Smart Technology for Anorexia Nervosa Recovery: A Pilot Intervention for the Post-Acute Treatment of Anorexia Nervosa

项目摘要/摘要 神经性厌食症(AN)是所有精神疾病中死亡率最高的，典型的发病于青春期。 尽管以家庭为基础的干预措施对高达75%的患有老年痴呆症的青少年有效，但约30% 会在康复后复发。迫切需要优化治疗，防止出院后复发。 经过急性治疗以改善患有AN的青少年的预后。为了满足这一迫切需求，我们的团队 开发了一套数字工具，提高了评估、风险预测和临床决策的科学性 支持在急性后治疗窗口使用，称为“智能治疗厌食症恢复(STAR)”。 STAR使用尖端评估技术来缩短考试管理时间，并使用机器学习来预测 复苏的可能性。然后，该信息通过易于使用的临床-- 决策支持工具，当用户输入的数据显示患者没有进展时，提醒临床医生。在……里面 在当前的应用程序中，我们建议扩展STAR以测试在 出院后窗口。我们的科学前提是跨诊断评估和临床决策 STAR套件中提供的支持工具将优化面对面的临床服务，并增加 适应性移动健康干预将改善青少年的门诊治疗反应并减少复发 因急性呼吸窘迫综合征接受强化治疗后出院。我们之前的工作支持了我们的科学前提。具体地说，我们的 研究为我们的预测评估工具的预测有效性和临床实用性提供了强有力的支持 与ED相关的精神损害和康复。然而，我们基于纸张的项目数量 评估工具为144，对于常规使用来说太长了。为了克服这一挑战，我们开发了一种 使用计算机化自适应测试的手机应用程序将评估时间缩短高达50%，而 保留了原始纸笔测量的可靠性和有效性。我们建议利用这一点 创新以优化AN的面对面治疗和mHealth治疗。我们的目标是：1)发展 移动健康干预(由临床医生和利益相关者参与)和2)建立可行性、可接受性和 使用临床医生和患者数据的我们的移动健康干预的初步效果大小。为了实现我们的目标 目标，我们将采用计算机化的自适应测试与机器学习算法相结合，提供 在我们的应用程序中，当他们的客户面临不良结果和复发的风险时，向临床医生发出信号。具体目标 包括：1)调整我们现有的临床工具，以提供治疗师支持模块和患者mHealth消息； 2)对我们的综合评估和mHealth进行初步随机对照试验(RCT) 干预工具 3)测试导致AN症状变化的初步机制。假设有一个 缺乏对后续急性治疗的专门护理，但95%的青少年拥有智能手机， 拟议的研究具有创新性和重要意义，因为它有可能在未来减少复发和 优化现有的社区提供的干预措施，以便在急性发作后的治疗窗口内采取有效的干预措施。

PROJECT SUMMARY/ABSTRACT Anorexia nervosa (AN) has the highest mortality rate of any mental illness, with a typical onset in adolescence. Although family-based interventions are efficacious for up to 75% of adolescents with AN, approximately 30% will relapse after recovery. There is a critical need to optimize treatments and prevent post-discharge relapse following acute treatment to improve outcomes for adolescents with AN. To address this critical need, our team developed a suite of digital tools that advance the science of assessment, risk prediction, and clinical-decision support for use in the post-acute treatment window, called “Smart Treatment for Anorexia Recovery (STAR).” STAR uses cutting-edge assessment technology to shorten test administration and machine-learning to predict likelihood of recovery. This information is then provided back to the clinician via an easy-to-use clinical- decision support tool to alert the clinician when user-entered data suggests the patient is not progressing. In the current application, we propose to expand STAR to test an adaptive mHealth intervention delivered in the post-discharge window. Our scientific premise is that a transdiagnostic assessment and clinical-decision support tool delivered within the STAR suite will optimize face-to-face clinical service and the addition of an adaptive mHealth intervention will improve outpatient treatment response and reduce relapse in adolescents discharged from intensive treatment for AN. Our previous work supports our scientific premise. Specifically, our studies provide robust support for the predictive validity and clinical utility of our assessment tool for predicting ED-related psychiatric impairment and recovery. However, the number of items across our paper-based assessment tool is 144, which is overly long for routine use. To overcome this challenge, we developed a mobile phone app that uses computerized adaptive testing to reduce assessment length by up to 50% while retaining the reliability and validity of the original paper-and-pencil measure. We propose to leverage this innovation to optimize both face-to-face and mHealth treatment for AN. Our objectives are to: 1) develop the mHealth intervention (with clinician and stakeholder input) and 2) establish feasibility, acceptability, and preliminary effect size of our mHealth intervention using both clinician and patient data. To accomplish our objectives, we will employ a computerized adaptive test coupled with machine learning algorithms, delivered within our app to signal clinicians when their clients are at-risk for poor outcomes and relapse. Specific aims include: 1) adapt our existing clinical tool to provide therapist support modules and patient mHealth messages; 2) conduct a preliminary randomized controlled trial (RCT) of our integrated assessment and mHealth intervention tool ; 3) test preliminary mechanisms that lead to changes in AN symptoms. Given there is a scarcity of specialty care for AN following acute treatment, yet 95% of adolescents have smart phones, the proposed research is innovative and significant because it has the future potential to reduce relapse and optimize existing community-delivered interventions for AN over the post-acute treatment window.