More (#2) from Social Physics:

For the entire community, we measured activity levels by using the accelerometer sensors embedded in their mobile phones. Unlike typical social science experiments, FunFit was conducted out in the real world, with all the complications of daily life. In addition, we collected hundreds of thousands of hours and hundreds of gigabytes of contextual data, so that we could later go back and see which factors had the greatest effect.

On average, it turned out that the social network incentive scheme worked almost four times more efficiently than a traditional individual-incentive market approach. For the buddies who had the most interactions with their assigned target, the social network incentive worked almost eight times better than the standard market approach.

And better yet, it stuck. People who received social network incentives maintained their higher levels of activity even after the incentives disappeared. These small but focused social network incentives generated engagement around new, healthier habits of behavior by creating social pressure for behavior change in the community.

And:

Unexpectedly, we found that the factors most people usually think of as driving group performance—i.e., cohesion, motivation, and satisfaction—were not statistically significant. The largest factor in predicting group intelligence was the equality of conversational turn taking; groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn taking. The second most important factor was the social intelligence of a group’s members, as measured by their ability to read each other’s social signals. Women tend to do better at reading social signals, so groups with more women tended to do better...