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Physical activity is an area of life in which social influence plays a major role. Observing the activity of a sedentary person may cause the observer to exercise less; observing a persistently active person can serve as a motivating factor. The goal of this research is to determine how to optimally pair individuals in order to facilitate motivational relationships with respect to physical activity. This research performs an observational study of data collected from a mobile health and fitness application, iBurnCalorie, which allows users to follow each other in addition to tracking physical activity. Through this social feature, this study examines the influence of users on each other's activity patterns. Our preliminary results indicate that some users have chosen effective role models without any intervention. If this natural effect can be replicated, such a novel interventional networking feature could have a significant impact within iBurnCalorie and all similar applications.

Walking is a fundamental human activity and its diminution a potential morbidity factor. Recent developments in mobile computing have enabled ubiquitous monitoring of walking activity via the smartphone accelerometers. Typically, walking apps map accelerometer values to caloric values through calibration algorithms. However, these calibration algorithms assume a flat surface, which is not always true and can introduce significant errors. In this paper, we outline a novel calibration method that estimates surface inclination for uphill walking, thus, improving the caloric estimation in walking apps.

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