TY - JOUR
T1 - A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms
AU - Vignapiano, Annarita
AU - Monaco, Francesco
AU - Pagano, Claudio
AU - Piacente, Martina
AU - Farina, Federica
AU - Petrillo, Gianvito
AU - Sica, Raffaella
AU - Marenna, Alessandra
AU - Shin, Jae Il
AU - Solmi, Marco
AU - Corrivetti, Giulio
N1 - Publisher Copyright:
Copyright © 2023 Vignapiano, Monaco, Pagano, Piacente, Farina, Petrillo, Sica, Marenna, Shin, Solmi and Corrivetti.
PY - 2023
Y1 - 2023
N2 - Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients’ state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression’s stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.
AB - Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients’ state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression’s stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.
KW - artificial intelligence
KW - digital biomarkers
KW - major depressive disorder
KW - mental healthcare
KW - personalized treatment
KW - wearable devices
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U2 - 10.3389/fpsyt.2023.1321345
DO - 10.3389/fpsyt.2023.1321345
M3 - Short survey
AN - SCOPUS:85178873900
SN - 1664-0640
VL - 14
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
M1 - 1321345
ER -