Collecting Health Data with Apple ResearchKit

Advantages & Limitations

By Renee Petrie | @reneepetrie

If you ask any researcher about the biggest challenges they face with observational research and health interventions, participant engagement and recruitment are usually at the top of the list. Apple forever changed this with its release of ResearchKit in April. By providing an open source framework that allows developers to create iPhone apps for conducting health research, Apple provided a direct channel to a huge population (over 44M iPhone users in the U.S. alone) of potential study participants.

By all appearances, people are willing and eager to crowdsource their health data to help advance medical research. This is due to the convenience of participating – a key element of sticky technology. ResearchKit’s core modules for a visual consent flow, survey engine, and active tasks allow interested study participants to easily go from downloading the study app to providing informed consent and Patient Reported Outcomes through surveys delivered through their iPhone.

Seamlessly integrating with HealthKit takes convenience one step further by enabling participants to contribute their Patient Generated Data being passively captured through their iPhone’s built-in capabilities (e.g., accelerometer, gyroscope, GPS, etc). These new health data sources provide subjective measures that can be captured on a more frequent and ongoing basis than with traditional self-reporting.

Limitations of Apple ResearchKit

These remarkable advancements do not come without key limitations which researchers must carefully evaluate when designing their study.  Although ResearchKit provides study participants with the convenience of participating via an iPhone app, it doesn’t give options for participating from other devices.  Being unable to acquire health data from non-iPhone users leads to one of the most often cited limitations: people who own iPhones are not representative of the general population. iPhone users in the U.S. are disproportionally more educated, white males with higher income.  This coverage bias may skew results and can be problematic for health research, especially on disease conditions where significant health disparities exist.

iPhone users are disproportionally more educated white males with higher income. This coverage bias may skew results and can be problematic for health research. {Click to Tweet}

Another potential limitation of the ResearchKit is the need for iOS programmers to develop a research study app. Apple’s release of an open source framework is admirable and will help to streamline development of research apps, however it’s impact may be limited because it is designed for iOS developers rather than today’s researcher.

Finally, data storage and security are important considerations to keep in mind before utilizing ResearchKit. The framework does not include a backend data storage or data management solution. By taking this approach, Apple provides researchers with the peace of mind that they maintain ownership and control over study data. The downside is that the researcher must identify a backend solution that provides the necessary privacy and security protections for their participant’s health data.

Accelerating Research with a Multi-mode Approach

Utilizing a multi-mode approach that combines ResearchKit with current online data capture solutions can maximize the ability to reach willing study participants while remaining convenient for them to provide their health data. Furthermore, a multi-mode approach can address the limitations of ResearchKit by:

  • Allowing researchers to reach participants on devices other than the iPhone, limiting coverage bias
  • Providing an interface for creating surveys and forms that can be accessed across devices, eliminating the need for costly iOS developer resources
  • Establishing a secure, centralized database that stores data from all sources, alleviating concerns of data security and privacy.

DatStat’s Research Kit module addresses the limitations of the Apple ResearchKit by enabling customers to publish DatStat surveys and study workflows in ResearchKit and all other devices. Learn how you can leverage this module to reach participants on a wider scale and accelerate your research by contacting us.


Renee2016

Renee helps customers apply DatStat technology solutions across health research and patient-centered population health management.  Before joining DatStat, she spent 17 years in working at nationally recognized research centers. While at University of Washington’s Social Development Research Group (SDRG), she set the standard for maximizing engagement and retention in landmark longitudinal studies. Renee now combines her practical research experience and a fascination with technology to help customers understand how to leverage technology in patient interactions to create ‘sticky’ relationships – ultimately achieving better engagement and outcomes.

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