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

研究發現，你的朋友圈對你的健康更有預見性

上海譯銳翻譯         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.

可穿戴的健身追蹤器讓我們對自己的健康判斷變得非常容易。我們可以通過我們的心律來判斷我們在今天早上的工作匯報中是不是真的感到緊張，或根據我們在一天結束前所行走的步數而認為自己更加健康。

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.

發表在公共科學圖書館期刊PLOS ONE中的一項新研究發現，要想進一步了解你整體的健康狀況和幸福程度，則最好查看一下你朋友圈的優勢和結構。

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.

盡管此前的研究已經表明信仰、觀念以及態度以何種方式在社交網絡中流傳，但是圣母大學的研究人員卻對社交網絡結構對健康、幸福和壓力的表達感興趣。

"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."

圣母大學計算機科學和工程系Frank M. Freimann教授、網絡科學和應用跨學科中心主任以及本研究的首席作者Nitesh V. Chawla表示：“我們對社交網絡的拓撲結構感興趣-我在社交網絡中的位置對我的健康和幸福有怎樣的預測？我們所發現的是，與僅僅使用可穿戴設備中的數據，比如步數或心律相比，社交網絡結構極大地改善了個人幸福狀況的可預測性。”

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.

為開展研究，參與者通過佩戴Fitbits來記錄健康行為數據，比如步數、睡眠、心律以及活動水平-并針對壓力、幸福以及積極心態完成問卷調查和自我評估。Chawla和他的團隊隨后利用機器學習技術和個人的社交網絡特點，包括程度、中心性、集聚系數以及各種三角形對這一數據進行分析和模擬。這些特性表明了連接度、社會平衡、相互作用以及在社交網絡中親密度方面的特性。研究表明，社交網絡結構、心律、步數以及活動程度之間有非常緊密的相關性。

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.

與僅僅通過Fitbis查看健康行為數據相比，社交網絡結構極大地改善了一個人健康和幸福的可預測性。比如說，當社交網絡結構與可穿戴設備中的數據相結合時，機器學習模塊在預測幸福、一個人的自我評估健康、積極態度和成功方面分別提高了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：“該研究聲稱，如果沒有社交網絡信息，我們則無法了解到一個人幸福狀態的全貌。要想完全預測并能夠推導出干預措施，同時了解社交網絡結構特點至關重要。”

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.

研究結果能夠讓希望通過可穿戴健身設備來激勵員工改善健康的雇主有更為深刻的看法。為某人提供一個可以記錄步數并監控健康的設備并希望以此來改善他們的健康也許還無法看到有意義或有效的結果。Chawla表示，鼓勵員工建立一個平臺并在這個平臺記錄并分享他們的體會可以讓雇主獲益。社交網絡結構可以讓我們更全面的了解健康和幸福狀況。

"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認為：“我相信，我們在工作中所采取的激勵機制是有意義的，但是我也相信，我們之所以沒有看到結果，是因為我們可能沒有按照正確的方式去利用他們。當我們聽到由可穿戴設備所推動的健康和幸福計劃在工作場所并未奏效時，我們應該問問自己，是不是因為我們只是采取了一個單一的視角。在這個視角中，我們只是為員工提供了可穿戴設備，并隨之將其拋在腦后并且沒有采取行動去思考社交網絡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.

本研究獲得了美國國家衛生研究院國家心肺血液研究所以及波蘭國家科學中心的資助。

需要了解的詞：

Circle of friends:朋友圈

Wearable fitness tracker：可穿戴的健身追蹤器

Better off+doing sth.:最好去做某事

文章來源：科學日報  編輯：質控部Susan