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Posted at: Jun 18, 2019, 4:03 PM; last updated: Jun 18, 2019, 4:03 PM (IST)

Friend circle more predictive of your health than Fitbit alone: Study

Friend circle more predictive of your health than Fitbit alone: Study
Photo for representation only.

WASHINGTON

Your circle of friends may help you get a better reading on your overall health and wellness than just using wearable devices such as Fitbit, according to researchers, including one of Indian origin.

The study, published in the journal PLOS ONE, analysed what the structure of social networks says about the state of health, happiness and stress.

"We were interested in the topology of the social network—what does my position within my social network predict about my health and well-being?" said Nitesh V Chawla, a professor at the University of Notre Dame in the US.

"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," Chawla said.

For the study, participants wore Fitbits 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.

Chawla and his team then analysed and modelled the data, using machine learning, alongside an individual's social network characteristics including degree, centrality, clustering coefficient and number of triangles.

These characteristics are indicative of properties like connectivity, social balance, reciprocity and closeness within the social network.

The study showed a strong correlation between social network structures, heart rate, number of steps and level of activity.

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.

The model also achieved 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.

The findings could provide insight to employers who look to wearable fitness devices to incentivise employees to improve their health.

Handing someone a means to track their steps and monitor their health in the hopes that their health improves simply may not be enough to see meaningful or significant results.

Those employers, Chawla said, would benefit from encouraging employees to build a platform to post and share their experiences with each other.

Social network structure helps complete the picture of health and well-being.

"I do believe these incentives that we institute at work are meaningful, but I also believe we're not seeing the effect because we may not be capitalising on them the way we should," Chawla said. — PTI 

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