Knowing Social Network and Using Health-Tracking Device Can Help Predict One’s Well-Being
According to the article, the researchers at the University of Notre Dame found that using a health-tracking device (namely Fitbit) and knowing one’s social network increases the predictability of one’s mental and physical health than using Fitbit alone.
For the study, the participants wore Fitbits to keep track of basic health data, including steps walked, hours of sleep, and heart rate, etc. and self-assessed about their stress level, happiness, and other mental qualities. In addition, the researchers analyzed participants’ social networks, so that they could find a correlation between the participants’ overall health and well-being and social networks. This involved machine learning and calculating metrics such as clustering coefficient and the number of “triangles” within the network. The study showed that there is a strong correlation between social network structures and heart rate, steps walked, etc.
This research is closely related to the topics that we learned in class; this research counted the number of triangles in the network and derived a clustering coefficient. This clustering coefficient would be valuable because it may give the researchers an insight on how extensive one’s social relationships are, since higher value of clustering coefficient would mean that one’s friends are more likely to know each other. This may help predict one’s social health and ultimately, one’s mental health and well-being.
I believe that this research may help improve the predictability of Fitbit and other devices on one’s well-being by encouraging the implementation of social networking function to the devices. However, I believe there might be a potential for creating unnecessary competition among friends — for example, on how much they have walked in each day — which may not help them on predicting their well-being.