Trial reportPloS one2026
Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study.
Trial report in PloS one, 2026. 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. Not yet cited in PubMed.
What it found
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The trial behind it
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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
Who cites it
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe predictD intervention, delivered by family physicians (FPs), has demonstrated effectiveness and cost-efficiency in preventing depression and anxiety. The e-predictD study aims to design, develop, and evaluate a novel personalized intervention for depression prevention by integrating information and communication technologies (ICTs), risk prediction algorithms, and decision support systems (DSS) for both patients and FPs.
objectiveTo evaluate the satisfaction, usability, and acceptability, of a beta version of the e-predictD intervention in primary care settings.
methodsThe e-predictD intervention follows a biopsychosocial approach, including an initial patient-FP interview, specific FP training, and an app. A β-version was tested in a pilot study without a control group over three months. The app integrates a validated depression risk prediction algorithm, decision algorithms, and a monitoring system supporting the DSS. The DSS generates a personalized prevention plan (PPP) from eight intervention modules: physical exercise, social relationships, problem-solving, communication skills, decision-making, assertiveness, sleep improvement, and cognitive restructuring. Patients and FPs discussed the PPP in a 15-minute baseline interview, selecting modules for implementation over three months. Semi-structured interviews gathered feedback. Assessments included depression (PHQ-9), anxiety (GAD-7), quality of life (SF-12), and major depression risk (predictD algorithm).
resultsSix FPs from six Spanish cities enrolled 56 non-depressed patients at moderate-to-high risk of depression; 47 (84%) completed follow-up. The app was used for a median of six days (interquartile range: 1-30). Both FPs and patients expressed satisfaction, leading to incorporated improvements. After three months, significant reductions in major depression risk and anxiety symptoms were observed, alongside improved mental quality of life. However, no significant changes were found in depressive symptoms or physical quality of life.
conclusionThis pilot study supports the feasibility and acceptability of the e-predictD β-version, despite lower-than-expected app usability. Health improvements were observed, warranting confirmation in a randomized controlled trial.
trial registrationClinicalTrials.gov NCT03990792.
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