Many behaviors that lead to worsened health outcomes are modifiable, social, and visible. Social influence has thus the potential to foster adoption of habits that promote health and improve disease management. In this study, we consider the evolution of the physical activity of 44.5 thousand Fitbit users as they interact on the Fitbit social network, in relation to their health status.
The users collectively recorded 9.3 million days of steps over the period of a year through a Fitbit device. 7,515 of the users also self-reported whether they were diagnosed with a major chronic condition. A time aggregated analysis shows that ego net size, average alter physical activity, gender, and body mass index (BMI) are significantly predictive of ego physical activity. For users who self-reported chronic conditions, the direction and effect size of associations varied depending on the condition, with diabetic users specifically showing almost a 6-fold increase in additional daily steps for each additional social tie.
Each additional social tie corresponds to an increase of 6.5 steps on average
Subsequently, we consider the co-evolution of activity and friendship longitudinally on a month by month basis. We show that the fluctuations in average alter activity significantly predict fluctuations in ego activity. By leveraging a class of novel non-parametric statistical tests we investigate the causal factors in these fluctuations. We find that under certain stationarity assumptions, non-null causal dependence exists between ego and alter’s activity, even in the presence of unobserved stationary individual traits.
We believe that our findings provide evidence that the study of online social networks have the potential to improve our understanding of factors affecting adoption of positive habits, especially in the context of chronic condition management.