抑郁症和焦虑症数字干预中的症状轨迹使用例行结果监测数据:二次分析研究研究
Diana Catalina Cumpanasoiu1, Angel Enrique1,2, Jorge E Palacios1,2
1SilverCloud Science, SilverCloud Health, Dublin, Ireland.
JMIR mHealth and uHealth
|July 12, 2023
概括
大多数患者通过互联网提供的认知行为疗法 (iCBT) 得到改善,但症状变化轨迹各不相同. 了解这些模式可以帮助量身定制ICBT交付,以获得更好的结果.
科学领域:
- 心理学 心理学 心理学
- 数字健康数字健康
- 临床研究 临床研究
背景情况:
- 通过互联网提供的认知行为疗法 (iCBT) 显示了各种治疗反应.
- 在ICBT中,个体症状变化轨迹的研究不足.
- 大量的数据集可以分析iCBT的结果和平台使用情况.
研究的目的:
- 在iCBT期间确定抑郁和焦虑的潜在症状变化轨迹.
- 调查与每个轨迹相关的患者特征.
- 研究平台使用与症状轨迹之间的关系.
主要方法:
- 在英国IAPT计划中对随机对照试验 (N=256) 的二次分析.
- 使用患者健康问卷-9 (PHQ-9) 和通用焦虑障碍-7 (GAD-7) 评分的纵向回顾设计.
- 潜在类增长分析以确定症状轨迹和相关因素.
主要成果:
- 确定了五类模型,用于抑郁和焦虑症状轨迹.
- 大约70%的患者表现出改善轨迹,基线分数,速度和结果各不相同.
- 两个较小的组表现出最小的收益或持续高的症状得分.
- 基线严重程度,药物状态和程序分配显著预测了轨迹.
- 平台使用在最初的4周是最高的,与轨迹没有时间变化的关系.
结论:
- 大多数患者从ICBT中受益,但存在不同的改善模式.
- 识别响应的预测因素可以为量身定制的支持和监测提供信息.
- 需要进一步的研究,以了解iCBT中的"什么对谁最有效".
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