Different Aspects of Physical Load in Small-Sided Field Hockey Games.

ABSTRACT: Wilmes, E, de Ruiter, CJ, van Leeuwen, RR, Banning, LF, van der Laan, D, and Savelsbergh, GJP. Different aspects of physical load in small-sided field hockey games. J Strength Cond Res 38(2): e56-e61, 2024-Running volumes and acceleration/deceleration load are known to vary with different formats of small-sided games (SSGs) in field hockey. However, little is known about other aspects of the physical load. Therefore, the aim of this study was to gain a more thorough understanding of the total physical load in field hockey SSGs. To that end, 2 different SSGs (small: 5 vs. 5, ∼100 m 2 per player; large: 9 vs. 9, ∼200 m 2 per player) were performed by 16 female elite field hockey athletes. A range of external physical load metrics was obtained using a global navigational satellite system and 3 wearable inertial measurement units on the thighs and pelvis. These metrics included distances covered in different velocity ranges (walk, jog, run, and sprint), mean absolute acceleration/deceleration, Hip Load, and time spent in several physically demanding body postures. The effects of SSG format on these external physical load metrics were assessed using linear mixed models ( p < 0.05). Running volumes in various speed ranges were higher for the large SSG. By contrast, mean absolute acceleration/deceleration and time spent in several demanding body postures were higher for the small SSG. This study shows that changing the SSG format affects different aspects of physical load differently.

New training load metrics in field hockey using inertial measurement units.

Field hockey players are exposed to high biomechanical loads. These loads often cannot be adequately estimated with global navigational satellite systems (GNSS) since on-field displacements during these movements are often small. Therefore, this study aims to explore the potential of different proxies of biomechanical load in field hockey with use of a simple inertial measurement unit (IMU) system. Sixteen field hockey players performed a range of field hockey specific exercises, including running with stick on the ground, running upright, and different types of shots and passes. All exercises were performed at two different frequencies (i.e. number of actions per minute). A variety of proxies of biomechanical load (time spent with forward tilted pelvis, time spent in lunge position, time spent with flexed thighs, and Hip Load) were obtained using wearable IMUs. In addition, total distance was quantified using a GNSS system. Linear mixed models were constructed to determine the effects of the different exercises and action frequency on all quantified metrics. All metrics increased approximately proportional to the increase in action frequency. Total distance and Hip Load were greatest for the running exercises, but the different types of shots and passes had greater effects on specific on the times spent in the demanding body postures. This shows that these proxies of biomechanical load can be used to estimate field hockey-specific biomechanical loads. The use of these metrics may provide coaches and medical staff with a more complete view of the training load that field hockey players experience.Highlights New proxies of biomechanical load derived with inertial measurement units were used to quantify field hockey specific biomechanical loads.These new biomechanical metrics are complementary to metrics obtained with global navigation satellite systems and increased proportionally to a doubling of the exercise intensity.The presented biomechanical load metrics can help field hockey coaches to achieve a better balance between load and recovery for their players.

Construct Validity and Test-Retest Reliability of Hip Load Compared With Playerload During Football-Specific Running, Kicking, and Jumping Tasks.

PURPOSE: To determine the test-retest reliability of the recently developed Hip Load metric, evaluate its construct validity, and assess the differences with Playerload during football-specific short-distance shuttle runs. METHODS: Eleven amateur football players participated in 2 identical experimental sessions. Each session included 3 different shuttle runs that were performed at 2 pace-controlled running intensities. The runs consisted of only running, running combined with kicks, and running combined with jumps. Cumulative Playerload and Hip Loads of the preferred and nonpreferred kicking leg were collected for each shuttle run. Test-retest reliability was determined using intraclass correlations, coefficients of variation, and Bland-Altman analyses. To compare the load metrics with each other, they were normalized to their respective values obtained during a 54-m run at 9 km/h. Sensitivity of each load metric to running intensity, kicks, and jumps was assessed using separate linear mixed models. RESULTS: Intraclass correlations were high for the Hip Loads of the preferred kicking leg (.91) and the nonpreferred kicking leg (.96) and moderate for the Playerload (.87). The effects (95% CIs) of intensity and kicks on the normalized Hip Load of the kicking leg (intensity: 0.95 to 1.50, kicks: 0.36 to 1.59) and nonkicking leg (intensity: 0.96 to 1.53, kicks: 0.06 to 1.34) were larger than on the normalized Playerload (intensity: 0.12 to 0.25, kicks: 0.22 to 0.53). CONCLUSIONS: The inclusion of Hip Load in training load quantification may help sport practitioners to better balance load and recovery.

Concurrent validity of an easy-to-use inertial measurement unit-system to evaluate sagittal plane segment kinematics during overground sprinting at different speeds.

This study investigated concurrent validity of inertial measurement units (IMUs) and high-speed video for sagittal plane kinematics during overground sprinting. The practical relevance is demonstrated by reporting the changes in thigh kinematics in relation to toe-off and touch-down of the feet at near maximal to maximal (80-100%) speeds. Sixteen athletes ran multiple 60 m sprints with IMUs on their feet, shanks, thighs, pelvis and trunk. High-speed video data were captured of the start strides and of one complete stride at full speed. Coefficients of multiple correlation with video were >0.99 for angles and angular velocities of the thigh and shank but low for the pelvis and trunk (0.13-0.66). For the limb segment angles (minimum, maximum, at toe-off and at touch-down) absolute biases (limits of agreement) were ≤2.9°(≤7.7°) and for angular velocities the values were ≤57°.s-1(≤93°.s-1). Many of the expected speed-related changes in thigh kinematics were significant (linear mixed effect regression; p < 0.05).In conclusion, an easy-to-use IMU system has good concurrent validity with video, especially for the thigh. It registers the kinematics of all strides in multiple sprints and can detect relatively small changes thereof, including those at key moments of foot-touch-down and toe-off.

Biomechanical Load Quantification Using a Lower Extremity Inertial Sensor Setup During Football Specific Activities.

Training load monitoring systems in football do not focus on lower extremities and therefore potentially neglect important information to optimise performance or reduce injury risk. The current study aims to present joint and segment angular accelerations as novel indicators to quantify lower extremity biomechanical load measured by a new inertial sensor setup. Relationships were explored with commonly used whole-body training load indicators using principal component analysis (PCA). Sixteen male amateur football players performed a linear sprint and an agility T-test. An inertial sensor setup, and local position measurement system were used to collect training load data. Hip Load, Knee Load, Thigh Load and Shank Load were introduced to quantify lower extremity biomechanical load. Three principal components were identified for both tests, explaining 91% and 86% of the variance. The indicators for the lower extremities contributed to the second principal component for both tests and provide distinct information compared to whole-body load indicators. The results show the potential to use an inertial sensor setup combined with common monitoring systems to evaluate training load, which may help optimise future performance and reduce injury risk. These relationships should be further examined during other football specific activities such as shooting or jumping.

Stride Lengths during Maximal Linear Sprint Acceleration Obtained with Foot-Mounted Inertial Measurement Units.

Inertial measurement units (IMUs) fixed to the lower limbs have been reported to provide accurate estimates of stride lengths (SLs) during walking. Due to technical challenges, validation of such estimates in running is generally limited to speeds (well) below 5 m·s-1. However, athletes sprinting at (sub)maximal effort already surpass 5 m·s-1 after a few strides. The present study aimed to develop and validate IMU-derived SLs during maximal linear overground sprints. Recreational athletes (n = 21) completed two sets of three 35 m sprints executed at 60, 80, and 100% of subjective effort, with an IMU on the instep of each shoe. Reference SLs from start to ~30 m were obtained with a series of video cameras. SLs from IMUs were obtained by double integration of horizontal acceleration with a zero-velocity update, corrected for acceleration artefacts at touch-down of the feet. Peak sprint speeds (mean ± SD) reached at the three levels of effort were 7.02 ± 0.80, 7.65 ± 0.77, and 8.42 ± 0.85 m·s-1, respectively. Biases (±Limits of Agreement) of SLs obtained from all participants during sprints at 60, 80, and 100% effort were 0.01% (±6.33%), -0.75% (±6.39%), and -2.51% (±8.54%), respectively. In conclusion, in recreational athletes wearing IMUs tightly fixed to their shoes, stride length can be estimated with reasonable accuracy during maximal linear sprint acceleration.

Associations between Hamstring Fatigue and Sprint Kinematics during a Simulated Football (Soccer) Match.

PURPOSE: Neuromuscular fatigue is considered to be important in the etiology of hamstring strain injuries in football. Fatigue is assumed to lead to decreases in hamstring contractile strength and changes in sprinting kinematics, which would increase hamstring strain injury risk. Therefore, the aim was to examine the effects of football-specific fatigue on hamstring maximal voluntary torque (MVT) and rate of torque development (RTD), in relation to alterations in sprinting kinematics. METHODS: Ten amateur football players executed a 90-min running-based football match simulation. Before and after every 15 min of simulated play, MVT and RTD of the hamstrings were obtained in addition to the performance and lower body kinematics during a 20-m maximal sprint. Linear mixed models and repeated measurement correlations were used to assess changes over time and common within participant associations between hamstring contractile properties and peak knee extension during the final part of the swing phase, peak hip flexion, peak combined knee extension and hip flexion, and peak joint angular velocities, respectively. RESULTS: Hamstring MVT and sprint performance were significantly reduced by 7.5% and 14.3% at the end of the football match simulation. Unexpectedly, there were no indications for reductions in RTD when MVT decrease was considered. Decreases in hamstring MVT were significantly correlated to decreases in peak knee angle (R = 0.342) and to increases in the peak combined angle (R = -0.251). CONCLUSIONS: During a football match simulation, maximal voluntary isometric hamstring torque declines. This decline is related to greater peak knee extension and peak combined angle during sprint running, which indicates a reduced capacity of the hamstrings to decelerate the lower leg during sprint running with fatigue.

The biomechanics of running and running styles: a synthesis.

Running movements are parametrised using a wide variety of devices. Misleading interpretations can be avoided if the interdependencies and redundancies between biomechanical parameters are taken into account. In this synthetic review, commonly measured running parameters are discussed in relation to each other, culminating in a concise, yet comprehensive description of the full spectrum of running styles. Since the goal of running movements is to transport the body centre of mass (BCoM), and the BCoM trajectory can be derived from spatiotemporal parameters, we anticipate that different running styles are reflected in those spatiotemporal parameters. To this end, this review focuses on spatiotemporal parameters and their relationships with speed, ground reaction force and whole-body kinematics. Based on this evaluation, we submit that the full spectrum of running styles can be described by only two parameters, namely the step frequency and the duty factor (the ratio of stance time and stride time) as assessed at a given speed. These key parameters led to the conceptualisation of a so-called Dual-axis framework. This framework allows categorisation of distinctive running styles (coined 'Stick', 'Bounce', 'Push', 'Hop', and 'Sit') and provides a practical overview to guide future measurement and interpretation of running biomechanics.

Sweat rate and sweat composition during heat acclimation.

The purpose of this study was to determine local sweat rate (LSR) and sweat composition during heat acclimation (HA). For ten consecutive days of HA, eight participants cycled in 33 °C and 65% relative humidity at an intensity such that a rectal temperature of 38.5 °C was reached within ~40 min, followed by a 60-min clamp of this rectal temperature (i.e., controlled hyperthermia). Four participants extended HA by a 28-day decay period and five consecutive days of heat re-acclimation (HRA) using controlled hyperthermia. Sweat from the upper arm and upper back was collected three times during each heat exposure session. LSR and sweat sodium, chloride, lactate, and potassium concentrations were determined. Relative to HA day 1, LSR was increased at the final day of HA (day 10) (arm: +58%, P < 0.001; back: +36%, P < 0.05). Concentrations of sodium, chloride, and lactate significantly (P < 0.05) decreased to ~60% at HA day 10 compared to day 1 on the arm and back. Potassium concentration did not significantly differ on HA day 10 compared to day 1 (arm: +11%, P > 0.05; back: +8%, P > 0.05). The induction patterns of the sudomotor adaptations were different. Whilst LSR increased from HA day 8 on the arm and from HA day 7 on the back, sodium and chloride conservation already occurred from HA day 3 on both skin sites. Lastly, the sweat lactate reduction occurred from HA day 6 on the arm and back. Initial evidence is provided that adaptations were partly conserved after decay (28 days) and that a 5-day HRA may be sufficient to restore HA adaptations. In conclusion, ten days of exercise-induced HA using controlled hyperthermia led to increases in LSR and concomitant reductions of sweat sodium, chloride, and lactate concentrations, whilst potassium concentrations remained relatively constant.

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field.

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The disadvantage of these measurement systems is that they do not register lower extremity kinematics, which could be a useful measure for identifying injury-risk factors. Rapid development in sensor technology may overcome the limitations of the current measurement systems. With inertial measurement units (IMUs) securely fixed to body segments, sensor fusion algorithms and a biomechanical model, joint kinematics could be estimated. The main purpose of this article is to demonstrate a sensor setup for estimating hip and knee joint kinematics of team sport athletes in the field. Five male subjects (age 22.5 ± 2.1 years; body mass 77.0 ± 3.8 kg; height 184.3 ± 5.2 cm; training experience 15.3 ± 4.8 years) performed a maximal 30-meter linear sprint. Hip and knee joint angles and angular velocities were obtained by five IMUs placed on the pelvis, both thighs and both shanks. Hip angles ranged from 195° (± 8°) extension to 100.5° (± 8°) flexion and knee angles ranged from 168.6° (± 12°) minimal flexion and 62.8° (± 12°) maximal flexion. Furthermore, hip angular velocity ranged between 802.6 °·s-1 (± 192 °·s-1) and -674.9 °·s-1 (± 130 °·s-1). Knee angular velocity ranged between 1155.9 °·s-1 (± 200 °·s-1) and -1208.2 °·s-1 (± 264 °·s-1). The sensor setup has been validated and could provide additional information with regard to athlete monitoring in the field. This may help professionals in a daily sports setting to evaluate their training programs, aiming to reduce injury and optimize performance.

Inertial Sensor-Based Motion Tracking in Football with Movement Intensity Quantification.

Inertial sensor-based measurements of lower body kinematics in football players may improve physical load estimates during training sessions and matches. However, the validity of inertial-based motion analysis systems is specific to both the type of movement and the intensity at which movements are executed. Importantly, such a system should be relatively simple, so it can easily be used in daily practice. This paper introduces an easy-to-use inertial-based motion analysis system and evaluates its validity using an optoelectronic motion analysis system as a gold standard. The system was validated in 11 football players for six different football specific movements that were executed at low, medium, and maximal intensity. Across all movements and intensities, the root mean square differences (means ± SD) for knee and hip flexion/extension angles were 5.3° ± 3.4° and 8.0° ± 3.5°, respectively, illustrating good validity with the gold standard. In addition, mean absolute flexion/extension angular velocities significantly differed between the three movement intensities. These results show the potential to use the inertial based motion analysis system in football practice to obtain lower body kinematics and to quantify movement intensity, which both may improve currently used physical load estimates of the players.

Sweat rate and sweat composition following active or passive heat re-acclimation: A pilot study.

The purpose of this study was to investigate local sweat rate (LSR) and sweat composition before and after active or passive heat re-acclimation (HRA). Fifteen participants completed four standardized heat stress tests (HST): before and after ten days of controlled hyperthermia (CH) heat acclimation (HA), and before and after five days of HRA. Each HST consisted of 35 min of cycling at 1.5W·kg-1 body mass (33°C and 65% relative humidity), followed by a graded exercise test. For HRA, participants were re-exposed to either CH (CH-CH, n = 6), hot water immersion (water temperature ~40°C for 40 min; CH-HWI, n = 5) or control (CH-CON, n = 4). LSR, sweat sodium, chloride, lactate and potassium concentrations were determined on the arm and back. LSR increased following HA (arm +18%; back +41%, P ≤  0.03) and HRA (CH-CH: arm +31%; back +45%; CH-HWI: arm +65%; back +49%; CH-CON arm +11%; back +11%, P ≤ 0.021). Sweat sodium, chloride and lactate decreased following HA (arm 25-34; back 21-27%, P < 0.001) and HRA (CH-CH: arm 26-54%; back 20-43%; CH-HWI: arm 9-49%; back 13-29%; CH-CON: arm 1-3%, back 2-5%, P < 0.001). LSR increases on both skin sites were larger in CH-CH and CH-HWI than CH-CON (P ≤ 0.010), but CH-CH and CH-HWI were not different (P ≥ 0.148). Sweat sodium and chloride conservation was larger in CH-CH than CH-HWI and CH-CON on the arm and back, whilst CH-HWI and CH-CON were not different (P ≥ 0.265). These results suggest that active HRA leads to similar increases in LSR, but more conservation of sweat sodium and chloride than passive HRA. Abbreviations: ANOVA: Analysis of variance; ATP: Adenosine triphosphate; BSA (m2): Body surface area; CH: Controlled hyperthermia; CH-CH: Heat re-acclimation by controlled hyperthermia; CH-CON: Control group (no heat re-acclimation); CH-HWI: Heat re-acclimation by hot water immersion; CV (%): Coefficient of variation; dt (min): Duration of a stimulus; F: Female; GEE: Generalized estimating equations; HA: Heat acclimation; HRA : Heat re-acclimation; HST: Heat stress test; LSR (mg·cm-2·min-1) : Local sweat rate; LOD (mmol·L-1): Limit of detection; M: Male; m x (mg): Mass of x; RH (%): Relative humidity; RT: Recreationally trained; SA (cm2): Surface area; t (min): Time; T: Trained; Tsk (°C): Skin temperature; Tre (°C): Rectal temperature; USG : Urine specific gravity; VO2peak (mL·kg-1·min-1): Peak oxygen uptake; WBSL (L): Whole-body sweat loss; WBSR (L·h-1): Whole-body sweat rate.

Individual optimal step frequency during outdoor running.

This is the first study investigating preferred (self-selected) step frequency (PSF) in relation to optimal step frequency (OSF) during running at different speeds outside the laboratory. OSF was defined as the SF at which heartrate (HR) showed a minimum during running at a constant speed. We aimed for a high ecological validity, which prohibits the direct measurement of oxygen consumption. HR was used as a proxy of energy cost, as has previously been validated. After a practice session, 21 experienced recreational runners (12 female) ran in an out-and-back manner on a straight flat tarmac lane in three main sessions: (1) 27 min at fixed comfortable speed with PSF, (2) at the same speed but with different imposed SFs (range 140-200 steps min-1) and at PSF and (3) similarly but at 15% higher speed. Runners were paced by a cyclist. Second order polynomials fitted the individual SF-HR relations well. At comfortable speed (3.04 ± 0.28 m s-1) mean r2 was 0.81 (range: 0.40-0.99) and PSF and OSF respectively were 165 ± 8 and 171 ± 8 steps min-1 (p < .05). Both increased (p < .05) by 3 steps min-1 at the 15% higher speed. SFs were negatively (-0.66 < r < -0.49, p < .05) related to body height. From the individual SF-HR relations we deduced that changing PSF to OSF would decrease median HR only by 0.5 beats min-1, but for two participants meaningful reductions (1-3 beats min-1) were predicted. Outdoors, experienced recreational runners ran at a PSF slightly below OSF, but for most of them, there was little to gain in terms of HR reductions by increasing their SF to OSF.

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.

Stride and Step Length Obtained with Inertial Measurement Units during Maximal Sprint Acceleration.

During sprint acceleration, step length, step rate, ground contact, and airtime are key variables for coaches to guide the training process and technical development of their athletes. In the field, three of these variables are easily obtained with inertial measurement units (IMUs), but, unfortunately, valid estimates of step length with IMUs currently are limited to low speeds (<50% max). A simple method is proposed here to derive step length during maximal sprint acceleration, using IMUs on both feet and two timing gates only. Mono-exponential velocity-time functions are fitted to the 30-m (split) and 60-m times, which in combination with IMU-derived step durations yield estimates of step length. To validate this approach, sixteen well-trained athletes with IMUs on the insteps of both feet executed two 60-m maximal sprints, starting from a three-point position. As a reference, step lengths were determined from video data. The reference step lengths combined with IMU-derived step durations yielded a time series of step velocity that confirmed the appropriateness of a mono-exponential increase of step velocity (R2 ≥ 0.96). The comparison of estimated step lengths to reference measurements showed no significant difference (p > 0.05) and acceptable agreement (root mean square error, RMSE = 8.0 cm, bias ± Limits of Agreement = -0.15 ± 16 cm). Step length estimations further improved (RMSE = 5.7 cm, -0.16 ± 11 cm) after smoothing the original estimated step lengths with a third order polynomial function (R2 = 0.94 ± 0.04). In conclusion, during maximal sprint acceleration, acceptable estimates of stride and step length were obtained from IMU-derived step times and 30-m (split) and 60-m sprint times.

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.

Optimal stride frequencies in running at different speeds.

During running at a constant speed, the optimal stride frequency (SF) can be derived from the u-shaped relationship between SF and heart rate (HR). Changing SF towards the optimum of this relationship is beneficial for energy expenditure and may positively change biomechanics of running. In the current study, the effects of speed on the optimal SF and the nature of the u-shaped relation were empirically tested using Generalized Estimating Equations. To this end, HR was recorded from twelve healthy (4 males, 8 females) inexperienced runners, who completed runs at three speeds. The three speeds were 90%, 100% and 110% of self-selected speed. A self-selected SF (SFself) was determined for each of the speeds prior to the speed series. The speed series started with a free-chosen SF condition, followed by five imposed SF conditions (SFself, 70, 80, 90, 100 strides·min-1) assigned in random order. The conditions lasted 3 minutes with 2.5 minutes of walking in between. SFself increased significantly (p<0.05) with speed with averages of 77, 79, 80 strides·min-1 at 2.4, 2.6, 2.9 m·s-1, respectively). As expected, the relation between SF and HR could be described by a parabolic curve for all speeds. Speed did not significantly affect the curvature, nor did it affect optimal SF. We conclude that over the speed range tested, inexperienced runners may not need to adapt their SF to running speed. However, since SFself were lower than the SFopt of 83 strides·min-1, the runners could reduce HR by increasing their SFself.

Functioning Without Cartilage: Older People With Radiographic Knee Osteoarthritis Who Self-Report No Functional Limitations Do Score Lower on a Performance Battery.

The majority of the older population shows signs of radiographic knee osteoarthritis. However, many remain without functional complaints for a long period. This study aims to find early functional changes associated with stages of radiographic knee osteoarthritis. A group of older people without self-reported complaints was divided in two groups: knee osteoarthritis (K&L = 2-4, N = 29) and control (K&L = 0-1, N = 31). Muscle function was assessed with voluntary and electrically-stimulated isometric knee contractions, including a fatigue test. Physical functioning was assessed with a 6-min walk test (6MWT), a stair climb test (SCT), and a short performance battery. There were no differences in muscle function parameters, 6MWT, and SCT between groups. A clinically relevant lower score on the performance battery was found in participants with knee osteoarthritis. In conclusion, even when older people indicate to have no functional limitations, a decline in functional outcome can be measured with a physical performance battery.