mHealth has the potential to improve precision medicine with applications and wearables to collect patient data at a low-cost, but the technology often goes underutilized. In a recent evaluation by the Duke University Margolis Center for Health Policy, researchers developed recommendations to improve mHealth use in clinical research.

mHealth adoption for clinical use allows data to be reused as evidence in future studies. The path to achieving secure and reliable data for validation is a rocky one. In response, Duke assembled a team of experts to develop recommendations for creating collaborative communities for the advancement of data collection, enable mHealth developers to build guidelines and ensure users understand how data is being used.

Recommendations listed in the report included:

1. Establish a learning community to develop patient/consumer mHealth technologies for data creation. Create a list of priority areas that could benefit most from mHealth.
2. Provide research-capable designs to mHealth companies to save time and improve marketability to analyze patient/consumer feedback to encourage long-term use. Use platforms that provide open source information on technology standards like Open mHealth, Github, ResearchKit and ResearchStack.
3. Access to standardized health data should be ensured. The continuous development of standards should be routinely updates, such as the physical activity and sleep monitor standards set by the Consumer Technology Association.
4. Use mHealth technologies to collect feedbacks and insights on research from participants. Academic journals should be able to allow researchers to send study participants articles for free.
5. Use mHealth to encourage participation in research by utilizing awareness and adoption of standard practices for consent. Using mobile-read frameworks like Eureka Research Platform, Hugo, and Sage’s Participant-Centered Consent toolkit for enrollment and informed consent.

“This is a crucial time for mHealth, with growing attention to overcoming the challenges of interoperability, common data elements, and data definitions in order to allow disparate data streams to combine to create actionable insights for improving or maintaining an individual’s health and treating disease,” stated the report.