Inter-Individual Variability in The Joint Negative Work During Running

Training alters joint power distributions during running in National Football League Draft Preparation Players

The aim of this study was to compare joint-specific contributions to total lower limb joint power during a speed run in American style football players before and after a 6-week National Football League (NFL) draft preparation camp. Fifty-two players performed one 5-second treadmill run at 5.5-6.5 m/s before and after camp. Speed was consistent between tests. Bilateral hip, knee, and ankle power generation (positive) and absorption (negative) were calculated from kinematics and kinetics using inverse dynamics. Negative total limb power decreased for both limbs, significantly for the left (Z = -2.113, p = .035, r = .30) due to significant decreases in negative ankle power bilaterally (left, Z = -1.967, p = .049, r = .27; right, Z = -2.623, p = .009, r = .36). The contribution of negative left hip power significantly increased (Z = -2.398, p = .016, r = .31). While no other significant changes in positive or negative joint-specific magnitudes or contributions occurred, there were general distal-to-proximal shifts in joint power contributions. Players appear to have adopted more energy efficient running through moderate joint-specific mechanistic training adaptations such as a reduction in ankle power absorption.

Subject-specific sensitivity of several biomechanical features to fatigue during an exhaustive treadmill run

The aim of the present study was to examine the sensitivity of several movement features during running to exhaustion in a subject-specific setup adopting a cross-sectional design and a machine learning approach. Thirteen recreational runners, that systematically trained and competed, performed an exhaustive running protocol on an instrumented treadmill. Respiratory data were collected to establish the second ventilatory threshold (VT2) in order to obtain a reference point regarding the gradual accumulation of fatigue. A machine learning approach was adopted to analyze kinetic and kinematic data recorded for each participant, using a random forest classifier for the region pre and post the second ventilatory threshold. SHapley Additive exPlanations (SHAP) analysis was used to explain the models' predictions and to provide insight about the most important variables. The classification accuracy value of the models adopted ranged from 0.853 to 0.962. The most important feature in six out of thirteen participants was the angular range in AP axis of upper trunk C7 (RTAPu) followed by maximum loading rate (RFDmaxD) and the angular range in the LT axis of the C7. SHAP dependence plots also showed an increased dispersion of predictions in stages around the second ventilatory threshold which is consistent with feature interactions. These results showed that each runner used the examined features differently to cope with the increase in fatigue and mitigate its effects in order to maintain a proper motor pattern.