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A new study from the US suggests that social interaction should be considered an important factor for extending lifespan, on a par with other health and lifestyle factors, to the extent that low social interaction harms longevity as much as alcoholism and smoking, has more impact than lack of exercise, and is twice as harmful as obesity.

Researchers at Brigham Young University (BYU) in Provo, Utah, conducted a meta-analysis of published studies and found that having social ties with friends, family, neighbours and colleagues can improve our odds of survival by 50 per cent. You can read about their study online in a paper published in the July issue PLoS Medicine.

“The idea that a lack of social relationships is a risk factor for death is still not widely recognized by health organizations and the public,” noted the journal editors in their summary.

First author Dr Julianne Holt-Lunstad, a professor in the Department of Psychology at BYU, and co-authors Dr Timothy Smith, a professor of Counseling Psychology at BYU, and Brad Layton, formerly of BYU and now working towards a PhD in epidemiology at the University of North Carolina at Chapel Hill, suggest that social relationships should be added to the shortlist of factors that impact a person’s odds of living or dying.

For their analysis, they pooled data from 148 published longitudinal studies (the sort that track groups of people over time, taking observations now and again), and found that low social interaction had a similar impact on lifespan as being an alcoholic or smoking 15 cigarettes a day. It was also more harmful than physical inactivity, and twice as harmful as obesity, they suggested.

The studies they examined measured the frequency of human interaction and tracked a range of health outcomes for an overall average period of 7.5 years. If the studies had also yielded data on quality of relationships, the authors suggest the impact of healthy social interaction on odds of survival could be higher than 50 per cent.

To work out the impact statistically, Holt-Lunstad and colleagues extracted an “effect size” from each study: this quantifies the difference between two groups, in this case, the likelihood of death between groups that differed in terms of their social ties.

Using a statistical method known as “random effects modeling” they then worked out the average effect size as an odds ratio (OR), which essentially expresses the chance of something happening (in this case death) in one group with the chance of it happening in another group, as a ratio.

Holt-Lunstad told the press that the data they analyzed only showed whether the participants were “integrated in a social network”; there wasn’t enough detail to enable them to separately examine the negative and positive effects of being in the network, “they are all averaged together,” she added.
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This post was submitted by Harshit Agrawal.

Thanks to www.medicalnewstoday.com