2 28 CHAPTER 2 algorithms and evaluating their performance using a train-test split dataset from the HNGW data. We apply techniques like grid search and cross-validation to optimize each model in order to find their best configuration. To show the applicability of this research in practice, we developed a proof-of-concept Web application, which has, to the best of our knowledge, not been done before. With the personalized actionable information, the application provides, we demonstrate that machine learning automating is feasible as a part of the coaching process. The techniques described in this work could serve two goals in the field of personalized coaching. Firstly, we envision how coaches can use such applications and how these applications can provide them with detailed insight about the participants’ activity during the day. Secondly, the tool could be used as a selfsupport tool, in which the participants’ engagement with their lifestyle might increase as a result of the extra feedback. 2. RELATED WORK A number of studies have been performed on physical activity over days, where the sources of variance in activity is related to the subject, the day of the week, the season, and occupational and non-occupational days [5]. Tudor-Locke et al. (2005) showed that the individual is the main source of variability in physical activity next to the difference between the Sunday and the rest of the week [6]. Another study identified physical inactivity being lower on weekend days, and Saturday was the most active day of the week for both men and women [5]. To reduce sedentary time and increase physical activity levels, individuals need to change their behaviour and daily routines. This is hard to achieve because of various reasons and requires interventions and coaching strategies that use well-established techniques to induce a behaviour change. A review by Gardner et al. (2016) found that self-monitoring, problem solving, and restructuring the social or physical environment were the most promising behaviour change strategies, and—although the evidence base is quite weak—advises environmental restructuring, persuasion, and education to enhance self-regulatory skills [7]. Interventions aimed at increasing physical activity levels or reducing sedentary time varies widely in content and in effectiveness. For example, studies focusing on exercise training and behavioural approaches have demonstrated conflicting results, whereas interventions focusing on reducing sedentary time seem to be more promising [8]–[12]. The use of active video games seems to be effective in increasing physical activity, but has inconsistent findings on whether they are suitable to meet the recommended levels [13]. Also, interventions targeting recreational screen time reduction might be effective when using health promotion curricula or counselling [14]. Web- or app-based interventions to improve diet, physical activity, and sedentary
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