Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study

@article{morenoperal2026toward,
  title = {{Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study}},
  author = {Patricia Moreno-Peral and Henar Campos-Paíno and Alberto Rodríguez-Morejón and Sonia Conejo-Cerón and Antonina Rodríguez-Bayón and María Isabel Ballesta-Rodríguez and Emiliano Rodríguez-Sánchez and Juan Manuel Mendive and Yolanda López del Hoyo and Juan de Dios Luna and Olaya Tamayo and Juan Ángel Bellón},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0355675},
  url = {https://doi.org/10.1371/journal.pone.0355675},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study
AU  - Patricia Moreno-Peral
AU  - Henar Campos-Paíno
AU  - Alberto Rodríguez-Morejón
AU  - Sonia Conejo-Cerón
AU  - Antonina Rodríguez-Bayón
AU  - María Isabel Ballesta-Rodríguez
AU  - Emiliano Rodríguez-Sánchez
AU  - Juan Manuel Mendive
AU  - Yolanda López del Hoyo
AU  - Juan de Dios Luna
AU  - Olaya Tamayo
AU  - Juan Ángel Bellón
PY  - 2026
DA  - 2026/09/11
JO  - PLOS One
DO  - 10.1371/journal.pone.0355675
UR  - https://doi.org/10.1371/journal.pone.0355675
AB  - Background: The 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. Objective: To evaluate the satisfaction, usability, and acceptability, of a beta version of the e-predictD intervention in primary care settings. Methods: The 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). Results: Six 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. Conclusion: This 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 registration: ClinicalTrials.gov NCT03990792
SN  - 1932-6203
ER  -