Use of Framework Matrix and Thematic Coding Methods in Qualitative Analysis for mHealth: NIRUDAK Study Data

Objective: Framework Matrix Analysis (FMA) and Applied Thematic Analysis (ATA) are qualitative methods that have not been as widely used/cited compared to content analysis or grounded theory. This paper compares methods of FMA with ATA for mobile health (mHealth) research. The same qualitative data were analyzed separately, using each methodology. The methods, utility, and results of each are compared, and recommendations made for their effective use.

Methods: Formative qualitative data were collected in eight focus group discussions with physicians and nurses from three hospitals in Bangladesh. Focus groups were conducted via video conference in the local language, Bangla, and audio recorded. Audio recordings were used to complete a FMA of participants' opinions about key features of a novel mHealth application (app) designed to support clinical management in patients with acute diarrhea. The resulting framework matrix was shared with the app design team and used to guide iterative development of the product for a validation study of the app. Subsequently, focus group audio recordings were transcribed in Bangla then translated into English for ATA; transcripts and codes were entered into NVivo qualitative analysis software. Code summaries and thematic memos explored the clinical utility of the mHealth app including clinicians' attitudes about using this decision support tool.

Results: Each of the two methods contributes differently to the research goal and have different implications for an mHealth research timeline. Recommendations for the effective use of each method in app development include: using FMA for data reduction where specific outcomes are needed to make programming and design decisions and using ATA to capture the more nuanced issues that guide use, product implementation, training, and workflow.

Conclusions: By describing how both analytical methods were used in this context, this paper provides guidance and an illustration for use of these two methods, specifically in mHealth design.