Fatigue-Related Changes in Running Gait Patterns Persist in the Days Following a Marathon Race

Abstract

Context: The risk of experiencing an overuse running-related injury can increase with atypical running biomechanics associated with neuromuscular fatigue and/or training errors. While it is important to understand the changes in running biomechanics within a fatigue-inducing run, it may be more clinically relevant to assess gait patterns in the days following a marathon to better evaluate the effects of inadequate recovery on injury. Objective: To use center of mass (CoM) acceleration patterns to investigate changes in running patterns prior to (PRE) and at 2 (POST2) and 7 (POST7) days following a marathon race. Design: Pre–post intervention study. Setting: A 200-m oval track surface. Participants: Seventeen recreational marathon runners (10 females, age = 34.2 [5.67] y; 7 males, age = 47.41 [15.32] y). Intervention: Marathon race. Main Outcome Measures: An inertial measurement unit was placed near the CoM to collect triaxial acceleration data during overground running for PRE, POST2, and POST7 sessions. Twenty-two features were extracted from the acceleration waveforms to characterize different aspects of running gait. Lower-limb musculoskeletal pain was also recorded at each session with a visual analog scale. Results: At POST2, runners reported higher self-reported pain and exhibited elevated peak mediolateral acceleration with an increased mediolateral ratio of acceleration root mean square compared with PRE. At POST7, pain was reduced and more similar to PRE, with runners demonstrating increased stride regularity in the vertical direction and decreased peak resultant acceleration. Conclusions: The observed changes in CoM motion at POST2 may be associated with atypical running biomechanics that can translate to greater mediolateral impulses, potentially increasing the risk of injury. This study demonstrates the use of an accelerometer as an effective tool to detect atypical CoM motion for runners due to fatigue, recovery, and musculoskeletal pain in real-world environments.

The Use of a Single Trunk-Mounted Accelerometer to Detect Changes in Center of Mass Motion Linked to Lower-Leg Overuse Injuries: A Prospective Study

“…Furthermore, some of these compensations were associated with the development of LLOIs. These findings build further on limited available literature that reported movement compensations and changes in movement dynamics after a fatiguing running [9][10][11][12]14] and prior LLOI occurrence [11,12]. In addition, in contrast to our previous study [10], we did find differences between the injured group and the uninjured group during steady state unfatigued running.…”

“…Winter et al [11] did not find a decrease in step regularity, but did find a similar increase in RMS ratio in the ML direction in their male runners. In addition, Clermont et al [14] found that a marathon race induced a prolongated fatigue state 2 days following the race, characterized by an elevated peak ML acceleration and an increased RMS ratio ML in comparison to the day prior to the marathon race. Together with the results from this study, evidence is growing that running fatigue results in an increased variability in the ML direction already early in the run (from 30% onwards), and that these changes even persist days after the fatiguing run.…”

“…Dynamic stability represents the runners' capability of maintaining an optimal variability, uniformity, regularity and complexity of the acceleration signal. Dynamic stability decreases with fatigue accumulation during running, predominantly through an increase in variability (RMS ratio) in the horizontal plane (i.e., sway) [9][10][11]14]. In addition, a larger decrease in dynamic stability with running fatigue was observed in participants with a history of medial tibial stress syndrome compared to controls [10].…”

“…The recent availability of wearable accelerometers that can measure changes in movement dynamics during running in the ecological environment of the runner allowed the first steps towards providing a completer picture of how runners behave during a training session, adapting their pattern to fatigue, or potentially prior to the onset of injury. Previous research showed that wearable trunk accelerometers can successfully identify movement deviations caused by running fatigue in indoor [9] and outdoor running conditions [10][11][12][13][14]. In addition, a very recent study by Winter et al demonstrated the potential of wearable trunk accelerometers to identify movement deviations prior to injury occurrence [11,12].…”

Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach

“…Correcting abnormal kinematics has been suggested to reduce the risk of injury [3,4]. Runners' gait parameters can also change during a prolonged run due to fatigue [5][6][7], which may increase injury risk as the runner deviates from their normal gait pattern [8,9]. Reducing injury risk factors via altering running biomechanics has been demonstrated to be feasible in a lab environment [4,10], but this requires sophisticated equipment and software.…”