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数字心理健康干预中的个性化策略:对抑郁症状的系统审查和概念框架

Silvan Hornstein¹, Kirsten Zantvoort², Ulrike Lueken¹

¹Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.

Frontiers in digital health

|June 7, 2023

概括

在数字心理健康干预 (DMHI) 中定义了个性化,并评估了其流行率. 虽然很常见,但其益处的证据仍然有限,这表明机器学习的未来使用.

科学领域:

数字心理健康干预 (DMHIs) 是指数字心理健康干预.
在医疗保健中的个性化.
机器学习应用机器学习应用

背景情况:

个性化被认为是提高数字心理健康干预措施 (DMHI) 的坚持和结果的关键策略.
然而,明确的定义,流行率和DMHI个性化证明的好处仍未得到充分探索.
这项研究解决了该领域的这些关键知识缺口.

研究的目的:

系统地审查和概念化成人抑郁症状的DMHIs个性化.
为了确定个性化策略和机制在当前的DMHIs的流行程度.
为了确定跨不同干预维度的个性化程度.

主要方法:

对针对成人抑郁症状的DMHIs实证研究进行了系统的文献审查.
在PubMed,SCOPUS和PsycINFO中搜索了2015年至2022年9月期间发表的研究.
包括138篇文章,详细介绍了94个不同的DMHIs,约有24,300名个人.

主要成果:

个性化被概念化为治疗元素或结构的有目的变化,按个性化方面 (内容,顺序,指导,沟通) 和机制 (用户/提供者选择,决策规则,机器学习) 分类.
个性化在66%的DMHIs中被发现,其中定制内容 (32%) 和沟通 (30%) 是最常见的.
决策规则 (48%) 和用户选择 (36%) 是常见的机制;机器学习 (ML) 的使用很少 (3%),大多数干预措施只针对一个维度进行个性化.

结论:

未来的DMHIs可以增强个性化,特别是通过整合机器学习模型来实现更复杂的定制.
支持DMHIs个性化的好处的经验证据目前很少,也没有确定性.
进一步的研究对于确定个性化数字心理健康方法的有效性和优势至关重要.

关键词:

抑郁抑郁症抑郁症抑郁症抑郁症数字心理健康数字心理健康个性化个性化个性化精密护理,ICBT,机器学习

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