Dietary behavior surveillance is central for monitoring both public and individual health, as well as understanding its underlying cultural, social, and psychological mechanisms. Heart disease, diabetes, osteoarthritis, and even cancer have all been linked to weight gain, and the US Center for Disease Control and Prevention (CDC) estimates some 35% of adults in US are obese, with medical care and other expenses associated with obesity costing up to $190 billion a year (as of 2012). Large-scale dietary studies of food consumption use questionnaires and food diaries to keep track of the daily activities of their participants. Public health awareness campaigns then use data on dietary behavior across various population segments to tailor their message and intervention techniques. Recently, the Quantified Self revolution has involved the individual in personalized health management, with easy diet tracking, gamification approaches to a more active lifestyle, and online social support networks, opening opportunities for quantitative study both at individual and population levels.
The availability of online data is creating unprecedented opportunities to study these issues from new angles as many dietary related behaviors leave digital traces: users search for diet advice on Google, they upload cooking suggestions to allrecipes.com, they turn to online communities for help with eating disorders, they check in at their gym on Foursquare, and they share their #FoodPorn pictures on Instagram. Though such online data comes with its own set of challenges, in particular related to quality and selection biases, recent work shows the potential benefits online data offers concerning scale, richness, social network information, as well as low latency and cost. While computer scientists have the technical expertise to collect and analyze such data, they typically lack the domain expertise to ask the right, impactful research questions and to position their work within the existing body of knowledge. At the same time nutritionists and public health experts might not know how tools such as machine learning can be used to deal with large amounts of messy online data. Through this special issue we hope to strengthen interdisciplinary work that critically analyzes the value that online data holds for dietary studies.
We welcome novel contributions that use online data -- including, but not limited to, social media, web logs, search logs, multimedia, social network data, online communities, smartphone applications and mobility data -- to study dietary behavior and healthy weight management for applications both in individual and public health.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
total views article views article downloads topic views