The Mental Health Score (MHS): A Composite Wellness Metric Integrating Validated Clinical Assessments and Ecological Momentary Data

Mental health monitoring in digital health applications is commonly fragmented across multiple validated instruments, each capturing a distinct clinical dimension in isolation. This paper introduces the Mental Health Score (MHS), a novel composite wellness metric scaled 0–1000 that integrates three validated clinical assessments — the Patient Health Questionnaire-9 (PHQ-9), the Generalised Anxiety Disorder-7 (GAD-7), and the Work and Social Adjustment Scale (WSAS) — with ecological momentary assessment (EMA) data in the form of daily mood check-ins. The MHS provides a single, interpretable, longitudinally comparable indicator of overall mental wellness. A fixed weighting schema assigns 200 points to each clinical instrument and 400 points to the mood component, with all inputs normalised to a common 0–1 scale prior to aggregation. Temporal tracking is supported via historical score persistence, enabling within-person comparison across time. A dual-ring circular gauge visualisation presents the current and previous scores simultaneously, accompanied by a percentage-change indicator. The MHS system is designed for both self-monitoring and clinical oversight contexts, including a professional dashboard supporting patient-level monitoring with consent-gated data access. This article describes the theoretical basis, computational specification, software architecture, and user-interface design of the MHS, and discusses its potential applications in digital mental health monitoring, clinical decision support, and population-level wellness research.ResearchGate: https://www.researchgate.net/publication/401942301