多式联运数据驱动个性化与StayFine的共享决策的一致性:基于应用程序的个性化预防青少年焦虑和抑郁的复发
Bas E A M Kooiman1,2, Suzanne J Robberegt2,3, Casper J Albers4
1Department of Clinical Psychology and Experimental Psychopathology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands.
Frontiers in psychiatry
|October 16, 2023
概括
个性化数字心理健康干预通过整合多个数据源来改善治疗. 患者偏好指导模块选择,补充数据驱动的建议,以获得更好的结果.
科学领域:
- 数字心理健康数字心理健康
- 临床心理学 临床心理学
- 青少年精神病学 青少年精神病学
背景情况:
- 个性化干预可以提高治疗效率和治疗坚持.
- 在心理健康领域,数据驱动的个性化正在出现,特别是将多样化的数据与共享决策集成在一起.
- StayFine应用程序为患有焦虑或抑郁症的青少年提供指导性复发预防.
研究的目的:
- 描述和评估数字心理健康干预的创新数据驱动的个性化方法.
- 将诊断,问卷和网络数据中的模块建议与患者偏好的一致性进行比较.
- 评估共享决策如何与模块选择中的数据驱动咨询相结合.
主要方法:
- 利用了四个个性化来源:诊断面试,在线问卷,生态瞬间评估 (EMA) 症状网络和根据患者偏好共享决策.
- 分析了74名青少年 (13-21岁) 从焦虑或抑郁障碍中缓解的数据.
- 将数据驱动模块建议 (来源1-3) 与通过共享决策 (来源4) 选择的模块进行比较.
主要成果:
- 数据驱动的来源提供了补充,而不是确认的模块建议.
- 诊断面试建议"暴露"和"行为激活";问卷显示"睡眠";网络模型指出"增强积极影响".
- 共享决策优于模块增强积极的方面,而不是那些针对负面的,补充数据驱动的建议.
结论:
- 描述的个性化方法集成了多个数据流和患者偏好.
- 不同的数据来源为定制干预提供了独特的见解.
- 需要进一步的研究来确认这种个性化方法是否能改善治疗结果并降低学率.
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