1 16 CHAPTER 1 known as the endogeneity problem [53]. A potential solution to the fourth problem is using statistical methods that account for the absence of confounding variables. One such method involves utilising an ensemble of machine learning techniques in conjunction with Targeted Maximum Likelihood Estimation (TMLE). The goal of this strategy is to minimise the impact of the missing variables by using TMLE, which is known to be more robust to inaccuracies in modelling the underlying reality compared to traditional statistical methods [44], [45], [54]. As an alternative potential solution to the fourth problem, we take a two-way approach by applying traditional statistical analysis and machine learning techniques to the same incomplete data set. The use of traditional statistical analysis on the one hand and machine learning on the other provides insight into their relative performance when dealing with incomplete data [55]. This approach allows us to understand the applicability and discuss the strengths of traditional statistical analysis and machine learning. AIM AND OUTLINE This thesis aimed to reduce the data analytics gap represented by the four identified problems while examining the associated potential solutions to enable meaningful insights and predictions related to physical activity and physical performance. These insights and predictions can be used to make more informed decisions regarding physical activity and physical performance interventions. In Figure 1, we present an outline of the thesis and visualise how each chapter is mapped to one or more problems and solutions to reduce the identified data analytics gap. The structure of this thesis is as follows: In Chapter 2, we investigated the possibility of predicting the daily physical performance of employees based on wearable data. The study involved coaching Hanze University of Applied Science employees to increase their physical activity during the daytime and monitor their steps using Fitbits. These steps were subsequently transformed into physical performance measures such as number of steps per hour and total number of steps until a particular hour. In addition, we applied machine learning to predict whether an employee would achieve his or her overall daily step goal during the working day. Through automated analysis of physical activity and physical performance, timely detection of anomalies in behaviour and identifying effective coaching strategies may become feasible.
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