2 42 CHAPTER 2 5. DISCUSSION We investigated machine learning as a means to support personalized coaching on physical activity. We demonstrated that for our particular data sets, the tree algorithms and tree-based ensemble algorithms performed especially well. To demonstrate how the results of machine learning techniques could be used in practice, an application was used to aid the coaching of the physical activity process. Furthermore, the analysis shows that selecting the right algorithm, using the dataset of the individual participant, and tuning its individual algorithm parameters, can lead to significant improvements in predictive performance and is a critical step in machine learning application. All source code, including the different notebooks and the proof-of-concept Web application is available online as open-source software. The source code can serve as a blueprint for other researchers when aiming to apply machine learning for coaching. Although Random Forest outperformed most of the other algorithms, it is problematic to provide a generalized recommendation for specific algorithms, parameters, or parameter settings [44]. Presumably due to individually different physical activity patterns, different algorithms and parameters have to be considered. As a starting point, we selected the algorithms based on well-established sources [41], [42], applied cross-validation, and grid-searched the values of the selected parameters. Nevertheless, it’s important to note that these algorithms, parameters, and grid search values might not work best on all individual physical patterns, and the algorithms, parameters, and grid search values should only be used as starting points. Future work might consist of investigating the underlying mechanisms to be able to choose the best algorithm beforehand. We based the prediction solely on the hour of the day and the number of steps. These steps are naturally increasing over the day, and as such, not independent from each other. By including the cumulative number of steps for each block of data, and by including the number of steps made in the past hour, we assume each block to be independent from the other blocks, and as such, are still able to use the regular machine learning methods. A limitation of the present work is that all participants included in this study participated in an intervention. This intervention might have made the participants more aware and engaged with the project, and as such, the individualized models might be biased towards the best scenario. When people are not extrinsically motivated to meet their daily physical activity goal, and lower their physical activity, the predictive power of the models and therefor the effect of automated intervention will lessen. On the other hand, when an intervention like the health promotion program ends, the individualized models check the participant on his or her performance as if the program is supporting the participant.
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