Native vs PWAs – An overview for health researchers

When health researchers begin planning a digital intervention, they usually begin with the idea to develop ‘an app,’ without necessarily being aware there is a choice to make on what type of app platform: native apps and progressive web apps (PWAs). Choosing native versus PWA is a critical choice that will have substantial impacts on development costs and sustainability. In this post, we’ll introduce the features and functionality of native apps versus PWAs and analyse the various pros and cons respective to the needs of academic researchers working on health promotion projects.

What are native and PWAs?

Most people are familiar with native apps (even if they don’t know what they are called). Native apps are the apps you can download from app stores, such as the Apple Store and Google Play. A progressive web app (PWA) is a website that is built to look, feel, and act like a native app. It can be accessed via a web browser or downloaded to the user’s phone, tablet, or computer.

Differences in functionality

When it comes to functionality, native and PWAs have nearly identical capabilities. Once installed, there are only minor differences in the way an app is launched or used.

*Currently, Apple phones do not support the ability for PWAs to send push notifications. However, they have recently announced that this feature will be released in 2023.
**PWAs can be packaged for Apple/Google Play stores using web languages such as React Native or by using services such as PWABuilder.

Differences in cost and sustainability

Native apps are written in languages specific to Apple or Android platforms, while a PWA is written in the same languages used to build websites. This means that a PWA will work on any device that has a browser, while a native app needs to be created for each platform. This is one reason why native apps are more expensive to build than PWAs. A moderately complex native digital health app may cost around $200,000 USD to build for iPhones and Android phones. In contrast, a PWA can be nearly half as expensive to build.

PWAs also have an advantage in sustainability. Native apps must be continuously updated throughout the year to meet the requirements of operating system updates from Apple and Google – this typically costs about 10% of an app’s initial development cost (e.g., $20,000 a year for a $200,000 app). PWAs, on the other hand, usually costs less than $1,000 USD a year to host and maintain.

Considerations for data and privacy

Native apps are subject to the terms and conditions of the Apple and Google Play stores, especially with respect to app data usage and storage. Generally, these terms and conditions have been evolving to better protect users’ privacy. For example, Apple recently updated its App Store Review Guidelines, stating that apps allowing for account creation must also allow users to automatically delete their accounts and all personal data from within the app. This means that all data collected on an individual must be automatically (not manually) deleted from where it is stored. For a research app, this would likely include any participant data collected via surveys (e.g., RedCap, Qualtrics), and stored on university research data drives. This presents obvious challenges for apps used for research purposes.

Working with users’ request to delete data is easier when using a PWA, as the best practise privacy guidelines, such as GDPR, allow for users to send requests to delete data and allow for up to a month to complete data deletion. This process is more in line with most researchers’ current practises for participant withdrawal.

Summary

While PWAs are relatively new to app platform conversation, they are quickly gaining wider use across a range of industries and use cases. They potentially offer a better choice for academic researchers conducting digital health interventions – as their small disadvantages in functionality are usually outweighed by their substantially lower costs, easier maintenance, and more practical integration into usual study data management practices.

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