A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study
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DepressionPreventionInternet-based interventionsMobile applicationsPrimary health careRandomized controlled trial
Bellón JA, Rodríguez-Morejón A, Conejo-Cerón S, Campos-Paíno H, Rodríguez-Bayón A, Ballesta-Rodríguez MI, Rodríguez-Sánchez E, Mendive JM, López del Hoyo Y, Luna JD, Tamayo-Morales O and Moreno-Peral P (2023) A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study. Front. Psychiatry 14:1163800. [doi: 10.3389/fpsyt.2023.1163800]
SponsorshipSpanish Ministry of Health, the Institute of Health Carlos III; The European Regional Development Fund Una manera de hacer Europa (grant references: PI15/00401; PI15/01035, and PI15/01021), the Andalusian Council of Health (grant reference: AP-0095-2016);; Prevention and Health Promotion Research Network redIAPP (RD16/0007/0010; RD16/0007/0005, RD16/0007/0003, and RD16/0007/0001), Ministry of Health of Andalusia (PS-0330- 2016); The Chronicity, Primary Care, and Prevention and Health Promotion Research Network RICAPPS (RD21/0016/0012; RD21/0016/0005, RD21/0016/0010, and RD21/0016/0001); The Ministry of Science and Innovation, the Institute of Health Carlos III (SCIII); The European Funds of the Recovery, Transformation and Resilience Plan, and by the EU funds Next-Generation
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.