2 29 MACHINE LEARNING ENABLED PERSONALIZED PHYSICAL ACTIVITY COACHING behaviour can be effective. Multi-component interventions appear to be more effective than stand-alone app interventions, although the optimal number and combination of app features and level of participant contact needed remain to be confirmed [15], [16]. The workplace is often used for health promotion interventions. Recent reviews on workplace interventions for reducing sitting at work found initial evidence that the use of alternative workstations (sit-stand desks or treadmills) can decrease workplace sitting by thirty minutes to two hours. In addition, one review found that interventions promoting stair use and personalized behavioural interventions increase physical activity, while the other found no considerable or inconsistent effects of various interventions [17], [18]. Step counters provide an objective measure of activity levels and enable self-monitoring. Furthermore, most modern consumer-based activity trackers already contain several behaviour change models or theories [19], [20]. Therefore, based on the aforementioned, using activity trackers in interventions to promote healthy lifestyles is promising. From meta-analyses by Qiu et al. and Stephenson et al. it was concluded that step counter use was indeed associated with small but significant effects in reducing sedentary time [21], [22]. Adding an activity tracker to physical therapy or counselling was effective in some populations [23]–[25]. Besides collecting activity data for therapy or counselling, it is known that the Fitbit itself also serves as an intervention mechanism [26]. The mere fact of wearing an activity tracker (even without any form of coaching) could motivate physical activity and improve health-related quality of life [27], [28]. On the other hand, studies on workplace interventions using activity trackers report conflicting results [29]– [33]. There are several studies that use sensor or activity tracker data to build a custom-made application to support research. An example is the social computer game, Fish’n’Steps, which connects the daily steps of an employee to the growth and activity of the individual avatar fish in a virtual fish tank. The more one is active, the faster the fish grows and prospers [34]. Another example is the study on increased physical activity as the effect of social support groups using pedometers and an app [35]. Although applying machine learning to coaching is new, machine learning techniques in combination with sensors have been applied before to identify the type of activity. Identifying human activity using machine learning and sensor data have been studied, for example, by Wang et al. for recognizing human daily activities from an accelerometer signal [36], by Li et al. on the quantification of the lifetime circadian rhythm of physical activity [37], or by Catal et al. on the use of an ensemble of classifiers for accelerometerbased activity recognition [38]. Only a few studies have investigated the use of actionable, data-driven predictive models. A study on creating a predictive physical fatigue model based on sensors identified relevant features for predicting physical fatigue, however
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