2 27 MACHINE LEARNING ENABLED PERSONALIZED PHYSICAL ACTIVITY COACHING 1. INTRODUCTION Unhealthy lifestyles lead to increased premature mortality and are a risk factor for sustaining noncommunicable diseases (NCDs) such as cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes [1]. NCDs caused 63% of all deaths that occurred globally in 2008 [1]. There are four behavioural factors that have a significant influence on the prevention of NDCs: healthy nutrition, not smoking, maintaining a healthy body weight, and sufficient physical activity. Insufficient physical activity is one of the leading risk factors for the major NCDs and not meeting the recommended level of physical activity is associated with approximately 5.3 million deaths that occurred globally in 2008 [2]. A high amount of sedentary time without sufficient daily physical activity leads to a higher rate of all-cause mortality [3]. Besides the increased risk of premature mortality in the long term, the short-term quality of life, being able to work, and social participation is also threatened by insufficient physical activity [4]. Fortunately, these risks are eliminated when this sedentary time is compensated for with sufficient physical activity of moderate intensity [3]. In Western civilization, living a sedentary lifestyle is the rule rather than the exception, as many people work in office environments. In pursuance of preventing the negative effects of insufficient physical activity in the workplace, the Hanze University of Applied Sciences Groningen (HUAS), a large university in the northern part of the Netherlands, started a novel initiative named (in Dutch): ‘Het Nieuwe Gezonde Werken’ (The New Healthy Way of Working; HNGW). With HNGW, the HUAS aims to promote a healthy lifestyle and physical activity during the workday. HNGW consists of providing participants with educational group meetings, food boxes with healthy recipes, and individual coaching sessions supplemented with an activity tracker. Despite the fact that participants are coached every two weeks and measured continuously, it remains difficult for a coach to provide timely personalized feedback. The manual task of creating personalized feedback is time consuming, and as such it is not always possible for the participants to get in-depth and timely daily feedback on their progression. Furthermore, current activity trackers do not provide a prediction for reaching the daily goal. In order to fill this gap, we propose a novel, personalized, and flexible machine-learningbased procedure that can automate a part of the coaching process and serve as a source of information on a participant’s progress with physical activity during the day. The personalized model provides, throughout the day, information on the probability of the participant meeting his or her daily physical activity goal. We demonstrate the accuracy and effectiveness of this solution in practice by training different machine learning
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