Personalized Prevention of Depression in Primary Care

Not Applicable

Active, not recruiting

• Conditions: Depression
• Interventions: Behavioral: e-predictD intervention; Other: Brief psychoeducational intervention

• Registration Number: NCT03990792
• Lead Sponsor: The Mediterranean Institute for the Advance of Biotechnology and Health Research

Brief Summary

The main goal is to design, develop and evaluate a personalized intervention to prevent the onset of depression based on Information and Communications Technology (ICTs), risk predictive algorithms and decision support systems (DSS) for patients and general practitioners (GPs). The specific goals are 1) to design and develop a DSS, called e-predictD-DSS, to elaborate personalized plans to prevent depression; 2) to design and develop an ICT solution that integrates the DSS on the web, a mobile application (App), the risk predictive algorithm, different intervention modules and a monitoring-feedback system; 3) to evaluate the usability and adherence of primary care patients and their GPs with the e-predictD intervention; 4) to evaluate the effectiveness of the e-predictD intervention to reduce the incidence of major depression, depression and anxiety symptoms and the probability of major depression next year; 5) to evaluate the cost-effectiveness and cost-utility of the e-predictD intervention to prevent depression.

Methods: This is a randomized controlled trial with allocation by cluster (GPs), simple blind, two parallel arms (e-predictD vs "active m-Health control") and 1 year follow-up including 720 patients (360 in each arm) and 72 GPs (36 in each arm). Patients will be free of major depression at baseline and aged between 18 and 55 years old. Primary outcome will be the incidence of major depression at 12 months measured by CIDI. As secondary outcomes: depressive and anxiety symptomatology measured by PHQ-9 and GAD-7 and the risk probability of depression measured by predictD algorithm, as well as cost-effectiveness and cost-utility. The e-predictD intervention is multi-component and it is based on a DSS that helps the patients to elaborate their own personalized depression prevention plans, which the patient approves, and implements, and the system monitors offering feedback to the patient and to the GPs. It is an e-Health intervention because it is based on a web and m-Health because it is also implemented on the patient's smartphones through an App. In addition, it integrates a risk algorithm of depression, which is already validated (the predictD algorithm). It also includes an initial GP-patient interview and a specific training for the GP. Finally, a map of potentially useful local community resources to prevent depression will be integrated into the DSS.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2019-06-17
• Study First Submitted QC: 2019-06-17
• Study First Posted: 2019-06-19
• Study Start: 2020-02-01
• Status Verified: 2023-03
• Last Update Submitted: 2023-03-01
• Last Update Posted: 2023-03-02
• Primary Completion: 2023-08-31
• Study Completion: 2023-12-31

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 663

Inclusion Criteria

• PHQ-9 <10 at baseline
• Moderate-high risk of depression (predictD risk algorithm score ≥ 10%)

Exclusion Criteria

• Not have a smartphone and internet for personal use
• Unable to speak Spanish
• Documented terminal illness
• Documented cognitive impairment
• Limiting sensory disorder (e.g. deafness)
• Documented serious mental illness (psychosis, bipolar, addictions, etc.)

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
e-predictD intervention	e-predictD intervention	In this arm, patients will receive a personalized intervention to prevent depression based on ICTs, risk predictive algorithms and decision support systems (DSS) for patients and General Practitioners (GPs).
m-Health control	Brief psychoeducational intervention	In this arm, patients will continue receiving the usual care from their GPs. In addition, they will use an App with the same appearance as the e-predictD App but it will only send weekly messages about physical and mental health management. This intervention is not personalized and does not include GP training and GP-patient interview.

Primary Outcome Measures

Name	Time	Method
Incidence of major depression measured by the Composite International Diagnostic Interview (CIDI)	12 months	Composite International Diagnostic Interview (CIDI) is a structured diagnostic interview that provides current diagnoses of major depression

Secondary Outcome Measures

Name	Time	Method
Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9)	12 months	The Patient Health Questionnaire-9 (PHQ-9) measures symptoms of depression through 9 items, each of which is scored 0 ('not at all') to 3 ('nearly every day'). Low scores are equivalent to less symptoms of depression, the scale range is 0 to 27 (9 items)
Anxious symptoms measured by the General Anxiety Questionnaire (GAD-7)	12 months	The General Anxiety Questionnaire (GAD-7) measures generalized anxiety disorder through 7 items, each of which is scored 0 ('not at all') to 3 ('nearly every day'). Low scores are equivalent to less symptoms of anxiety, the scale range is 0 to 21 (7 items)
Probability of depression (predictD risk algorithm)	12 months
Cost-effectiveness and cost-utility	12 months

Trial Locations

Locations (6)

María Isabel Ballesta Rodríguez; 🇪🇸 Jaén, Spain

Juan M. Mendive; 🇪🇸 Barcelona, Spain

Antonina Rodríguez Bayón; 🇪🇸 Linares, Spain

Juan Á Bellón; 🇪🇸 Málaga, Spain

Yolanda López del Hoyo; 🇪🇸 Zaragoza, Spain

Emiliano Rodríguez; 🇪🇸 Salamanca, Spain