There is no objective way to monitor mechanical loading characteristics during exercise for bone health improvement. We developed accelerometry-based equations to predict ground reaction force (GRF) and loading rate (LR) in normal weight to severely obese subjects. Equations developed had a high and moderate accuracy for GRF and LR prediction, respectively, thereby representing an accessible way to determine mechanical loading characteristics in clinical settings.
There is no way to objectively prescribe and monitor exercise for bone health improvement in obese patients based on mechanical loading characteristics. We aimed to develop accelerometry-based equations to predict peak ground reaction forces (pGRFs) and peak loading rate (pLR) on normal weight to severely obese subjects.
Sixty-four subjects (45 females; 84.6 ± 21.7 kg) walked at different speeds (2–6 km·h−1) on a force plate–equipped treadmill while wearing accelerometers at 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. Actual and predicted values at different speeds were compared by repeated measures ANOVA.
Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration transient rate for pLR prediction. All pGRF equation coefficients of determination were above 0.89, a good agreement between actual and predicted pGRFs, with a mean absolute percent error (MAPE) below 6.7%. No significant differences were observed between actual and predicted pGRFs at each walking speed. Accuracy indices from our equations were better than previously developed equations for normal weight subjects, namely a MAPE approximately 3 times smaller. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF.
Walking pGRF and pLR in normal weight to severely obese subjects can be predicted with moderate to high accuracy by accelerometry-based equations, representing an easy and accessible way to determine mechanical loading characteristics in clinical settings.
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The study was developed in the Research Centre in Physical Activity, Health and Leisure (CIAFEL) funded by ERDF through the COMPETE and by the FCT (grant UIDB/00617/2020). The authors would like to thank the participants who took part in this research and all that have collaborated in the project.
This study was funded by the Foundation for Science and Technology of Portugal (FCT) (grant PTDC/DTP-DES/0968/2014) and by the European Regional Development Fund (ERDF) through the Operational Competitiveness Programme (COMPETE) (grant POCI-01-0145-FEDER-016707). Florêncio Diniz-Sousa was supported by the FCT (grant SFRH/BD/117622/2016), and Giorjines Boppre was supported by the FCT (grant SFRH/BD/146976/2019).
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Veras, L., Diniz-Sousa, F., Boppre, G. et al. Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects. Osteoporos Int 31, 1239–1250 (2020). https://doi.org/10.1007/s00198-020-05295-2
- Activity monitor
- Force plates
- Mechanical loading
- Raw acceleration