Mid Sweden University

Purpose To compare the anaerobic work capacity (AnWC, i.e., attributable anaerobic mechanical work) assessed using four different approaches/models applied to time-trial (TT) cycle-ergometry exercise. Methods Fifteen male cyclists completed a 7 x 4-min submaximal protocol and a 3-min all-out TT (TTAO). Linear relationships between power output (PO) and submaximal metabolic rate were constructed to estimate TT-specific gross efficiency (GE) and AnWC, using either a measured resting metabolic rate as a Y-intercept (7 + Y-LIN) or no measured Y-intercept (7-Y-LIN). In addition, GE of the last submaximal bout (GE(LAST)) was used to estimate AnWC, and critical power (CP) from TTAO (CP3'AO) was used to estimate mechanical work above CP (W', i.e., "AnWC"). Results Average PO during TTAO was 5.43 +/- 0.30 and CP was 4.48 +/- 0.23 W.kg(-1). The TT-associated GE values were similar to 22.0% for both 7 + Y-LIN and 7-Y-LIN and similar to 21.1% for GE(LAST) (both P < 0.001). The AnWC were 269 +/- 60, 272 +/- 55, 299 +/- 61, and 196 +/- 52 J.kg(-1) for the 7 + Y-LIN, 7 Y-LIN, GE(LAST), and CP3'AO models, respectively (7 + Y-LIN and 7-Y-LIN versus GE(LAST), both P <0.001; 7+ Y-LIN, 7 Y-LIN, and GE(LAST) versus CP3'AO, all P < 0.01). For the three pair-wise comparisons between 7+ Y-LIN, 7-Y-LIN, and GE(LAST), typical errors in AnWC values ranged from 7 to 11 J.kg(-1), whereas 7+ Y-LIN, 7-Y-LIN, and GE(LAST) versus CP3'AO revealed typical errors of 55-59 J.kg(-1). Conclusion These findings demonstrate a substantial disagreement in AnWC between CP3'AO and the other models. The 7 + Y-LIN and 7-Y-LIN generated 10% lower AnWC values than the GE(LAST) model, whereas 7 + Y-LIN and 7-Y-LIN generated similar values of AnWC.

Aim

To assess the effect of rifle carriage on skiing performance and angular kinematics in biathletes,and whether the rifle’s effect on angular kinematics could explain its effect on skiing performance, under ecologically valid conditions.

Methods

Twenty-nine biathletes (11 females, 18 males) performed two maximum effort skiing time-trials on snow, once with and once without rifle. During time-trials, biathletes wore a three-dimensional motion analysis system to measure maximum angle, minimum angle and range of motion of knee, hip and elbow flexion. A two-way mixed model analysis of variance and multilevel linear model were used to assess the effect of rifle carriage on skiing performance and angular kinematics respectively. In case of a significant effect, a Pearson correlation coefficient was calculated to assess the relation between the rifle’s effect on the respective angular measure and the rifle’s effect on skiing speed.

Results

Skiing performance was impaired with rifle compared to without rifle (P<.001; ƞ2p=0.04). No effect of rifle carriage was observed on any angular measure, except for maximum elbow flexion(P=.03; Cohen f 2<0.01). Maximum elbow flexion was reduced with rifle compared to without rifle (mean difference 11.9±37.6°). Pearson correlation coefficient revealed a positive correlation between the rifle’s effect on maximum elbow flexion and its effect on skiing speed (r=0.19; P=.04).

Practical application

The results demonstrate the negative impact of rifle carriage on biathlon skiing performance and suggest that maximum elbow flexion plays a role. Therefore, upper body contributions, elbow flexion angles in particular, might be of interest for biathlon coaches.

Despite not being recognized by the International System of Units, explosive is a term often applied in sports science and professional practice. While associated with force, strength, power, performance, exercise, movements, contraction, action, and training, readers may be misled to believe that there are further analyses beyond power output, peak force, contractile rate of force development, and impulse. This critical review discusses the misuse of the term explosive in sports science literature, proposes alternatives, and encourages correct definitions of terms, units, and nomenclature to describe exercise performance. The suggestions provided in this review can help to reduce the confusion and perpetuation of an erroneous understanding of mechanical work, energy, and power in sports science.

The current paper discusses the concepts and definitions of external and internal loads in sports science and the quantification of athletes’ performance and psychobiological responses. We provide practical solutions for improving human performance assessment by suggesting related terms and consistent terminology that align with biomechanical standards. This will help to avoid discrepancies in the meaning of terms across various subdisciplines of sport and exercise science and medicine. Where possible, exercise performance should be characterized and quantified according to physical quantities such as time, distance, displacement, speed, velocity, acceleration, force, torque, work, power, and the International System of Units. These quantifications can be performed for exercises, sessions, microcycles, and mesocycles. Standardization of these terms and measurements would enable consistent communication among scientists of all knowledge areas.

The aim was to investigate whether rifle carriage affects gear distribution during on-snow skiing in highly-trained biathletes, and whether there were any associated sex differences. Twenty-eight biathletes (11 women, 17 men) skied a 2230-m lap at competition speed twice, one lap with the rifle (WR) and the other lap without the rifle (NR). The biathletes wore a portable 3D-motion analysis system while skiing, which enabled characterisation of distance and time in different gears. Skiing WR increased lap time compared to NR (412 (90) vs. 395 (91) s, p < 0.001). The biathletes used gear 2 to a greater extent WR compared to NR (distance: 413 ± 139 vs. 365 ± 142 m; time: 133 (95) vs. 113 (86) s; both p < 0.001) and gear 3 less (distance: 713 ± 166 vs. 769 ± 182 m, p < 0.001; time: 141 ± 33 vs. 149 ± 37 s, p = 0.008), with similar patterns for women and men. Differences between WR and NR in the use of gears 3 and 2 were more extensive for moderate compared to steeper uphill terrain. Rifle carriage increased the use of gear 2, which was negatively associated with performance. Therefore, preparing biathletes to be able to cover more distance in gear 3 WR, especially in moderate uphill terrain, may improve biathlon skiing performance.

INTRODUCTION: A greater usage of gear 3 is associated with improved performance in cross-country skiing. However, it remains unclear whether rifle carriage in biathlon affects sub-technique choice, and therefore biathlon skiing performance. Therefore,this study aimed to investigate whether rifle carriage affects gear distribution during on-snow skiing in biathletes, and whether there are any associated sex differences.

METHODS: Twenty-eight tier 3 biathletes (17 men, 11 women) skied a 2230-m lap at competition speed twice, once with the rifle (WR) and once without the rifle (NR) in a counter-balanced and randomized order. The course was divided into seven uphill sections, three flat sections and seven downhill sections. The biathletes wore aportable 3D-motion analysis system while skiing, which together with GNSS data enabled the characterization of distance covered and time spent in gears 2–4 and skiing without poles (NP). A two-way mixed model ANOVA was used to investigate whether rifle carriage and/or sex influenced gear distribution or performance.

RESULTS: Skiing WR increased the lap time compared to NR (412 (90) vs.395 (91) s, p < 0.001). The biathletes used gear 2 to a greater extent during WR compared to NR (distance: 413 ± 139 vs. 365 ± 142 m; time: 133 (95) vs. 113 (86) s;both p < 0.001) and gear 3 to a lesser extent (distance: 713 ± 166 vs. 769 ± 182 m,p < 0.001; time: 141 ± 33 vs. 149 ± 37 s, p < 0.01) without differences in gear 4 or NP.The women demonstrated a greater proportional use of gear 2 compared to the men (p < 0.05). For the two longest uphill sections, the uphill with a more moderate incline (4.5°) revealed more differences between WR and NR in the use of gears 2 and 3 compared to the uphill with a steeper incline (6.7°). Increased usage of gear 2 (distance and time) was also negatively related to performance during WR and NR (r = 0.516–0.901, p < 0.01).

DISCUSSION/CONCLUSION: Rifle carriage decreases the distance covered and time spent in gear 3 and increases these metrics in gear 2, and the use of gear 2 was negatively related to performance. Rifle carriage had a greater impact on gear distribution when skiing in moderate uphill terrain, where changes between gears 2 and 3 were more common, compared to in steeper terrain. This information can be useful for athletes and coaches when developing and optimizing sport-specific biathlon training to improve performance.

Purpose: To examine the effect of freediving depth on risk for hypoxic blackout by recording arterial oxygen saturation (SpO2) and heart rate (HR) during deep and shallow dives in the sea. Methods: Fourteen competitive freedivers conducted open-water training dives wearing a water-/pressure proof pulse oximeter continuously recording HR and SpO2. Dives were divided into deep (&gt; 35 m) and shallow (10–25 m) post-hoc and data from one deep and one shallow dive from 10 divers were compared. Results: Mean ± SD depth was 53 ± 14 m for deep and 17 ± 4 m for shallow dives. Respective dive durations (120 ± 18 s and 116 ± 43 s) did not differ. Deep dives resulted in lower minimum SpO2 (58 ± 17%) compared with shallow dives (74 ± 17%; P = 0.029). Overall diving HR was 7 bpm higher in deep dives (P = 0.002) although minimum HR was similar in both types of dives (39 bpm). Three divers desaturated early at depth, of which two exhibited severe hypoxia (SpO2 ≤ 65%) upon resurfacing. Additionally, four divers developed severe hypoxia after dives. Conclusions: Despite similar dive durations, oxygen desaturation was greater during deep dives, confirming increased risk of hypoxic blackout with increased depth. In addition to the rapid drop in alveolar pressure and oxygen uptake during ascent, several other risk factors associated with deep freediving were identified, including higher swimming effort and oxygen consumption, a compromised diving response, an autonomic conflict possibly causing arrhythmias, and compromised oxygen uptake at depth by lung compression possibly leading to atelectasis or pulmonary edema in some individuals. Individuals with elevated risk could likely be identified using wearable technology. 

This study investigated micro-pacing strategies during sit para-biathlon. Six elite sit para-biathletes wore a positioning system device during the world-championships in three different competition formats (Sprint, Middle-distance, and Long-distance). Total Skiing Time (TST), penalty-time, shooting-time, and Total Race Time (TRT) were analysed. One-way analyses of variance were used to compare the relative contributions of TST, penalty-time, and shooting-time to TRT across the three race formats. Statistical parametric mapping (SPM) was used to determine the course positions (clusters) where instantaneous skiing speed was significantly associated with TST. The contribution of TST to TRT was lower for the Long-distance (80 & PLUSMN; 6%) compared to the Sprint (86 & PLUSMN; 5%) and Middle-distance (86 & PLUSMN; 3%) races, however this difference was not statistically significant (p > 0.05). The proportional contribution of penalty-time to TRT was significantly greater (p < 0.05) for the Long-distance (13 & PLUSMN; 6%) compared to the Sprint (5 & PLUSMN; 4%) and Middle-distance (4 & PLUSMN; 3%) races. Statistical parametric mapping (SPM) revealed specific clusters where instantaneous skiing speed was significantly associated with TST. For example, over all laps during the Long-distance race, the fastest athlete gained 6.5 s over the slowest athlete in the section with the steepest uphill. Overall, these findings can provide insights into pacing strategies and help para-biathlon coaches and athletes optimise training programmes to improve performance.

This study compared performance strategies and sub-technique selection in cross-country skate skiing sprint races, specifically individual time-trial (ITT) and head-to-head (H2H) formats. Fourteen male cross-country skiers from the Chinese national team participated in the FIS-sanctioned sprint race day. GNSS and heart rate sensors recorded positioning, skiing speeds, heart rate, sub-technique usage, and skiing kinematics. Statistical parametric mapping (SPM) was used to determine the course positions (clusters) where instantaneous skiing speed was significantly associated with section time. One-way analyses of variance were used to examine differences between the ITT and H2H. H2H race speeds were 2.4 +/- 0.2% faster than the ITT race (p < 0.05).Variations in sub-technique and skiing kinematics were observed between race formats, indicating different strategies and tactics employed by athletes. SPM identified specific clusters (primarily uphill) where the fastest athlete gained significant time over the slowest. The greatest time gains were associated with higher G3 sub-technique usage and longer G3 cycle length on steep uphill terrain (9-13% gradients). Integrating SPM analyses and sub-technique assessments can help optimise performance and tactics in sprint races. This study enhances our understanding of cross-country skiing dynamics and performance variations among elite competitors.

Despite the International System of Units (SI), as well as several publications guiding researchers on correct use of terminology, there continues to be widespread misuse of mechanical terms such as ‘work’ in sport and exercise science. A growing concern is the misuse of the term ‘load’. Terms such as ‘training load’ and ‘PlayerLoad’ are popular in sport and exercise science vernacular. However, a ‘load’ is a mechanical variable which, when used appropriately, describes a force and therefore should be accompanied with the SI-derived unit of the newton (N). It is tempting to accept popular terms and nomenclature as scientific. However, scientists are obliged to abide by the SI and must pay close attention to scientific constructs. This communication presents a critical reflection on the use of the term ‘load’ in sport and exercise science. We present ways in which the use of this term breaches principles of science and provide practical solutions for ongoing use in research and practice. 

Purpose: To identify differences in heart rate (HR) and concentration of blood lactate ([La]) relationships between laboratory and field-based skate-roller-skiing tests.

Methods: Fourteen world-class biathletes (8 women, 6 men) completed a laboratory- and field-based roller-skiing test using the skate technique. The laboratory-based test comprised 5 to 7 submaximal steps at a fixed incline and speed on a roller-skiing treadmill. The field-based test comprised 5 steps on a course where the final hill was designed to mimic the conditions of the laboratory test. HR and [La] were measured for each step. The HR associated with 2 mmol·L−1(HR@2 mmol) and 4 mmol·L−1 (HR@4 mmol) of [La] was calculated using an interpolation method. A 1-way analysis of variance and Bland–Altman analyses with 95% limits of agreement (LoA) were used to determine if test type influenced HR@2 mmol orHR@4 mmol. A second-order polynomial was fitted to group-level data to highlight the HR–[La] relationships for laboratory- and field-based tests.

Results: HR@2 mmol was lower for field tests than for laboratory tests (mean bias: 1.9%HRmax; 95% LoA: −4.5 to+8.3%HRmax; P < .001). HR@4 mmol was also lower for field tests compared to laboratory tests (mean bias: 2.4%HRmax; 95%LoA: −1.2 to +6.0%HRmax; P < .001). On the group level, the lactate threshold occurred at a lower HR during roller skiing in the field compared to the laboratory.

Conclusions: The findings from this study confirm that for a given HR, [La] was greater in field compared with laboratory-based conditions. These results might have implications for how coaches define training-intensity“zones” during skate roller skiing based on laboratory tests.

Purpose:

To critically appraise the utility of heart rate (HR) and power output (PO) to predict metabolic rate (MR) and oxygen consumption ( ˙VO2 ) during variable-intensity roller skiing and cycling.

Methods:

National-level cyclists (n = 8) and cross-country skiers (n = 9) completed a preliminary session to determine ˙VO2max , and a variable-intensity protocol with 3 high-intensity stages at 90% ˙VO2max for 3 minutes interspersed with 3 moderate-intensity stages at 70% ˙VO2max for 6 minutes. Cardiorespiratory measures were recorded throughout. Linear HR–MR, HR–˙VO2 , PO–MR, and PO–˙VO2 regressions were computed from the preliminary session, individually, for all athletes and used to predict MR and ˙VO2 from both HR and PO, separately, during the variable-intensity protocol. Mean differences with 95% limits of agreement (LOA) between measured and predicted MR and ˙VO2 were calculated.

Results:

MR and ˙VO2 estimated from HR displayed a mean bias close to zero but wide LOA. HR overestimated MR and ˙VO2 during moderate intensity but underestimated MR and ˙VO2 during high intensity, for both roller skiing and cycling. MR and ˙VO2 estimated from PO were more consistent across the experimental trial, displaying a mean bias farther from zero but with tighter LOA.

Conclusions:

This study has demonstrated that HR has limited utility to predict metabolic intensity during variable-intensity roller skiing and cycling because of wide LOA. On the other hand, metabolic intensity predicted from PO had tighter LOA, suggesting better consistency. PO might provide a better prediction of metabolic intensity compared with HR, particularly when longer-duration steps are performed during preliminary testing.

Background:

Compared with other major global team sports such as football or basketball, ice hockey has received considerably less attention in sport-science research. However, the research focus on ice hockey performance is growing rapidly. Unfortunately, despite the growing interest in ice hockey, among the little research that has been conducted there are inconsistencies in terminology and methodology in the study of physiology and performance during games. The need for systematic and standardized reporting of study methodology is vital, as a lack of methodological detail or methodological inconsistencies make it impossible to replicate published studies, and alterations in the methodologies used can influence the measured demands imposed on players. Accordingly, this prohibits the ability of coaches to generate game-replicating training programs, decreasing the application of research findings to practice. In addition, a lack of methodological detail or methodological inconsistencies can result in incorrect conclusions being made from research.

Purpose:

In this invited commentary, we aim to increase awareness regarding the current standard of methodological reporting in ice hockey game-analysis research. In addition, we have developed a framework for the standardization of game analysis in ice hockey in order to allow for greater replication in future research and to increase the application of published findings to practice.

Conclusions:

We implore researchers in the field to consult the Ice Hockey Game Analysis Research Methodological Reporting Checklist in order to adopt a detailed reporting standard of methodologies in future work to help improve the applicability of research outcomes.

This study examined the micro-pacing strategies during a distance freestyle cross-country (XC) skiing competition. Nine female and 10 male highly trained XC skiers wore a GNSS device during a FIS-sanctioned race. The course was ~4900 m; women completed two-laps; men completed three-laps. The course was divided into uphill (S1, S3, S5, S7), downhill (S2, S4, S6, S8), and flat (S9) sections for analyses. Statistical parametric mapping was used to determine the course positions (clusters) where total race time or section time was significantly associated with instantaneous skiing speed. Total race time was associated with instantaneous skiing speed during a cluster in S1 on lap 2 for both sexes (t ≥ 5.899, p ≤ 0.008). The two longest uphill sections (S1; S5) and the flat section (S9) contained clusters where section times were related to instantaneous skiing speed for both sexes (p < 0.05). The fastest woman gained 6.9 s on the slowest woman during a cluster in S1 on lap 1 and 7.3 s during a cluster in S9 on lap 1. The fastest man gained 51.7 s on the slowest man over all clusters in S5 over the 3 laps combined. Compared to skiers with longer total race times, skiers with shorter race times skied with faster instantaneous speeds in some clusters of the uphill sections, as well as on the flat section of the course. This study also identified different relative micro-pacing strategies for women and men during freestyle distance XC skiing races. Finally, statistical parametric mapping analyses can help to identify individual strengths and weaknesses for guiding training programs and optimise competition pacing strategies.

Purpose To examine the micro-pacing (within-lap) strategies during biathlon skiing with and without the biathlon rifle. Methods Twenty biathletes (7 women, 13 men) performed two crosscountry skiing time-trials on a ≈2300 m course, once with and once without the biathlon rifle. During time-trials, biathletes wore a sensor that recorded position, distance and skiing-speed. A trajectory correction and statistical parametric mapping procedure determined the course positions (clusters) where instantaneous skiing-speed was significantly related to time-trial performance. The time differences between the fastest and slowest skier in these clusters were calculated. Results The fastest biathletes skied with greater instantaneous speeds in specific clusters, which included both uphill and downhill sections. The clusters represented time gains for the fastest skier over the slowest skier of between 16.1 and 25.8 s for the women and between 18.9 and 21.9 s for the men. The largest time gains between the fastest and slowest biathletes were observed in a downhill section that was preceded by a 180° turn, where time gains were between 2.9 and 4.1 s in clusters of between just 12 to 62 m. In biathlon skiing with-rifle, there were more clusters that were not present during without-rifle. When skiing with-rifle, there were additional clusters in the uphill sections that represented time gains of 5.2 s and 2.3 s for the women and men, respectively. Conclusions Statistical parametric mapping can be used in biathlon to provide pacing and performance feedback to athletes and coaches.

Pacing strategies in cross-country skiing (XCS) are largely influenced by the course topography [1]. Recent research has identifiedthat skiing velocity during some key track sections (i.e. the micro-pacing strategy) was related to faster race times [2]. Specifically, skiing velocity during transitions between uphill, flat and downhill sections were strongly related to faster classic XCS sprint race times among female skiers [2]. Previous research has also shown that pacing strategies might vary between techniques (i.e. classic vs.skate) [1] and sexes [3]. Therefore, this study aimed to examine the relationship between micro-pacing strategies and performance during a freestyle XCS distance race in women and men.

METHODS: Nine female and 10 male XCS athletes wore a GNSS device (Catapult OptimEye S5; 10 Hz) during a senior FIS-sanctioned freestyle distance race. The race lap was 4892 m in distance with 165 m of vertical climbing. The women completed two laps (9964 m), while the men completed three laps (14678 m). The course was divided into uphill (S1, S3, S5, S7), downhill (S2, S4, S6, S8) and flat sections (S9) for analyses. In order to compare skiers’ velocities at the same track locations, GNSS positioning data were resampled at every 1-m interval along the course. Statistical parametric mapping (SPM; linear regression) was used to determine the track positions where instantaneous skiing velocity was significantly associated with race time.

RESULTS: Race time was associated with velocity during parts of S1 (the second longest climb) on lap 2 for both sexes (t ≥ 4.206, p < 0.001)and on lap 3 for men (t = 4.308, p = 0.047). By contrast, race time was unrelated to velocity during S3 (the shortest climb) for both sexes. Race time was related to velocity during parts of S5 (the longest climb) on lap 1 for women (t = 4.663, p < 0.001) and on laps 2and 3 for men (t ≥ 4.031, p ≤ 0.009). While race time was not related to velocity during S7 (the second shortest climb) for men, it was for women on lap 1 (t = 3.871, p = 0.002). Race time was largely unrelated to velocity during all downhill sections for both sexes. Finally, race time was related to velocity through S9 (the flat section) for women (t = 3.716, p = 0.001), but not for men.

CONCLUSION: SPM analyses revealed specific uphill and flat track sections where athletes with faster race times skied with higher velocities, compared to athletes with slower race times. Specifically, more successful women adopted more aggressive micro-pacing strategies on some parts of the three longer uphill sections (S1, S5, S7), as well as on the flat section of the course (S9). The more successful men also adopted more aggressive micro-pacing strategies on some parts of the two longest uphill sections (S1, S5). Coaches and athletes can use this information to optimise micro-pacing strategies and improve performance.

1. Stöggl et al. (2018); 2. Ihalainen et al. (2020); 3. Andersson et al. (2019)

This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women’s freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA −7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs −0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training. 

ObjectiveThis study aimed to identify Key Performance Indicators (KPIs) for men’s swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men’s individual 100 m races of the 2019 European Short-Course Swimming Championships.

ResultsDuration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs − 2.3% to + 2.3%), Breaststroke (0% bias, LOAs − 0.9% to + 0.9%), Butterfly (0% bias, LOAs − 1.2% to + 1.2%), and Freestyle (0% bias, LOAs − 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.

Purpose: Investigate the effect of biathlon rifle carriage on physiological and accelerometer-derived responses during biathlon skiing.

Methods: Twenty-eight biathletes (11F, 17M) completed two XC skiing time-trials (~2,300 m), once with and once without the biathlon rifle, with concurrent measurements of HR, skiing speed and accelerations recorded from three triaxial accelerometers attached at the Upper-spine, Lower-spine and Pelvis. Exercise intensity was quantified from HR, skiing speed as well from accelerometry-derived PlayerLoad™ per minute (PL·min−1) and average net force (AvFNet). All metrics were analyzed during Uphill, Flat and Downhill sections of the course. Relationships between accelerometry-derived metrics and skiing speed were examined.

Results: Time-trials were faster for males compared with females (mean difference: 97 ± 73 s) and No-Rifle compared to With-Rifle (mean difference: 16 ± 9 s). HR was greatest during Downhill (183 ± 5 bpm), followed by Uphill (181 ± 5 bpm) and was lowest in the Flat sections (177 ± 6 bpm, p <0.05). For PL·min−1 and AvFNet there were 3-way Rifle x Gradient x Sensor-Position interactions. Typically, these metrics were greatest during Uphill and Flat sections and were lowest during Downhill sections. Rifle carriage had no impact on the AvFNet at the Lower-Spine or Pelvis. Significant positive linear relationships were identified between skiing speed and accelerometer-derived metrics during Uphill, Flat and Downhill skiing (r = 0.12–0.61, p < 0.05).

Conclusions: The accelerometry-derived approach used in this study provides the potential of a novel method of monitoring the external demands during skiing. In particular, AvFNet with sensors located close to the center of mass displayed greatest utility because it followed the expected response of external intensity where responses were greatest during uphill sections, followed by flats and lowest during downhills. In addition, there were significant positive relationships between AvFNet and skiing speed ranging from small to large. Accelerometry-derived measures could provide useful estimates of the external demands in XC skiing and biathlon.

Purpose: To examine the relationship between cardiorespiratory and accelerometer-derived measures of exercise during trailrunning and determine the influence of accelerometer location.

Methods: Eight trail runners (7 males and 1 female; age 26 [5] y;maximal oxygen consumption [  ̇VO2] 70 [6] mL·kg−1·min−1) completed a 7-km trail run (elevation gain: 486 m), with concurrentmeasurements of  ̇VO2, heart rate, and accelerations recorded from 3 triaxial accelerometers attached at the upper spine, lowerspine, and pelvis. External exercise intensity was quantified from the accelerometers using PlayerLoad™ per minute andaccelerometry-derived average net force. External exercise volume was calculated using accumulated PlayerLoad and the product of average net force and duration (impulse). Internal intensity was calculated using heart rate and  ̇VO2-metrics; internal volume was calculated from total energy expenditure (work). All metrics were analyzed during both uphill (UH) and downhill(DH) sections of the trail run.

Results: PlayerLoad and average net force were greater during DH compared with UH for all sensorlocations (P ≤ .004). For all accelerometer metrics, there was a sensor position × gradient interaction (F2,1429.003;P <.001). The upper spine was lower compared with both pelvis (P ≤ .003) and lower spine (P ≤ .002) for all accelerometermetrics during both UH and DH running. Relationships between accelerometer and cardiorespiratory measures during UH running ranged from moderate negative to moderate positive (r = −.31 to .41). Relationships were stronger during DH running where there was a nearly perfect correlation between work and impulse (r = .91; P < .001).

Conclusions: Simultaneous monitoring of cardiorespiratory and accelerometer-derived measures during trail running is suggested because of the disparity between internal and external intensities during changes in gradient. Sensor positioning close to the center of mass isrecommended.

Aim To identify how riding rein direction (left and right) and rider asymmetry affect tölt performance in Icelandic horses. Methods Two horses were ridden in tölt by four riders on both left and right reins. Riders wore pressure insoles that measured the total absolute force (FAbs) and absolute force difference (FDiff) in their left and right feet in the stirrups. A 3D motion-analysis system recorded the degrees of side-to-side movement in the pelvis (RollP) and in the thoracolumbar region (RollT). Lateral advanced placement (LAP) and duty factor (DF) were calculated to determine tölt performance. One-way ANOVAs were used to assess the effect of rein direction on rider asymmetry variables (FAbs, FDiff, RollP and RollT) and tölt performance (LAP, DF) on a group level (n = 8). Within-subject Spearman rank correlations (ρ) were computed to determine the effect of rider asymmetry variables on tölt performance on an individual level. Results LAP was closer to 25% on the left rein compared to the right rein (mean difference: 1.8 ±1.2%; F(1,7) = 16.333; p = 0.005, η2p = 0.700). In addition, DF was lower on the left rein compared to the right rein (mean difference: 1.9±0.8%; F(1,7) = 41.299; p<0.001, η2p = 0.855). Individual relationships between RollT and LAP ranged from small negative to very large positive and reached significance for one rider (ρ = 0.730; p = 0.040). Individual relationships between RollP and DF ranged from very large negative to very large positive and reached significance for two riders (ρ = 0.731; p = 0.040; ρ = -0.723 p = 0.043). Conclusion Rein direction might influence tölt performance. Individual relationships between rider asymmetry and tölt performance were highly variable and reached significance in some instances, indicating that the relationship between rider asymmetry and tölt performance is highly individual. This type of biomechanical data can be used to provide valuable feedback to guide equestrians and coaches. 

Introduction: Icelandic horse competitions are popular in Scandinavia, yet understanding the rider-horse interaction in the tölt gait is currently limited. It is known that a heavier rider leads to shorter and more frequent steps during tölt.1 Also, an imbalanced rider can result in overextension in the horses’ limbs, which can cause lameness.2 Therefore, understanding the effects of rider asymmetry (i.e.,being unable to stay balanced) is important for both performance and horse welfare. Aim: The aim of this study was to identify whether rider asymmetry affects tölt performance in Icelandic horses. Method: Two different horses were ridden in tölt by four riders on separate occasions. Riders were equipped with Pedar foot pressureinsoles (Novel Electronics, Germany) to measure rider balance and a 3D motion-analysis system (Noraxon, Arizona, USA) to examine movement around the pelvisin both left and right rein directions. Lateral Advanced Placement (LAP)3 was used to determine tölt performance and was calculated from visual inspection of 60 Hzvideo footage. Paired samples t-tests were used to determine differences in LAP, lateral pelvic movement, and foot pressure between left and right rein. Results are expressed as mean±SD Results: LAP was significantly greater for left compared with right rein (23±1% vs. 21±0%; p = 0.002) and riders had significantly more lateral pelvic movement for right compared with left rein (2.6±0.5° vs. 1.5±0.5°;p=0.002). However, there was no foot pressure asymmetry for left vs. right rein(12±6 N vs. 10±6 N; p=0.670). Conclusions: Riders perform better in tölt on left rein and lateral pelvic movement appears to influence tölt quality. Coaches should focus on instructing riders to balance their seat to improve performance and equine welfare.

We investigated the effects of playing surfaces with different impact absorption characteristics on external demand and physiological responses. Fifteen participants completed a soccer match simulation on natural grass, synthetic turf and concrete surfaces. Accelerometry-derived PlayerLoadTM per minute (PL·min−1) and average net force (AvFNet) were used to quantify external demands at the centre of mass (CoM), upper-back, mid-back and hip. Heart rate, oxygen uptake, energy expenditure and RPE quantified physiological responses. The concrete surface exhibited the least impact absorption, with peak decelerations ~3.5x synthetic turf and ~10x natural grass (p &lt; 0.001). Despite this, there was no differences in external demand between surfaces (surface: p ≥ 0.194; η2p≤0.092). Both AvFNet and PL·min−1 (location: p &lt; 0.001; η2p≥0.859) were higher at the hip (613(91)N; 12.5(1.2)arb.u), reduced at the mid-back (521(67)N; 8.8(0.7)arb.u) and upper-back (502(60)N; 8.8(0.7)arb.u) when compared to CoM (576(78)N; 10.7(1.0)arb.u). Although playing surface did not influence the external demands, heart rate or oxygen uptake (p &gt; 0.05), energy expenditure was highest on natural grass compared to synthetic turf (P = 0.034) and RPE was highest on synthetic turf compared to concrete (p = 0.026). Different playing surfaces can alter physiological responses to soccer-specific activity even when the external demands are similar.