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在基于网络的解释偏见培训中,行为参与模式和心理社会结果用于焦虑的偏见培训
, Jeremy William Eberle1,2, Sonia Baee3
1Department of Psychology, University of Virginia, Charlottesville, Virginia, United States of America.
PLOS digital health
|July 24, 2025
概括
对焦虑的数字心理健康干预表明参与度低. 这项研究发现,在认知偏差修改程序中花费更少的时间,最初令人惊地改善了负面偏差,但后来损失了收益,突出了参与复杂性.
科学领域:
- 心理学 心理学 心理学
- 数字健康数字健康
- 心理健康 心理健康
背景情况:
- 数字心理健康干预 (DMHI) 提供了更多的焦虑治疗机会,但在用户参与方面存在困难.
- 解释认知偏差修改 (CBM-I) 是一种DMHI,旨在通过重新训练解释偏差来减少焦虑.
研究的目的:
- 在基于网络的CBM-I焦虑计划中分析基于不同行为参与模式的心理社会结果.
- 调查训练和评估任务所花费的时间是否与解释偏见和焦虑的变化相关.
主要方法:
- 在一项CBM-I临床试验中,一个集群分析从697名焦虑成年人中确定了两个参与组 (花费更多时间和花费更少时间).
- 多层次模型被用来比较参与小组之间的解释偏差和焦虑的变化.
主要成果:
- 参与组没有显著预测积极解释偏差或焦虑的变化.
- 花费较少时间的参与者在消极解释偏差方面初步表现出更大的改善,但在随访时这种情况没有持续下去.
- 那些花费较少时间的人年龄较大,受过教育程度略低.
结论:
- 行为参与,特别是花费的时间,是DMHI在焦虑中有效性的复杂标志物.
- 仅仅依靠花费的时间可能会误解参与;认知和情感标记对于改善DMHI至关重要.
- 需要对各种参与模式进行进一步的研究,以提高DMHI对焦虑障碍的疗效.
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