Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters

Vanwanseele, Benedicte; Beéck, Tim Op De; Schütte, Kurt; Davis, Jesse

doi:10.3389/fspor.2020.575596

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…However, these new equations described in the current study have the advantage of being able to be applied to both types of locomotion (walking and running) and not just walking. Another possible application is in sports training, by monitoring the amount of mechanical loading sustained by athletes and using this information for training loading monitoring and for establishing adequate measures to prevent osteoarticular overload and reduce the risk of osteoarthritis (Vanwanseele, Op De Beéck, Schütte, & Davis, 2020).…”

Section: Discussionmentioning

confidence: 99%

Mechanical loading prediction through accelerometry data during walking and running

Veras

Diniz‐Sousa

Boppre

et al. 2022

European Journal of Sport Science

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Currently, there is no way to assess mechanical loading variables such as peak ground reaction forces (pGRF) and peak loading rate (pLR) in clinical settings. The purpose of this study was to develop accelerometry-based equations to predict both pGRF and pLR during walking and running. One hundred and thirty one subjects (79 females; 76.9 ± 19.6 kg) walked and ran at different speeds (2-14 km•h −1 ) on a force plate-instrumented treadmill while wearing accelerometers at their ankle, lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Our pGRF prediction equation was compared with a reference equation previously published. Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration rate for pLR prediction. All pGRF equation coefficients of determination were above 0.96, and a good agreement between actual and predicted pGRF was observed, with a mean absolute percent error (MAPE) below 7.3%. Accuracy indices from our equations were better than previously developed equations. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF. Walking and running pGRF can be predicted with high accuracy by accelerometry-based equations, representing an easy way to determine mechanical loading in free-living conditions. The pLR prediction equations yielded a somewhat lower prediction accuracy compared with the pGRF equations.

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Section: Discussionmentioning

confidence: 99%

Mechanical loading prediction through accelerometry data during walking and running

Veras

Diniz‐Sousa

Boppre

et al. 2022

European Journal of Sport Science

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“…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]. Dynamic loading represents the magnitude of impacts on the human body during ground contact while running [15]. An increase in dynamic loading in the anterior-posterior direction during a fatiguing protocol has been observed prior to the onset of RRIs [12].…”

Section: Introductionmentioning

confidence: 98%

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

Pla

Hollville

Schütte

et al. 2021

Sensors

Self Cite

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Movement dynamics during running was previously characterized using a trunk-mounted accelerometer, and were associated with a history of overuse injuries. However, it remains unknown if these measures are also linked to the development of overuse injuries. The aim of this study was therefore to determine how movement dynamics alter in response to fatigue, and the possible link with developing lower-leg overuse injuries during a six-month follow-up period. Two hundred and eight movement science university students completed a 12-min all-out run while wearing a trunk-mounted accelerometer. Dynamic stability, dynamic loading and spatiotemporal measures were extracted from the accelerometer. Participants sustaining an injury within the 6-month period demonstrated significantly higher RMS ratio values in the vertical direction and lower RMS ratio values in the anteroposterior direction, and lower impact acceleration values in the anteroposterior direction in an unfatigued state compared to the uninjured group. They also demonstrated an increase in dynamic loading in the horizontal plane during the run. In addition, with running fatigue both groups exhibited changes in dynamic stability and loading measures. These results show the potential of using a single trunk-mounted accelerometer to detect changes in movement dynamics that are linked to lower-leg overuse injuries.

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“…To our point, starting from 2015, some studies have followed athletes for uncontrolled training runs or races [188,[200][201][202]206,207,[210][211][212][213]220,221,[225][226][227]230]. A myriad of external factors, such as weather, traffic, and surface conditions, could influence how someone runs and therefore, it is crucial to capture running patterns in the same settings that runners actually run.…”

Section: Running Environmentsmentioning

confidence: 99%

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

Benson

Räisänen

Clermont

et al. 2022

Sensors

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Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

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Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters

Cited by 10 publications

References 22 publications

Mechanical loading prediction through accelerometry data during walking and running

Mechanical loading prediction through accelerometry data during walking and running

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

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

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