Sedentary behavior is considered as a major public health challenge, linked with many chronic diseases and premature mortality. In Ageing@Work, we developed a machine learning approach to integrate prediction of sedentary behavior within our Virtual Coach, using daily step counts as input. Machine learning algorithms are using as input the daily steps of the last seven days in order to predict the possibility of sedentary behavior for the upcoming day. Virtual Coach uses these predictions in order to personalize suggestions so as to avoid sedentarism beforehand and emphasizes the importance of following these suggestions to the user.
Surprisingly, the predictive capabilities of relevant ICT systems have not been thoroughly investigated for sedentary behavior, nor exploited, thereby motivating the current work. Existing approaches focus on predicting sedentary behavior rhythms on an hourly rather than a daily basis, and they are based on activity states. In contrast with the above works, our Ageing@Work implementation focused on daily predictions of step-defined sedentary behavior using objective monitoring methods, while following widely accepted physical activity recommendations. As such, we prevent sedentary behaviors a day before they occur towards encouraging individuals to become more active. Our outcomes, following the experimentation with different machine learning algorithms, provide an important ground towards the development of real-life artificially intelligent systems for sedentary behavior prediction on a large-scale.
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