Beta version of mHealth evidence database to go live soon

The Center for Communication Programs at Johns Hopkins University's Bloomberg School of Public Health is developing an online mHealth Evidence Database that James BonTempo, the Center's Director of ICT & Innovation, hopes will one day serve as a global resource for the worldwide mobile healthcare community.

A U.S. Agency for International Development-funded Knowledge for Health (K4Health) project, the database has an ambitious goal: to catalog, categorize and grade all of the known peer-reviewed and grey literature on mHealth in high-, middle- and low-income countries.   

BonTempo told FierceMobileHealthcare he is the first to admit that "mHealth is a young field, and the evidence base for its effectiveness and cost-effectiveness is still limited." However, he argues that "there is evidence out there but it's not always easy to find it and it's not always clear where to go to find that evidence."

Moreover, BonTempo believes the failure to incorporate known best practices or to learn from past failures reduces the effectiveness of new mHealth interventions, and resources may be wasted on mHealth interventions that have already been shown to be ineffective or not cost-effective. By the same token, he warns that mHealth interventions that do have a solid evidence base may not receive the funding they deserve due to a lack of awareness.

In order to address these challenges, the mHealth Evidence database (mHE) will contain information on peer-reviewed literature as well as the broad array of grey literature, including evaluations, project reports, white papers, blog posts and discussion boards, among other sources. In addition, the database will be curated using strict but open and explicit inclusion criteria.

"While we will encourage the submission of resources for consideration, we do not plan to allow for automatic uploading by users," states BonTempo. "Each potential evidence source will proceed through a workflow to be classified using a harmonized taxonomy and assessed using expert, consensus-based criteria, ultimately enabling easy, flexible and comprehensive searching, sorting, and filtering by virtually any relevant metadata."

The taxonomy for categorizing the evidence database and the criteria for assessing it are being developed in collaboration with a World Health Organization-sponsored mHealth Technical and Evidence Review Group. This classification model will be used to tag the resources to make it easier to search the database. Currently, a public alpha version of the database site exists including approximately 1,500 records, with the public beta version planned for release in a few weeks with more than 2,000 records. 

"I think we'll soon find ourselves inundated with a lot of evidence. And, hopefully, a lot of the kind of evidence that we really need to be able to make informed decisions about not just how to design and implement projects but also more on the policy level and decision-making level," he asserts.  

According to BonTempo, the inability to access high-quality mHealth evidence quickly and easily limits the ability of Ministries of Health, multilateral institutions and donors to make well-informed decisions about which mHealth interventions merit additional investment, and creates uncertainty on the part of innovators, implementers and researchers regarding which mHealth interventions to include in their field-based programs. 

"We saw this need to create a tool that would allow us to effectively uncover or unearth in some ways the existing evidence, but also to be a registry for the evidence that will be coming down the pike in the future, creating one place that really starts to pull together all of these different sources of 'evidence' for mHealth," he states. 

Nevertheless, a recent article in PLoS Medicine found that despite hundreds of mHealth pilot studies there has been "insufficient programmatic evidence to inform implementation and scale-up of mHealth." After more than 500 pilot studies tracked by the World Bank, the article concludes that, "we know almost nothing about the likely uptake, best strategies for engagement, efficacy, or effectiveness of these initiatives." As a result, the authors say mHealth interventions "lack a foundation of basic evidence, let alone a foundation that would permit evidence-based scale up."