Converting and expanding mobile support tools for tuberculosis treatment support: Design recommendations from domain...

Tuberculosis (TB) remains one of the top ten causes of death globally despite it being largely treatable. Poor adherence to treatment directly contributes to poor outcomes, such as, prolonged infectivity and the development of drug resistance. Mobile phone-based interventions have the potential to improve treatment outcomes.

The purpose of this study was to solicit design and domain expert feedback of a previously developed TB support intervention converted to a mobile application.

We used prototyping in iterative cycles that included integrating findings from prior formative research with endusers and soliciting feedback from design and content experts. In this project, we used low-fidelity prototype evaluation to inform the design of high-fidelity prototypes for further testing and iterative refinement.

We received 12 survey results. Overall, the participants agreed that the functions would be easy to learn/use. Recommendations for improvement included: simplify the reporting by offering broad categories; split complex screens to be more intuitive and user friendly; modify feedback graphics to display data more clearly; incorporate instructions for each task/function to guide users and collapse the information once users had viewed it; display navigation icons on each screen and add a main menu button; have medication tracker be homepage and limit redundancies. Several potential functionalities were suggested, such as adding a notes/journal and a social feature. We were able to easily incorporate recommendations and feedback into the high-fidelity prototypes and continue testing and refinement. After we came to a stable prototype through testing, we gave the interactive prototype to our developers to program a base functioning model.

The proposed design recommendations provide valuable insight to inform initial conversion of an interactive intervention to customize patient support, which include a smartphone app and a direct drug metabolite test reengineered for home use. We argue that iteratively developing low- and high-fidelity prototypes with content and design experts to guide initial programming of a functional beta app paves the way to better explore further refinement needs and recommendations with endusers rather than using hypothetical scenarios.