Circle of Friends Vs. Fitness Trackers: Your friends can predict your overall health and wellbeing much better than a Fitbit.
Your friends can read your overall health and wellness better than a wearable fitness tracker, reports a new study. Wearable fitness trackers have made it all too easy for us to make assumptions about our health. We may look to our heart rate to determine whether we really felt the stress of that presentation at work this morning, or think ourselves healthier based on the number of steps we've taken by the end of the day.
‘Circle of Friends Vs. Fitness Trackers: Your friends can track your overall health and wellbeing much better than a Fitbit.’
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But to get a better reading on your overall health and wellness, you'd be better off looking at the strength and structure of your circle of friends, according to a new study in the Public Library of Science journal, PLOS ONE.Read More..
While previous studies have shown how beliefs, opinions, and attitudes spread throughout our social networks, researchers at the University of Notre Dame were interested in 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, Frank M. Freimann Professor of Computer Science and Engineering at Notre Dame, director of the Interdisciplinary Center for Network Science and Applications and a lead author of the study.
"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."
For the study, participants wore Fitbits to capture health behavior 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 analyzed and modeled the data, using machine learning, alongside an individual's social network characteristics including degree, centrality, clustering coefficient and a 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.
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"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.
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"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 capitalizing on them the way we should," Chawla said.
"When we hear that health and wellness programs driven by wearables at places of employment aren't working, we should be asking, is it because we're just taking a single dimensional view where we just give the employees the wearables and forget about it without taking the step to understand the role social networks play in health?"
Source-Eurekalert