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

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-b...

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Autores principales: Tomczyk, Samuel, Barth, Simon, Schmidt, Silke, Muehlan, Holger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136409/
https://www.ncbi.nlm.nih.gov/pubmed/33882016
http://dx.doi.org/10.2196/25447
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author Tomczyk, Samuel
Barth, Simon
Schmidt, Silke
Muehlan, Holger
author_facet Tomczyk, Samuel
Barth, Simon
Schmidt, Silke
Muehlan, Holger
author_sort Tomczyk, Samuel
collection PubMed
description 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(2)=56%-63%) and frequency of current app use (R(2)=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.
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spelling pubmed-81364092021-05-25 Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study Tomczyk, Samuel Barth, Simon Schmidt, Silke Muehlan, Holger J Med Internet Res Original Paper 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(2)=56%-63%) and frequency of current app use (R(2)=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. JMIR Publications 2021-05-19 /pmc/articles/PMC8136409/ /pubmed/33882016 http://dx.doi.org/10.2196/25447 Text en ©Samuel Tomczyk, Simon Barth, Silke Schmidt, Holger Muehlan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.05.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Tomczyk, Samuel
Barth, Simon
Schmidt, Silke
Muehlan, Holger
Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study
title Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study
title_full Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study
title_fullStr Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study
title_full_unstemmed Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study
title_short Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study
title_sort utilizing health behavior change and technology acceptance models to predict the adoption of covid-19 contact tracing apps: cross-sectional survey study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136409/
https://www.ncbi.nlm.nih.gov/pubmed/33882016
http://dx.doi.org/10.2196/25447
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