利用健康行为改变和技术接受模型预测 COVID-19 接触者追踪应用程序的采用:横断面调查研究。

Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study.

机构信息

Department Health and Prevention, Institute of Psychology, University of Greifswald, Greifswald, Germany.

出版信息

J Med Internet Res. 2021 May 19;23(5):e25447. doi: 10.2196/25447.

Abstract

BACKGROUND

To combat the global COVID-19 pandemic, contact tracing apps have been discussed as digital health solutions to track infection chains and provide appropriate information. However, observational studies point to low acceptance in most countries, and few studies have yet examined theory-based predictors of app use in the general population to guide health communication efforts.

OBJECTIVE

This study utilizes established health behavior change and technology acceptance models to predict adoption intentions and frequency of current app use.

METHODS

We conducted a cross-sectional online survey between May and July 2020 in a German convenience sample (N=349; mean age 35.62 years; n=226, 65.3% female). To inspect the incremental validity of model constructs as well as additional variables (privacy concerns, personalization), hierarchical regression models were applied, controlling for covariates.

RESULTS

The theory of planned behavior and the unified theory of acceptance and use of technology predicted adoption intentions (R=56%-63%) and frequency of current app use (R=33%-37%). A combined model only marginally increased the predictive value by about 5%, but lower privacy concerns and higher threat appraisals (ie, anticipatory anxiety) significantly predicted app use when included as additional variables. Moreover, the impact of perceived usefulness was positive for adoption intentions but negative for frequency of current app use.

CONCLUSIONS

This study identified several theory-based predictors of contact tracing app use. However, few constructs, such as social norms, have a consistent positive effect across models and outcomes. Further research is required to replicate these observations, and to examine the interconnectedness of these constructs and their impact throughout the pandemic. Nevertheless, the findings suggest that promulgating affirmative social norms and positive emotional effects of app use, as well as addressing health concerns, might be promising strategies to foster adoption intentions and app use in the general population.

摘要

背景

为了应对全球 COVID-19 大流行,接触者追踪应用程序已被讨论为数字健康解决方案,以追踪感染链并提供适当的信息。然而,观察性研究指出,在大多数国家,这些应用程序的接受程度较低,并且很少有研究基于理论来预测普通人群中应用程序使用的预测因素,以指导健康传播工作。

目的

本研究利用既定的健康行为改变和技术接受模型来预测采用意愿和当前应用程序使用频率。

方法

我们于 2020 年 5 月至 7 月期间在德国便利样本中进行了横断面在线调查(N=349;平均年龄 35.62 岁;n=226,65.3%为女性)。为了检查模型构念以及其他变量(隐私问题、个性化)的增量有效性,我们应用了分层回归模型,控制了协变量。

结果

计划行为理论和统一技术接受与使用理论预测了采用意愿(R=56%-63%)和当前应用程序使用频率(R=33%-37%)。一个综合模型仅略微增加了约 5%的预测值,但较低的隐私问题和较高的威胁评估(即预期焦虑)在将其作为附加变量包含在内时,显著预测了应用程序的使用。此外,感知有用性对采用意愿的影响为正,但对当前应用程序使用频率的影响为负。

结论

本研究确定了接触者追踪应用程序使用的几个基于理论的预测因素。然而,很少有构念,如社会规范,在模型和结果中具有一致的积极影响。需要进一步的研究来复制这些观察结果,并研究这些构念的相互关系及其在整个大流行期间的影响。尽管如此,研究结果表明,宣扬肯定的社会规范和应用程序使用的积极情感效应,以及解决健康问题,可能是促进普通人群采用意愿和应用程序使用的有前途的策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8df8/8136409/e8fe574e0c45/jmir_v23i5e25447_fig1.jpg

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