# External Validation of Accelerometry-Based Mechanical Loading Prediction Equations
## 4 Discussion
The controlled setting might have reduced external factors like terrain variability, environmental conditions, and daily life movements, potentially influencing the prediction models' performance when compared to their application in real life settings. Furthermore, as the researchers positioned the accelerometers and ensured that their position remained correct for the duration of the trials, the results cannot account for the effect of increased error due to accelerometer misplacement and shifting that can occur in free-living conditions [37]. Finally, this study's sample is relatively small and homogenous in characteristics such as age and body mass index. Therefore, we may not know if these results hold true for people with diverse characteristics, such as the elderly and people with obesity. Despite these limitations, the study's strengths lie in its rigorous methodological approach and the comprehensive evaluation of multiple prediction models across various activities, providing valuable insights into their external validity. Future studies aiming to improve the accuracy of mechanical loading prediction equations could explore the use of advanced machine learning techniques, which are better equipped to capture the complexity and variability inherent to biomechanical data. Additionally, employing accelerometers with higher sampling frequencies would provide more detailed data, which is essential for accurately detecting critical events like peak loading rates. Techniques that enable the reconstruction of ground reaction force signals from accelerometer data could further enhance accuracy, particularly in pLR predictions, making these models more reliable for diverse real-world applications. Moreover, prediction accuracy could be potentially improved by using data from sensors in two or more placements simultaneously. Although this strategy would not be feasible when collecting daily living data due to a probable decrease in the participant's adherence to the protocol, in more controlled scenarios, such as gathering mechanical loading data from athletes during their sports practice, this could be a valid strategy. Together, these approaches could not only enhance the accuracy of the models but also improve their consistency by mitigating the systematic biases observed in our analysis, reducing the tendency to either consistently overestimate or underestimate the predicted values, and making the bias values closer to zero. In conclusion, this study demonstrates the necessity and value of external validation for mechanical loading prediction equations. Our findings indicate that while existing models can be useful, their accuracy is context-dependent, and further refinement and validation are needed. This research highlights the importance of continuous evaluation and adaptation of prediction models to enhance their applicability and reliability in diverse real-world settings. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142210292/sl](https://www.mdpi.com/article/10.3390/app142210292/sl)]([https://www.mdpi.com/article/10.3390/app142210292/sl](https://www.mdpi.com/article/10.3390/app142210292/sl)). Table S1: Accuracy indices for pGRF and pLR prediction for different walking and running speeds based on the [PERSON] et al. (2022) [20] equations; Table S2: Accuracy indices for pGRF and pLR prediction for different types of jumping based on the [PERSON] et al. (2023) [21] equations; Video S1: Example of the activities included in the protocol. Conceptualization, L.V., F.D.-S., G.B., J.O. and H.F.; formal analysis, L.V. and D.O. ; investigation, L.V., D.O., F.D.-S., G.B., A.R.-C., J.O. and H.F.; methodology, L.V., F.D.-S., G.B., J.O. and H.F.; software, L.V. and D.O. ; writing--original draft, L.V., D.O. and H.F.; writing--review and editing, L.V., D.O., F.D.-S., G.B., A.R.-C., J.O. and H.F. All authors have read and agreed to the published version of the manuscript.