Your circle of friends is more predictive of your health, study finds

研究发现，你的朋友圈对你的健康更有预见性

上海译锐翻译         2019-6-21            15:47 p.m.

© Drobot Dean / Adobe Stock

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.

可穿戴的健身追踪器让我们对自己的健康判断变得非常容易。我们可以通过我们的心律来判断我们在今天早上的工作汇报中是不是真的感到紧张，或根据我们在一天结束前所行走的步数而认为自己更加健康。

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.

发表在公共科学图书馆期刊PLOS ONE中的一项新研究发现，要想进一步了解你整体的健康状况和幸福程度，则最好查看一下你朋友圈的优势和结构。

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.

尽管此前的研究已经表明信仰、观念以及态度以何种方式在社交网络中流传，但是圣母大学的研究人员却对社交网络结构对健康、幸福和压力的表达感兴趣。

"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."

圣母大学计算机科学和工程系Frank M. Freimann教授、网络科学和应用跨学科中心主任以及本研究的首席作者Nitesh V. Chawla表示：“我们对社交网络的拓扑结构感兴趣-我在社交网络中的位置对我的健康和幸福有怎样的预测？我们所发现的是，与仅仅使用可穿戴设备中的数据，比如步数或心律相比，社交网络结构极大地改善了个人幸福状况的可预测性。”

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 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.

为开展研究，参与者通过佩戴Fitbits来记录健康行为数据，比如步数、睡眠、心律以及活动水平-并针对压力、幸福以及积极心态完成问卷调查和自我评估。Chawla和他的团队随后利用机器学习技术和个人的社交网络特点，包括程度、中心性、集聚系数以及各种三角形对这一数据进行分析和模拟。这些特性表明了连接度、社会平衡、相互作用以及在社交网络中亲密度方面的特性。研究表明，社交网络结构、心律、步数以及活动程度之间有非常紧密的相关性。

Social network structure provided significant improvement in predicting one's health and well-being compared to just looking at health behavior 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 percent improvement in predicting happiness, 54 percent improvement in predicting one's self-assessed health prediction, 55 percent improvement in predicting positive attitude, and 38 percent improvement in predicting success.

与仅仅通过Fitbis查看健康行为数据相比，社交网络结构极大地改善了一个人健康和幸福的可预测性。比如说，当社交网络结构与可穿戴设备中的数据相结合时，机器学习模块在预测幸福、一个人的自我评估健康、积极态度和成功方面分别提高了65%、54%、55%和38%。

"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.

Chawla表示：“该研究声称，如果没有社交网络信息，我们则无法了解到一个人幸福状态的全貌。要想完全预测并能够推导出干预措施，同时了解社交网络结构特点至关重要。”

The findings could provide insight to employers who look to wearable fitness devices to incentivize 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.

研究结果能够让希望通过可穿戴健身设备来激励员工改善健康的雇主有更为深刻的看法。为某人提供一个可以记录步数并监控健康的设备并希望以此来改善他们的健康也许还无法看到有意义或有效的结果。Chawla表示，鼓励员工建立一个平台并在这个平台记录并分享他们的体会可以让雇主获益。社交网络结构可以让我们更全面的了解健康和幸福状况。

"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?"

Chawla认为：“我相信，我们在工作中所采取的激励机制是有意义的，但是我也相信，我们之所以没有看到结果，是因为我们可能没有按照正确的方式去利用他们。当我们听到由可穿戴设备所推动的健康和幸福计划在工作场所并未奏效时，我们应该问问自己，是不是因为我们只是采取了一个单一的视角。在这个视角中，我们只是为员工提供了可穿戴设备，并随之将其抛在脑后并且没有采取行动去思考社交网络在健康中所起到的作用。”

The study was funded by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health and the National Science Centre, Poland.

本研究获得了美国国家卫生研究院国家心肺血液研究所以及波兰国家科学中心的资助。

需要了解的词：

Circle of friends:朋友圈

Wearable fitness tracker：可穿戴的健身追踪器

Better off+doing sth.:最好去做某事

文章来源：科学日报  编辑：质控部Susan