评估应用程序推和持续参与的数字表型:队列研究
Bridget Dwyer1, Matthew Flathers1, James Burns1
1Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
JMIR formative research
|November 19, 2024
概括
数字表型化可以将个人与心理健康应用程序相匹配,从而潜在地提高参与度. 这种使用智能手机数据的方法在试点研究中显示出更高的参与度,这表明现实应用的可行性.
科学领域:
- 数字健康数字健康
- 心理健康技术 心理健康技术
- 行为科学是一种行为科学.
背景情况:
- 低参与度的心理健康应用程序限制了他们的有效性.
- 个性化应用程序匹配可以提高用户参与度.
- 需要新的方法来将个人与合适的数字心理健康工具联系起来.
研究的目的:
- 试点数字表型化,将个人与心理健康应用程序相匹配.
- 评估数据驱动的建议是否增加了应用程序的参与度.
- 评估使用智能手机传感器数据用于应用程序选择的可行性.
主要方法:
- 使用mindLAMP应用程序收集的数字表型化数据.
- 将随机分配的参与者分配到数字表型化手臂 (附建议) 或对照手臂.
- 使用客观的屏幕时间,自我报告和数字工作联盟库存来衡量参与度.
主要成果:
- 在数字表型化部门观察到更高的参与度 (屏幕时间,联盟分数).
- 大多数参与者使用了应对抑郁或焦虑的应用程序.
- 在自我报告和客观参与指标之间没有发现相关性.
结论:
- 心理健康应用程序推的数字表型是可行的,可能会改善参与度.
- 这种方法是通用和实用的,可用于现实世界的实施.
- 数字表型的未来进步有望提供更个性化的建议.
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