Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
8-1-2019 12:00 AM
End Date
11-1-2019 12:00 AM
Description
Individuals are influenced by both direct and indirect interaction with their social contacts. While peer influence is known to affect health-related outcomes such as exercise, limited work has fully explored how social networks are structured to support (or inhibit) interaction that could lead to positive health behaviors. With the development of pervasive technology and rise of personal health and wellness tracking, increasing attention has been paid to promoting positive fitness behaviors through social interaction mechanisms in online fitness communities. This trend offers a unique opportunity to understand the opportunity structures for personal health and wellness support. Utilizing a large-scale behavioral trace dataset from the online fitness community Strava, we examine how the size of people's personal network is structured by demographics (e.g. gender and age) and an economic indicator (i.e. if they pay for a premium account). We employ stochastic process models to characterize the empirical network degree distributions in this population of fitness community members. We find that gender, age and account status are associated with distinct network structure. Results have implications in the analysis and the design of health interventions that make use of network relationships in online settings.
"Friending" in Online Fitness Communities: Exploring Activity-Based Online Network Structure
Grand Wailea, Hawaii
Individuals are influenced by both direct and indirect interaction with their social contacts. While peer influence is known to affect health-related outcomes such as exercise, limited work has fully explored how social networks are structured to support (or inhibit) interaction that could lead to positive health behaviors. With the development of pervasive technology and rise of personal health and wellness tracking, increasing attention has been paid to promoting positive fitness behaviors through social interaction mechanisms in online fitness communities. This trend offers a unique opportunity to understand the opportunity structures for personal health and wellness support. Utilizing a large-scale behavioral trace dataset from the online fitness community Strava, we examine how the size of people's personal network is structured by demographics (e.g. gender and age) and an economic indicator (i.e. if they pay for a premium account). We employ stochastic process models to characterize the empirical network degree distributions in this population of fitness community members. We find that gender, age and account status are associated with distinct network structure. Results have implications in the analysis and the design of health interventions that make use of network relationships in online settings.
https://aisel.aisnet.org/hicss-52/dsm/dsm_and_communities/7