<p>Basic AitaHealth Process Flow.</p>

Mobile health (mHealth) technologies are increasingly used to support community-based healthcare. However, their real-world impact often remains unclear. Understanding implementation factors is essential for advancing their use and achieving meaningful health outcomes. We evaluated an mHealth tool (AitaHealth) after customising its modules and workflows for household Tuberculosis (TB) contact tracing and other community-based data collection by community health workers (CHWs). We describe the acceptability, feasibility, and implementation fidelity of this approach. We conducted a mixed-methods evaluation in two South African districts: uMkhanyakude and Ekurhuleni. We collected qualitative data through focus group discussions (FGDs) and in-depth interviews (IDIs) with CHWs, team leaders, and key stakeholders. We used deductive thematic analysis grounded in the Technology Acceptance Model (TAM) to assess the acceptability and implementation feasibility of the mHealth tool. We used quantitative data from the AitaHealth metadata to assess implementation fidelity. CHWs appreciated AitaHealth’s efficiency, data security, and credibility. Across the two districts, 103 CHWs recorded data for 2,452 households and 10,649 household members. However, they reported challenges in ease of use, with unreliable devices, weak support, and safety concerns hindering data collection. These issues led to inconsistent engagement, with 48.5% of CHWs logging in fewer than 15 times during implementation. Despite these challenges, when used, AitaHealth ensured high-quality data collection and household coverage, with TB-related fields completed in over 94% of households, demonstrating its potential under better conditions. AitaHealth`s limitations stemmed from system constraints rather than user resistance. To achieve full impact, mHealth tools require reliable infrastructure and supportive environments for both the tools and their implementers.