. 1999; Corbett 2009) or running (Sandals et al. 2006; Tucker et al. 2006), pacing in different sports disciplines including XC skiing, has been much less investigated. Pacing analysis in biathlon is rare and to the best knowledge only Luchsinger et al. examined pacing strategies in sprint and individual biathlon competition events. Their findings revealed J-shaped pacing for both disciplines in both analyzed performance groups, athletes ranked top 10 as well as ranked 21-30. Though, contrary to the present investigation, pacing analyzed by Luchsinger et al. was dependent only on kinematic data represented by speed levels, but not on physiological parameters1 (Luchsinger et al., 2017; 2018). Compared to biathlon, pacing in XC skiing has been investigated to a much greater extent. Findings from a review of results by Stöggl et al. showed that pacing in XC skiing was mostly positive, irrespective of distance and as for this, research from Abbiss and Laursen defined positive pacing as a gradual decline of an athletes’ speed throughout the duration of an event (Abbiss and Laursen, 2008; Stöggl et al., 2018). With regard to different groups of performance, Stöggl et al. stated that while better skiers tended to use a more even pacing strategy, skiers with a lower level of performance/or experience exhibited more pronounced pacing. Furthermore, researchers pointed out that XC skiing might differ from other endurance sports due to the undulating terrain and various techniques involved in XC skiing races, subsequently leading to more complex situations by influencing how skiers regulate their exercise intensity, work rate, and their pacing (Stöggl et al., 2018). By looking at time trial races, terrain as a factor was also
1 see explanations on page 11 11
taking into consideration by Abbiss and Laursen. Researchers underlined, that optimal pacing strategies required are still unclear when athletes are competing under variable environmental conditions, by referring to different climate/altitude levels and varying terrain/wind (Abbis and Laursen, 2008). By looking at the aforementioned findings, it appears likely, that a general comparison of pacing strategies appears even more complex in biathlon, since the shooting process must also to be taken into consideration. Research from Formenti et al. in a 10-km skating simulated race, included analyses in pacing strategies depending on speed and HR responses. In their evaluations they reported a reverse J-paced strategy for male skiers finishing in the top 9, while HR gradually increased from the first to the other laps. The documented pacing strategy displayed a very high intensity reflecting the distribution of speed in the first lap, following a decrease of speed and a final spurt during the end of the race. If competitors finished outside the top 9, a different pacing strategy was observed by skiing the second lap fastest, the third lap worst, and the first and fourth lap equally fast. By looking at physiological variables, HR profiles of above 90% were characteristic of flat and uphill parts, whereas recorded HR between 80-90% were representative of downhill track parts. Due to their findings demonstrating a significant lower HR in the first lap although skiing at the highest speed, Formenti et al. emphasized a need to consider HR variables in relation to the pacing strategy occupied during a race. This therefore leads to a combined observation of exercise intensity monitored by HR as internal load and speed monitored by an assigned pacing profile as external load. The inability in synchronizing HR to speed levels might be due to a fast start strategy of the skiers, thus resulting in relatively constant HR intensity from the end of the first lap (Formenti et al. 2015). Different findings in XC skiing from Losnegard et al. demonstrated positive pacing accompanied by a slower skied final lap compared to the first lap in World Cup, World Championships, and Olympic events. Researcher examined women’s 10-km and men’s 15-km interval start races in classic and freestyle events, stating a quick start of the slower skiers relative to their average velocity followed by a greater decrease during the race compared with the fastest skiers (Losnegard et al., 2016). Pacing observations in distances close to individual races in biathlon were monitored by Hanley while examining elite half marathon runners from both sexes. Their results showed reverse J-shaped pacing profiles for nearly all performance groups, emphasizing relatively fast initial 5-km following a progressively slow down until 20-km, and finally speeding up again during the final 1.1-km (Hanley, 2016). Though, the substantially separating and most noticeable factor between endurance disciplines like half marathon running, respectively,
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traditional XC-skiing races compared with biathlon races has to be highlighted as the penalization of missed targets, potentially resulting in pacing profiles which are considered with a greater degree of caution. To the best knowledge, no previous research has compared sprint and individual races in biathlon as for IBU standard distances and only Hoffman and Street (1992) did so in an intervention with similar race distances over two decades ago. The purpose of the present thesis was to investigate pacing strategies in sprint and individual biathlon competition by calculating speed data, and designanting the revealed pacing profile to exercise intensity monitored by HR. It was therefore hypothesized that both, sprint and individual events, would show a J-shaped pacing structure. Precisely, we expected higher average speed throughout the race and especially in the uphill sections in sprint competition in comparison with individual event. With regard to physiological responses it was presumed that HR profiles in sprint races show a more excessive and gradual increase from the first to the last lap while HR profiles in individual race would remain more evenly. Additionally, we hypothesized higher speed and heart rate profiles in uphill sections in connection with higher HR profiles in the beginning of the following downhill section in sprint event compared to the same sections in individual distance race.
Methods
Subjects
Nine elite male biathletes (body height 184 ± 5 cm, weight 78 ± 7 kg, HRpeak 197 ± 7 beats min-1), who were regularly competing in national level (NL) races, volunteered to participate in the study. Subjects were either recruited in regard to their involvement in a Global Positioning System (GPS) intervention or received a request upfront. An introduction relating the procedures was forwarded to the athletes in advance, who signed an informed consent allowing them to withdraw from the study at any time without providing further explanation. Pre-information also included that athletes were aware of the fact that they have the right to know what info is stored and withdraw that info anytime if desired. The Norwegian Centre for Research Data (NSD) approved the study, which was conducted in accordance with the
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Declaration of Helsinki and reviewed by the Regional Ethics Committee of Southern Norway prior to the start of the study.
Self-Perception Before the start of each race participants were asked for self-assessed values of their motivation (on a range scaling from 1-10, starting from 1 = very low level of perceived motivation and ending with 10 = very high level of perceived motivation) as well as a one- time general assessment of their state of health (on a range scaling from 1-10, starting with 1 = very bad health state & ending with 10 = very good health state). The scaling was formerly used in testing at the National Centre for Elite Sports (Olympiatoppen, Granåsen, Norway).
Overall Design The races were held based on the official rules of the IBU. The race program included a sprint biathlon competition of 10-km on day 1 and an individual biathlon competition of 20-km on day 2, both in skating technique. The competition was organized in Granåsen (Trondheim, Norway) and the athletes were familiar to the track profile due to previous training and races. All of the athletes had to race 3 laps in the sprint event and 5 laps in the individual event. Whereas athletes in sprint competition had to execute 1 prone shooting between lap 1 & 2 as well as 1 standing shooting between lap 2 & 3, individual competition included shooting twice in prone position (midst lap 1 & 2 and lap 3 & 4) and standing position (midst lap 1 & 2 and lap 3 & 4). In case of a shooting error an athlete had to run a 150-m penalty loop of approximately 25 seconds of time loss in the sprint event resp. were sanctioned with a one- minute penalty in the individual event. Both events were run in skating technique while a GPS system and a heart-rate monitor was applied for continuously measuring athletes’ time, speed and heart rate during the whole race. In both competitions racers used their own equipment which included the rifle (3.9 ± 0.2 kg), pole length (91 ± 1% of body height), skating XC skiing shoes and skating XC skies. Weather conditions were stable in sprint and individual competition, the race course was machine-groomed and athletes’ skies were prepared to meet the requirements of the present snow conditions. All athletes completed their warm-up prior to the competition in order of their personalized protocols.
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Race profile analyses Race profiles were divided into three types of sections: uphill terrain sections (red labelled), downhill terrain sections (green labelled) and flat terrain sections (grey labelled) (Figures 2 & 3). Section boundaries were defined as points where a change between positive and negative gradient in the course profile was present. This did not apply for the flat terrain section, which was created in order to enable direct comparability between laps and sections/half-sections/quarter-sections in sprint and individual competition. The flat terrain section was therefore excluded from the analyses, including a total of 206-m in sprint and 841-m in individual race to compare adjusted laps of total 2726-m in sprint with laps of total 2720-m in individual discipline. Due to the difference in laps by aiming to compare sprint with individual races, the following 3 considerations were applied: (I) comparison of sprint laps (1-3) with laps 1, 2 & 3 in individual discipline, (II) comparison of sprint laps (1-3) with laps 1, 3, 5 in individual discipline and (III) comparison of sprint laps (1-3) with laps 3, 4, & 5 in individual discipline. This has been selected in order to couple sprint discipline directly to (I) beginning till middle of individual races, (II) beginning, middle and finish of individual races and (III) middle till finish of individual races. All Out of these 3 comparisons, (II) was chosen for direct comparison of sprint with individual distance in detail, and to ensure the simultaneous integration of both, beginning and end of a race. This approach was selected since positive pacing was found in a systematic review evaluating pacing strategies in 10-50-km XC skiing races, highlighting considerable lap to lap fluctuations in speed level throughout the entire races (Stöggl et al., 2018). Only sections with lengths of 100-m or longer were selected for comparison tests of specific sections. This is in accordance with research from Aughey in GPS technologies in field sports, stating that the longer the duration of a measured task, the more valid a GPS measured distance becomes. Aughey further named an example in sprinting, in which the standard error was reduced by 2/3 when sprints of 40-m where juxtaposed to sprints of 10-m (Aughey, 2011). In order to collect the required data to look at speed differences across disciplines, total meters of a lap/section/subsection measured by GPS were divided by total seconds of the corresponding lap/section/subsection. Speed data of the shooting range was also used for analyses between the disciplines. Average differences between uphill-/downhill sections in sprint and individual discipline were 9.3 ± 5.4-m. During the laps athletes had a total altitude gain of 103-m in sprint and 127-m in individual as for the uphill sections and a total altitude difference of 110- m in sprint and 135-m in individual when racing downhill. 15
Figure 2 ⎢Graphic Representation of the Altitude Track Profile in Sprint Competition.
Red labelling: uphill sections. Green labelling: downhill sections. Grey labelling: flat section, excluded from analysis. Light blue arrow: Start (in arrow direction).
Figure 3 ⎢Graphic Representation of the Altitude Track Profile in Individual Competition.
Instruments and Materials The entire course in both competitions was set in an open area and coupled to a GPS system which was only minimally influenced by tree cover. To achieve the calculation of valid data, the course and elevation profiles were standardized by integrating a Garmin Forerunner GPS with a heart-rate monitor and barometry in an Apertus inertial navigation system, which was connected to the racetrack (Apertus Skiing Sensor, Apertus AS, Asker, Norway; Garmin Ltd., Olathe, Kansas). The Apertus navigaton system therefore allowed to collect accurate position and heart-rate data at a 1-Hz sampling rate. To ensure proper GPS fixing and a low resultant inaccuracy in GPS data athletes had to turn on the GPS at least 10-min before the start of the race and wait until the GPS-watch signals that contact with satellites and HR-belt are achieved before actual tracking started. With reference to research from Bolger et al., the recorded position data of each participant was subsequently projected onto the standard course to further reduce inaccuracy Furthermore, virtual split time positions for each participant and lap were defined to each point on the standard course, which were assigned by
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a projection algorithm (Bolger et al. 2015). Based on these virtual split times, it became possible to collect data of time and HR every 10-15 meters along the course. The development of the software for data gathering was achieved by a project at the National Centre for Elite Sports (“Analysis of sensor and GPS-data for optimizing technical and tactical abilities”). Collected data from the Apertus inertial navigation system was post processed in Matlab 2014b (The MathWorks Inc., Natick, Massachusetts).
Statistical analyses Analyses of data with Shapiro-Wilk test showed normal distribution and are presented as means ± SD (standard deviation). Paired sample t-tests were used to test, if significant differences between disciplines in overall average speed and overall average speed in uphill sections were found. Disciplines and laps for speed values respectively disciplines, laps and sections for HR values were analyzed by a one-way repeated measures analysis of variance (MANOVA), with Bonferroni correction for multiple comparisons, to determine if significance differences were reached. Cohen’s d was calculated post-hoc to determine the effect size (Cohen, 1988; Sawilowsky, 2009). Due to the sensitivity of the GPS-system and resulting inaccuracy, MANOVA-analyses of sections (0-100%) were only applied for HR. Relationship between variables were assessed by a bivariate Pearson’s correlation coefficient test. The level of statistical significance was set at an alpha level of <.05. Greenhouse-Geisser correction was applied when sphericity revealed significance (p <.05) and Greenhouse- Geisser epsilon was < .75. Statistical analyses were processed using IBM SPSS statistics version 25 (SPSS Inc, Chicago, IL, USA), Matlab 2014b, Office Excel 2011 (Microsoft, Redmond, USA) and G*Power 3.1 (Heinrich-Heine-Universität, Düsseldorf, Germany).
Results
Whilst average finishing time for total laps in sprint biathlon competition was 25:13 ± 0:27 min, finishing time in individual competition was 52:56 ± 0:14 min. The skiers average lap times in ascending order were 500 ± 16 s, 511 ± 13 and 501 ± 15 in sprint as well as 611 ± 18 s, 629 ± 13, 642 ± 19, 651 ± 17 and 641 ± 15 in individual race. The total lap time was divided into uphill, flat and downhill sections corresponding to 38.9%, 7% and 54.1% in sprint resp. 31.2%, 23.6% and 45.2% in individual competition. Only uphill and downhill
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sections were used for further analysis. All data expressed as mean ± SD (Table 1).
Table 1 ⎢ Percentage in Uphill and Downhill Sections in a male 10- and 20-km Biathlon Race on National Level (mean ± SD ). Sprint Individual
Uphill (%) 38.9 ± 5.0 31.2 ± 4.3
Downhill (%) 54.1 ± 9.8 45.2 ± 7.9
Excluded from analysis
Flat (%, 1 section) 7.0 23.6
Average speed values in laps 1-3 in sprint and laps 1-5 in individual race are illustrated in Table 2. Athletes in sprint competition (M=6.1, sd = .1) showed significant higher average speed (+3.7%, in m/s) in uphill and downhill sections compared to the individual event (M=5.7, sd = .1) p = .001 . In uphill sections of the race, sprint discipline (M=4.3, sd= .1) revealed significant higher average speed (+9.6%) than in individual race (M=3.9, sd =.1) p = .001 .
Table 2 ⎢Average Skiing Speed (m/s) in a male 10- and 20-km Biathlon Race on National Level (mean ± SD). Terrain Sprint Individual
Total uphill/downhill (m/s) 6.1 ± 0.2 5.7 ± 0.2
Uphill 4.3 ± 0.1 3.9 ± 0.2
Downhill 8.5 ± 0.2 7.4 ± 0.2
By viewing pacing based on speed levels, graphic presentation over laps revealed J-shaped pacing for sprint race for both, uphill and downhill sections and uphill sections (Figure 2). Pacing in individual competition showed a pattern of a reverse J-shaped profile in uphill- and downhill sections as well as when only uphill sections were displayed (Figure 3).
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Figure 2 ⎢Pacing profiles of Uphill and Downhill Sections in Sprint and Individual Biathlon Competition.
Figure 3 ⎢Pacing profiles of Uphill Sections in Sprint and Individual Biathlon Competition.
Average speed and HR matched to the track profile in laps 1-3 in sprint and laps 1, 3, 5 in individual race are illustrated in Figure 4 & 5.
19
Figure 4 ⎢Percentage of Average Skiing Speed for Laps 1-3 in Sprint and Laps 1/3/5 in Individual Biathlon Competition.
Figures 4-5: Exclusion of flat section for comparison purposes is marked by grey arrows (excluded distance: Sprint 206.0-m, Individual 840.8-m). Significances for disciplines x laps are highlighted. Standard deviation, SD, represented by broken lines (Sprint: above speed graph; Individual: below speed graph).
20
Figure 5 ⎢Percentage of Average HRmax at Uphill Sections for Laps 1-3 in Sprint and Laps 1/3/5 in Individual Biathlon Competition.
21
A one-way MANOVA revealed a significant main effect for disciplines in all cases on the speed of the athletes by indicating higher speed in sprint competition (+4.8%, Sprint laps 1-3 vs. Individual laps 1-3; +5.2%, Sprint laps 1-3 vs. Individual laps 1/3/5; +7.5%, Sprint laps 1- 3 vs. Individual laps 3-5). For interactive effects, disciplines x laps showed significant differences by looking at Sprint laps 1-3 vs. Individual laps 1-3 (+1.7%, lap 1 vs. lap 1; +3.4%, lap 2 vs. lap 2; +7.0%, lap 3 vs. lap 3) and Sprint laps 1-3 vs. Individual laps 1/3/5 (+1.7%, lap 1 vs. lap 1; +5.3% lap 2 vs. lap 3; +7.0%, lap 3 vs. lap 5). Cohen’s d reported huge effect sizes for disciplines in all cases. For disciplines x laps, effect sizes were very small (Sprint laps 1-3 vs. Individual laps 3-5), large (Sprint laps 1-3 vs. Individiual laps 1/3/5) and very large (Sprint laps 1-3 vs. Individiual laps 1-3) (Table 3). Average speed in uphill sections was analyzed by a one-way MANOVA demonstrating significant results for disciplines in all cases by revealing higher values in sprint race (+8.1%, Sprint laps 1-3 vs. Individual laps 1-3; +9.0%, Sprint laps 1-3 vs. Individual laps 1/3/5; +9.8%, Sprint laps 1-3 vs. Individual laps 3-5). For interactive effects, disciplines x laps showed significant results for sprint laps 1-3 with individual laps 1-3 (+5.8%, lap 1 vs. lap 1; +6.4%, lap 2 vs. lap 2; +12.2% lap 3 vs. lap 3) and sprint laps 1-3 vs. individual laps 1/3/5 (+5.8%, lap 1 vs. lap 1; +9.9%, lap 2 vs. lap 3; +11.5%, lap 3 vs. lap 5). Cohen’s d effect sizes were huge (Sprint laps 1-3 vs. Individiual laps 1-3 and 1/3/5) and very large (Sprint laps 1-3 vs. Individiual laps 1/3/5) for disciplines. For disciplines x laps, huge (Sprint laps 1-3 vs. Individual laps 1/3/5), large (Sprint laps 1-3 vs. Individual laps 1-3) and medium (Sprint laps 1-3 vs. Individual laps 3-5) effects were found (Table 4).
22
Table 3 ⎢Comparison of Speed in a Sprint and Individual Biathlon Race on National Level.
D ISCIPLINE F P d
WITHIN- SPRINT I NDIVIDUAL SUBJECTS TESTS
Lap 1/2/3 Lap 1/2/3 Lap 1/3/5 Lap 3/4/5
6.1 ± 0.0 5.8 ± 0.0 a ***
`````` (1,8)= 2.84 F 64.691 <.001
6.1 ± 0.0 5.8 ± 0.0 a *** `````` (1,8)=
84.707 F <.001 ) 3.25 1 (Sprintx Individual)
-
6.1 ± 0.0 5.7 ± 0.0 a ***
`````` (1,8)= 3.62 Disciplines F 104.913 <.001
6.1 ± 0.1 6.0 ± 0.1
b ** speed(ms 6.0 ± 0.0 5.8 ± 0.0 ````` .001 2,16) = 2,16) ( 1.20 6.1 ± 0.1 5.7 ± 0.1 F 11.546
6.1 ± 0.1 6.0 ± 0.1 **
b 6.0 ± 0.0 5.7 ± 0.1 ```` .98 .004 2,16) = 2,16)
(
6.1 ± 0.1 5.7 ± 0.0 F 7.750 x laps (Sprint x Individual) Individual) x (Sprint laps x 6.1 ± 0.1 5.7 ± 0.1
`
6.0 ± 0.0 5.6 ± 0.1 b .477 .10 (1.056,
Disciplines (Lap1/2/3 Sprint x X/Y/Z Individual) 5.7 ± 0.1 5.7 ± 0.0 F = 8.445) 1.175
Values in Tables 3-9 are presented as means ± SD. F- and P- values obtained by One-Way repeated-measures ANOVA (2 disciplines x 3 laps), significance, p < .05; **, high significance, p < 0.01; highly significant, ***, p <.001. Cohen’s d- values obtained by G*Power data analysis, ` (1), very small effect size, d ≥ .01; `` (2), small effect size, d ≥ .2; ``` (3), medium effect size, d ≥ .5; ```` (4), large effect size, d ≥ .8. ````` (5), very large effect size, d ≥ 1.2; `````` (6), huge effect size, d ≥ 2.0. aMain effect between disciplines. binteractive effect between discipline x laps.
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Table 4 ⎢ Comparison of Speed in Uphill Sections in a Sprint and Individual Biathlon Race on National Level.
D ISCIPLINE F P d
WITHIN- SPRINT I NDIVIDUAL SUBJECTS
TESTS
Lap 1/2/3 Lap 1/2/3 Lap 1/3/5 Lap 3/4/5
4.3 ± 0.0 4.0 ± 0.0 a ***
`````` (1,8)= <.001 4.30 F 148.570
4.3 ± 0.0 4.0 ± 0.0 a *** `````
) (1,8)= <.001 1.65 1.65 F 216.097 1 - (Sprintx Individual)
4.3 ± 0.0 3.9 ± 0.0 a *** ``````` (1,8)= <.001 4.61 Disciplines F 169.797
4.4 ± 0.0 4.1 ± 0.1 #
speed(ms
*
```` 4.2 ± 0.0 4.0 ± 0.0 b Laps .010 1.260, 1.260, 1.07 (
4.3 ± 0.1 3.9 ± 0.0 F = 10.077) 9.116
4.4 ± 0.0 4.1 ± 0.1
*
b 4.2 ± 0.0 3.9 ± 0.0 `````` .023 2,16) = 2,16) ( 2.46
4.3 ± 0.1 3.9 ± 0.0 F 4.840 (Sprintx Individual) x 4.4 ± 0.0 3.9 ± 0.0
b 4.2 ± 0.0 3.8 ± 0.0 ``` (2,16)= .102 .58 Disciplines (UphillSections inLap 1/2/3 Sprint x Individual) X/Y/Z Lap in Sections Uphill 4.3 ± 0.1 3.9 ± 0.0 F 2.646
Values obtained by One-Way repeated-measures ANOVA (2 disciplines x 3 laps). #Greenhouse-Geisser- Correction was applied for discipline x laps (Sprint Uphill Sections in Lap 1-3 vs. Individual Uphill Sections in Lap 1-3).
Average speed within sprint competition for lap 1 vs. lap 2 was significantly higher in the first lap (+2.3%) p <.05 . For individual discipline, average speed in lap 1 revealed to be significantly higher in lap 1 vs. lap 2 (+3.4%), lap 1 vs. lap 3 (+5.2%), lap 1 vs. lap 4 (+6.6%) and lap 1 vs. lap 5 (+4.7%) p <.001 . For uphill comparisons within sprint race, significantly higher average speed was found in lap 1 vs. lap 2 (+3.4%) p <.01 . Average uphill speed within individual discipline was significantly higher in lap 1 vs. lap 2 (+4.0%), lap 1 vs. lap 3 (+7.4%), lap 1 vs. lap 4 (+8.9%) p <.001 and lap 1 vs. lap 5 (+6.7%) p <.01 (Figure 6).
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Figure 6 ⎢Speed profiles in each lap on Uphill and Downhill Race sections and Uphill Sections in Sprint and Individual Biathlon Competition.
Values in Figures 6 are presented as means ± SD. P- values obtained by Paired-Samples T-Tests, significance, *, p < .05 vs. lap 1; **, high significance, p < 0.01 vs. lap 1; highly significant, ***, p <.001 vs. lap 1. Significances for different laps indicated within parenthesis.
Average HR in laps 1-3 of sprint and laps 1-5 in individual race is shown in Table 6 and
Figure 7 (in % HRmax).
Table 6 ⎢Mean Heart Frequency (HF) for the Total Race (mean ± SD). Average values in Uphill and Downhill Sections in a male 10- and 20-km Biathlon Race on National Level.
Discipline Sprint Individual
Average HRmax (%) Average HR HRmax HR (bpm)
Total 173.5 ± 7.4 88.2 ± 3.0 174.1 ± 5.7 88.5 ± 2.9 (beats per minute) Uphill 173.3 ± 7.4 87.9 ± 3.4 173.3 ± 5.7 88.1 ± 2.9
Downhill 174.0 ± 7.2 88.5 ± 2.6 174.4 ± 5.8 88.7 ± 3.0
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Figure 7 ⎢Heart profiles in each lap in Sprint and Individual Biathlon Competition.
Values in Figure 7 are presented as means ± SD. P- values obtained by Paired-Samples T-Tests, significance, *, p < .05 vs. lap 1; **, high significance, p < 0.01 vs. lap 1; highly significant, ***, p <.001 vs. lap 1. Significances for different laps indicated within parenthesis.
A one-way MANOVA analysis for HR (beats per minute, bpm) showed significant higher average HR for disciplines in individual laps 1-3 vs. sprint laps 1-3 (+0.8%). For disciplines x laps, significant interactive effects were found in all cases. By comparing sprint laps 1-3 with individual laps 1-3, average HR in individual revealed to be higher in lap 1 and lap 2 (+2.9% and +1.0%), and average HR in lap 3 was higher in sprint (+1.5%). Comparison of laps 1-3 in sprint with laps 1/3/5 in individual showed higher average HR in individual in lap 1, negligible higher average HR in sprint lap 2 (+0.1%) and higher average HR in sprint lap 3 (+1.5%). For sprint laps 1-3 vs. individual laps 3-5 higher average HR was found in individual in lap 3 (+3.4%), and average HR in sprint were shown to be higher in lap 2 (+1.5%) and lap 3. Manova results for disciplines x laps x sections revealed significant interactive effects for sprint laps 1-3 vs. individual laps 1-3 and sprint laps 1-3 vs. individual laps 3-5. Cohen’s d showed large (Sprint laps 1-3 vs. Individiual laps 1-3), small (Sprint laps 1-3 vs. Individiual laps 1/3/5) and very small (Sprint laps 1-3 vs. Individual laps 3-5) effect sizes for average HR between disciplines. Very large (Sprint laps 1-3 vs. Individual laps 1-3 and 1/3/5) and huge effects sizes (Sprint laps 1-3 vs. Individual laps 3-5) for disciplines x laps were found, whereas disciplines x laps x sections revealed medium (Sprint laps 1-3 vs. Individual laps 1-3), large (Sprint laps 1-3 vs. Individual laps 1-3 and 1/3/5) and huge (Sprint laps 1-3 vs. Individual laps 3-5) effect sizes for average HR (Table 6).
One-way MANOVA for HR in uphill sections revealed significant higher average HR for
26
disciplines in individual laps 1-3 vs. sprint laps 1-3 (+0.9%). Interactive effects for disciplines x laps showed significant effects in all cases. For sprint laps 1-3 vs. individual laps 1-3 higher average HR was found in lap 1 (+2.9%) and lap 2 (+1.0%) in individual, while HR showed higher values in lap 3 in sprint (+1.3%). Comparison of laps 1-3 in sprint vs. laps 1/3/5 in individual demonstrated higher HR in individual in lap 1, negligible higher HR in sprint in lap 2 (+0.1%) as well as higher HR in sprint lap 3 (+1.5%). By comparing HR in sprint laps 1-3 with individual laps 3-5, higher HR was shown in individual lap 1 (+4.2%), while HR revealed to be higher in lap 2 (+1.6%) and 3 (+1.7%) in sprint. Manova results for disciplines x laps x sections demonstrated significant effects in all cases. Cohens’s d for average HR in uphill sections for disciplines x laps showed very large (Sprint laps 1-3 vs. Individiual laps 1-3 and Sprint laps 1-3 vs. Individual laps 1/3/5) and huge (Sprint laps 1-3 vs. Individual laps 3-5) effects sizes. Disciplines x laps x sections appeared to have medium (Sprint laps 1-3 vs. Individiual laps 1-3 and Sprint laps 1-3 vs. Individual laps 1/3/5) and large (Sprint laps 1-3 vs. Individual laps 3-5) effect sizes (Table 7).
Average HR for the first half of downhill sections was analyzed by a one-way MANOVA and showed significant higher average values for disciplines in individual laps 1-3 vs. sprint laps 1- 3 (+0.9%). Interactive effects for disciplines x laps revealed significant results for all cases. Higher HR was found in individual during lap 1 (+2.4%), while HR values were higher in lap 2 & lap 3 in sprint (+1.0% & +2.7%). HR in sprint laps 1-3 vs. individual laps 3-5 was higher in individual in lap 3 (+1.3%), whereas sprint showed higher HR in lap 2 (+1.9%) and lap 3 (+1.5%). By looking at downhill speed in uphill sections (0-50%), Cohen’s d effect sizes were medium (Sprint laps 1-3 vs. individual laps 3-5), small (Sprint laps 1-3 vs. individual laps 1/3/5) and very small (Sprint laps 1-3 vs. individual laps 1-3). For disciplines x laps (Sprint laps 1-3 vs. individual laps 1-3, sprint laps 1-3 vs. individual laps 1/3/5 and sprint laps 1-3 vs. individual laps 3-5) huge effect sizes were found in all cases (Table 8).
Average HR (bpm, % HRmax) within sprint race showed significant higher values in lap 2 (+3.8%) and lap 3 (+5.1%) vs. lap 1 . In individual discipline average HR was significantly higher in lap 2 (+1.5%) vs. lap 1.
27
Table 7 ⎢Comparison of Heart Rate in a Sprint and Individual Biathlon Race on National Level.
D ISCIPLINE F P d
WITHIN- SPRINT I NDIVIDUAL SUBJECTS
TESTS
Lap 1/2/3 Lap 1/2/3 Lap 1/3/5 Lap 3/4/5 Sections 1-13 Sections 1-13 Sections 1-13 Sections 1-13
`
` a
173.3 ± 2.2 174.7 ± 2.0 * ``
(1,8)= .011 F 10.646 1.15
173.3 ± 2.2 174.0 ± 1.9 a
` ` (1,8)= F 1.478 .259 .43 (Sprintx Individual)
173.3 ± 2.2 173.4 ± 1.8
a
` (1,8)=
Disciplines F 0.26 .876 .06 ) 1 -
168.7 ± 2.2 173.6 ± 2.0
``
b *** in ```
Laps 174.5 ± 2.1 176.2 ± 1.8
2,16) = 2,16) ( F (m 1.46
176.6 ± 1.8 174.4 ± 1.9 17.112 <.001
168.7 ± 2.5 173.6 ± 2.3
b *** ```` 174.5 ± 2.1 174.4 ± 2.1 ` 2,16) = 2,16) <.001 ( 1.59
176.6 ± 1.8 174.0 ± 2.2 F 20.277 (Sprintx Individual) x heart rate heart
#
168.7 ± 2.5 174.4 ± 1.8 b ```` ` ***
174.5 ± 2.1 171.9 ± 1.8 `` 1.160, 1.160, (
Disciplines Disciplines (Lap1/2/3 Sprint x X/Y/Z Individual) 176.6 ± 1.8 174.0 ± 1.9 F = 9.283) 77.546 <.001 3.11 Sprint Lap 1/2/3
`
*** 13) vs. Individual Lap 1/2/3 c - `` Laps Laps
(24,192)= .76 F 4.648 <.001
Sprint Lap 1/2/3
c `````` 13 x Sections 1 Sections x 13 - vs. Individual Lap 1/3/5 ` (2,24)=
.095 ° F 1.436 3.17
Sprint Lap 1/2/3
c ***
vs. Individual Lap 3/4/5 ````
(24,192) Disciplines (Sprint x Individual) x Individual) x (Sprint Disciplines (Lap1/2/3 Sprint x X/Y/Z Individual) x 1 (Sections Sections F 9.117 = <.001 1.07
Values obtained by One-Way repeated-measures ANOVA (2 disciplines x 3 laps x 13 sections, °Significant trend. #Greenhouse-Geisser-Correction was applied for discipline x laps (Sprint Lap 1/2/3 vs. Individual Lap 3/4/5).
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Table 8 ⎢ Comparison of Heart Rate in Uphill Sections in a Sprint and Individual Biathlon Race on National Level.
D ISCIPLINE F P d
WITHIN- SPRINT I NDIVIDUAL SUBJECTS
TESTS
Lap 1/2/3 Lap 1/2/3 Lap 1/3/5 Lap 3/4/5 Sections Sections Sections Sections 1/3/5/8/10/12 1/3/5/8/10/12 1/3/5/8/10/12 1/3/5/8/10/12
172.5 ± 2.2 174.0 ± 1.9
* a ```` .022 (1,8)=
1.00
F 8.118
172.5 ± 2.2 173.2 ± 1.9 a
`` 447 (1,8)= . F 1 .263 .43 (Sprintx Individual)
172.5 ± 2.2 173.4 ± 2.1
a
`
(1,8)= ) Disciplines F .001 .972 .17 1 -
#
166.9 ± 2.3 172.4 ± 2.0 b
in ***
Laps
174.2 ± 2.1 175.6 ± 2.0 ````` (1.909 (m F 24.531 ,15.275) =
176.3 ± 2.5 173.9 ± 1.8 <.001 1.75
#
166.9 ± 2.3 172.4 ± 2.0 b *** `````
174.2 ± 2.1 173.9 ± 1.8 1.864, ( 24.513 F 14.912) = 14.912) 1.75 <.001 176.3 ± 2.5 173.4 ± 1.9 heart rate heart (Sprintx Individual) x
#
166.9 ± 2.3 173.9 ± 1.8 b *** ) =) 1 174.2 ± 2.1 171.4 ± 1.8 `````` 1.158, 1.158, 40 (
Disciplines Disciplines (Lap1/2/3 Sprint x X/Y/Z Individual) 176.3 ± 2.5 173.4 ± 1.9 F 9.262 53.832 <.00 3.
Sprint Lap 1/2/3
vs. Individual Lap 1/2/3
` *** c Laps ` ` (12,96)= .75 F 4.465 <.001
Sprint Lap 1/2/3
c
vs. Individual Lap 1/3/5 ** ```
(12,96)= F 2.763 .006 .59 (Sprintx Individual) x
Sprint Lap 1/2/3
c *** vs. Individual Lap 3/4/5 ````
(12,96) 1.13 Disciplines (UphillSections inLap 1/2/3 Sprint x Individual) X/Y/Z Lap in Sections Uphill F 4.414 = <.001
Values obtained by One-Way repeated-measures ANOVA (2 disciplines x 3 laps x 6 sections). #Greenhouse- Geisser-Correction was applied for discipline x laps (Sprint Lap 1-3 vs. Individual Lap 1-3 and Sprint Lap 1-3 vs. Individual Lap 1/3/5 and Sprint Lap 1-3 vs. Individual Lap 3-5).
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Table 9 ⎢ Comparison of Heart Rate in First Half of Downhill Sections in a Sprint and Individual Biathlon Race on National Level.
D ISCIPLINE F P d
WITHIN- SPRINT I NDIVIDUAL SUBJECTS
TESTS
Lap 1/2/3 Lap 1/2/3 Lap 1/3/5 Lap 3/4/5 Sections Sections Sections Sections 2/4/6/7/9/11/13 2/4/6/7/9/11/13 2/4/6/7/9/11/13 2/4/6/7/9/11/13 (0-50%) (0-50%) (0-50%) (0-50%)
175.3 ± 2.3 175.5 ± 1.8 a ` .09 .813 .060 (1,8)=
F
) 1
-
175.3 ± 2.3 174.5 ± 1.7
a ` ` in (1,8)= 455 28 F .616 . . (Sprintx Individual)
(m
175.3 ± 2.3 173.4 ± 1.7 a `
`` (1,8)= 126 60 Disciplines F 2.911 . .
s 172.0 ± 2.2 176.2 ± 1.7 b `
` * * Lap heart rate heart 175.9 ± 2.3 176.0 ± 2.2 ``` x
(2,16)=
21 10.055 .001 F 1.
177.9 ± 2.6 174.2 ± 1.9
172.0 ± 2.2 176.2 ± 1.7 b *** `````
175.9 ± 2.3 174.2 ± 1.9 37 2,16) = 2,16) ( <.001 1. 14.907
F
177.9 ± 2.6 173.2 ± 2.1
(Sprintx Individual)
172.0 ± 2.2 174.2 ± 1.9 `` *** `
b `` 175.9 ± 2.3 172.7 ± 1.5 2 <.001 1.66 2,16) = 2,16) 1/2/3 Sprint x X/Y/Z Individual) X/Y/Z x Sprint 1/2/3
Disciplines Disciplines ( 177.9 ± 2.6 173.2 ± 2.1 F 2.082 Values obtained by One-Way repeated-measures ANOVA (2 disciplines x 3 laps).
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Discussion
With an emphasis on pacing strategies in XC skiing, the presented thesis aimed to further investigate the rather scarce researched field of track analyses in biathlon competition. Outcomes of speed profiles in the present investigation were viewed in relation to exercise intensity expressed by HR, showing J-shaped pacing in sprint and reverse J-shaped pacing in individual race. Average skiing speed in sprint and individual biathlon competition revealed significant differences over the race, by demonstrating higher speed in favour of sprint, both in uphill and downhill (+3.7%), respectively, uphill (+9.6%) race sections. In all sections of the race, profiles of HR in sprint revealed to have a more excessive increase than in individual competition by illustrating a gradual increase in exercise intensity from the first to the last lap. In contrast, HR profiles in individual race were shown to be variable pronounced by indicating high exercise intensity in the starting lap in all sections of the race, after decreasing intensity substantially following the subsequent laps, and raising it again in the final lap of the competition.
Speed profiles/Pacing In line with the hypothesis, findings of the present investigation demonstrated that biathletes in sprint competition were able to show higher speed values than in individual competition, both for average skiing speed throughout the race and in specific race sections. Athletes therefore reached significant higher speed levels in uphill and downhill (+6.6%, p <.01) as well as in uphill sections (+9.3%, p <.05) of the sprint race, while maintaining their speed levels close to even. Distinguishing from sprint event, biathletes in individual competition showed the highest speed values at the beginning of the race before consistently slowing down from lap-to-lap and raising their speed again after finishing the last shooting. The observed findings of speed levels just above 6 m/s in sprint are considerably lower than results from former investigations in sprint biathlon world cup (WC) events stating 7- 7.1 m/s. Speed values of 5.7 m/s in individual also revealed to be lower, although to a remarkable smaller extent when compared to former findings of approximately 6 m/s in WC biathlon races (Cholewa et al., 2005; Luchsinger et al., 2017). A major discriminating factor between former research in biathlon analysing WC, World Championships and Olympic Games and the present data demonstrating substantially lower speed differences between disciplines might be associated with the lower performance level of the athletes. Subjects tested in the
31
current examination were mostly competing on national-level (NL) and thus likely differ in regard to intraindividual prerequisites, potentially leading to lower physical and/or technical abilities. Indications for such assumptions are on the basis of previous investigations, and Luchsinger et al. related shooting performance in biathlon individual competition largely to overall differences between performance groups, especially in the standing shooting position (Luchsinger et al., 2018). Moreover, research from Mahood et al. demonstrated significant correlations between VO2max, lactate threshold and ski economy to competitive season performance in collegiate skiers tested on field roller-skis (Mahood et al., 2001). In this context, previous research has named roller skiing as a valid model for testing XC skiing performance (Watts et al.,1993). The imbalance in relation to differences in disciplines amongst these results and previous analyses in sprint and individual races may also be connected to natural upper limits of the best possible performance in individual discipline. Though it seems obvious that athletes’ requirements differ considering 20-km total XC skiing distance in individual vs. 10-km XC skiing in sprint discipline, the combination of long-lasting physiological loading, the risk of a 1-min penalisation due to a missed target in competition and 2 rifle shot series in standing position instead of just 1 in sprint discipline, might deliver considerable arguments why athletes try to act particularly careful in adapting their upper speed limits in individual races. This is also elucidated by research from Luchsinger et al. naming course time and shooting performance as factors which seem equally important to improve individual race performance, distinguishing to sprint discipline, in which 59-65% of the overall performance was explained by course time (Luchsinger et al., 2017; 2018). Results of the present data in sprint competition by investigating pacing in uphill and downhill sections, respectively, uphill sections, revealed noticeable differences to former examinations. Even though HR in the present event was significantly lower in the first lap compared to lap 2 and 3 of the sprint race, a high level of speed was selected from the beginning, and speed values remained stable from start to finish. Contrary, comparable results from Luchsinger et al. in WC events, demonstrated that both of their analysed performance groups showed slower skiing times in the second and third lap compared to the first lap in sprint discipline (Luchsinger et al., 2017). Even though these findings are slightly contrary to the present data revealing a significant decrease of speed from the first to the second lap, and no differences between the first to the third lap, the exclusion of flat sections for comparison reasons might falsified the current outcomes considerably. Further, due to results from Noakes et al., it might be the case that the pacing during the latter stages of the race was
32
subconciously positively modified, whilst WC biathletes in the aformentioned analyses were unable to do so. Such an executing mechanism was thus explained by researchers as a result of a never absolute state of fatigue, and a continous oscillation of exercise intensity and the activity of different metabolic systems (Noakes et al., 2005). Contrary to the expected outcomes, reverse J-shaped pacing in individiual competition was found by showing significant differences between lap 1 vs. lap 2-5. However, since results from Luchsinger et al. (2018) revealed J-shaped pacing in individual WC races for both performance groups, this has to be adressed as an unexpected outcome and results leave some room open for interpretation. At this point, a more passive pacing in XC skiing from the beginning of the race was assumed, to ensure the reduction of excessive stress for the cardiovascular system, potentially resulting in the avoidance of unsuccessful shooting attempts. Though, contrary to the aforementioned investigation analyzing world-class biathletes (Luchsinger et al., 2018), the present examination tested biathletes competiting in NL races. However, a strong argument for selecting higher speed from the beginning of the race might be related to higher tolerance limits in performing well in prone shooting attempts, which is in line with previous findings (Hoffman et al., 1992). At this, most researchers connected decreases in shooting results after XC skiing exercise primarily to the standing shooting event, by emphasizing the importance of postural control and stability of hold during shooting performance (Groslambert et al 1998, 1999; Hoffman et al. 1992; Ihalainen et al. 2018; Laaksonen et al., 2018). By looking at the present outcomes, only 1 previous investigation in a simulated 10-km race in the skating technique was able to demonstrate reverse J-shaped pacing during XC skiing. Analysis by Formenti et al. found non-constant speed levels throughout 4 laps of skiing by stating a constant reduction in speed after a fast start from lap 1 to 3, until a final increment in speed in the last lap (Formenti et al., 2015). Overall, the selection of a fast start followed by a final spurt in racing might strongly be connected to tactical manoeuvring which has been discussed by Abbiss et al. in their pacing-based review (Abbis et al., 2008). Even though such a reverse J-shaped pacing pattern in XC skiing may likely be related to tactics, e.g. as described by former investigations to reserve some ability to expend energy anaerobically for a terminal acceleration (Foster et al., 2004; Abbiss et al., 2008), the chosen pacing most likely reflects the specific requirements in biathlon to outbalance the metabolic system economically, and therefore avoid the risk of an impairment in cognitive functioning after intensive exercise. Earlier research from Grebot et al. demonstrating difficulties of recalling
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shooting performance due to intensive exercise, thus is in line with such insights (Grebot et al., 2003). The present results in individual race therefore revealed high speed accompanied by high exercise intensity illustrated by HR in the beginning of the race, which suggests the selection of a fast start strategy. Results indicating J-shaped pacing in sprint and reverse J-shaped pacing in individual discipline may reflect the endeavour to pace intensively in sprint race throughout the race, whilst in individual competition the disproportionate nature of taking the risk of a 1-min penalization as a result of a missed shot, especially in standing position, seemed to influence the pacing behaviour substantially.
Heart-Rate Profiles A second central variable of the present results was HR and in association with the hypothesis results showed a more excessive increase of HR in sprint competition, with HR curves following a gradual increase from the first to the last lap (p <.001). Slightly differing from expectations, HR in individiual race revealed a variable course profile by demonstrating high HR in the second lap, and when athletes were appoaching the finish. Findings from the present examination showed very high HR during competition revealing values close to 90% of HRmax, which is only marginally above HRmax values of high performance biathletes during competition analysed by Hoffman and Street over two decades ago (Hoffman and Street, 1992). Sprint race in the current project showed significant lap-to-lap variation in HR from the first to the other laps (p <.001) and a lower HR profile than in individual competition during the first lap, accompanied by relatively high speed levels from the beginning of the race. This is closely related to results from Formenti et al. stating a significant increase in HR from the first to the other laps in connection with a fast starting strategy. Researchers further related physiological variables of HR to the selected pacing strategy illustrated by speed to put relevant elements involved in skiing together and illustrate their version of a holistic performance profile for XC skiing (Formenti et al., 2015). In the present investigation, the inability of athletes in synchronizing HR to speed levels, illustrated by a conspicious relative difference of internal (HR) and external load (Speed), is most likely. Moreover, results relating to the final stages of competition revealed significant HR increases in both, sprint and individual discipline, by demonstrating higher HR in sprint discipline. Such HR adaptions during exercise may occur to maintain a nearly constant cardiac output until the end of a XC
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skiing race and limit the so called cardiovascular drift phenomena consequently resulting in a decrement in stroke volume (Coyle and Gonzálezalonso, 2001; Formenti et al., 2015) and thus support athletes’ abilities in maintaining appropriate SVmax- and VO2max- values during athletic competition (Colakoglu et al., 2018). However, the importance to avoid such a drift phenomena in biathlon may likely be of exceptional importance. At first, the aforementioned results support evidence that XC skiing performance might be impaired due to reduced metabolic capabilities after cardiovascular drifting. Thus, raised HR could be critical when it comes to the shooting range since former research reported increases in HR under aerobic and anaerobic load, further leading to destabilization of posture (Laaksonen et al., 2018) and alterations in standing shooting performance (Groslambert et al., 1998; 1999). These findings are in accordance with experimental data from Gallichhio et al., stating an influence in shooting accuracy by the cardiac cycle phase (Gallichhio et al., 2016). By looking at specific sections of the race, HR profiles between disciplines demonstrated variable higher pronounced profiles both, in uphill sections and in sections which only included the first half of the following downhill part. Whilst HR revealed to be higher in individual competition at the beginning of the race, a clear distinguishing trend between disciplines in favour of sprint occurred when athletes were approaching the final lap. Such a turnaround was found in both, uphill sections as well as in sections which only included the first half of the downhill section. Though a multitude of studies have examined athletic performance during XC skiing competition with regard to terrain structure (Andersson et al., 2010; Sandbakk et al., 2011; Sandbakk and Holmberg, 2014), no results are currently availabe in which track analyses in biathlon competition have been investigated. Such analyses seem not least worth exploring, since previous research extensively discussed physical, cognitive and neuronal requirements in biathlon (Hoffman et al., 1992; Groslambert et al., 1995; Gallicchio et al. 2016). It seems therefore obvious that the necessity in outbalancing the aforementioned demands may be very challenging, especially in connection to varying terrain structure during competition. Though results from track analysis in biathlon are scarce, findings from Hoffman et al. in biathlon race distances close to sprint and individual discipline stated an increase in HR from the first to the last lap in both, sprint (+ 6.7%) and individual (+5.4%) event (Hoffman and Street, 1992). Furthermore, Welde et al. reported a significant variation of HR in relation to the trail profile during a 6-km simulated XC skiing race. Researchers analyzed female junior XC skiers in both, classical and skating technique and revealed a rose in HR during the first uphill climb (190 ± 5), followed by a HR decrease during the downhill skiing (171 ± 6) and a subsequent increase in HR during the second uphill
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climb (192 ± 5) (Welde et al., 2003). Though, in contrast to the present investigation showing a more variable profile in altitude during competition, the previous described race track was seperated into a more pronounced profile by including 2 long uphill parts followed by 2 long downhill sections. In line with present findings, research by Bolger et al. within terrain revealed only marginal differences in individual XC time trials in skating and classical technique. In their examinations they tested men in a 15-km and women in a 10-km competition on 2 consecutive days, by emphasizing a lack of relationship between changes in HR and differences in speed. Bolger et al. further concluded that a drift in HR occurred as a result of increased intensity during the uphill sections. Such a drift in HR therefore reflects the delayed HR response and was visible in the beginning of the following flat or downhill section of the race, consequently resulting in permanently falsified HR data along the track (Bolger et al., 2015). Similar findings were also made by Solli et al., who tested athletes from both genders on a 5-km XC skiing track on varying terrain. In their discussion it was mentioned that HR values were higher during downhill sections and researcher thus concluded that a subsequent oxygen deficit increased HR in the following race period (Solli et al., 2018). Hence, results from the present biathlon competitions also suggest HR drifting since, in comparison to the preceding uphill section, HR in most sections was found to be raised when biathletes were skiing on the first half of a downhill section. According to current evidence and when viewed individually, HR data, recorded in specific race sections in biathlon and XC skiing races, does not appear to be sufficient in order to make definite conclusions on the physical load of the cardiovascular system. This is thus in line with results from Karlsson et al. in XC skiing research, who stated a limited ability of the HR to reflect rapid-intensity transients and supramaximal exercise intensities (Karlsson, 2018). Nonetheless, the present findings need to be confirmed in further investigations. Due to the aforementioned results, the importance of connecting HR to speed values appears convincing. Consequently, this should enable a realistic view of the existing pacing structure during an event.
Conclusions
The selection of pacing in sprint and individual biathlon competition seems to be related to distinguishing impact of shooting on overall performance, and thus influencing the chosen
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exercise intensity during XC skiing. Whilst approaches in sprint races reflect a very high intensity close to a “all-out” strategy, pacing in individual races seems to be selected with greater care which is likely due to the longer distance skied and to avoid the possible negative impact on shooting misses. Overall, the chosen exercise intensity during competition indicates that biathletes are balancing out the physiological load with the risk of failed shooting.
Limitations
One limitation may had considerable impact on the outcomes of the present work. Since laps in individual race had substantially longer flat sections, all flat sections were excluded from analysis, which might have had a significant influence on the present outcomes.
References
Abbiss CR, Laursen PB (2008). Describing and understanding pacing strategies during athletic competition. Sports Med, 38(3): 239-252.
Andersson E, Supej M, Sandbakk Ø, Sperlich B, Stöggl T, Holmberg HC (2010). Analysis of sprint cross-country skiing using a differential global navigation satellite system. Eur J Appl Physiol. 110(3): 585-95.
Aughey RJ (2011). Applications of GPS technologies to field sports. Int J Sports Physiol Perform. 6(3): 295-310.
Bolger CM, Kocbach J, Hegge AM, Sandbakk Ø (2015). Speed and heart-rate profiles in skating and classical cross-country skiing competitions. Int J Sports Physiol Perform. 10(7):873-80.
Cholewa J, Gerasimuk D, Szepelawy M, Zajac A (2005). Analysis of structure of the biathlon runs. Acta Gymnica. 35(1):35-41.
Cohen J (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic.
Corbett J (2009). An analysis of the pacing strategies adpoted by elite athletes during track cycling. Int J Sports Physiol Perform. 4:195-205.
37
Coyle EF, Gonzalez-Alonso J (2001). Cardiovascular drift during prolonged exercise: new perspectives. Exerc Sport Sci Rev, 29(2): 88-92.
De Koning JJ, Bobbert MF, Foster C (1999). Determination of optimal pacing strategy on cycle time trial performance. Med Sci Sports Exerc. 25(3): 383:8.
Formenti D, Trecroci A, Cavaggioni L, Caumo A, Alberti G (2014). Heart rate response to a marathon cross-country skiing race: a case study. Sport Sci Health, 1-4.
Formenti D, Rossi A, Calogiuri G, Thomassen TO, Scurati R, Weydahl A (2015). Exercise Intensity and Pacing Strategy of Cross-country Skiers during a 10 km Skating Simulated Race. Res Sports Med. 23:126-139.
Foster C, Green MA, Snyder AC, Thompson NN (1993). Physiological responses during simulated competition. Med Sci Sports Exerc, 25(7): 877-882
Foster C, de Koning JJ, Hettinga F, Lampen J, Dodge C, Bobbert M, Porcari JP (2004) Effect of competitive distance on energy expenditure during simulated competition (2004). Int J Sports Med, 25(3). 198-204.
Gallichhio G, Finkenzeller T, Sattlecker G, Lindinger S, Hoedlmoser K. (2016). Shooting under cardiosvascular load: Electroencephalocgraphic activity in preparation for biathlon shooting. Int Jour Psychophys. 109:92-99.
Gilman MB (1996). The use of heart rate to monitor the intensity of endurance training. Sports Med, 21:73– 79.
Gonsalez-Millan C, Perez-Brunicardi D, Salinero JJ, Lara B, Abian-Vicen J, Areces F, Ruiz Vicente D, Soriano L, Del Coso J (2017). Physiological demands of elite cross- country skiing during a real competition. Jour Strength Cond Res. 31(6):1536-1543.
Grebot C, Groslambert A, Pernin J-N, Burtheret A (2003). Effects of exercise on perceptual estimation and short-term recall of shooting performance in biathlon. Percept Mot Skills. 97:1107-1114.
Groslambert A, Candau R, Hoffman MD, Bardy B, Rouillon JD (1995). Influence de l’exercice physique sur la qualité. Sci Sports. 10:47-48.
Groslambert A, Grappe F, Rouillon JD (1998). Adaptation ventilatoire au tir en biathlon [Ventilatory adaptations during biathlon shooting]. Sciences et Sport. 13:135–137.
Groslambert A, Candau R, Hoffman MD, Bardy B, Rouillon JD (1999). Validation of simple tests of biathlon shooting ability. Int Jour Sports Med. 20:179–182.
Hanley B (2016). Pacing profil and pack running at the IAAF World Half Marathon Championships. Jour Sports Sci. 33(11):1189-1195.
Hébert-Losier K, Zinner C, Platt S, Stöggl T, Holmberg H-C (2017). Factors that Influence the Performance of Elite Sprint Cross-Country Skiers. Sports Med, 47(2):319-342.
38
Hoffman MD, Gilson PM, Westenburg TM, Spencer WA (1992). Biathlon shooting performance after exercise of different intensities. Int J Sports Med. 13(3):270-3.
Hoffman MD, Street GM (1992). Characterization of the Heart Rate Response during Biathlon. Int J Sports Med. 13:390-394.
Ihalainen S, Laaksonen MS, Kuitunen S, Leppävuori A, Mikkola J, Lindinger SJ, Linnamo V (2018). Technical determinants of biathlon standing shooting performance before and after race simulation. Scand J Med Sci Sports. 28:1700-1707.
Karlsson Ø, Gilgien M, Gløersen Ø, Rud B, Losnegard, T (2018). Exercise Intensity During Cross-Country Skiing Described by Oxygen Demands in Flat and Uphill Terrain. Front Physiol, 9:846.
Laaksonen MS, Ainegren M, Lisspers J (2011). Evidence of Improved Shooting Precision in Biathlon After 10 Weeks of Combined Relaxation and Specific Shooting Training. Cogn behav therap, 40(4):237-50.
Laaksonen M, Finkenzeller T, Holmberg HC, Sattlecker G (2018). The influence of physiobiomechanical parameters, technical aspects of shooting, and psychophysiological factors on biathlon performance: A review. Jour Sport Health Sc, 7: 394-404.
Losnegard T, Kjeldsen K, Skattebo Ø (2016). An Analysis of the Pacing Strategies Adopted by Elite Cross-Country Skiers. J Strength Cond Res. 30(11):3256-3260.
Luchsinger H, Kocbach J, Ettema G, Sandbakk Ø (2017). Comparison of Performance-Levels and Sex on Sprint Race Performance in the Biathlon World Cup. IJSPP, 1-24. Doi:10.1123/ijspp.2017-0112
Luchsinger H, Kocbach J, Ettema G, Sandbakk Ø (2018). The contribution from cross- country skiing and shooting variables on performance level and sex differences in biathlon World Cup individual races. IJSPP, 14(2)190-195. Doi:10.1123/ijspp.2018- 0134
Mahood NV, Kenefick, RW, Kertzer R, Quinn TJ (2001). Physiological determinants of cross-country ski racing performance. Med Sci Sports Exerc, 33(8):1379-1384.
Mognoni P, Rossi G, Gastaldelli F, Canclini A, Cotelli F (2001). Heart rate profiles and energy cost of locomotion during cross-country skiing races. Eur J Appl Physiol. 85(1-2):62–67.
Noakes TD, St Clair Gibson A, Lambert EV (2005). From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans: summary and conclusions. Br J Sports Med, 39:120-124.
Colakoglum M, Ozkaya O, Balci A (2018). Moderate Intensity Intermittent Exercise Modality May Prevent Cardiovascular Drift. Mdpi, 6:98.
39
Pustovrh J, Jost B, Vodicar J (1995). Analysis of the structure of competitive successfulness in the biathlon. Acta Kinesiologiae. 4:171–185.
Evans RK, Scoville CR, Ito MA, Mello RP (2003). Upper Body Fatiguing Exercise and Shooting Performance. Mil Med. 168(6):451-6.
Sandals LE, Wood DM, Draper S, James DV (2006). Influence of pacing strategy on oxygen uptake during treadmill middle-distance running. Int J Sports Med. 27(1):37-42.
Sandbakk Ø, Ettema G, Leirdal S, Jakobsen V, Holmberg HC (2011a). Analysis of a sprint ski race and associated laboratory determinants of worldclass performance. Eur J Appl Physiol, 111(6):947–957
Sandbakk Ø, Holmberg HC (2014). A Reappraisal of Success Factors for Olympic Cross- Country Skiing. Int J Sports Physiol Perform. 9(1):117-129.
Sandbakk Ø, Losnegard T, Skattebo O, Hegge AM, Tonnessen E, Kocbach J (2016). Analysis of classical time-trial performance and technique-specific physiological determinants in elite female cross-country skiers. Front Physiol, 7:1-9.
Sawilowsky S (2009). New effect size rules of thumb. Jour Mod Appl Stat Methods. 8(2):467- 474.
Skorski S & Abbiss CR (2017). The Manipulation of Pace within Endurance Sport. Front Physiol, 8:102.
Solli GS, Kocbach J, Seeberg TM, Tjønnås J, Torvik PØ, ... , Sandbakk Ø (2018). Sex-based differences in speed, sub-technique selection, and kinematic patterns during low- and high-intensity training for classical cross-coutnry skiing. PloS ONE, 13(11):e0207195.
Stickland MK, Jones RL, Haykowsky MJ, Petersen SR (2004). Lactate steady state during 20km simulated cycle racing. Res Sports Med. 12(1): 59– 70.
Stöggl T, Pellegrini B, Holmberg HC (2018). Pacing and predictors of performance during cross-country skiing races: A systematic review. JSHS, 7: 381-393.
Tucker R, Noakes TD (2009). The physiological regulation of pacing during exercise. A critical review. Br J Sports Med. 43(6):e1.
Watts PB, Hoffman MD, Sulentic JE, Drobish KM, Gibbons TP, Newbury VS, Mittelstadt SW, O'Hagan KP, Clifford PS (1993). Physiological responses to specific maximal exercise tests for cross-country skiing. Can J Appl Physiol, 18(4):359-65.
Welde B, Evertsen F, Von Heimburg E, Medbø JI (2003). Energy Cost of Free Technique and Classical Cross-Country Skiing at Racing Speeds. Med Sci Sports Exerc, 35(5):818- 825.
Zinner C, Willis SJ, Jonsson M, Sperlich B, Holmberg HC (2014). Heart rate response during biathlon races of different lengths in elite athletes. In Science & Skiing VI. Meyer & Meyer Sport. pp. 483-494.
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