The emergence of wearables for body care and fitness seems to have made it easier for us to gather more information about our health . Now with this technology you can check your heart rate to know your stress level during a presentation at work or believe that we are more fit after counting the number of steps you have taken throughout the day. But to have a more complete and accurate reading about your general health and well-being, you would do better to look at the strength and structure of your circle of friends, according to a new study published in the Public Library of Science journal (PLOS ONE) .
While previous studies had revealed how beliefs, opinions and attitudes spread through social networks, research carried out by experts from the University of Notre Dame has been interested in what your social relationships say about your health , happiness and stress level. “We were interested in the topology of the social network, what the position of each one with respect to their group says about their health,” says Nitesh V. Chawla, Professor of Computer Science and Engineering at the American University of Notre Dame (Indiana ), director of the Interdisciplinary Center for Network and Application Science and main author of the study. “We have found that the structure of the social network and personal connections makes it much easier to predict an individual’s states of well-being than using data from wearables, such as heart rate or walking distance.”
The importance of the social network
Study participants wore Fitbit wearables and apps to collect health and behavioral data, such as steps, sleep, pulse and heart rate, and activity level, and answered questionnaires about their feelings and perceptions of stress. happiness and positive thinking. Chawla and his team then analyzed the data using machine learning and artificial intelligence models along with the characteristics of the individuals’ social network, such as their level, central role in the network, the clustering coefficient and the number of triangles . All of them are indicators of connectivity, social balance, reciprocity and closeness to the social network. The study showed a strong correlation between social network structures with heart rate, number of steps and activity level.
Knowing the structure of the social network significantly improved the predictability of health and well-being than if you only looked at the Fitbit data. For example, when the structure of the social network was combined with the data collected by wearables, the machine learning model improved 65% in the prediction of happiness, 54% in the self-prediction of health, 55% in predicting positive attitude and 38% predicting success. “ The study confirms that without the information from the social network we only have an incomplete vision of the individual’s state of well-being. For the prediction to be effective and to be able to focus on an intervention, it is essential to know that information, ”says Chawla.
This finding may help employers exploring fitness wearables in order to incentivize their employees to improve their health and fitness. Chawla believes that it would be better to create a platform for employers and employees to share their experiences and that this would help improve the health and well-being of everyone.
