ResearchKit: 7 Implications For Developers

ResearchKit has finally arrived for iOS devices. Back at the launch of the Apple Watch and the new Macbook Apple also announced a new open source platform for Medical research called ResearchKit. They spoke about the possibilities and how this would be a true step forward in Medical Research, however the details of how this would come together were few and far between (read our post ReasearchKit: 3 Reasons For Pharma to be Optimistic)

Now ResearchKit has finally been launched and the developer kit made available for all. Having had a chance to work through the sample and also read through the extensive support documentation here are my 7 thoughts thoughts on the impact that ResearchKit will have on developers:

1. A simplified registration process

The workflow to get someone to sign up to your research project is already in place. This includes explaining the purpose of the study, the time commitment required from the user and what activities you will be asking the user to participate in. The framework also presents your terms and conditions and presents a confirmation dialogue. The final part of the registration process gathers a signature from the user to authorise their participation in the programme. As a developer you can use this framework off the shelf and simply add your own copy (and get approval from the appropriate ethics committee of course).

2. Standard tasks

Apple are already providing a number of standard tasks that you can simply include in your research project. This include a two finger tap test, the 6 minute walk test, a spacial memory test, audio test and a short walk test to measure gait and balance. These tasks include all the steps the user will need to complete in order to complete the test, these are supplied as clear instructions to the user and include Siri audio instructions and vibrations as well where required.

3. Custom tasks 

You can also use the framework to build your own tasks using the same framework that is used for the standard tasks. This gives developers the chance to create their own tasks for users to complete but tap into the standard data logging and export features that are part of the ResearchKit framework.

4. Standard data format

All of the data that is generated by the tasks is stored in a standard JSON format that you can then export to your server and process as part of your research project. ResearchKit does not provide any analysis of the data for you so it is down to you to develop. If you wish to provide instant results to your user then you will need to develop this within the app. But as this is aimed at large research projects the vast majority of the analysis will of course be done with the aggregated data that you have sent to your back end server.

5. Access to the sensors on the device

As part of the tasks you get access to raw data gathered by items such as the microphone, accelerometer, core motion chip and the pedometer. Additionally if the user has an Apple Watch you will also be able to gather data such as pulse rate from its sensors (read our post 5 Reasons Pharma Should Care About Apple Watch)

6. Integration with Health

You will be able to directly link with Health in order to gain access to external data sources such as calories, sleep, blood pressure that the user may have entered into Health or linked into Health from 3rd party apps that they are using for tracking their own health and lifestyle.

7. Its Open Source

In a major departure from the normal for Apple, ResearchKit has been release as Open Source via GitHub. This means that researchers and developers will be able to expand ResearchKit. The huge advantage of this is that we are likely to see more standard tasks being added as they are developed by the community.

Clearly ResearchKit opens up a great number of opportunities for pharmaceutical companies and research institutions. Distribution via the app store means that there is the potential to reach a far wider audience (there are 700 million iPhone owners in the world) and obtain research data from people who may not otherwise have ever dreamt of taking part in a clinical trial.