Designing an Appropriate Climate Health Interface for Guided Self-Diagnosis in Low-income Communities within Nigeria

In Nigeria, many climate-related illnesses affect the health and well-being of vulnerable populations in low income communities. Due to high upfront costs and substandard facilities, low-income healthcare relies on self-diagnoses and self-medication which is often inaccurate. Furthermore, due to high illiteracy levels, multiple Nigerian languages, and a low technology adoption rate - especially in older generations - traditional telemedicine application interfaces may be unsuccessful in promoting easy usage and comprehension by the target population. Consequently, this research proposes the development of an algorithmically guided self-diagnostic tool to increase accuracy in self-diagnosis and provide more affordable means to healthcare. The preliminary process described in this paper involves determining the appropriate form of information transfer to the multiple user types within this study population. A remote user-interview was conducted to evaluate the efficiency of visuals, text, and a combination of both, in representing medical and technical jargon. Findings indicated that a combination of visuals and text in lay terms was most effective in communication, and the placement of the text in the background was more efficient at information transfer and comprehension of technical jargon and descriptions. These findings will support next steps for selecting an appropriate self-diagnostic interface for Nigeria.