Next-generation Clinical Assessment Using Mobile Devices

抑郁和焦虑是美国残疾和死亡的主要原因之一。虽然 虽然存在有效的治疗方法，但对特定治疗的反应在个体之间差异很大。更准确 将个体与治疗相匹配可以显著提高总体成功率。匹配或 个性化可以通过治疗前预测反应或通过早期检测来实现 治疗失败（缩短试错周期）。 最近的证据表明，神经心理学表现的测量（如NIMH RDoC） 结构）或直接观察到的行为可以更准确地预测或检测对治疗的反应 而不是传统的症状测量。越来越多的移动的电子设备的使用将使得有可能 广泛和迅速地传播这些下一代评估工具。 该试点项目的目的是评估消费者参与度、预测效用和临床 移动的、IT支持的认知、身体和社交活动评估在寻求 治疗抑郁和焦虑 第一个目标将是确定三个卫生系统的成员是否开始治疗 抑郁和/或焦虑将通过互联网和移动的设备完成认知评估， 成员将愿意通过移动的传感收集和共享身体和社会活动数据 设备或安装在智能手机上的行为分析应用。 第二个目的是探索治疗前和治疗期间收集的数据的效用， 治疗结果的预测或治疗失败的早期检测。 最终的目标是确定提供者对访问这些信息的兴趣和经验， 治疗计划的目的。

Depression and anxiety are among the leading causes of disability and mortality in the US. Although effective treatments exist, response to specific treatments varies widely among individuals. More accurate matching of individuals with treatments could significantly increase overall success rates. Matching or personalization could be accomplished either by pre-treatment prediction of response or by earlier detection of treatment failure (shorting the cycle time of trial-and-errbr). Recent evidence suggests that measures of neuropsychological performance (such as the NIMH RDoC constructs) or directly observed behaviors could predict or detect response to treatment more accurately than traditional symptom measures. Increasing use of mobile electronic devices will make it possible to widely and rapidly disseminate tools for these next-generation assessments. The purpose of this pilot project is to assess consumer engagement, predictive utility, and clinical applicability of mobile, IT-enabled assessment of cognitive, physical and social activity in patients seeking treatment for depression and anxiety. The first aim will be to determine whether members of three health systems initiating treatment for depression and/or anxiety will complete cognitive assessment via internet and mobile devices, and whether members would be willing to collect and share physical and social activity data through mobile sensing devices or behavioral analytic apps installed on their smart phones. The second aim will be to explore the utility of the data collected prior to and during treatment in either prediction of treatment outcomes or early detection of treatment failure. The final aim will be to determine provider interest in and experience with accessing this information for treatment planning purposes.