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基于风险预测算法和决策支持系统的初级保健中预防抑郁症的个性化干预措施:e-predictD研究方案

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

作者信息

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

机构信息

Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.

Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain.

出版信息

Front Psychiatry. 2023 Jun 2;14:1163800. doi: 10.3389/fpsyt.2023.1163800. eCollection 2023.

Abstract

UNLABELLED

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.

摘要

未标注

PredictD是全科医生实施的一项预防抑郁症的干预措施,该措施降低了抑郁焦虑症的发病率且具有成本效益。e-PredictD研究旨在设计、开发并评估一种基于信息通信技术、预测风险算法、决策支持系统(DSS)和个性化预防计划(PPP)的改进型PredictD干预措施,以预防初级保健中重度抑郁症的发作。正在进行一项多中心整群随机试验,将全科医生随机分配到e-PredictD干预+常规护理(CAU)组或积极对照组+CAU组,并进行1年的随访。所需样本量为720名无抑郁症患者(年龄在18至55岁之间),抑郁风险为中到高,由西班牙六个城市的72名全科医生负责护理。分配到e-PredictD干预组的全科医生接受简短培训,而分配到对照组的则不接受培训。分配到e-PredictD组的全科医生招募的患者下载e-PredictD应用程序,该应用程序包含经过验证的预测抑郁症的风险算法、监测系统和DSS。综合所有输入信息,DSS会根据八个干预模块自动为患者提出抑郁症的PPP:体育锻炼、社会关系、改善睡眠、解决问题、沟通技巧、决策、自信和思维训练。这个PPP会在一次15分钟的半结构化全科医生-患者访谈中进行讨论。然后患者从DSS提出的一个或多个干预模块中选择,在接下来的3个月内自行实施。这个过程将在3个月、6个月和9个月时重新制定,但不进行全科医生-患者访谈。分配到对照组+CAU的全科医生招募的患者下载另一个版本的e-PredictD应用程序,但他们通过该应用程序获得的唯一干预是每周的简短心理教育信息(积极对照组)。主要结局是通过综合国际诊断访谈在6个月和12个月时测量的重度抑郁症的累积发病率。其他结局包括抑郁症状(PHQ-9)和焦虑症状(GAD-7)、抑郁风险(PredictD风险算法)、心理和生理生活质量(SF-12)以及对干预措施的可接受性和满意度(“电子健康影响”问卷)。患者在基线以及3个月、6个月、9个月和12个月时接受评估。还将从社会和卫生系统两个角度进行经济评估(成本效益和成本效用分析)。

试验注册

ClinicalTrials.gov,标识符:NCT03990792。

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