Development of a 4-Horizon Prognostic System for Mental States via Multimodal Circadian Rhythm Imaging

Abstract

Background: The diagnosis and follow-up of mental disorders still rely heavily on subjective clinical assessments, highlighting the need for objective and quantitative monitoring methods. Although wearable devices enable continuous collection of digital behavioral data, the predictive characteristics associated with different psychiatric conditions remain insufficiently understood. Objective: This study proposes a novel four-horizon regression model that predicts future mental-state risk at 1, 3, 7, and 10 days using multimodal inputs integrating wearable activity data with comorbidity information (migraine). Methods: Fifteen circadian-rhythm features and one static comorbidity feature were extracted from time-series activity data and converted into 7-day heatmap images. These images served as inputs for a fine-tuned ResNet50 model, which predicted a composite risk score across multiple future time horizons. Results: The model demonstrated stable predictive performance (mean RMSE = 0.081). Notably, condition-specific predictive characteristics emerged. In the bipolar disorder group, the 7-day-ahead prediction achieved the highest accuracy ( R 2 = 0.65), reflecting the disorder’s intrinsic cyclic fluctuations. In contrast, in the unipolar depression group, the 10-day-ahead prediction substantially outperformed short-term forecasts ( R 2 = 0.48), suggesting that changes in activity levels may precede subsequent mood alterations. Conclusions: These findings indicate that the proposed approach enables quantitative mental-state monitoring and captures condition-specific temporal dynamics, such as periodicity and early predictive signals, embedded within daily activity patterns.