Does Characterizing Global Running Pattern Help to Prescribe Individualized Strength Training in Recreational Runners?

This study aimed to determine if concurrent endurance and strength training that matches the global running pattern would be more effective in increasing running economy (RE) than non-matched training. The global running pattern of 37 recreational runners was determined using the Volodalen® method as being aerial (AER) or terrestrial (TER). Strength training consisted of endurance running training and either plyometric (PLY) or dynamic weight training (DWT). Runners were randomly assigned to a matched (n = 18; DWT for TER, PLY for AER) or non-matched (n = 19; DWT for AER, PLY for TER) 8 weeks concurrent training program. RE, maximal oxygen uptake V̇O2max) and peak treadmill speed at V̇O2max (PTS) were measured before and after the training intervention. None of the tested performance related variables depicted a significant group effect or interaction effect between training and grouping (p ≥ 0.436). However, a significant increase in RE, V̇O2max, and PTS (p ≤ 0.003) was found after the training intervention. No difference in number of responders between matched and non-matched groups was observed for any of the performance related variables (p ≥ 0.248). In recreational runners, prescribing PLT or DWT according to the global running pattern of individuals, in addition to endurance training, did not lead to greater improvements in RE.

Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter

Equations predicting stride frequency (SF) and duty factor (DF) solely based on running speed have been proposed. However, for a given speed, kinematics vary depending on the global running pattern (GRP), i.e., the overall individual movement while running, which depends on the vertical oscillation of the head, antero-posterior motion of the elbows, vertical pelvis position at ground contact, antero-posterior foot position at ground contact, and strike pattern. Hence, we first verified the validity of the aforementioned equations while accounting for GRP. Kinematics during three 50-m runs on a track (n = 20) were used with curve fitting and linear mixed effects models. The percentage of explained variance was increased by ≥133% for DF when taking into account GRP. GRP was negatively related to DF (p = 0.004) but not to SF (p = 0.08), invalidating DF equation. Second, we assessed which parameters among anthropometric characteristics, sex, training volume, and GRP could relate to SF and DF in addition to speed, using kinematic data during five 30-s runs on a treadmill (n = 54). SF and DF linearly increased and quadratically decreased with speed (p < 0.001), respectively. However, on an individual level, SF was best described using a second-order polynomial equation. SF and DF showed a non-negligible percentage of variance explained by random effects (≥28%). Age and height were positively and negatively related to SF (p ≤ 0.05), respectively, while GRP was negatively related to DF (p < 0.001), making them key parameters to estimate SF and DF, respectively, in addition to speed.

There Is No Global Running Pattern More Economic Than Another at Endurance Running Speeds

Purpose: The subjective Volodalen® score (V®score) and the objective duty factor metric can both assess global running patterns. The authors aimed to investigate the relation between running economy (RE) at endurance running speeds and the global running pattern quantified using both subjective and objective measures. Methods: RE and 3-dimensional whole-body kinematics were acquired by indirect calorimetry and an optoelectronic system, respectively, for 52 trained runners during treadmill runs at 10, 12, and 14 km/h. Results: Correlations between RE and V®score and RE and duty factor were negligible and nonsignificant across speeds tested (P ≥ .20), except for a low and significant correlation between RE and V®score at 10 km/h. Conclusions: These findings suggest there is no global running pattern more economic than another at endurance running speeds. Therefore, there is no advantage of choosing, favoring, or prescribing one specific global running pattern along a continuum based on V®score or duty factor metrics, and coaches should not try to modify the spontaneous running pattern of runners at endurance running speed to improve RE.

High-intensity Activity in European vs. National Rugby Union Games in the best 2014–2015 Team

The purpose of this study is to evaluate the influence of competition level on running patterns for five playing position in the most successful 2014–2015 European rugby union team. Seventeen French rugby union championship and seven European rugby Champions Cup games were analysed. Global positioning system (sampling: 10 Hz) were used to determine high-speed movements, high-intensity accelerations, repeated high-intensity efforts and high-intensity micro-movements characteristics for five positional groups. During European Champions Cup games, front row forwards performed a higher number of repeated high-intensity efforts compared to National championship games (5.8±1.6 vs. 3.6±2.3; +61.1%), and back row forwards travelled greater distance both at high-speed movements (3.4±1.8 vs. 2.4±0.9 m·min-1; +41.7%) and after high-intensity accelerations (78.2±14.0 vs. 68.1 ±13.4 m; +14.8%). In backs, scrum halves carried out more high-intensity accelerations (24.7±3.1 vs. 14.8±5.0; +66.3%) whereas outside backs completed a higher number of high-speed movements (62.7±25.4 vs. 48.3±17.0; +29.8%) and repeated high-intensity efforts (13.5±4.6 vs. 9.7±4.9; +39.2%). These results highlighted that the competition level affected the high-intensity activity differently among the five playing positions. Consequently, training programs in elite rugby should be tailored taking into account both the level of competition and the high-intensity running pattern of each playing position.

Biomechanical Analyses of Professional Ultramarathon Athletes: The Effect of Repeated Long Distances on the Gait Kinematic and Kinetics

Constant monitoring of an athlete allows to foresee any injuries by acting on the dynamics of the movements. For runners, the conduct of a correct athletic gesture according to the athlete’s specific body biomechanics guarantees the minimization of non-accidental injury factors. For athletes who are engaged in endurance sports such as marathon runners, the long distances to which they are subjected increases the importance of this type of monitoring. This work reports the results of a study carried out on three IUTA (Italian Ultramarathon and Trail Association) athletes during a routine check carried out at a reference healthcare facility that takes care of their care. These athletes are all specialized in the 24-hour race in which they try to reach the most distance in this time. This type of effort can be made if the athlete undergoes an adequate training regime over long distances. The execution of the running pattern in such a repeated way can lead to the accentuation of postural and joint problems. It is therefore necessary to monitor the biomechanics parameters. In this work, therefore, various gestures are analyzed to show potential movement deficits in order to act in advance on the running technique.

The Gaitprint: Identifying Individuals by Their Running Style

Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement.