Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression

作者：Abhishek Pratap, Ava Homiar, Luke Waninger, Calvin Herd, Christine Suver, Joshua Volponi, Joaquin A. Anguera, Patricia A. Areán · 发表于：Scientific Data · 年份：2022 · DOI：10.1038/s41597-022-01633-7 · 被引用次数：20 · 研究领域：Digital Mental Health Interventions、Mental Health Research Topics、Mobile Health and mHealth Applications

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.