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.

We hypothesised that experienced runners would select a stride frequency closer to the optimum (minimal energy costs) than would novice runners. In addition, we expected that optimal stride frequency could simply be determined by monitoring heart rate without measuring oxygen consumption (V̇O₂). Ten healthy males (mean±s: 24±2 year) with no running training experience and 10 trained runners of similar age ran at constant treadmill speed corresponding to 80% of individual ventilatory threshold. For two days, they ran at seven different stride frequencies (self‐selected stride frequency±18%) imposed by a metronome. Optimal stride frequency was based on the minimum of a second‐order polynomial equation fitted through steady state V̇O₂ at each stride frequency. Running cost (mean±s) at optimal stride frequency was higher (P < 0.05) in novice (236±31 ml O₂·kg^−1.km⁻¹) than trained (189±13 ml O₂·kg^−1.km⁻¹) runners. Self‐selected stride frequency (mean±s; strides^.min⁻¹) for novice (77.8±2.8) and trained runners (84.4±5.3) were lower (P < 0.05) than optimal stride frequency (respectively, 84.9±5.0 and 87.1±4.8). The difference between self‐selected and optimal stride frequency was smaller (P < 0.05) for trained runners. In both the groups optimal stride frequency established with heart rate was not different (P > 0.3) from optimal stride frequency based on V̇O₂. In each group and despite limited variation between participants, optimal stride frequencies derived from V̇O₂ and heart rate were related (r > 0.7; P < 0.05). In conclusion, trained runners chose a stride frequency closer to the optimum for energy expenditure than novices. Heart rate could be used to establish optimal stride frequency.

Purpose: To reduce the need for invasive and expensive measures of human biomarkers, sweat is becoming increasingly popular in use as an alternative to blood. Therefore, the (in)dependency of blood and sweat composition has to be explored.Methods: In an environmental chamber (33 °C, 65% relative humidity; RH), 12 participants completed three subsequent 20-min cycling stages to elicit three different local sweat rates (LSR) while aiming to limit changes in blood composition: at 60% of their maximum heart rate (HR_max), 70% HR_maxand 80% HR_max, with 5 min of seated-rest in between. Sweat was collected from the arm and back during each stage and post-exercise. Blood was drawn from a superficial antecubital vein in the middle of each stage. Concentrations of sodium, chloride, potassium, ammonia, lactate and glucose were determined in blood plasma and sweat.Results: With increasing exercise intensity, LSR, sweat sodium, chloride and glucose concentrations increased (P ≤ 0.026), while simultaneously limited changes in blood composition were elicited for these components (P ≥ 0.093). Sweat potassium, lactate and ammonia concentrations decreased (P ≤ 0.006), while blood potassium decreased (P = 0.003), and blood ammonia and lactate concentrations increased with higher exercise intensities (P = 0.005;P = 0.007, respectively). The vast majority of correlations between blood and sweat parameters were non-significant (P > 0.05), with few exceptions.Conclusion: The data suggest that sweat composition is at least partly independent of blood composition. This has important consequences when targeting sweat as non-invasive alternative for blood measurements.

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.

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.

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 (SF_self) 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 (SF_self, 70, 80, 90, 100 strides·min^-1) assigned in random order. The conditions lasted 3 minutes with 2.5 minutes of walking in between. SF_self 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 SF_self were lower than the SF_opt of 83 strides·min^-1, the runners could reduce HR by increasing their SF_self.