LAUSR

Although mobile health (mHealth) technologies have shown promise in improving clinical care in resource-limited settings (RLS), they are infrequently brought to scale. One limitation to the success of many mHealth interventions is inattention to end-user acceptability, which is an important predictor of technology adoption. We conducted in-depth interviews with 43 people living with HIV in rural Uganda who had participated in a clinical trial of a short messaging system (SMS)-based intervention designed to prompt return to clinic after an abnormal laboratory test. Interviews focused on established features of technology acceptance models, including perceived ease of use and perceived usefulness, and included open-ended questions to gain insight into unexplored issues related to the intervention’s acceptability. We used conventional (inductive) and direct content analysis to derive categories describing use behaviors and acceptability. Interviews guided development of a proposed conceptual framework, the technology acceptance model for resource-limited settings (TAM-RLS). This framework incorporates both classic technology acceptance model categories as well as novel factors affecting use in this setting. Participants described how SMS message language, phone characteristics, and experience with similar technologies contributed to the system’s ease of use. Perceived usefulness was shaped by the perception that the system led to augmented HIV care services and improved access to social support from family and colleagues. Emergent themes specifically related to mHealth acceptance among PLWH in Uganda included (1) the importance of confidentiality, disclosure, and stigma, and (2) the barriers and facilitators downstream from the intervention that impacted achievement of the system’s target outcome. The TAM-RLS is a proposed model of mHealth technology acceptance based upon end-user experiences in rural Uganda. Although the proposed model requires validation, the TAM-RLS may serve as a useful tool to guide design and implementation of mHealth interventions.