CHRONOBIOLOGICAL PREDICTORS OF DEPRESSION RELAPSE FOR PROGNOSTIC MODEL DEVELOPMENT - A SYSTEMATIC REVIEW

Abstract

Background: Depressive disorders are characterized by fluctuating symptom severity, and developing an individual prognostic model for relapse is crucial for effective prevention. Chronobiological factors are poorly understood in this context. Methods: A systematic search was conducted to identify articles related to the prognosis of depression recurrence based on chronobiological factors. Relevant clinical studies were included, while reviews and case reports were excluded. A total of 14 articles were selected for review. Results: The included articles focused on various chronobiological factors, including circadian biorhythms, individual chronotype, mood swings, seasonal patterns, diurnal cortisol fluctuations, and light therapy. The accuracy of personified prognosis ranged from 22.7% to 93.8%, and the prognostic value of specific predictors in group prognosis varied from 23.9% to 54%. Methodological differences and limitations hindered direct comparison and clinical applicability. Conclusions: Developing precise and practical models for depression recurrence prognosis remains limited. Parameters of circadian rhythm showed the highest accuracy for short-term prognosis, and the use of digital technologies, including AI, enhanced prognostic value. Relapse seasonality had limited practical applicability. Integrating other chronobiological factors into prognostic models requires further research. Utilizing digital technologies, including AI, can improve the accuracy and range of personified prognosis. Only a few selected parameters of the human chronobiological system were considered in the examined studies. There are indications of the other chronobiological factors that could be included in the integrated prognostic model of recurrence for its further improvement.