Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
Sci Data. 2022 Aug 27;9(1):522. doi: 10.1038/s41597-022-01633-7.
Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a timely and long-term data resource to evaluate analytical approaches for developing digital behavioral markers and understand the effectiveness of mental health care delivered continuously and remotely.
尽管医疗系统投入了数十亿美元，但大多数精神健康障碍患者仍无法及时获得基于证据的治疗。研究人员一直在探索使用数字健康技术来衡量现实环境中的行为，但结果喜忧参半。需要创建可访问和可计算的数字心理健康数据集，以推进包容性和透明验证的研究，从而创建稳健的真实世界数字心理健康生物标志物。在这里，我们分享并描述了来自美国两千多人的最大和最多样化的真实世界行为数据集之一。这些数据是作为两项 NIH 资助的随机临床试验的一部分生成的，该试验旨在评估远程连续提供心理健康护理的有效性。纵向数据集包括在现实环境中自我评估情绪、抑郁、焦虑以及被动收集的基于电话的行为数据流。该数据集将提供一个及时和长期的数据资源，用于评估开发数字行为标志物的分析方法，并了解远程连续提供心理健康护理的效果。
