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

项目概要/摘要 神经性厌食症（AN）是所有精神疾病中死亡率最高的，通常在青春期发病。 虽然以家庭为基础的干预措施对高达75%的AN青少年有效，但大约30%的AN青少年患者的治疗效果不佳。 康复后会复发。迫切需要优化治疗方法，防止出院后复发 急性治疗后，以改善与AN青少年的结果。为了满足这一关键需求，我们的团队 开发了一套数字化工具，以推进评估、风险预测和临床决策的科学 支持在急性期后治疗窗口中使用，称为“厌食症恢复智能治疗（星星）”。 星星使用尖端的评估技术来缩短测试管理和机器学习预测 复苏的可能性。然后，通过易于使用的临床- 决策支持工具，用于在用户输入的数据表明患者没有进展时提醒临床医生。在 在当前的应用中，我们建议扩展星星，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 星星套件中提供的支持工具将优化面对面的临床服务， 适应性移动健康干预将改善门诊治疗反应，减少青少年复发 因AN接受强化治疗后出院。我们以前的工作支持我们的科学前提。具体来说，我们 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 ED相关的精神损害和恢复。然而，我们的纸质文件中的项目数量 评估工具是144，这对于常规使用来说过长。为了克服这一挑战，我们开发了一个 移动的手机应用程序，使用计算机化的自适应测试，以减少评估时间长达50%， 保持了原有纸笔测量的可靠性和有效性。我们建议利用这个 创新以优化AN的面对面和mHealth治疗。我们的目标是：1）发展 移动健康干预（临床医生和利益相关者的输入）和2）建立可行性，可接受性， 使用临床医生和患者数据的移动健康干预的初步效果大小。完成我们 目标，我们将采用计算机化的自适应测试加上机器学习算法，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移动健康进行初步随机对照试验（RCT） 介入工具 3）测试导致AN症状变化的初步机制。鉴于有一个 AN急性治疗后缺乏专业护理，但95%的青少年拥有智能手机， 拟议的研究是创新的和重要的，因为它有未来的潜力，以减少复发， 在急性期后治疗窗口期优化现有的AN社区干预措施。

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.