JMIR Publications

ABSTRACT

With thousands of mHealth solutions on the market, patients and healthcare providers struggle to identify which solution to use/prescribe. The lack of evidence-based mHealth solutions may be due to limited research on intervention development and continued use of traditional research methods for mHealth evaluation. The Multiphase Optimization Strategy (MOST) is a framework which aids in developing interventions which are economical, affordable, scalable, and effective (EASE). MOST Phase I highlights the importance of formative intervention development, a stage often overlooked and rarely published. The aim of MOST Phase I is to identify candidate intervention components, create a conceptual model, and define the optimization objective. While MOST sets these three targets, the framework itself does not provide robust guidance on how to conduct quality research within Phase I, and what steps can be taken to identify potential intervention components, develop the conceptual model, and achieve intervention EASE with the implementation context in mind. To advance the applicability of MOST within the field of implementation science, this paper provides an account of the methods used to develop an mHealth intervention. Specifically, we provide a comprehensive example of how to achieve the goals of MOST Phase I by outlining the formative development of an mHealth prompting intervention within a diabetes prevention program. Additionally, recommendations are proposed for future researchers to conduct formative research on mHealth interventions with implementation in mind. Given its considerable reach, mHealth has the potential to positively impact public health by decreasing implementation costs and improving accessibility. MOST is well-suited for the efficient development and optimization of mHealth interventions. By using an implementation-focused lens and outlining the steps in developing an mHealth intervention using MOST Phase I, this work can may guide future intervention developers towards maximizing the impact of mHealth outside of the research laboratory.