Feinstein Institute for Medical Research Assistant Professor Amit Baumel, PhD, has developed Enlight, a tool to evaluate the quality and therapeutic potential of computer and mobile-based eHealth applications. The tool could help end users (health consumers), health IT, researchers and clinicians compare different eHealth applications, identifying those with the most promising benefit for use. The testing and development of this tool are part of a paper published in the Journal of Medical Internet Research.
The wide distribution of personal digital devices has the potential to dramatically enhance public access to health interventions. As a result, tens of thousands of health, wellness, and medical apps are now available for download. The large number of eHealth intervention programs makes it impossible to evaluate them within traditional research study settings.
"Previous studies have shown that we can objectively evaluate eHealth interventions without performing testing with end users," said Dr. Baumel. "However, previous tools do not use measurements directly associated with therapeutic benefit, and were not tested for their ability to compare eHealth program types across different delivery mediums (mobile/computer platforms) and settings. More study is required to test the validity of such tools, but we think that Enlight has the potential to provide a clear measurement of whether an eHealth program can be effective - mainly due to the incorporation of quality measures that target app potential to encourage its users to create beneficial outcomes in their life and not only the potential to engage users with the app itself."
Dr. Baumel's team found that achieving a certain standard of quality in terms of the content being delivered or engaging design does not necessarily mean that this program is also designed to change people's behaviors in the real world, making the case to incorporate principles of persuasive design and therapeutic alliance qualities when evaluating such programs. His team plans to examine which quality aspects are better at predicting user behaviors and also testing the feasibility of an Enlight-based recommender system for clinicians and end-users.