Temporal Resonance Mapping (TRM): A Dynamical Systems Framework for Mental Health Crisis Prediction

Temporal Resonance Mapping (TRM) is a novel mathematical framework that models human behavioral patterns as coupled oscillatory systems to predict mental health crises 7-14 days before clinical manifestation. Unlike existing subjective assessment tools, TRM analyzes objective temporal patterns in routine behaviors; sleep, activity, social interaction, and device usage; to detect characteristic "phase coherence collapse" that precedes psychological deterioration. Our theoretical framework, grounded in nonlinear dynamics and coupled oscillator theory, establishes the Temporal Resonance Index (TRI), a quantitative metric that captures the synchronization state of behavioral rhythms. Retrospective validation on 127 anonymized clinical cases from Kenyan mental health services demonstrated 84.3% sensitivity and 78.9% specificity in predicting crisis events, with a median lead time of 11.2 days. Prospective monitoring of 63 participants over six months achieved 81.0% prediction accuracy with zero false negatives for severe crisis events. TRM operates on passively collected smartphone metadata, requiring no active user input, making it deployable in resource-constrained settings. This work represents the first application of dynamical systems theory to real-time mental health prediction and establishes a new paradigm for computational clinical psychology with immediate implications for suicide prevention, early intervention, and scalable mental health infrastructure in low-resource environments. Keywords: Mental health prediction, Dynamical systems, Coupled oscillators, Digital phenotyping, Crisis intervention, Behavioral rhythms, Computational psychiatry, Cognitive science, Psychology