Are impact accelerations during treadmill running representative of those produced overground?

Dillon, Sarah; Burke, Aoife; Whyte, Enda; O’Connor, Siobhán; Gore, Shane; Moran, Kieran

doi:10.1016/j.gaitpost.2022.09.076

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…This finding indicates that the differences between in-lab and real-world gait patterns seen in this study cannot be solely attributed to inclines, declines, and turns: runners run differently in the real world versus in the lab, even when running on flat, straight segments. Notably, other work has shown that sensor-measured tibial acceleration differs between in-lab treadmill running and real-world running on flat, straight ground [34], as well as between in-lab overground running and real-world running on flat, straight ground [35].…”

Section: Effects Of Inclines Declines and Turnsmentioning

confidence: 99%

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study

Davis,

Meardon,

Brown

et al. 2024

Sensors

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Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3–90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner’s in-lab data. Researchers and clinicians should consider “borrowing” information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

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Section: Effects Of Inclines Declines and Turnsmentioning

confidence: 99%

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study

Davis,

Meardon,

Brown

et al. 2024

Sensors

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“…However, the equipment size and installation requirements have constrained study protocols to treadmills and/or within lab-settings, which could impact the natural running pattern. In fact, there has been increasing evidence of biomechanical differences between running indoors and running in ‘real-world’ environments [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Restoration Algorithm Applied to Clipped Tibial Acceleration Signals

Chan

Angel

Thomson

et al. 2023

Sensors

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Wireless accelerometers with various operating ranges have been used to measure tibial acceleration. Accelerometers with a low operating range output distorted signals and have been found to result in inaccurate measurements of peaks. A restoration algorithm using spline interpolation has been proposed to restore the distorted signal. This algorithm has been validated for axial peaks within the range of 15.0–15.9 g. However, the accuracy of peaks of higher magnitude and the resultant peaks have not been reported. The purpose of the present study is to evaluate the measurement agreement of the restored peaks using a low-range accelerometer (±16 g) against peaks sampled using a high-range accelerometer (±200 g). The measurement agreement of both the axial and resultant peaks were examined. In total, 24 runners were equipped with 2 tri-axial accelerometers at their tibia and completed an outdoor running assessment. The accelerometer with an operating range of ±200 g was used as reference. The results of this study showed an average difference of −1.40 ± 4.52 g and −1.23 ± 5.48 g for axial and resultant peaks. Based on our findings, the restoration algorithm could skew data and potentially lead to incorrect conclusions if used without caution.

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Fatigue assessment in distance runners: A scoping review of inertial sensor-based biomechanical outcomes and their relation to fatigue markers and assessment conditions

McConnochie,

Fox,

Badger

et al. 2025

Gait & Posture

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Are impact accelerations during treadmill running representative of those produced overground?

Cited by 3 publications

References 40 publications

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study

Evaluation of a Restoration Algorithm Applied to Clipped Tibial Acceleration Signals

Fatigue assessment in distance runners: A scoping review of inertial sensor-based biomechanical outcomes and their relation to fatigue markers and assessment conditions

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