2 30 CHAPTER 2 the model was not proven to be predictive enough to be applied [39]. In order to improve physical activity in combination with activity trackers, a coaching feature is helpful, but only when the messages are personal and placed in context [40]. Perceiving the coaching information as personal and relevant is crucial for the effectiveness of (e)Coaching [41]. Such tailored (e)Coaching has many aspects, two of which are personalization and timing [42]. Timeliness of information is important for participants to be able to process the information and apply the advice while it is still relevant for them. In order to provide such advice, access to real-time predictions is vital, as it allows for timing the moment of coaching, either virtual or in real life and as flexible as needed. To the best of our knowledge, no studies exist about the use of sensor data combined with machine learning techniques for creating validated and individualized predictive models on physical activity. The individualized models could help the coach and the participant in the process of behaviour change and increased physical activity. 3. MATERIALS AND METHODS The present work revolves around the HNGW project. This project was started in 2015 and focuses on promoting a healthy lifestyle. We describe the design of this study and how the resulting data is used in the present work. Next, we describe our analysis pipeline. We describe the conversion of the raw data set into a feature set, the evaluation methods of the predictive models, and the choice of the algorithms. Finally, we shed light on the proof-of-concept application we created to demonstrate how these techniques could be used in practice. 3.1. Study Design The goal of the workplace health promotion intervention HNGW at the HUAS was to increase physical activity during workdays, by improving both physical and mental health, and several work-related variables. In the study, several performance-based tests and self-reported questionnaires were used to assess its effectiveness on a group level. Forty-eight eligible participants from the HUAS were randomized into two groups, stratified according to age, gender, BMI, and baseline self-reported health. One group followed a twelve-week workplace health promotion intervention; the other served as a control during the first twelve weeks and thereafter received the twelve-week workplace health promotion intervention. During the study, minutely step count data of the participants was collected. Step count was measured using a wrist-worn activity tracker, the Fitbit Flex. The Fitbit Flex has
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