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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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CPP Model Definition (1)

• Log likelihood of the traces given

• Users’ responsibility:

p

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CPP Model Definition (2)

• CPP model learning

• Probability function

• is a selection functionf

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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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Detected Communities with Health Outcome Measures

avg(BMI) avg(WS)

avg(#steps)

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Consistency of Detected Communities

Standard deviation of BMI Standard deviation of WS

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CPP vs Social Link, CSI Model

• Apply optimal clustering on friend network

Wellness score #steps

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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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ACM International Conference on Information and Knowledge Management (CIKM) - 2014

Thanks you!

{haiphan, dou}@cs.uoregon.edu