Randomized Controlled Study of a Digital Data Driven Intervention for Depressive and Generalized Anxiety Symptoms

Overview

Journal NPJ Digit Med

Specialty Medical Informatics

Date 2025 Feb 19

PMID 39972054

Authors

Panagiotis Fatouros

Charalampos Tsirmpas

Dimitrios Andrikopoulos

Sharon Kaplow

Konstantinos Kontoangelos

Charalabos Papageorgiou

Affiliations

Soon will be listed here.

Abstract

As mental health disorders like Major Depressive Disorder and Generalized Anxiety Disorder rise globally, effective, scalable, and personalized treatments are urgently needed. This 16-week prospective, decentralized, randomized, waitlist-controlled study investigated the effectiveness of a digital data-driven therapeutic integrating wearable sensor data with a mobile app to deliver personalized CBT-based interventions for individuals with depressive and generalized anxiety symptoms. 200 adults were randomized to intervention or control groups, with 164 completing the study. The intervention group demonstrated significant reductions in depressive (mean change = -5.61, CI = -7.14, -4.08) and anxiety symptoms (mean change = -5.21, CI = -6.66, -3.76), compared to the control group, with medium-to-large effect sizes (r = 0.64 and r = 0.62, P < 0.001). Notably, these improvements were also observed in participants with clinically significant depression and anxiety, further reinforcing the potential of digital therapeutics in targeting more severe cases. These findings, combined with high engagement levels, suggest that data-driven digital health interventions could complement traditional treatments, though further research is needed to assess their long-term impact.

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