Search

The term ‘stiffness’ is commonly used in the literature to refer to various components of ‘hyperresistance’ by which spastic muscles oppose to their passive lengthening, especially in children with cerebral palsy (CP). Originally, stiffness consists of mechanical resistance to passive movement in the absence of any muscle activation. Increased muscle stiffness in CP therefore refers to alterations to the mechanical properties of the tissue. It is closely linked to muscle shortening, yet the two phenomena are not equivalent. Both increased stiffness and shortening are present early in childhood in the plantarflexor muscles of children with spastic CP. This narrative review provides a comprehensive overview of the literature on passive stiffness of the plantarflexor muscles measured at the joint, muscles, fascicles, and fiber level in children with CP. Articles were searched through the Pub’Med database using the keywords “cerebral palsy” AND “stiffness”. The ambiguous use of the term ‘stiffness’ has been supported by discrepancies in available results, influenced by heterogeneity in materials, methodologies and characteristics of the participants among studies. Increased stiffness at the joint and muscle belly level may be explained by altered structural properties at the microscopic level. This thorough investigation of the literature suggests that the pathophysiology and the time course of the development of stiffness and contracture remain to be elucidated. A consideration of both morphological and mechanical measurements in children with CP is important when describing the alterations in their plantarflexors.

Scientific experiments on running mainly consider level running. However, the magnitude and etiology of fatigue depend on the exercise under consideration, particularly the predominant type of contraction, which differs between level, uphill, and downhill running. The purpose of this review is to comprehensively summarize the neurophysiological and biomechanical changes due to fatigue in graded running. When comparing prolonged hilly running (i.e., a combination of uphill and downhill running) to level running, it is found that (1) the general shape of the neuromuscular fatigue-exercise duration curve as well as the etiology of fatigue in knee extensor and plantar flexor muscles are similar and (2) the biomechanical consequences are also relatively comparable, suggesting that duration rather than elevation changes affects neuromuscular function and running patterns. However, 'pure' uphill or downhill running has several fatigue-related intrinsic features compared with the level running. Downhill running induces severe lower limb tissue damage, indirectly evidenced by massive increases in plasma creatine kinase/myoglobin concentration or inflammatory markers. In addition, low-frequency fatigue (i.e., excitation-contraction coupling failure) is systematically observed after downhill running, although it has also been found in high-intensity uphill running for different reasons. Indeed, low-frequency fatigue in downhill running is attributed to mechanical stress at the interface sarcoplasmic reticulum/T-tubule, while the inorganic phosphate accumulation probably plays a central role in intense uphill running. Other fatigue-related specificities of graded running such as strategies to minimize the deleterious effects of downhill running on muscle function, the difference of energy cost versus heat storage or muscle activity changes in downhill, level, and uphill running are also discussed.

The aim of this study was to investigate lactate recovery kinetics after high-intensity exercises. Six competitive middle-distance runners performed 500-, 1000-, and 1500-m trials at 90 % of their current maximal speed over 1500 m. Each event was followed by a passive recovery to obtain blood lactate recovery curves (BLRC). BLRC were fitted by the bi-exponential time function: La(t) = La(0) + A 1(1–e −γ1t ) + A 2(1–e −γ2t ), where La(0) is the blood lactate concentration at exercise completion, and γ 1 and γ 2 enlighten the lactate exchange ability between the previously active muscles and the blood and the overall lactate removal ability, respectively. Applications of the model provided parameters related to lactate release, removal and accumulation rates at exercise completion, and net amount of lactate released during recovery. The increase of running distance was accompanied by (1) a continuous decrease in γ 1 (p

We sought to compare force–velocity relationships developed from unloaded sprinting acceleration to that compiled from multiple sled-resisted sprints. Twenty-seven mixed-code athletes performed six to seven maximal sprints, unloaded and towing a sled (20–120% of body-mass), while measured using a sports radar. Two methods were used to draw force–velocity relationships for each athlete: A multiple trial method compiling kinetic data using pre-determined friction coefficients and aerodynamic drag at maximum velocity from each sprint; and a validated single trial method plotting external force due to acceleration and aerodynamic drag and velocity throughout an acceleration phase of an unloaded sprint (only). Maximal theoretical force, velocity and power were determined from each force–velocity relationship and compared using regression analysis and absolute bias (± 90% confidence intervals), Pearson correlations and typical error of the estimate (TEE). The average bias between the methods was between − 6.4 and − 0.4%. Power and maximal force showed strong correlations (r = 0.71 to 0.86), but large error (TEE = 0.53 to 0.71). Theoretical maximal velocity was nearly identical between the methods (r = 0.99), with little bias (− 0.04 to 0.00 m s−1) and error (TEE = 0.12). When horizontal force or power output is considered for a given speed, resisted sprinting is similar to its associated phase during an unloaded sprint acceleration [e.g. first steps (~ 3 m s−1) = heavy resistance]. Error associated with increasing loading could be resultant of error, fatigue, or technique, and more research is needed. This research provides a basis for simplified assessment of optimal loading from a single unloaded sprint.

Impact reduction has become a factor of interest in the prevention of running-related injuries such as stress fractures. Currently, the midfoot strike pattern (MFS) is thought as a potential way to decrease impact. The purpose was to test the effects of two long-term interventions aiming to reduce impact during running via a transition to an MFS: a foot strike retraining versus a low-drop/low-heel height footwear. Thirty rearfoot strikers were randomly assigned to two experimental groups (SHOES and TRAIN). SHOES progressively wore low-drop/low-heel height shoes and TRAIN progressively adopted an MFS, over a 3-month period with three 30-min running sessions per week. Measurement sessions (pre-training, 1, 2 and 3 months) were performed during which subjects were equipped with three accelerometers on the shin, heel and metatarsals, and ran for 15 min on an instrumented treadmill. Synchronized acceleration and vertical ground reaction force signals were recorded. Peak heel acceleration was significantly lower as compared to pre-training for SHOES (−33.5 ± 12.8 % at 2 months and −25.3 ± 18.8 % at 3 months, p

We investigated the changes in constant velocity spring-mass behavior after high intensity sprint fatigue in order to better interpret the results recently reported after ultra-long distance (ULD) exercises. Our hypothesis was that after repeated sprints (RS), subjects may likely experience losses of force such as after ULD, but the necessity to modify their running pattern to attenuate the overall impact at each step (such as after ULD) may not be present. Eleven male subjects performed four sets of five 6-s sprints with 24-s recovery between sprints and 3 min between sets, on a sprint treadmill and on a bicycle ergometer. For each session, their running mechanics and spring-mass characteristics were measured at 10 and 20 km h(-1) on an instrumented treadmill before and after RS. Two-way (period and velocity) ANOVAs showed that high-intensity fatigue did not induce any change in the constant velocity running pattern at low or high velocity, after both running and cycling RS, despite significant decreases (P 0.001) in maximal power (-27.1 ± 8.2% after running RS and -15.4 ± 11.5 % after cycling RS) and knee extensors maximal voluntary force (-18.8 ± 6.7 % after running RS and -15.0 ± 7.6 % after cycling RS). These results bring indirect support to the hypothesis put forward in recent ULD studies that the changes in running mechanics observed after ULD are likely not related to the decrease in strength capabilities, but rather to the necessity for subjects to adopt a protective running pattern.

The author would like to correct the reference in the publication of the original article. The corrected reference is given below for your reading.

This report describes the changes in physiological capacity of a heavy-weight rower who obtained seven medals in World Championships and Olympic Games. The investigation was carried out over the last 6 years of the rower’s international competition career in comparison with peer champions, and the following 4 years. Over the first period, maximal oxygen uptake ( $$ \dot{V}{\text{O}}_{2\max } $$ ) remained above 6 l min−1 which is an outstanding value. The training load measured over the last 18 months of the period increased from 119 to 142 km wk−1 of rowing. Four years after the international competition period, $$ \dot{V}{\text{O}}_{2\max } $$ had only declined by 3.6% although the training load had declined by 35%. These data suggest that the ability of this rower to compete at top level for years was related to ability to maintain an outstanding $$ \dot{V}{\text{O}}_{2\max } $$ . Gross efficiency and ability to rely on anaerobic glycolysis did not emerge as relevant factors.

Our aim was to assess whether we can predict satisfactorily performance in swimming and high frequency power (HF power) of heart rate variability from the responses to previous training. We have tested predictions using the model of Banister and the variable dose-response model.Data came from ten swimmers followed during 30 weeks of training with performance and HF power measured each week. The first 15-week training period was used to estimate the parameters of each model for both performance and HF power. Both were then predicted in response to the training done during the second 15-week training period. The bias and precision were estimated from the mean and SD of the difference between prediction and actual value expressed as a percentage of performance or HF power at the first week.With the variable-dose response model, the bias for performance prediction was -0.24 ± 0.06 and the precision 0.69 ± 0.24% (mean ± between-subject SD). For HF power, the bias was 0 ± 21 and the precision 22 ± 8%. When HF power was transformed into performance using a quadratic relation in each swimmer established from the first 15-week period, the bias was 0.18 ± 0.74 and the precision 0.80 ± 0.30%. No clear trend in the error was observed during the second period.This study showed that the modeling of training effects on performance allowed accurate performance prediction supporting its relevance to control and predict week after week the responses to future training.

International audience; Chronic Achilles tendon vibration has previously shown its effectiveness in improving plantar flexor's strength and activation capacities. The present study investigated the related neural mechanisms by analyzing H-reflexes and V-waves of the soleus (SOL) and gastrocnemii (GM gastrocnemius medialis; GL gastrocnemius lateralis) muscles under maximal isometric plantar flexion. Moreover, recordings were conducted bilaterally to address potential crossed effects. 11 subjects were engaged in this study. Maximal voluntary contraction and superimposed H-reflexes and V-waves were quantified in both legs at baseline (PRE) and 2 weeks later to verify repeatability of data (CON). Then, subjects were retested after 14 days of daily unilateral Achilles tendon vibration (VIB; 1 h per day; frequency: 50 Hz). No changes were reported between PRE and CON data. In the VIB condition, there was an increase in MVC for both the vibrated (+9.1 %; p = 0.016) and non-vibrated (+10.2 %; p = 0.009) legs. The H-reflex increased by a mean 25 % in the vibrated SOL (p < 0.001), while it remained unchanged for the contralateral side (p = 0.531). The SOL V-wave also increased in the vibrated limb (+43.3 %; p < 0.001), as well as in the non-vibrated one (+41.9 %; p = 0.006). Furthermore, the GM V-wave increased by 37.8 % (p = 0.081) in the vibrated side and by 39.4 % (p = 0.03) in the non-vibrated side. However, no changes were reported for the GL muscles. While the present study confirmed the strength gains induced by chronic Achilles tendon vibration, the results indicated a cross-education phenomenon with differences in neural adaptations between the vibrated leg and non-vibrated leg.

We investigated the differences in performance between 100-m sprints performed on a sprint treadmill recently validated versus on a standard track. To date, studies comparing overground and treadmill running have mainly focused on constant and not maximal “free” running speed, and compared running kinetics and kinematics over a limited number of steps, but not overall sprint performance. Eleven male physical education students including two sprinters performed one 100-m on the treadmill and one on a standard athletics track in a randomized order, separated by 30 min. Performance data were derived in both cases from speed–time relationships measured with a radar and with the instrumented sprint treadmill, which allowed subjects to run and produce speed “freely”, i.e. with no predetermined belt speed imposed. Field and treadmill typical speed–distance curves and data of maximal and mean speed, 100-m time and acceleration/deceleration time constants were compared using t tests and field–treadmill correlations were tested. All the performance parameters but time to reach top speed and deceleration time constant differed significantly, by about 20% on average, between field and treadmill (e.g. top speed of 8.84 ± 0.51 vs. 6.90 ± 0.39 m s−1). However, significant correlations were found (r > 0.63; P