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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 2, 2020
Date Accepted: Apr 11, 2021
Date Submitted to PubMed: Apr 21, 2021

The final, peer-reviewed published version of this preprint can be found here:

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

Tomczyk S, Barth S, Schmidt S, Muehlan H

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

J Med Internet Res 2021;23(5):e25447

DOI: 10.2196/25447

PMID: 33882016

PMCID: 8136409

#StrongerTogether – Utilizing Health Behavior Change and Technology Acceptance Models to Predict Adoption of COVID-19 Contact Tracing Apps: A Cross-Sectional Survey

  • Samuel Tomczyk; 
  • Simon Barth; 
  • Silke Schmidt; 
  • Holger Muehlan

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 a 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:

Therefore, 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; 65% female). To inspect 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 (R2=56% to 63%) and frequency of current app use (R2=33% to 37%). A combined model only marginally increased predictive value by about 5%, but lower privacy concerns and higher threat appraisals (i.e. 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, 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.


 Citation

Please cite as:

Tomczyk S, Barth S, Schmidt S, Muehlan H

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

J Med Internet Res 2021;23(5):e25447

DOI: 10.2196/25447

PMID: 33882016

PMCID: 8136409

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