Analysis of Physical Activity Propagation in a Health Social Network

Abstract

Modeling physical activity propagation, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. However, there has been lacking of scientific and quantitative study to elucidate how social communication may deliver physical activity interventions. In this work we introduce a Community-level Physical Activity Propagation (CPP) model to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level). CPP is a novel model which is inspired by the well-known Independent Cascade and Community-level Social Influence models. Given a social network, we utilize a hierarchical approach to detect a set of communities and their reciprocal influence strength of physical activities. CPP provides a powerful tool to discover, summarize, and investigate influence patterns of physical activities in a health social network. The detail experimental evaluation shows not only the effectiveness of our approach but also the correlation of the detected communities with various health outcome measures (i.e., both existing ones and our novel measure, named Wellness score, which is a combination of lifestyle parameters, biometrics, and biomarkers). Our promising results potentially pave a way for knowledge discovery in health social networks.

Characterizing Physical Activity in a Health Social Network

“…Better outcomes were identified when interventions had more than five contacts with participants and when the time to follow-up was short (≤3 months; 60% positive outcomes), compared to medium-term (3-6 months, 50%) and long-term (≥6 months, 40%). Phan et al [25] introduced a hierarchical approach to analyze physical activity propagation through social communications at the community level. Their findings show that: (a) social networks have great potential to propagate physical activities via social communications; and (b) physical activity-based influence behavior has a strong correlation to health outcome measures such as BMI.…”

“…To investigate this, we must equip ourselves with methods that can model influence in a social network. To model physical activity propagation, Phan et al [25] introduced the Community-level Physical Activity Propagation (CPP) model, which was inspired by the ideas of the Independent Cascade (IC) model [17], and the Community-level Social Influence (CSI) model [21]. Similar to our Topic aware Community-level Physical Activity Propagation (TaCPP) model [26], we take the content of the posts into account.…”

“…The building block for activity propagation modeling is the definition of a trace [25]. Assume that at time t, user v makes a posting on which user u comments; given a ∆t, v is considered to activate u at time t if the total number of (walking & running) steps of u in [t, t + ∆t] is larger than or equal to the total number of steps of u in the past period Let Iα,u be the set of users that potentially influence u's activation in the trace α:…”

“…And likewise, a negative post can indicate a decrease in the activity compared to (the average of) previous and future days. To study the group-level behavior caused by the posts, we build on our previous research [25] that demonstrated how social influence can contribute to physical activity propagation. Findings of the current paper show that there is a significant difference between positive and negative posts in how they influence other members of the network.…”

Topic-Aware Physical Activity Propagation in a Health Social Network

“…To achieve this goal, we were inspired by the well-known Independent Cascade (IC) model, 7 the Community-level Social Influence (CSI) model, 8 and the Physical Activity Propagation (CPP) 9 model (see the sidebar for “Related Work in Online Social Networks”). In this article, we extend our previous work by taking into account the content of social communication instead of a binary status (message sent or not sent) between two users.…”