There have been considerable advances in monitoring training load in running-based team sports in recent years. Novel technologies nowadays offer ample opportunities to continuously monitor the activities of a player. These activities lead to internal biochemical stresses on the various physiological subsystems; however, they also cause internal mechanical stresses on the various musculoskeletal tissues. Based on the amount and periodization of these stresses, the subsystems and tissues adapt. Therefore, by monitoring external loads, one hopes to estimate internal loads to predict adaptation, through understanding the load-adaptation pathways. We propose a new theoretical framework in which physiological and biomechanical load-adaptation pathways are considered separately, shedding new light on some of the previously published evidence. We hope that it can help the various practitioners in this field (trainers, coaches, medical staff, sport scientists) to align their thoughts when considering the value of monitoring load, and that it can help researchers design experiments that can better rationalize training-load monitoring for improving performance while preventing injury.

Purpose: The aim of this study was to investigate the relationship between wholebody accelerations and body-worn accelerometry during team sports movements. Methods: Twenty male team sport players performed forward running, and anticipated 45° and 90° side-cuts at approach speeds of 2, 3, 4 and 5 m·s -1 . Wholebody Centre of Mass (CoM) accelerations were determined from ground reaction forces collected from one foot-ground-contact and segmental accelerations were measured from a commercial GPS/accelerometer unit on the upper trunk. Three higher specification accelerometers were also positioned on the GPS unit, the dorsal aspect of the pelvis, and the shaft of the tibia. Associations between mechanical load variables (peak acceleration, loading rate and impulse) calculated from both CoM accelerations and segmental accelerations were explored using regression analysis. In addition one-dimensional Statistical Parametric Mapping (SPM) was used to explore the relationships between peak segmental accelerations and CoM acceleration profiles during the whole foot-ground-contact. Results: A weak relationship was observed for the investigated mechanical load variables regardless of accelerometer location and task (R 2 values across accelerometer locations and tasks: peak acceleration 0.08-0.55, loading rate 0.27-0.59 and impulse 0.02-0.59). Segmental accelerations generally overestimated whole-body mechanical load. SPM analysis showed that peak segmental accelerations were mostly related to CoM accelerations during the first 40-50% of contact phase. Conclusions: Whilst body-worn accelerometry correlates to whole-body loading in team sports movements and can reveal useful estimates concerning loading, these correlations are not strong. Body-worn acclerometry should therefore be used with caution to monitor whole-body mechanical loading in the field.

The mechanics of cutting movements have been investigated extensively, but few studies have considered the rapid deceleration phase prior to turning which has been linked to muscle damage. This study used accelerometry to examine the influence of turning intensity on the last three steps of a severe turn. Ten soccer players performed 135° "V" cuts at five different intensities. Resultant decelerations were recorded from a trunk-mounted tri-axial accelerometer. Lower limb kinematics and ground reaction forces (GRF) from the pivot foot-ground contact (FGC) were also monitored. Average peak trunk decelerations were larger at the two preceding steps (4.37 ± 0.12 g and 4.58 ± 0.11 g) compared to the PIVOT step (4.10 ± 0.09 g). Larger peak joint flexion angular velocities were observed at PRE step (ankle: 367 ± 192 deg.s; knee 493 ± 252  deg.s) compared to PIVOT step (ankle 255 ± 183 deg.s; knee 377 ± 229 deg.s). Turn intensity did not influence peak GRF at PIVOT step. This study highlights the importance of steps prior to turning and their high-frequency loading characteristics. It is suggested that investigations of lower limb loading during turning should include this deceleration phase and not focus solely on pivot FGC.

The aim of the present study was to examine reliability and construct convergent validity of Player Load™ (PL) from trunk-mounted accelerometry, expressed as a cumulative measure and an intensity measure (PL · min). Fifteen male participants twice performed an overground football match simulation that included four different multidirectional football actions (jog, side cut, stride and sprint) whilst wearing a trunk-mounted accelerometer inbuilt in a global positioning system unit. Results showed a moderate-to-high reliability as indicated by the intra-class correlation coefficient (0.806-0.949) and limits of agreement. Convergent validity analysis showed considerable between-participant variation (coefficient of variation range 14.5-24.5%), which was not explained from participant demographics despite a negative association with body height for the stride task. Between-task variations generally showed a moderate correlation between ranking of participants for PL (0.593-0.764) and PL · min (0.282-0.736). It was concluded that monitoring PL in football multidirectional actions presents moderate-to-high reliability, that between-participant variability most likely relies on the individual's locomotive skills and not their anthropometrics, and that the intensity of a task expressed by PL · min is largely related to the running velocity of the task.

The benefits of differentiating between the physiological and biomechanical load-response pathways in football and other (team) sports have become increasingly recognised. In contrast to physiological loads however, the biomechanical demands of training and competition are still not well understood, primarily due to the difficulty of quantifying biomechanical loads in a field environment. Although musculoskeletal adaptation and injury are known to occur at a tissue level, several biomechanical load metrics are available that quantify loads experienced by the body as a whole, its different structures and the individual tissues that are part of these structures. This paper discusses the distinct aspects and challenges that are associated with measuring biomechanical loads at these different levels in laboratory and/or field contexts. Our hope is that through this paper, sport scientists and practitioners will be able to critically consider the value and limitations of biomechanical load metrics and will keep pursuing new methods to measure these loads within and outside the lab, as a detailed load quantification is essential to better understand the biomechanical load-response pathways that occur in the field.

The aim of this study was to compare external (EL) and internal loads (IL) during training sessions compared to official matches in elite female soccer players according to their playing position. Training and match data were obtained during the 2017/18 season from eighteen players (age: 26.5±5.7 years; height: 164.4±5.3 cm; body mass: 58.56±5.58 kg) from a first Division Spanish team. The EL (total distance covered; high speed running distance; number of accelerations and decelerations) was assessed with a global positioning system (GPS) and triaxial accelerometer. The IL was assessed with ratings of perceived exertion (RPE; and session-RPE). The EL and the IL from official matches were higher compared to training sessions (p<0.05; effect size [ES]: 0.6-5.4). In official matches, the EL was greater in Attackers (AT) and Central Midfielders (CM) versus Central Backs (p<0.05; ES: 0.21-1.74). During training sessions, the EL was similar between playing positions (p>0.05; ES: 0.03-0.87). The EL and the IL are greater in official matches compared to training sessions, with greater match-related EL in AT and CM players. Current results may help practitioners to better understand and modulate training session's loads according to their playing position, potentially contributing to their performance readiness and injury risk reduction.

BackgroundMonitoring the external ground reaction forces (GRF) acting on the human body during running could help to understand how external loads influence tissue adaptation over time. Although mass-spring-damper (MSD) models have the potential to simulate the complex multi-segmental mechanics of the human body and predict GRF, these models currently require input from measured GRF limiting their application in field settings. Based on the hypothesis that the acceleration of the MSD-model’s upper mass primarily represents the acceleration of the trunk segment, this paper explored the feasibility of using measured trunk accelerometry to estimate the MSD-model parameters required to predict resultant GRF during running.MethodsTwenty male athletes ran at approach speeds between 2–5 m s−1. Resultant trunk accelerometry was used as a surrogate of the MSD-model upper mass acceleration to estimate the MSD-model parameters (ACCparam) required to predict resultant GRF. A purpose-built gradient descent optimisation routine was used where the MSD-model’s upper mass acceleration was fitted to the measured trunk accelerometer signal. Root mean squared errors (RMSE) were calculated to evaluate the accuracy of the trunk accelerometry fitting and GRF predictions. In addition, MSD-model parameters were estimated from fitting measured resultant GRF (GRFparam), to explore the difference between ACCparam and GRFparam.ResultsDespite a good match between the measured trunk accelerometry and the MSD-model’s upper mass acceleration (median RMSE between 0.16 and 0.22 g), poor GRF predictions (median RMSE between 6.68 and 12.77 N kg−1) were observed. In contrast, the MSD-model was able to replicate the measured GRF with high accuracy (median RMSE between 0.45 and 0.59 N kg−1) across running speeds from GRFparam. The ACCparam from measured trunk accelerometry under- or overestimated the GRFparam obtained from measured GRF, and generally demonstrated larger within parameter variations.DiscussionDespite the potential of obtaining a close fit between the MSD-model’s upper mass acceleration and the measured trunk accelerometry, the ACCparam estimated from this process were inadequate to predict resultant GRF waveforms during slow to moderate speed running. We therefore conclude that trunk-mounted accelerometry alone is inappropriate as input for the MSD-model to predict meaningful GRF waveforms. Further investigations are needed to continue to explore the feasibility of using body-worn micro sensor technology to drive simple human body models that would allow practitioners and researchers to estimate and monitor GRF waveforms in field settings.

Biomechanical loading during running: can a two mass-spring-damper model be used to evaluate ground reaction forces for high-intensity tasks?