Advancing the field of mHealth with mEvidence

Oct 7, 2011, 12:40 PM

In August, Pioneer's Al Shar shared his takeaways from the 2011 mHealth Evidence Workshop that we sponsored along with NIH, NSF and the McKesson Foundation. In that post, Al mentioned that the participants were eagerly putting together a statement of direction and would soon publish the key outcomes of the meeting.

We are pleased to report that the group has since shared those thoughts, which we have included below. Additionally, we encourage you to watch the archived webcast of the event.

Let us know how you think the mHealth ecosystem can be strengthened to deliver transformational improvements in the research and practice of health and well-being.

National and global scientists, policymakers, health professionals, technologists, and representatives from regulatory and funding agencies gathered for the invited mHealth Evidence Workshop at the National Institutes of Health August 2011 to discuss and identify more effective methods to generate evidence of efficacy and effectiveness for the unique emerging science of mobile health (mHealth).  mHealth draws from medical and clinical research, behavioral theory, user interface design, sensing technology, computer science and statistical inference to improve health outcomes. The meeting was sponsored by the Pioneer Portfolio of the Robert Wood Johnson Foundation, the McKesson Foundation, the Office of Behavioral and Social Sciences and National Heart, Lung and Blood Institute at the National Institutes of Health, and the National Science Foundation. The overall conclusion of workshop participants was that mHealth has great potential to support health and well-being worldwide, and, therefore, there is a need to enhance its scientific foundation. mHealth tools and interventions must be backed up by rigorous scientific development, evaluation, and evidence generation to enhance meaningful innovation and best practices, and to validate tools and methods for health professionals, consumers, payers, governments, and industry.

Meeting participants also concluded that the science of mHealth must use and further develop systematic research methods adapted to the technology, clinical or program intervention, in addition to analytic methods to process the vast amounts of streaming, tagged, complex and layered data that becomes available using mHealth technologies. 

This spectrum of methods will need to include not only randomized clinical trials, potentially optimized to leverage mHealth advancements, but also alternative study designs and methodologies that  ensure that research studies are able to provide timely information within a rapidly evolving field.  Evaluation methods that incorporate principles of existing study methodologies, including randomization, step-wedge design, n-of-1 trials, and Practice-Based-Evidence (PBE) methodology were discussed, in addition to methods that borrow from engineering, including Multiphase Optimization Strategy (MOST) and Sequential, Multiple Assignment  Randomized Trials (SMART).  Ethical issues related to collection, storage and use of real-time masses of identifiable personal data were also acknowledged as topics requiring updated guidance.

As a follow up to the workshop, participants are identifying and developing the methods needed to best generate mHealth evidence. They are forming working groups to engage the mHealth community in developing a research agenda centered on design methodology, analytic methods, and mHealth technologies. These efforts will support a rigorous and innovative mHealth ecosystem with promise to deliver transformational improvements in the research and practice of health and well-being.