JMIR Publications

ABSTRACT

Background:

Simulation study results suggest that COVID-19 contact-tracing apps have the potential to achieve pandemic control. Concordantly, high app adoption rates were a stipulated prerequisite for success. Early studies on potential adoption were encouraging. Several factors predicting adoption rates were investigated, especially pertaining to user characteristics. Since then, several countries have released COVID-19 contact-tracing apps.

Objective:

This study’s primary aim was to investigate the quality characteristics of national European COVID-19 contact-tracing apps, thereby shifting attention from user to application characteristics. Secondly, associations between app quality and adoption were investigated. Finally, app features contributing to higher app quality were identified.

Methods:

Eligible COVID-19 contact-tracing apps were those released by national health authorities of European Union member states, former member states, and countries of the European Free Trade Association, all countries with comparable legal standards concerning personal data protection and app-usage voluntariness. The Mobile App Rating Scale (MARS) was utilized to assess app quality. An interdisciplinary team, consisting of two health and two human-computer-interaction scientists, independently conducted MARS ratings. To investigate associations between app quality and adoptions rates and measures of pandemic control, a Bayesian linear regression analyses were conducted.

Results:

We discovered 21 national COVID-19 contact-tracing apps, all demonstrating high quality overall, and high-level functionality, aesthetics, and information-quality. However, the average app-adoption rate, of 22.9% (SD 12.5%), was far below the level recommended by simulation studies. Lower levels of engagement-oriented app design were detected, with substantial variations between apps. By regression analyses, the best-case adoption rate (BCAR) was calculated by assuming apps achieve highest ratings. The mean BCARs for engagement and overall app quality were 40% and 44%, respectively. Higher adoption rates were associated with lower cumulative infection rates. Overall, we identified five feature categories (symptom assessment and monitoring, regularly-updated information, individualization, tracing, and communication) and 14 individual features that contributed to higher app quality. These 14 features were: a symptom checker, a symptom diary, statistics on COVID-19, app usage, public health instructions/restrictions, information of burden on healthcare system, assigning personal data, regional updates, control over tracing activity, contact diary, venue check-in, chats, helplines, and app-sharing capacity.

Conclusions:

European national health authorities have generally released high-quality COVID-19 contact-tracing apps, with regards to functionality, aesthetics, and information quality. However, the app’s engagement-oriented design generally was of lower quality, even though regression analyses results identify engagement as a promising optimization target to increase adoption rates. Associations between higher app-adoption and lower infection rates are consistent with simulation study results, albeit acknowledging that app usage might be part of a broader set of protective attitudes and behaviors for self and others. Various features were identified that could guide further engagement-enhancing app development.