Smartphone-Based Bioelectric Coherence Index for Mental Health Diagnostics: A Digital Twin Study

Background: Mental health disorders affect over 1 billion people globally, with circadian rhythm disruption emerging as a key pathophysiological mechanism. Digital biomarkers offer potential for scalable assessment, but validation remains limited. Objective: To develop and validate a Bioelectric Coherence Index (BCI) using smartphone sensors for circadian dysfunction assessment across 50 mental health conditions through digital twin simulation. Methods: We developed a BCI using smartphone sensors (light exposure, sleep patterns, pupillometry) with the formula: BCI(t) = alpha * phi(t) + beta * delta(t) - gamma * psi(t), integrating phase alignment, circadian amplitude, and temporal variability components. We simulated 1,000 virtual subjects (20 per condition) across 50 mental health conditions over 365 days, stratified by circadian impact tier. Results: Digital twin validation achieved 71.2% diagnostic accuracy (95% CI: 68.8-73.6%) across all 50 conditions. Performance varied by circadian impact tier: Core Circadian (87.3%), High Circadian (76.5%), Moderate Circadian (65.2%), and Lower Circadian (52.8%). Cross-validation demonstrated robust performance with minimal overfitting. External benchmarking against published chronotherapy literature showed strong correlations (r = 0.84, p < 0.001). Conclusions: This digital twin validation provides preliminary computational evidence for smartphone-based circadian assessment utility across diverse mental health conditions. The tier-based performance hierarchy aligns with circadian biology principles. Clinical validation through prospective trials remains essential before clinical application.