{ "background": "Public health surveillance systems are critical for early disease detection and response in sub-Saharan Africa. However, their effectiveness is often limited by low adoption and inconsistent use by frontline health workers, with a paucity of rigorous field evidence on interventions to improve uptake.", "purpose and objectives": "This case study aimed to methodologically evaluate and compare the adoption rates of two distinct surveillance system interfaces among primary healthcare workers in a Nigerian state, using a randomised field trial design.", "methodology": "We conducted a cluster-randomised controlled trial across 60 primary healthcare centres. Centres were randomised to receive either a simplified, mobile-optimised reporting interface (intervention) or the existing standard web portal (control). Adoption was measured as the proportion of weekly reports submitted over a six-month period. The primary analysis used a generalised linear mixed model: $\\logit(P(Y{ij}=1)) = \\beta0 + \\beta1 X{ij} + ui + e{ij}$, where $Y{ij}$ is submission status for centre $i$ at time $j$, $X{ij}$ is the intervention indicator, and $u_i$ are random cluster effects.", "findings": "The mobile-optimised interface significantly increased adoption rates. The adjusted odds ratio for complete weekly reporting was 2.45 (95% CI: 1.78 to 3.36; p<0.001). The absolute difference in mean submission completeness was 28 percentage points (intervention: 89%, control: 61%).", "conclusion": "A user-centred redesign of surveillance system interfaces substantially improved adoption by frontline health workers under real-world conditions.", "recommendations": "National and state health ministries should prioritise human-centred design and mobile-first platforms when deploying or upgrading digital surveillance tools. Investment in iterative usability testing with end-users before system-wide rollout is critical.", "key words": "health surveillance, adoption, randomised trial, human-computer interaction, implementation science,