A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision...

Area of Science:

• Digital Health Interventions
• Primary Care Psychiatry
• Preventive Medicine

Background:

• Depression is a leading cause of disability worldwide, with primary care being a key setting for early intervention.
• The predictD intervention demonstrated success in reducing depression and anxiety, showing cost-effectiveness.
• There is a need for scalable, technology-based solutions to enhance depression prevention in primary care.

Purpose of the Study:

• To design, develop, and evaluate an evolved digital intervention (e-predictD) for preventing major depression onset in primary care.
• To integrate Information and Communication Technologies (ICT), predictive algorithms, decision support systems (DSS), and personalized prevention plans (PPPs).
• To assess the clinical effectiveness and cost-effectiveness of the e-predictD intervention compared to an active control.

Main Methods:

• A multicenter cluster randomized trial involving 720 patients at moderate-to-high risk for depression, managed by 72 general practitioners (GPs) across six Spanish cities.
• GPs in the intervention group receive training on the e-predictD system, which includes a DSS proposing personalized prevention modules via an app.
• Patients in the control group receive psychoeducational messages via the app; both groups are followed for 1 year, with primary outcomes assessed using the Composite International Diagnostic Interview.

Main Results:

• The primary outcome is the cumulative incidence of major depression at 6 and 12 months.
• Secondary outcomes include depressive and anxiety symptoms (PHQ-9, GAD-7), depression risk, quality of life (SF-12), and intervention acceptability.
• Economic evaluations (cost-effectiveness and cost-utility) will be conducted from societal and health system perspectives.

Conclusions:

• The e-predictD intervention has the potential to offer a scalable and personalized approach to depression prevention in primary care settings.
• This study will provide valuable data on the clinical effectiveness, patient acceptability, and economic impact of a technology-enhanced depression prevention strategy.
• Findings will inform the integration of digital tools into routine primary care for proactive mental health management.