Socially aware computation and communication
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
HealthGear: A Real-time Wearable System for Monitoring and Analyzing Physiological Signals
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
A sensor network for social dynamics
Proceedings of the 5th international conference on Information processing in sensor networks
Wireless sensor networks for personal health monitoring: Issues and an implementation
Computer Communications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
mConverse: inferring conversation episodes from respiratory measurements collected in the field
Proceedings of the 2nd Conference on Wireless Health
Modeling the co-evolution of behaviors and social relationships using mobile phone data
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Human behavior understanding for inducing behavioral change: application perspectives
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Situation fencing: making geo-fencing personal and dynamic
Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
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What is the role of face-to-face interactions in the diffusion of health-related behaviors- diet choices, exercise habits, and long-term weight changes? We use co-location and communication sensors in mass-market mobile phones to model the diffusion of health-related behaviors via face-to-face interactions amongst the residents of an undergraduate residence hall during the academic year of 2008--09. The dataset used in this analysis includes bluetooth proximity scans, 802.11 WLAN AP scans, calling and SMS networks and self-reported diet, exercise and weight-related information collected periodically over a nine month period. We find that the health behaviors of participants are correlated with the behaviors of peers that they are exposed to over long durations. Such exposure can be estimated using automatically captured social interactions between individuals. To better understand this adoption mechanism, we contrast the role of exposure to different sub-behaviors, i.e., exposure to peers that are obese, are inactive, have unhealthy dietary habits and those that display similar weight changes in the observation period. These results suggest that it is possible to design self-feedback tools and real-time interventions in the future. In stark contrast to previous work, we find that self-reported friends and social acquaintances do not show similar predictive ability for these social health behaviors.