Veerle Segers

Trained runners exhibit better running economy (RE) during level running than untrained individuals, though the underlying contributors remain unclear. Using an indirect approach, we explored whether more effective muscle–tendon interactions might explain this superior RE. We measured RE, biomechanics, and lower limb muscle activation in 16 runners and 16 aerobically fit nonrunners during level (1.7 and 3.5 m/s) and uphill (+8.1°, 1.7 m/s) running. Unlike level running, uphill running requires net positive mechanical work, reducing the contribution of fine-tuned muscle–tendon interactions to RE. Therefore, we hypothesized that RE differences between runners and nonrunners would be smaller during uphill running. Our results confirmed that runners had better RE on level ground ( P ≤ .046) but showed no RE advantage during uphill running ( P = 1.00). Across all conditions, runners ran with higher average positive and negative ankle joint power ( P ≤ .010) and a greater relative contribution of the ankle to the total positive joint power ( P = .037). Despite this increased ankle reliance, relative triceps surae muscle activation was similar between groups. These findings suggest that superior RE observed in runners on level ground may stem from more optimized interaction between the triceps surae muscles and the Achilles tendon.

Fatigue has been considered a risk factor for sports injuries, modulating full-body jump-landing biomechanics. Biomechanical assessments of jump-landing manoeuvres are typically performed before and after short-term fatigue protocols, but changes during the protocol are often neglected. Therefore, this study investigated spike jump-landing strategy alterations during and following a short-term fatigue protocol in volleyball. Forty-three healthy, adult, male volleyball players participated in this study. Three-dimensional full-body kinematics were collected when performing spike jump-landings before, during and after a short-term fatigue protocol specific for volleyball. Full-body sagittal plane joint angles were calculated and analysed with curve analysis using one-way repeated measures ANOVA and post-hoc paired sample t-tests to investigate fatigue effects (p < 0.05). A significant main effect of fatigue was found for all kinematic variables (p = 0.015−0.041). More specifically, more pelvis-trunk flexion and less hip, knee, and ankle (dorsi-) flexion were observed during and after the protocol compared to baseline (p = 0.001−0.003). Moreover, less hip and knee flexion was observed during the protocol compared to after fatigue (p = 0.001−0.005). In conclusion, significant kinematic changes were found with fatigue, and these were somehow more pronounced during fatigue, possibly due to decreased attention towards the jump-landing task execution. A decision tree was provided to help researchers, coaches and/or clinicians in determining whether screenings should be better performed during or after fatigue, based on practical considerations.

Running style is temporally defined by a duty factor and stride frequency and believed to be related to the loading experienced during ever step. However, the exact relationship between both temporal variables and loading magnitude is still unknown. We aimed to identify the relationship between a duty factor and stride frequency with external load measures, joint reaction forces and joint moments. Thirty-one healthy female recreational runners ran across a 25-m runway at a speed of 2.30 ± 0.05 m∙s−1. Ground reaction forces and motion capture data were used to determine the maximal vertical ground reaction force, the vertical instantaneous loading rate, peak braking force, peak joint extension moments and peak joint reaction forces at the knee and the ankle. The habitual duty factor and stride frequency of runners did not correlate with each other. The duty factor was found to be a significant predictor of maximal vertical ground reaction force (R2 = 0.585), peak braking force (R2 = 0.153), peak knee extension moment (R2 = 0.149), ankle plantar flexion moment (R2 = 0.225) and peak joint reaction forces at the knee (R2 = 0.591) and the ankle (R2 = 0.592), but not of the vertical instantaneous loading rate. Stride frequency had no significant predictive value. In conclusion, the maximal loading and potential injury risk of female recreational runners running with high duty factors are lower compared to those of peers running with lower duty factors.

The notion of gait retraining in human running has recently been studied in relation to the objective of impact reduction (Cheung et al., 2018; Clansey et al., 2014; Futrell et al., 2021) and, rela...

Background: Running retraining with the use of biofeedback on an impact measure has been executed or evaluated in the biomechanics laboratory. Here, the execution and evaluation of feedback‐driven retraining are taken out of the laboratory.Purpose: To determine whether biofeedback can reduce the peak tibial acceleration with or without affecting the running cadence in a 3‐week retraining protocol.Study Design: Quasi‐randomized controlled trial.Methods: Twenty runners with high peak tibial acceleration were allocated to either the retraining (n = 10, 32.1 ± 7.8 years, 10.9 ± 2.8 g) or control (n = 10, 39.1 ± 10.4 years, 13.0 ± 3.9 g) groups. They performed six running sessions in an athletic training environment. A body‐worn system collected axial tibial acceleration and provided real‐time feedback. The retraining group received music‐based biofeedback in a faded feedback scheme. Pink noise was superimposed on tempo‐synchronized music when the peak tibial acceleration was ≥70% of the runner's baseline. The control group received tempo‐synchronized music, which acted as a placebo for blinding purposes. Speed feedback was provided to obtain a stable running speed of ~2.9 m·s⁻¹. Peak tibial acceleration and running cadence were evaluated.Results: A significant group‐by‐feedback interaction effect was detected for peak tibial acceleration. The experimental group had a decrease in peak tibial acceleration by 25.5% (mean: 10.9 ± 2.8 g versus 8.1 ± 3.9 g, p = 0.008, d = 1.08, mean difference = 2.77 [0.94, 4.61]) without changing the running cadence. The control group had no statistically significant change in peak tibial acceleration nor in running cadence.Conclusion: The retraining protocol was effective at reducing the peak tibial acceleration in high‐impact runners by reacting to music‐based biofeedback that was provided in real time per wearable technology in a training environment. This reduction magnitude may have meaningful influences on injury risk.