{ "background": "The sustained adoption of public health surveillance systems in low-resource settings remains a critical challenge, with many digital health interventions failing to move beyond pilot phases. Evidence on the long-term drivers of adoption, particularly from randomised evaluations, is scarce.", "purpose and objectives": "This study aimed to quantify the long-term adoption rates of a digital public health surveillance system and to identify the key operational and contextual factors influencing its sustained use within a national health system.", "methodology": "A longitudinal, cluster-randomised field trial was conducted across 120 health facilities. Adoption was measured via system-use data logs and structured surveys administered at multiple time points. The primary analysis employed a mixed-effects logistic regression model: $\\logit(P(Y{it}=1)) = \\beta0 + \\beta1 Ti + \\beta2 X{it} + ui + \\epsilon{it}$, where $Y{it}$ is adoption at facility $i$ at time $t$, $Ti$ is the treatment assignment, and $X_{it}$ are time-varying covariates. Robust standard errors were clustered at the facility level.", "findings": "Findings are pending as the longitudinal trial is ongoing; final data collection and analysis will conclude in . Preliminary analysis of interim data indicates a strong positive association between the provision of dedicated technical support and system adoption, with supported facilities showing a 40% higher odds of sustained use (95% CI: 1.15 to 1.72).", "conclusion": "Final conclusions will be drawn upon trial completion. Interim evidence suggests that infrastructural interventions alone are insufficient for sustained adoption, with dedicated human resource support being a potentially critical moderating factor.", "recommendations": "Programme designers should prioritise funding for ongoing technical support roles alongside digital infrastructure. Policymakers should integrate adoption metrics with standard health system performance assessments.", "key words": "digital health, implementation science, health systems strengthening, cluster randomised trial, sustainability, health