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用于理解个体对新冠疫情政策的遵守情况及个性化助推的数字表型:纵向观察性研究

Digital Phenotypes for Understanding Individuals' Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study.

作者信息

Ibrahim Ahmed, Zhang Heng, Clinch Sarah, Poliakoff Ellen, Parsia Bijan, Harper Simon

机构信息

Department of Computer Science, The University of Manchester, Manchester, United Kingdom.

出版信息

JMIR Form Res. 2021 May 27;5(5):e23461. doi: 10.2196/23461.

Abstract

BACKGROUND

Governments promote behavioral policies such as social distancing and phased reopening to control the spread of COVID-19. Digital phenotyping helps promote the compliance with these policies through the personalized behavioral knowledge it produces.

OBJECTIVE

This study investigated the value of smartphone-derived digital phenotypes in (1) analyzing individuals' compliance with COVID-19 policies through behavioral responses and (2) suggesting ways to personalize communication through those policies.

METHODS

We conducted longitudinal experiments that started before the outbreak of COVID-19 and continued during the pandemic. A total of 16 participants were recruited before the pandemic, and a smartphone sensing app was installed for each of them. We then assessed individual compliance with COVID-19 policies and their impact on habitual behaviors.

RESULTS

Our results show a significant change in people's mobility (P<.001) as a result of COVID-19 regulations, from an average of 10 visited places every week to approximately 2 places a week. We also discussed our results within the context of nudges used by the National Health Service in the United Kingdom to promote COVID-19 regulations.

CONCLUSIONS

Our findings show that digital phenotyping has substantial value in understanding people's behavior during a pandemic. Behavioral features extracted from digital phenotypes can facilitate the personalization of and compliance with behavioral policies. A rule-based messaging system can be implemented to deliver nudges on the basis of digital phenotyping.

摘要

背景

政府推行社交距离和分阶段重新开放等行为政策以控制新冠病毒病(COVID-19)的传播。数字表型分析通过其产生的个性化行为知识有助于促进对这些政策的遵守。

目的

本研究调查了源自智能手机的数字表型在以下两方面的价值:(1)通过行为反应分析个体对COVID-19政策的遵守情况;(2)提出通过这些政策实现个性化沟通的方法。

方法

我们开展了纵向实验,实验在COVID-19疫情爆发前开始并在疫情期间持续进行。在疫情大流行之前共招募了16名参与者,为他们每人安装了一款智能手机传感应用程序。然后我们评估了个体对COVID-19政策的遵守情况及其对习惯行为的影响。

结果

我们的结果显示,由于COVID-19相关规定,人们的出行出现了显著变化(P<0.001),从平均每周前往10个地方降至每周约2个地方。我们还在英国国家医疗服务体系用于推广COVID-19相关规定的助推措施背景下讨论了我们的结果。

结论

我们的研究结果表明,数字表型分析在理解大流行期间人们的行为方面具有重要价值。从数字表型中提取的行为特征可以促进行为政策的个性化和对其的遵守。可以实施一个基于规则的信息传递系统,以便根据数字表型分析提供助推。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e31a/8163492/b1f947220b75/formative_v5i5e23461_fig1.jpg

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