基于移动应用程序的TEAM-CBT (测试同情心评估方法-认知行为疗法) 干预 (感觉良好) 抑郁症的可行性和可接受性:二次数据分析
Nicholas Bisconti1, Mackenzie Odier1, Matthew Becker1
1PGSP-Stanford PsyD. Consortium, Palo Alto, CA, United States.
JMIR mental health
|May 10, 2024
概括
感觉良好应用程序,一个数字心理健康工具,显示出绝望的显著下降,是安全的非临床用户. 需要进一步的研究来调查它在减少抑郁症症状方面的有效性.
科学领域:
- 数字心理健康数字心理健康
- 移动卫生干预措施 移动卫生干预
- 认知行为治疗 (CBT)
背景情况:
- 感觉良好应用程序是一个移动工具,提供基于CBT原则的自助课程,以减少抑郁症状.
- 它旨在提供无需直接提供者指导的可访问的心理健康支持.
- 该应用程序使用密集基础培训 (IBT) 和持续培训模型.
研究的目的:
- 对Feeling Good App的安全性,可行性和可访问性进行独立的学术评估.
- 为了评估一个自动化的数字移动心理健康应用程序,旨在减轻抑郁症症状.
- 通过非赞助大学协议分析产业生成的数据.
主要方法:
- 在290名参与者中采用了等候名单队列交叉设计.
- 使用PHQ-9和GAD-7测量了抑郁和焦虑症状,同时使用7-DES.
- 数据使用SPSS统计数据进行分析,并对可行性和可接受性进行了描述性评估.
主要成果:
- 没有观察到自杀倾向的统计学意义上的变化.
- 在统计学上显著地减少了绝望 (P<.01).
- 用户参与度在4周的协议中显示出65.22%的学率,其中72.2%完成了最初的2天IBT.
结论:
- 这项研究代表了Feeling Good App的第一个概念验证评估.
- 该应用程序被认为对精选的非临床群体是安全的,证明了可行的行业-学术合作.
- 参与的用户显示抑郁症严重程度降低,这表明进一步调查的潜在疗效.
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