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ACM International Conference on Information and Knowledge Management (CIKM) - 2014
Analysis of Physical Activity Propagation in a Health Social Network
Nhathai Phan, Dejing Dou, Xiao Xiao, Brigitte Piniewski, David Kil
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Outline
• SMASH Project & Motivation• Community-Level Physical Activity Propagation• Experimental Results• Conclusions & Future Works
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Obesity & Physical Activity Interventions
• 18 states (30% <35%), 2 states (>= 35%)• Medical cost:
– $147 billion (in 2008)
• 30 minutes, 5 days• Interventions
– Telephone (16)– Website (15)– Effective in
short term
Prevalence* of Self-Reported Obesity Among U.S. Adults
CDC, http://www.cdc.gov/obesity/data/prevalence-maps.html-2014
E.G. Eakin et al. 2007 C. Vadelanotte et al. 2007G.J. Norman et al. 2007
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SMASH Project• 254 Overweight and Obese individuals with personal
information in the YesiWell study• Social activities– Online social network, text messages, posts, comments, …– Social games, competitions, …
• Daily physical activities– Walking, running, jogging, distance, speed, intensity, …
• Biomarkers, biometric measures – Cholesterol, triglyceride, BMI, …
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Motivation
• Utilize social networks to• help the physical activity propagation process • improve the intervention approaches with
affordable cost• How can social communications effect the
physical activity propagations?– Social interactions– Different granularities– Physical activity propagations & health outcomes
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Outline
• SMASH Project & Motivation• Community-Level Physical Activity Propagation• Experimental Results• Conclusions & Future Works
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A Trace of Physical Activity Propagation
m, t
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Problem Statement
• A directed graph – represents an influence relationship– represents the strength of the arc
• A set of traces
K. Saito, R. Nakano, and M. Kimura. Prediction of information diffusion probabilities for independent cascade model. In KES’08, pages 67-75.Y. Mehmood, N. Barbieri, F. Bonchi, and A. Ukkonen. Csi: Community-level social inuence analysis. In ECML-PKDD’13, pages 48-63.
CPP Model
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Learning & Model Selection (1)
• Complete expectation log likelihood of the observed propagations:
• Solving • We have
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Learning & Model Selection (2)
• Users’ responsibilities will not change
• Run EM algorithm without clustering structure– step 1: estimate – step 2: update
• Keep fixed, update
• Bayesian Information Criterion (BIC)
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Outline
• SMASH Project & Motivation• Community-Level Physical Activity Propagation• Experimental Results• Conclusions & Future Works
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Experiment Setting
• YesiWell dataset – 254 users– Oct 2010 – Aug 2011
• BMI value• Wellness score
• Parameter setting: – tw is a day, is a week
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Detected Communities
• Influencers: circle nodes• Influenced users: rectangle nodes• Non-Influenced users: triangle nodes
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Outline
• SMASH Project & Motivation• Community-Level Physical Activity Propagation• Experimental Results• Conclusions & Future Works
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Conclusions and Future Works
• Propose the CPP model• Observations:– Social networks have great potential to propagate physical
activities– The propagation network found is almost acyclic– The physical activity-based influence behavior has a strong
correlation to health outcome measures (BMI, lifestyles, and Wellness score)
• Which types of messages are important?• Which messages could influence non-influenced users?
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