Ignite Creation Date:
2025-12-24 @ 3:30 PM
Ignite Modification Date:
2026-01-02 @ 2:57 AM
Study NCT ID:
NCT03990792
Status:
COMPLETED
Last Update Posted:
2025-05-28
First Post:
2019-06-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Personalized Prevention of Depression in Primary Care
Sponsor:
The Mediterranean Institute for the Advance of Biotechnology and Health Research
Organization:
Study Overview
Official Title:
Preventing the Onset of Depression Through a Personalized Intervention Based on ICTs, Risk Prediction Algorithms and Decision Support Systems for Patients and GPs: the e-predictD Study
Status:
COMPLETED
Status Verified Date:
2025-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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:
None
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: