基于风险预测算法和决策支持系统的个人化干预,以预防初级保健中的抑郁症:e-predictD研究协议
Juan A Bellón1,2,3,4,5, Alberto Rodríguez-Morejón1,2,3,6, Sonia Conejo-Cerón1,2,3
1Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain.
Frontiers in psychiatry
|June 19, 2023
概括
在e-predictD研究中,开发了一种数字干预措施,以预防初级保健中的严重抑郁症. 这种以技术为导向的方法,使用个性化的预防计划,旨在有效地减少抑郁症发病率.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 主要护理精神病学 精神病学
- 预防医学 预防医学
背景情况:
- 抑郁症是全球导致残疾的主要原因,初级保健是早期干预的关键设置.
- 预测D干预措施在减少抑郁和焦虑方面取得了成功,显示了成本效益.
- 需要可扩展的,基于技术的解决方案,以加强初级保健中的抑郁症预防.
研究的目的:
- 设计,开发和评估一种进化的数字干预 (e-predictD),以预防在初级保健中出现严重抑郁症.
- 整合信息和通信技术 (ICT),预测算法,决策支持系统 (DSS) 和个性化预防计划 (PPP).
- 评估e-predictD干预措施与活性对照相比的临床有效性和成本效益.
主要方法:
- 一个多中心集群随机试验,涉及720名患有抑郁症中度至高风险的患者,由6个西班牙城市的72名全科医生 (GPs) 管理.
- 干预组的全科医生接受了e-predictD系统的培训,其中包括一个DSS,通过应用程序提出个性化预防模块.
- 对照组的患者通过应用程序接收精神教育信息;两组患者都被跟踪了一年,主要结果使用综合国际诊断访谈进行评估.
主要成果:
- 主要结局是6个月和12个月的大型抑郁症的累积发病率.
- 二次结局包括抑郁和焦虑症状 (PHQ-9,GAD-7),抑郁风险,生活质量 (SF-12),以及干预的可接受性.
- 经济评估 (成本效益和成本效益) 将从社会和卫生系统的角度进行.
结论:
- 该e-predictD干预措施有可能提供一种可扩展和个性化的方法,用于在初级保健机构中预防抑郁症.
- 这项研究将提供有关技术增强抑郁症预防策略临床有效性,患者接受度和经济影响的宝贵数据.
- 调查结果将有助于将数字工具整合到常规初级保健中,以积极主动地管理心理健康.
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