Understanding and Predicting Weight Loss with Mobile Social Networking Data

It has become increasingly popular to use mobile social networking applications for weight loss and management. Users can not only create profiles and maintain their records but also perform a variety of social activities that shatter the barrier to share or seek information. Due to the open and connected nature, these applications produce massive data that consists of rich weight-related information which offers immense opportunities for us to enable advanced research on weight loss. In this paper, we conduct the initial investigation to understand weight loss with a large-scale mobile social networking dataset with near 10 million users. In particular, we study individual and social factors related to weight loss and reveal a number of interesting findings that help us build a meaningful model to predict weight loss automatically. The experimental results demonstrate the effectiveness of the proposed model and the significance of social factors in weight loss.

[1]  William G. Griswold,et al.  Usability and Feasibility of PmEB: A Mobile Phone Application for Monitoring Real Time Caloric Balance , 2006, 2006 Pervasive Health Conference and Workshops.

[2]  Victoria A. Andersen,et al.  Contributions of weight perceptions to weight loss attempts: differences by body mass index and gender. , 2009, Body image.

[3]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[4]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[5]  Wei Zhang,et al.  Consuming fructose-sweetened, not glucose-sweetened, beverages increases visceral adiposity and lipids and decreases insulin sensitivity in overweight/obese humans. , 2009, The Journal of clinical investigation.

[6]  Mandi Yu,et al.  Mining Health App Data to Find More and Less Successful Weight Loss Subgroups , 2016, Journal of medical Internet research.

[7]  Feiping Nie,et al.  Robust Manifold Nonnegative Matrix Factorization , 2014, ACM Trans. Knowl. Discov. Data.

[8]  Arild Faxvaag,et al.  What Influences Patient Participation in an Online Forum for Weight Loss Surgery? A Qualitative Case Study , 2014, Interactive journal of medical research.

[9]  Graham Cormode,et al.  Node Classification in Social Networks , 2011, Social Network Data Analytics.

[10]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[11]  Deborah F Tate,et al.  Maintaining large weight losses: the role of behavioral and psychological factors. , 2008, Journal of consulting and clinical psychology.

[12]  Alan Buckingham,et al.  Statistical Update , 1996, The Bulletin of the Ecological Society of America.

[13]  W. Snell,et al.  Willingness to Self-Disclose to Female and Male Friends as a Function of Social Anxiety and Gender , 1989 .

[14]  Huan Liu,et al.  Exploiting homophily effect for trust prediction , 2013, WSDM.

[15]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[16]  Rheanna N. Ata,et al.  The Effects of Gender and Family, Friend, and Media Influences on Eating Behaviors and Body Image During Adolescence , 2007 .

[17]  Subbarao Kambhampati,et al.  Venting Weight: Analyzing the Discourse of an Online Weight Loss Forum , 2016, AAAI Workshop: WWW and Population Health Intelligence.

[18]  Sasan Adibi,et al.  Mobile Health: A Technology Road Map , 2015 .

[19]  Paul W. Ballantine,et al.  Help me, I'm fat! Social support in online weight loss networks , 2011 .

[20]  Thomas D. Giles,et al.  Council on Nutrition, Physical Activity, and Metabolism Statement on Obesity and Heart Disease From the Obesity Committee of the Weight Loss: An Update of the 1997 American Heart Association Scientific Obesity and Cardiovascular Disease: Pathophysiology, Evaluation, and Effect of , 2006 .

[21]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[22]  Ilkka Korhonen,et al.  Use of a mobile phone diary for observing weight management and related behaviours , 2010, Journal of telemedicine and telecare.

[23]  M. Carter,et al.  Adherence to a Smartphone Application for Weight Loss Compared to Website and Paper Diary: Pilot Randomized Controlled Trial , 2013, Journal of medical Internet research.

[24]  R. Wing,et al.  Social Influences Are Associated With BMI and Weight Loss Intentions in Young Adults , 2011, Obesity.

[25]  Richard Elliott,et al.  Peer pressure and poverty: exploring fashion brands and consumption symbolism among children of the ‘British poor’ , 2004 .

[26]  Arik Azran,et al.  The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks , 2007, ICML '07.

[27]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[28]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[29]  Huan Liu,et al.  Embedded Unsupervised Feature Selection , 2015, AAAI.

[30]  Mowafa Househ,et al.  Mobile Social Networking Health (MSNet-Health): beyond the mHealth frontier. , 2012, Studies in health technology and informatics.

[31]  S. McKellar,et al.  Drug Use Amongst Peers: peer pressure or peer preference? , 1994 .

[32]  David B Allison,et al.  Clinical implications of obesity with specific focus on cardiovascular disease: a statement for professionals from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism: endorsed by the American College of Cardiology Foundation. , 2004, Circulation.

[33]  Lise Getoor,et al.  Link-Based Classification , 2003, Encyclopedia of Machine Learning and Data Mining.

[34]  Shankar Kumar,et al.  Video suggestion and discovery for youtube: taking random walks through the view graph , 2008, WWW.

[35]  Jennifer Neville,et al.  Iterative Classification in Relational Data , 2000 .

[36]  Nancy D. Albers-Miller.  Consumer misbehavior: why people buy illicit goods , 1999 .

[37]  Micah O. Mazurek,et al.  Prevalence and Correlates of Screen-Based Media Use Among Youths with Autism Spectrum Disorders , 2011, Journal of Autism and Developmental Disorders.

[38]  Mark D. Huffman,et al.  AHA Statistical Update Heart Disease and Stroke Statistics — 2012 Update A Report From the American Heart Association WRITING GROUP MEMBERS , 2010 .

[39]  M. Shiner,et al.  Defining peer education. , 1999, Journal of adolescence.

[40]  Eric Horvitz,et al.  Predicting Depression via Social Media , 2013, ICWSM.

[41]  Lise Getoor,et al.  Collective Classi!cation in Network Data , 2008 .

[42]  E. Arias,et al.  Mortality in the United States, 2013. , 2014, NCHS data brief.

[43]  Lucy Yardley,et al.  Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study , 2013, Journal of medical Internet research.

[44]  Foster Provost,et al.  A Simple Relational Classifier , 2003 .

[45]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[46]  Thomas D. Giles,et al.  Obesity and Cardiovascular Disease: Pathophysiology, Evaluation, and Effect of Weight Loss , 2006, Arteriosclerosis, thrombosis, and vascular biology.