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Training Load Monitoring in Team Sports: A Novel Framework Separating Physiological and Biomechanical Load-Adaptation Pathways
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.
Journal Article
Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power
One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA.
Journal Article
New Mutants : back to school, the complete collection
\"Dani Moonstar, Karma and Wolfsbane -- the former X-Men-in-training who helped define a generation -- are back to pass their wisdom on to the next one! But how will the New Mutants react to Professor X's up-and-coming students, who think of them as \"Old Mutants\"? Find out as a new class debuts at the Xavier School -- including Prodigy, Wallflower, Wither, Surge, Elixir, Wind Dancer and more! They may be the future of their species -- if they can survive threats like the Reavers and the hate group Purity! As the latest squad comes into its own, the originals settle into new roles as mentors -- but will Wolfsbane's desire to regain her powers cause her to cross a line?\"--Back cover.
BOOK
A force profile analysis comparison between functional data analysis, statistical parametric mapping and statistical non-parametric mapping in on-water single sculling
To examine whether the Functional Data Analysis (FDA), Statistical Parametric Mapping (SPM) and Statistical non-Parametric Mapping (SnPM) hypothesis testing techniques differ in their ability to draw inferences in the context of a single, simple experimental design.
The sample data used is cross-sectional (two-sample gender comparison) and evaluation of differences between statistical techniques used a combination of descriptive and qualitative assessments.
FDA, SPM and SnPM t-tests were applied to sample data of twenty highly skilled male and female rowers, rowing at 32 strokes per minute in a single scull boat. Statistical differences for gender were assessed by applying two t-tests (one for each side of the boat).
The t-statistic values were identical for all three methods (with the FDA t-statistic presented as an absolute measure). The critical t-statistics (tcrit) were very similar between the techniques, with SPM tcrit providing a marginally higher tcrit than the FDA and SnPM tcrit values (which were identical). All techniques were successful in identifying consistent sections of the force waveform, where male and female rowers were shown to differ significantly (p<0.05).
This is the first study to show that FDA, SPM and SnPM t-tests provide consistent results when applied to sports biomechanics data. Though the results were similar, selection of one technique over another by applied researchers and practitioners should be based on the underlying parametric assumption of SPM, as well as contextual factors related to the type of waveform data to be analysed and the experimental research question of interest.
Journal Article
The effect of running speed on knee mechanical loading in females during side cutting
Side cutting involves mechanical loading of the knee which has been associated with anterior cruciate ligament injury risk. Despite a fast growing body of research, the relationship between loading mechanisms and running speed is still unclear. The aim of this study was to investigate how running speed determines a likely trade-off between task achievement and actual mechanical loading.
Fourteen female participants (mean age=20.6±0.7yr, height=1.66±0.05m, mass=57.5±6.9kg) performed 45° side cutting manoeuvres at 2, 3, 4 and 5ms−1 approach speeds. Three dimensional motion and ground reaction forces were recorded to calculate whole body centre of mass (CoM) velocity and lower limb kinematics and kinetics, focusing on knee flexion angle at touch-down and peak knee valgus loading during weight acceptance. One-way repeated measures ANOVA and one-dimensional statistical parametric mapping were used to identify significant speed effects on task achievement and mechanical loading.
Analysis of CoM velocities revealed that side cutting manoeuvres at higher running speeds matched the task requirements to a lesser extent. Despite a gradual increase of anterior–posterior deceleration and medio-lateral acceleration with running speed, knee loading mechanisms only reached meaningful levels from a 4ms−1 running speed.
Our results confirmed a trade-off between task achievement and actual mechanical loading. This identified a need for standardisation of reporting running speeds. Taking into account also safety considerations, standardisation of a 4ms−1 running speed is proposed for female athletes.
Journal Article
Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units
Changes of direction (COD) are an important aspect of soccer match play. Understanding the physiological and biomechanical demands on players in games allows sports scientists to effectively train and rehabilitate soccer players. COD are conventionally recorded using manually annotated time-motion video analysis which is highly time consuming, so more time-efficient approaches are required. The aim was to develop an automated classification model based on multi-sensor player tracking device data to detect COD > 45°. Video analysis data and individual multi-sensor player tracking data (GPS, accelerometer, gyroscopic) for 23 academy-level soccer players were used. A novel ‘GPS-COD Angle’ variable was developed and used in model training; along with 24 GPS-derived, gyroscope and accelerometer variables. Video annotation was the ground truth indicator of occurrence of COD > 45°. The random forest classifier using the full set of features demonstrated the highest accuracy (AUROC = 0.957, 95% CI = 0.956–0.958, Sensitivity = 0.941, Specificity = 0.772. To balance sensitivity and specificity, model parameters were optimised resulting in a value of 0.889 for both metrics. Similarly high levels of accuracy were observed for random forest models trained using a reduced set of features, accelerometer-derived variables only, and gyroscope-derived variables only. These results point to the potential effectiveness of the novel methodology implemented in automatically identifying COD in soccer players.
Journal Article
Whole-body biomechanical load in running-based sports: The validity of estimating ground reaction forces from segmental accelerations
Unlike physiological loads, the biomechanical loads of training in running-based sports are still largely unexplored. This study, therefore, aimed to assess the validity of estimating ground reaction forces (GRF), as a measure of external whole-body biomechanical loading, from segmental accelerations.
Fifteen team-sport athletes performed accelerations, decelerations, 90° cuts and straight running at different speeds including sprinting. Full-body kinematics and GRF were recorded with a three-dimensional motion capture system and a single force platform respectively. GRF profiles were estimated as the sum of the product of all fifteen segmental masses and accelerations, or a reduced number of segments.
Errors for GRF profiles estimated from fifteen segmental accelerations were low (1–2Nkg−1) for low-speed running, moderate (2–3Nkg−1) for accelerations, 90° cuts and moderate-speed running, but very high (>4Nkg−1) for decelerations and high-speed running. Similarly, impulse (2.3–11.1%), impact peak (9.2–28.5%) and loading rate (20.1–42.8%) errors varied across tasks. Moreover, mean errors increased from 3.26±1.72Nkg−1 to 6.76±3.62Nkg−1 across tasks when the number of segments was reduced.
Accuracy of estimated GRF profiles and loading characteristics was dependent on task, and errors substantially increased when the number of segments was reduced. Using a direct mechanical approach to estimate GRF from segmental accelerations is thus unlikely to be a valid method to assess whole-body biomechanical loading across different dynamic and high-intensity activities. Researchers and practitioners should, therefore, be very cautious when interpreting accelerations from one or several segments, as these are unlikely to accurately represent external whole-body biomechanical loads.
Journal Article