Friend circle more predictive of your health than Fitbit, finds study
To get a better reading on your overall health and wellness, you should be better off looking at the strength and structure of your circle of friends, says a study. According to the study published in the journal PLOS ONE, the researchers were interested in what the structure of social networks says about the state of health, happiness and stress.
"What we found was the social network structure provides a significant improvement in predictability of wellness states of an individual over just using the data derived from wearables, like the number of steps or heart rate," said the study lead author Nitesh V. Chawla, a researcher of Indian origin from University of Notre Dame in the US.
For the study, participants wore Fitbit to capture health behaviour data -- such as steps, sleep, heart rate and activity level and completed surveys and self-assessments about their feelings of stress, happiness and positivity.
The research team then analysed the data, using machine learning, alongside an individual's social network characteristics, including degree, centrality, clustering coefficient and number of triangles.
The study showed a strong correlation between social network structures, heart rate, number of steps and level of activity.
According to the researchers, social network structure provided significant improvement in predicting one's health and well-being compared to just looking at health behaviour data from the Fitbit alone.
For example, when social network structure is combined with the data derived from wearables, the machine learning model achieved a 65 per cent improvement in predicting happiness, 54 per cent improvement in predicting one's self-assessed health prediction, 55 per cent improvement in predicting positive attitude, and 38 per cent improvement in predicting success.
"This study asserts that without social network information, we only have an incomplete view of an individual's wellness state, and to be fully predictive or to be able to derive interventions, it is critical to be aware of the social network structural features as well," Chawla said.
(With IANS Inputs)
