Next-generation Clinical Assessment Using Mobile Devices

在美国，抑郁和焦虑是导致残疾和死亡的主要原因之一。虽然 有有效的治疗方法，但对特定治疗的反应因人而异。更准确 个体与治疗的匹配可以显著提高总体成功率。匹配或 个性化可以通过对响应的预处理预测或通过更早的检测来实现 治疗失败(缩短试错周期时间)br 最近的证据表明，神经心理表现的指标(如NIMH RDoC 结构)或直接观察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方法。移动电子设备的使用增加将使以下情况成为可能 广泛和迅速地传播这些下一代评估的工具。 该试点项目的目的是评估消费者参与度、预测性效用和临床 移动IT支持的认知、体力和社会活动评估在寻求帮助患者中的适用性 治疗抑郁症和焦虑症。 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设备完成认知评估，以及 成员愿意通过移动传感收集和共享身体和社会活动数据 智能手机上安装的设备或行为分析应用程序。 第二个目标是探索在治疗前和治疗期间收集的数据在任何一种情况下的实用性 预测治疗结果或及早发现治疗失败。 最终目标将是确定提供商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.