This thesis was developed within a collaborative project between the University of Essex and Politecnico di Torino and focuses on strengthening the digital infrastructure of the Happy Again platform (https://happyagain.essex.ac.uk/). The platform is a web-based research tool designed to investigate the long-term neurocognitive consequences of COVID-19 by collecting behavioural and cognitive markers through online tasks and questionnaires. It enables the assessment of attention, perception, response timing, and cognitive processing in individuals experiencing post-COVID conditions. The work presented in this thesis involved coordinated development across backend and frontend components to improve system stability, data integrity, and research reproducibility. On the backend, Docker-based containerization was introduced to ensure consistent deployment, the configuration architecture was refactored, APIs and data models were updated, new cognitive and timing indicators (including task-specific lc_flag values) were integrated, and robustness of registration and email delivery workflows was enhanced.