Influence on health behavior from peers is well known and it has been shown that participants in an online physical activity promotion program are generally more successful when they share their achievements through an online community. However, more detailed insights are needed into the mechanisms that explain the influence of a community on physical activity levels (PAL). This paper discusses a detailed analysis of a data set of participants in an online physical activity promotion program. The analysis focuses on the comparison two groups of participants, namely participants who will join an online community at some point in time and participants who will never join such a community. A well-balanced selection is made to eliminate to a large extent factors that dilute the effect of the willingness to partake in a community. We create statistical models that describe the PAL increase at the end of the program. A comparison of these models shows that participants that will participate in a community not only have a higher PAL at the start of the program, but also that the PAL increase is significantly greater compared to participants that will not become community members. The results further support the hypothesis that the possibility to share achievements is an important feature of successful health promotion programs. At the same time, it raises the question whether part of the success is caused by a selection bias, as people that are willing to participate in a community are already more active at the start.

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Social and cognitive sciences' knowledge about social behavior and social networks combined with the new computational machine learning techniques can facilitate the creation of be er models. We propose and evaluate a new methodology for nding personality traits of young adults involved in a network using hyper optimization algorithms. We used a social contagion model for the spread of behavior (measured by the physical activity level) among the participants. A part of the Big-5 questionnaire was used to gather information about people regarding their traits of openness and expressiveness. en we try to ne tune the model using machine learning algorithms. e ne tuning of questions from an intake questionnaire can be very useful in validating a model. e accuracy delivered by machine learning pure algorithms is shown to be be er, but the inclusion of data related to people's traits is bene cial in de ning their characteristics.

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