Mechanical loading prediction through accelerometry data during walking and running
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- Research Center in Physical Activity, Health and Leisure (CIAFEL) Faculty of Sport University of Porto Porto Portugal, Laboratory for Integrative and Translational Research in Population Health (ITR) University of Porto Porto Portugal
- Obesity Integrated Responsability Unity (CRIO) São João Academic Medical Center Porto Portugal
- Center of Research, Education, Innovation and Intervention in Sport (CIFI2D) Faculty of Sport University of Porto Porto Portugal, Biomechanics Laboratory (LABIOMEP‐UP) University of Porto Porto Portugal
ABSTRACT 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.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 2281743
- Journal Information:
- European Journal of Sport Science, Journal Name: European Journal of Sport Science Journal Issue: 8 Vol. 23; ISSN 1536-7290
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Country unknown/Code not available
- Language:
- English
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