REVIEW article
Front. Digit. Health
Sec. Digital Mental Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1595243
This article is part of the Research TopicDigital Health Past, Present, and FutureView all 27 articles
The Implementation of Digital Biomarkers in the Diagnosis, Treatment and Monitoring of Mood Disorders: A Narrative Review Authors
Provisionally accepted
Andrea P Garzón-Partida1Citlali B Padilla-Gómez1
Diana Emilia Martínez-Fernández2Joaquín García-Estrada1Sonia Luquin1
David Fernández-Quezada1*- 1Instituto de Neurociencias Traslacionales, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara (UdeG), Guadalajara, Mexico
- 2Departamento de Farmacobiología, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara (UdeG), Guadalajara, Mexico
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Mood Disorders are a group of mental health conditions characterized by a disruption of the emotional state that affects the quality of life of the people living with them. Mental Disorders are difficult to diagnose and treat due to the complex processes involved and limitations of the healthcare system. Digital biomarkers have created accessible, long-term, non-invasive, and user-friendly alternatives for the diagnosis, treatment, and monitoring of these conditions. The use of everyday devices like smartphones and smartwatches and specialized tools like actigraphy, in conjunction with powerful statistical tools, artificial intelligence, and machine learning, represents a promising avenue for the implementation of personalized strategies to monitor and treat Mood Disorders, and potentially higher adherence to treatment. We conducted several studies that implement a variety of methodologies and tools to better understand Mood Disorders, using a patient-focused approach with the ultimate goal of identifying better strategies to improve their quality of life.
Keywords: digital biomarkers, Mood Disorders, predictive model, machine learning, Bipolar Disorder, Depression, Major Depressive Disorder
Received: 17 Mar 2025; Accepted: 04 Jun 2025.
Copyright: © 2025 Garzón-Partida, Padilla-Gómez, Martínez-Fernández, García-Estrada, Luquin and Fernández-Quezada. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: David Fernández-Quezada, Instituto de Neurociencias Traslacionales, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara (UdeG), Guadalajara, Mexico
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