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A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study

The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depres...

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Autores principales: Bellón, Juan A., Rodríguez-Morejón, Alberto, Conejo-Cerón, Sonia, Campos-Paíno, Henar, Rodríguez-Bayón, Antonina, Ballesta-Rodríguez, María I., Rodríguez-Sánchez, Emiliano, Mendive, Juan M., López del Hoyo, Yolanda, Luna, Juan D., Tamayo-Morales, Olaya, Moreno-Peral, Patricia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275079/
https://www.ncbi.nlm.nih.gov/pubmed/37333911
http://dx.doi.org/10.3389/fpsyt.2023.1163800
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author Bellón, Juan A.
Rodríguez-Morejón, Alberto
Conejo-Cerón, Sonia
Campos-Paíno, Henar
Rodríguez-Bayón, Antonina
Ballesta-Rodríguez, María I.
Rodríguez-Sánchez, Emiliano
Mendive, Juan M.
López del Hoyo, Yolanda
Luna, Juan D.
Tamayo-Morales, Olaya
Moreno-Peral, Patricia
author_facet Bellón, Juan A.
Rodríguez-Morejón, Alberto
Conejo-Cerón, Sonia
Campos-Paíno, Henar
Rodríguez-Bayón, Antonina
Ballesta-Rodríguez, María I.
Rodríguez-Sánchez, Emiliano
Mendive, Juan M.
López del Hoyo, Yolanda
Luna, Juan D.
Tamayo-Morales, Olaya
Moreno-Peral, Patricia
author_sort Bellón, Juan A.
collection PubMed
description The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18–55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP–patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction (‘e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT03990792.
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spelling pubmed-102750792023-06-17 A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study Bellón, Juan A. Rodríguez-Morejón, Alberto Conejo-Cerón, Sonia Campos-Paíno, Henar Rodríguez-Bayón, Antonina Ballesta-Rodríguez, María I. Rodríguez-Sánchez, Emiliano Mendive, Juan M. López del Hoyo, Yolanda Luna, Juan D. Tamayo-Morales, Olaya Moreno-Peral, Patricia Front Psychiatry Psychiatry The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18–55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP–patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction (‘e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT03990792. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10275079/ /pubmed/37333911 http://dx.doi.org/10.3389/fpsyt.2023.1163800 Text en Copyright © 2023 Bellón, Rodríguez-Morejón, Conejo-Cerón, Campos-Paíno, Rodríguez-Bayón, Ballesta-Rodríguez, Rodríguez-Sánchez, Mendive, López del Hoyo, Luna, Tamayo-Morales and Moreno-Peral. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Bellón, Juan A.
Rodríguez-Morejón, Alberto
Conejo-Cerón, Sonia
Campos-Paíno, Henar
Rodríguez-Bayón, Antonina
Ballesta-Rodríguez, María I.
Rodríguez-Sánchez, Emiliano
Mendive, Juan M.
López del Hoyo, Yolanda
Luna, Juan D.
Tamayo-Morales, Olaya
Moreno-Peral, Patricia
A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
title A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
title_full A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
title_fullStr A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
title_full_unstemmed A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
title_short A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
title_sort personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictd study
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275079/
https://www.ncbi.nlm.nih.gov/pubmed/37333911
http://dx.doi.org/10.3389/fpsyt.2023.1163800
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