Evidence map›Paper›PMID 37333911›Full record

ArticleFrontiers in psychiatry2023

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

Juan A Bellón, Alberto Rodríguez-Morejón, Sonia Conejo-Cerón, Henar Campos-Paíno, Antonina Rodríguez-Bayón, María I Ballesta-Rodríguez, Emiliano Rodríguez-Sánchez, Juan M Mendive, Yolanda López Del Hoyo, Juan D Luna and 2 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03990792 (Preventing the Onset of Depression Through a Personalized Intervention Based on ICTs, Risk Prediction Algorithms and Decision Support Systems for Patients and GPs), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03990792 nacompletednot on this map

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

TypeinterventionalSponsorThe Mediterranean Institute for the Advance of Biotechnology and Health ResearchRan2020 to 2023Enrolled663ConditionsDepressionArmse-predictD intervention, Brief psychoeducational intervention
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 9 institutions in 1 country.

Juan A BellónBiomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.
Alberto Rodríguez-MorejónBiomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.
Sonia Conejo-CerónBiomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.
Henar Campos-PaínoBiomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.
Antonina Rodríguez-BayónPrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
María I Ballesta-RodríguezPrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
Emiliano Rodríguez-SánchezPrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
Juan M MendivePrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
Yolanda López Del HoyoPrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
Juan D LunaPrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
Olaya Tamayo-MoralesPrevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.
Patricia Moreno-PeralBiomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.
Andalusian Centre for Nanomedicine and Biotechnology · ESInstituto de Investigación Biomédica de Salamanca · ESRed de Investigación en Actividades Preventivas y Promoción de la Salud · ESComplejo Hospitalario de Jaén · ESInstituto de Investigación Biomédica de Málaga · ESInstituto de Salud Carlos III · ESUniversidad de Granada · ESUniversidad de Málaga · ESUniversidad de Zaragoza · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

depressioninternet-based interventionsmobile applicationspreventionprimary health carerandomized controlled trial

Identifiers

PMID37333911
PMCPMC10275079
OpenAlexW4379232812

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.