Determining V̇O2max in competitive swimmers: Comparing the validity and reliability of cycling, arm cranking, ergometer swimming, and tethered swimming.

OBJECTIVES: This study aims to identify the optimal method for determining V̇O2max in competitive swimmers in terms of validity and test-retest reliability. DESIGN: Controlled experiment. METHODS: Twenty competitive swimmers performed four maximal incremental exercise tests: cycling, arm cranking, ergometer swimming, and tethered swimming. Gas analysis was conducted to estimate V̇O2max. Validity was assessed in terms of the amount of variance of the performance on a 1500-m time trial explained by the estimated V̇O2max . Test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). RESULTS: V̇O2max obtained from tethered swimming, ergometer swimming, and cycling explained a similar amount of variance of the 1500-m performance (R2 = 0.64, 0.64 and 0.65, respectively). However, ergometer swimming yielded significantly lower V̇O2max estimates (40.54 ±â€¯6.55 ml/kg/min) than tethered swimming (54.40 ±â€¯6.21 ml/kg/min) and cycling (54.39 ±â€¯5.63 ml/kg/min). Arm cranking resulted in both a lower explained variance (R2 = 0.41) and a significantly lower V̇O2max (43.14 ±â€¯7.81 ml/kg/min). Tethered swimming showed good reliability (ICC = 0.81). CONCLUSIONS: Bicycle and tethered swimming tests demonstrated high validity with comparable V̇O2max estimates, explaining a large proportion of differences in endurance performance. Choosing between these two methods involves a trade-off between a higher practical applicability and reliability of the bicycle test and the more sport-specific nature of the tethered swimming test.

Concurrent Strength and Endurance Training: A Systematic Review and Meta-Analysis on the Impact of Sex and Training Status.

BACKGROUND: Many sports require maximal strength and endurance performance. Concurrent strength and endurance training can lead to suboptimal training adaptations. However, how adaptations differ between males and females is currently unknown. Additionally, current training status may affect training adaptations. OBJECTIVE: We aimed to assess sex-specific differences in adaptations in strength, power, muscle hypertrophy, and maximal oxygen consumption ( V ˙ O2max) to concurrent strength and endurance training in healthy adults. Second, we investigated how training adaptations are influenced by strength and endurance training status. METHODS: A systematic review and meta-analysis was conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and a Cochrane risk of bias was evaluated. ISI Web of science, PubMed/MEDLINE, and SPORTDiscus databases were searched using the following inclusion criteria: healthy adults aged 18-50 years, intervention period of ≥ 4 weeks, and outcome measures were defined as upper- and lower-body strength, power, hypertrophy, and/or V ˙ O2max. A meta-analysis was performed using a random-effects model and reported in standardized mean differences. RESULTS: In total, 59 studies with 1346 participants were included. Concurrent training showed blunted lower-body strength adaptations in males, but not in females (male: - 0.43, 95% confidence interval [- 0.64 to - 0.22], female: 0.08 [- 0.34 to 0.49], group difference: P = 0.03). No sex differences were observed for changes in upper-body strength (P = 0.67), power (P = 0.37), or V ˙ O2max (P = 0.13). Data on muscle hypertrophy were insufficient to draw any conclusions. For training status, untrained but not trained or highly trained endurance athletes displayed lower V ˙ O2max gains with concurrent training (P = 0.04). For other outcomes, no differences were found between untrained and trained individuals, both for strength and endurance training status. CONCLUSIONS: Concurrent training results in small interference for lower-body strength adaptations in males, but not in females. Untrained, but not trained or highly trained endurance athletes demonstrated impaired improvements in V ˙ O2max following concurrent training. More studies on females and highly strength-trained and endurance-trained athletes are warranted. CLINICAL TRIAL REGISTRATION: PROSPERO: CRD42022370894.

Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach.

BACKGROUND: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. AIM: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. METHODS: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal-Wallis or Kolmogorov-Smirnov tests for training impulse and KDE scores, respectively. RESULTS: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. CONCLUSION: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.

Low myoglobin concentration in skeletal muscle of elite cyclists is associated with low mRNA expression levels.

Myoglobin is essential for oxygen transport to the muscle fibers. However, measurements of myoglobin (Mb) protein concentrations within individual human muscle fibers are scarce. Recent observations have revealed surprisingly low Mb concentrations in elite cyclists, however it remains unclear whether this relates to Mb translation, transcription and/or myonuclear content. The aim was to compare Mb concentration, Mb messenger RNA (mRNA) expression levels and myonuclear content within muscle fibers of these elite cyclists with those of physically-active controls. Muscle biopsies were obtained from m. vastus lateralis in 29 cyclists and 20 physically-active subjects. Mb concentration was determined by peroxidase staining for both type I and type II fibers, Mb mRNA expression level was determined by quantitative PCR and myonuclear domain size (MDS) was obtained by immunofluorescence staining. Average Mb concentrations (mean ± SD: 0.38 ± 0.04 mM vs. 0.48 ± 0.19 mM; P = 0.014) and Mb mRNA expression levels (0.067 ± 0.019 vs. 0.088 ± 0.027; P = 0.002) were lower in cyclists compared to controls. In contrast, MDS and total RNA per mg muscle were not different between groups. Interestingly, in cyclists compared to controls, Mb concentration was only lower for type I fibers (P < 0.001), but not for type II fibers (P > 0.05). In conclusion, the lower Mb concentration in muscle fibers of elite cyclists is partly explained by lower Mb mRNA expression levels per myonucleus and not by a lower myonuclear content. It remains to be determined whether cyclists may benefit from strategies that upregulate Mb mRNA expression levels, particularly in type I fibers, to enhance their oxygen supply.

Quadriceps Muscle Morphology Is an Important Determinant of Maximal Isometric and Crank Torques of Cyclists.

The aim of this study was to determine if quadriceps morphology [muscle volume (MV); cross-sectional area (CSA)], vastus lateralis (VL) muscle architecture, and muscle quality [echo intensity (ECHO)] can explain differences in knee extensor maximal voluntary isometric contraction (MVIC), crank torque (CT) and time-to-exhaustion (TTE) in trained cyclists. Twenty male competitive cyclists performed a maximal incremental ramp to determine their maximal power output (POMAX). Muscle morphology (MV; CSA), muscle architecture of VL and muscle quality (ECHO) of both quadriceps muscles were assessed. Subsequently, cyclists performed three MVICs of both knee extensor muscles and finally performed a TTE test at POMAX with CT measurement during TTE. Stepwise multiple regression results revealed right quadriceps MV determined right MVIC (31%) and CT (33%). Left MV determined CT (24%); and left VL fascicle length (VL-FL) determined MVIC (64%). However, quadriceps morphological variables do not explain differences in TTE. No significant differences were observed between left and right quadriceps muscle morphology (p > 0.05). The findings emphasize that quadriceps MV is an important determinant of knee extensor MVIC and CT but does not explain differences in TTE at POMAX. Furthermore, quadriceps morphological variables were similar between the left and right quadriceps in competitive cyclists.

Validity and Reliability of Facial Rating of Perceived Exertion Scales for Training Load Monitoring.

ABSTRACT: van der Zwaard, S, Hooft Graafland, F, van Middelkoop, C, and Lintmeijer, LL. Validity and reliability of facial rating of perceived exertion scales for training load monitoring. J Strength Cond Res 37(5): e317-e324, 2023-Rating of perceived exertion (RPE) is often used by coaches and athletes to indicate exercise intensity, which facilitates training load monitoring and prescription. Although RPE is typically measured using the Borg's category-ratio 10-point scale (CR10), digital sports platforms have recently started to incorporate facial RPE scales, which potentially have a better user experience. The aim of this study was to evaluate the validity and reliability of a 5-point facial RPE scale (FCR5) and a 10-point facial RPE scale (FCR10), using the CR10 as a golden standard and to assess their use for training load monitoring. Forty-nine subjects were grouped into 17 untrained (UT), 19 recreationally trained (RT), and 13 trained (T) individuals Subjects completed 9 randomly ordered home-based workout sessions (3 intensities × 3 RPE scales) on the Fitchannel.com platform. Heart rate was monitored throughout the workouts. Subjects performed 3 additional workouts to assess reliability. Validity and reliability of both facial RPE scales were low in UT subjects (intraclass correlation [ICC] ≤ 0.44, p ≤ 0.06 and ICC ≤ 0.43, p ≥ 0.09). In RT and T subjects, validity was moderate for FCR5 (ICC ≥ 0.72, p < 0.001) and good for FCR10 (ICC ≥ 0.80, p < 0.001). Reliability for these groups was rather poor for FCR5 (ICC = 0.51, p = 0.006) and moderate for FCR10 (ICC = 0.74, p < 0.001), but it was excellent for CR10 (ICC = 0.92, p < 0.001). In RT and T subjects, session RPE scores were also strongly related to Edward's training impulse scores ( r ≥ 0.70, p < 0.001). User experience was best supported by the FCR10 scale. In conclusion, researchers, coaches, strength and conditioning professionals, and digital sports platforms are encouraged to incorporate the valid and reliable FCR10 and not FCR5 to assess perceived exertion and internal training load of recreationally trained and trained individuals.

Co-Operative Design of a Coach Dashboard for Training Monitoring and Feedback.

Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development.

Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics.

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires. Match performance was derived from annotated actions by a video scout during matches. We identified conditions of predictor variables that related to attack and pass performance (p < 0.05). Better attack performance is related to heavy weights of lower-body strength training exercises in the preceding four weeks. However, worse attack performance is linked to large variations in weights of full-body strength training exercises, excessively heavy upper-body strength training, low jump heights and small variations in the number of high jumps in the four weeks prior to competition. Lower passing performance was associated with small variations in the number of high jumps in the preceding week and an excessive amount of high jumps performed, on average, in the two weeks prior to competition. Differences in findings with respect to passing and attack performance suggest that elite volleyball players can improve their performance if training schedules are adapted to the position of a player.

Perceived Training of Junior Speed Skaters versus the Coach's Intention: Does a Mismatch Relate to Perceived Stress and Recovery?

The aim of this observational study was to examine the differences between training variables as intended by coaches and perceived by junior speed skaters and to explore how these relate to changes in stress and recovery. During a 4-week preparatory period, intended and perceived training intensity (RPE) and duration (min) were monitored for 2 coaches and their 23 speed skaters, respectively. The training load was calculated by multiplying RPE by duration. Changes in perceived stress and recovery were measured using RESTQ-sport questionnaires before and after 4 weeks. Results included 438 intended training sessions and 378 executed sessions of 14 speed skaters. A moderately higher intended (52:37 h) versus perceived duration (45:16 h) was found, as skaters performed fewer training sessions than anticipated (four sessions). Perceived training load was lower than intended for speed skating sessions (-532 ± 545 AU) and strength sessions (-1276 ± 530 AU) due to lower RPE scores for skating (-0.6 ± 0.7) or shorter and fewer training sessions for strength (-04:13 ± 02:06 hh:mm). All training and RESTQ-sport parameters showed large inter-individual variations. Differences between intended-perceived training variables showed large positive correlations with changes in RESTQ-sport, i.e., for the subscale's success (r = 0.568), physical recovery (r = 0.575), self-regulation (r = 0.598), and personal accomplishment (r = 0.589). To conclude, speed skaters that approach or exceed the coach's intended training variables demonstrated an increased perception of success, physical recovery, self-regulation, and personal accomplishment.

Personalized machine learning approach to injury monitoring in elite volleyball players.

ABSTRACTWe implemented a machine learning approach to investigate individual indicators of training load and wellness that may predict the emergence or development of overuse injuries in professional volleyball. In this retrospective study, we collected data of 14 elite volleyball players (mean ± SD age: 27 ± 3 years, weight: 90.5 ± 6.3 kg, height: 1.97 ± 0.07 m) during 24 weeks of the 2018 international season. Physical load was tracked by manually logging the performed physical activities and by capturing the jump load using wearable devices. On a daily basis, the athletes answered questions about their wellness, and overuse complaints were monitored via the Oslo Sports Trauma Research Center (OSTRC) questionnaire. Based on training load and wellness indicators, we identified subgroups of days with increased injury risk for each volleyball player using the machine learning technique Subgroup Discovery. For most players and facets of overuse injuries (such as reduced sports participation), we have identified personalized training load and wellness variables that are significantly related to overuse issues. We demonstrate that the emergence and development of overuse injuries can be better understood using daily monitoring, taking into account interactions between training load and wellness indicators, and by applying a personalized approach.Highlights With detailed, athlete-specific monitoring of overuse complaints and training load, practical insights in the development of overuse injuries can be obtained in a player-specific fashion contributing to injury prevention in sports.A multi-dimensional and personalized approach that includes interactions between training load variables significantly increases the understanding of overuse issues on a personal basis.Jump load is an important predictor for overuse injuries in volleyball.

Training-Induced Muscle Adaptations During Competitive Preparation in Elite Female Rowers.

Training-induced adaptations in muscle morphology, including their magnitude and individual variation, remain relatively unknown in elite athletes. We reported changes in rowing performance and muscle morphology during the general and competitive preparation phases in elite rowers. Nineteen female rowers completed 8 weeks of general preparation, including concurrent endurance and high-load resistance training (HLRT). Seven rowers were monitored during a subsequent 16 weeks of competitive preparation, including concurrent endurance and resistance training with additional plyometric loading (APL). Vastus lateralis muscle volume, physiological cross-sectional area (PCSA), fascicle length, and pennation angle were measured using 3D ultrasonography. Rowing ergometer power output was measured as mean power in the final 4 minutes of an incremental test. Rowing ergometer power output improved during general preparation [+2 ± 2%, effect size (ES) = 0.22, P = 0.004], while fascicle length decreased (-5 ± 8%, ES = -0.47, P = 0.020). Rowing power output further improved during competitive preparation (+5 ± 3%, ES = 0.52, P = 0.010). Here, morphological adaptations were not significant, but demonstrated large ESs for fascicle length (+13 ± 19%, ES = 0.93), medium for pennation angle (-9 ± 15%, ES = -0.71), and small for muscle volume (+8 ± 13%, ES = 0.32). Importantly, rowers showed large individual differences in their training-induced muscle adaptations. In conclusion, vastus lateralis muscles of elite female athletes are highly adaptive to specific training stimuli, and adaptations largely differ between individual athletes. Therefore, coaches are encouraged to closely monitor their athletes' individual (muscle) adaptations to better evaluate the effectiveness of their training programs and finetune them to the athlete's individual needs.

Under the Hood: Skeletal Muscle Determinants of Endurance Performance.

In the past decades, researchers have extensively studied (elite) athletes' physiological responses to understand how to maximize their endurance performance. In endurance sports, whole-body measurements such as the maximal oxygen consumption, lactate threshold, and efficiency/economy play a key role in performance. Although these determinants are known to interact, it has also been demonstrated that athletes rarely excel in all three. The leading question is how athletes reach exceptional values in one or all of these determinants to optimize their endurance performance, and how such performance can be explained by (combinations of) underlying physiological determinants. In this review, we advance on Joyner and Coyle's conceptual framework of endurance performance, by integrating a meta-analysis of the interrelationships, and corresponding effect sizes between endurance performance and its key physiological determinants at the macroscopic (whole-body) and the microscopic level (muscle tissue, i.e., muscle fiber oxidative capacity, oxygen supply, muscle fiber size, and fiber type). Moreover, we discuss how these physiological determinants can be improved by training and what potential physiological challenges endurance athletes may face when trying to maximize their performance. This review highlights that integrative assessment of skeletal muscle determinants points toward efficient type-I fibers with a high mitochondrial oxidative capacity and strongly encourages well-adjusted capillarization and myoglobin concentrations to accommodate the required oxygen flux during endurance performance, especially in large muscle fibers. Optimisation of endurance performance requires careful design of training interventions that fine tune modulation of exercise intensity, frequency and duration, and particularly periodisation with respect to the skeletal muscle determinants.

Articles with impact: insights into 10 years of research with machine learning.

Worldwide scientific output is growing faster and faster. Academics should not only publish much and fast, but also publish research with impact. The aim of this study is to use machine learning to investigate characteristics of articles that were published in the Journal of Applied Physiology between 2009 and 2018, and characterize high-impact articles. Article impact was assessed for 4,531 publications by three common impact metrics: the Altmetric Attention Scores, downloads, and citations. Additionally, a broad collection of (more than 200) characteristics was collected from the article's title, abstract, authors, keywords, publication, and article engagement. We constructed random forest (RF) regression models to predict article impact and articles with the highest impact (top-25% and top-10% for each impact metric), which were compared with a naive baseline method. RF models outperformed the baseline models when predicting the impact of unseen articles (P < 0.001 for each impact metric). Also, RF models predicted top-25% and top-10% high-impact articles with a high accuracy. Moreover, RF models revealed important article characteristics. Higher impact was observed for articles about exercise, training, performance and V̇o2max, reviews, human studies, articles from large collaborations, longer articles with many references and high engagement by scientists, practitioners and public or via news outlets and videos. Lower impact was shown for articles about respiratory physiology or sleep apnea, editorials, animal studies, and titles with a question mark or a reference to places or individuals. In summary, research impact can be predicted and better understood using a combination of article characteristics and machine learning.NEW & NOTEWORTHY Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.

Effect of vasti morphology on peak sprint cycling power of a human musculoskeletal simulation model.

Fascicle length of m. vastus lateralis in cyclists has been shown to correlate positively with peak sprint cycling power normalized for lean body mass. We investigated whether vasti morphology affects sprint cycling power via force-length and force-velocity relationships. We simulated isokinetic sprint cycling at pedaling rates ranging from 40 to 150 rpm with a forward dynamic model of the human musculoskeletal system actuated by eight leg muscles. Input of the model was muscle stimulation over time, which was optimized to maximize the average power output over a pedal cycle. This was done for a reference model and for models in which the vasti had equal volume but different morphology. It was found that models with longer muscle fibers but a reduced physiological cross-sectional area of the vasti produced a higher sprint cycling power. This was partly explained by better alignment of the peak power-pedaling rate curve of the vasti with the corresponding curves of the other leg muscles. The highest sprint cycling power was achieved in a model in which the increase in muscle fiber length of the vasti was accompanied by a concomitant shift in optimum knee angle. It was concluded that muscle mechanics can partly explain the positive correlations between fascicle length of m. vastus lateralis and normalized peak sprint cycling power. It should be investigated whether muscle fiber length of the vasti and optimum knee angle are suitable training targets for athletes who want to concurrently improve their sprint and endurance cycling performance.NEW & NOTEWORTHY We simulated isokinetic sprint cycling at pedaling rates ranging from 40 to 150 rpm with a forward dynamic model of the human musculoskeletal system actuated by eight leg muscles. We selectively modified vasti morphology: we lengthened the muscle fibers and reduced the physiological cross-sectional area. The modified model was able to produce a higher sprint cycling power.

Anthropometric Clusters of Competitive Cyclists and Their Sprint and Endurance Performance.

Do athletes specialize toward sports disciplines that are well aligned with their anthropometry? Novel machine-learning algorithms now enable scientists to cluster athletes based on their individual anthropometry while integrating multiple anthropometric dimensions, which may provide new perspectives on anthropometry-dependent sports specialization. We aimed to identify clusters of competitive cyclists based on their individual anthropometry using multiple anthropometric measures, and to evaluate whether athletes with a similar anthropometry also competed in the same cycling discipline. Additionally, we assessed differences in sprint and endurance performance between the anthropometric clusters. Twenty-four nationally and internationally competitive male cyclists were included from sprint, pursuit, and road disciplines. Anthropometry was measured and k-means clustering was performed to divide cyclists into three anthropometric subgroups. Sprint performance (Wingate 1-s peak power, squat-jump mean power) and endurance performance (mean power during a 15 km time trial, V ˙ O2 peak) were obtained. K-means clustering assigned sprinters to a mesomorphic cluster (endo-, meso-, and ectomorphy were 2.8, 5.0, and 2.4; n = 6). Pursuit and road cyclists were distributed over a short meso-ectomorphic cluster (1.6, 3.8, and 3.9; n = 9) and tall meso-ectomorphic cluster (1.5, 3.6, and 4.0; n = 9), the former consisting of significantly lighter, shorter, and smaller cyclists (p < 0.05). The mesomorphic cluster demonstrated higher sprint performance (p < 0.05), whereas the meso-ectomorphic clusters established higher endurance performance (p < 0.001). Overall, endurance performance was associated with lean ectomorph cyclists with small girths and small frontal area (p < 0.05), and sprint performance related to cyclists with larger skinfolds, larger girths, and low frontal area per body mass (p < 0.05). Clustering optimization revealed a mesomorphic cluster of sprinters with high sprint performance and short and tall meso-ectomorphic clusters of pursuit and road cyclists with high endurance performance. Anthropometry-dependent specialization was partially confirmed, as the clustering algorithm distinguished short and tall endurance-type cyclists (matching the anthropometry of all-terrain and flat-terrain road cyclists) rather than pursuit and road cyclists. Machine-learning algorithms therefore provide new insights in how athletes match their sports discipline with their individual anthropometry.

Muscle morphology of the vastus lateralis is strongly related to ergometer performance, sprint capacity and endurance capacity in Olympic rowers.

Rowers need to combine high sprint and endurance capacities. Muscle morphology largely explains muscle power generating capacity, however, little is known on how muscle morphology relates to rowing performance measures. The aim was to determine how muscle morphology of the vastus lateralis relates to rowing ergometer performance, sprint and endurance capacity of Olympic rowers. Eighteen rowers (12♂, 6♀, who competed at 2016 Olympics) performed an incremental rowing test to obtain maximal oxygen consumption, reflecting endurance capacity. Sprint capacity was assessed by Wingate cycling peak power. M. vastus lateralis morphology (volume, physiological cross-sectional area, fascicle length and pennation angle) was derived from 3-dimensional ultrasound imaging. Thirteen rowers (7♂, 6♀) completed a 2000-m rowing ergometer time trial. Muscle volume largely explained variance in 2000-m rowing performance (R2 = 0.85), maximal oxygen consumption (R2 = 0.65), and Wingate peak power (R2 = 0.82). When normalized for differences in body size, maximal oxygen consumption and Wingate peak power were negatively related in males (r = -0.94). Fascicle length, not physiological cross-sectional area, attributed to normalized peak power. In conclusion, vastus lateralis volume largely explains variance in rowing ergometer performance, sprint and endurance capacity. For a high normalized sprint capacity, athletes may benefit from long fascicles rather than a large physiological cross-sectional area.

Critical determinants of combined sprint and endurance performance: an integrative analysis from muscle fiber to the human body.

Optimizing physical performance is a major goal in current physiology. However, basic understanding of combining high sprint and endurance performance is currently lacking. This study identifies critical determinants of combined sprint and endurance performance using multiple regression analyses of physiologic determinants at different biologic levels. Cyclists, including 6 international sprint, 8 team pursuit, and 14 road cyclists, completed a Wingate test and 15-km time trial to obtain sprint and endurance performance results, respectively. Performance was normalized to lean body mass2/3 to eliminate the influence of body size. Performance determinants were obtained from whole-body oxygen consumption, blood sampling, knee-extensor maximal force, muscle oxygenation, whole-muscle morphology, and muscle fiber histochemistry of musculus vastus lateralis. Normalized sprint performance was explained by percentage of fast-type fibers and muscle volume ( R2 = 0.65; P < 0.001) and normalized endurance performance by performance oxygen consumption ( V̇o2), mean corpuscular hemoglobin concentration, and muscle oxygenation ( R2 = 0.92; P < 0.001). Combined sprint and endurance performance was explained by gross efficiency, performance V̇o2, and likely by muscle volume and fascicle length ( P = 0.056; P = 0.059). High performance V̇o2 related to a high oxidative capacity, high capillarization × myoglobin, and small physiologic cross-sectional area ( R2 = 0.67; P < 0.001). Results suggest that fascicle length and capillarization are important targets for training to optimize sprint and endurance performance simultaneously.-Van der Zwaard, S., van der Laarse, W. J., Weide, G., Bloemers, F. W., Hofmijster, M. J., Levels, K., Noordhof, D. A., de Koning, J. J., de Ruiter, C. J., Jaspers, R. T. Critical determinants of combined sprint and endurance performance: an integrative analysis from muscle fiber to the human body.

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue.

The developmental goal of 3D ultrasound imaging (3DUS) is to engineer a modality to perform 3D morphological ultrasound analysis of human muscles. 3DUS images are constructed from calibrated freehand 2D B-mode ultrasound images, which are positioned into a voxel array. Ultrasound (US) imaging allows quantification of muscle size, fascicle length, and angle of pennation. These morphological variables are important determinants of muscle force and length range of force exertion. The presented protocol describes an approach to determine volume and fascicle length of m. vastus lateralis and m. gastrocnemius medialis. 3DUS facilitates standardization using 3D anatomical references. This approach provides a fast and cost-effective approach for quantifying 3D morphology in skeletal muscles. In healthcare and sports, information on the morphometry of muscles is very valuable in diagnostics and/or follow-up evaluations after treatment or training.