Summary

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.

Introduction

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.

Methods

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.

Results

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.

Conclusion

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.

中文翻译：

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基于加速度计的严重肥胖受试者正常体重行走期间骨骼机械负荷的预测。

概括

没有客观的方法来监测运动期间的机械负荷特性以改善骨骼健康。我们开发了基于加速度计的方程来预测严重肥胖受试者在正常体重下的地面反作用力 (GRF) 和负荷率 (LR)。所开发的方程分别对 GRF 和 LR 预测具有较高和中等的准确度，从而代表了一种确定临床环境中机械负荷特性的可行方法。

介绍

没有办法根据机械负荷特性客观地规定和监测肥胖患者骨骼健康改善的运动。我们旨在开发基于加速度计的方程来预测严重肥胖受试者正常体重的峰值地面反作用力 (pGRF) 和峰值负荷率 (pLR)。

方法

64 名受试者（45 名女性；84.6 ± 21.7 kg）在配备测力板的跑步机上以不同的速度（2-6 km·h ^-1 ）行走，同时在下背部和臀部佩戴加速度计。开发了回归方程来根据加速度数据预测 pGRF 和 pLR。留一法交叉验证用于计算预测精度和 Bland-Altman 图。通过重复测量方差分析比较不同速度下的实际值和预测值。

结果

pGRF 预测包括体重和峰值加速度，pLR 预测包括体重和峰值加速度瞬态速率。所有 pGRF 方程的决定系数均高于 0.89，实际和预测 pGRF 之间的一致性很好，平均绝对百分比误差 (MAPE) 低于 6.7%。在每种步行速度下，实际和预测的 pGRF 之间没有观察到显着差异。我们方程的准确度指标优于以前为正常体重受试者开发的方程，即 MAPE 大约小 3 倍。与为预测 pGRF 而开发的方程相比，所有 pLR 预测方程的准确度都较低。

结论

通过基于加速度计的方程，可以以中等到高精度预测体重正常到严重肥胖受试者的行走 pGRF 和 pLR，这代表了一种在临床环境中确定机械负荷特性的简单易行的方法。