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

神经性厌食症康复智能技术：神经性厌食症急性后治疗的试点干预

• 批准号: 10284797
• 负责人: Kelsie Terese Forbush
• 金额: $ 24.23万
• 依托单位: UNIVERSITY OF KANSAS LAWRENCE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-13 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10284797
• 关键词: Acute; Address; Adolescence; Adolescent; Ambulatory Care; Anorexia; Anorexia Nervosa; Anxiety; Assessment tool; Back; Body Weight decreased; Brain; Car Phone; Caring; Cellular Phone; Chronic; Chronic Disease; Client; Clinical; Clinical Services; Clinical Trials; Communities; Community Practice; Coupled; Data; Development; Diagnosis; Eating Disorders; Effectiveness; Effectiveness of Interventions; Emotions; Equipment and supply inventories; Evidence based intervention; Family; Feedback; Focus Groups; Future; Goals; Health Personnel; Impairment; Individual; Inosine Dialdehyde; Intervention; Lead; Left; Length; Machine Learning; Measures; Mental Depression; Mental disorders; Mission; Moods; National Institute of Mental Health; Notification; Organ; Osteopenia; Osteoporosis; Outcome; Outpatients; Output; Paper; Participant; Patients; Prevention; Process; Protocols documentation; Psyche structure; Public Health; Randomized; Randomized Controlled Trials; Recovery; Relapse; Reporting; Research; Risk; Science; Signal Transduction; Structure; Symptoms; Technology; Technology Assessment; Testing; United States National Institutes of Health; Validity and Reliability; Work; adolescent patient; anxiety symptoms; base; clinical decision support; clinical practice; computerized; depressive symptoms; digital; early onset; eating pathology; efficacious intervention; evidence base; improved; improved outcome; innovation; mHealth; machine learning algorithm; medical specialties; mortality; personalized medicine; post intervention; practice setting; predictive tools; prevent; response; risk prediction; routine practice; satisfaction; support tools; tool; treatment optimization; treatment response; usability

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.

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疾病，通常在青春期发病。 虽然基于家庭的干预措施对高达 75% 的 AN 青少年有效，但大约 30% 康复后会复发，迫切需要优化治疗并预防出院后复发。 为了改善 AN 青少年的治疗效果，我们的团队致力于满足这一迫切需求。 开发了一套数字工具，促进评估、风险预测和临床决策的科学发展 支持在急性后治疗窗口中使用，称为“厌食症恢复智能治疗（STAR）”。 STAR 使用尖端评估技术来缩短测试管理和机器学习来预测 然后通过易于使用的临床信息将这些信息返回给临床医生。 当用户输入的数据表明患者没有进展时，决策支持工具会提醒临床医生。 对于当前的应用程序，我们建议扩展 STAR 以测试在 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 适应性移动医疗干预将改善门诊治疗反应并减少青少年复发 我们之前的工作支持我们的科学前提。 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 相关的精神障碍和恢复 然而，我们纸质项目的数量。 评估工具为 144，对于日常使用来说太长了。为了克服这一挑战，我们开发了一个。 手机应用程序使用计算机化自适应测试将评估时间缩短多达 50%，同时 我们建议利用这一点，保留原始纸笔测量的可靠性和有效性。 创新以优化 AN 的面对面治疗和移动医疗治疗。我们的目标是：1) 开发 移动健康干预（根据临床医生和利益相关者的意见）以及 2) 建立可行性、可接受性和 使用临床医生和患者数据来确定我们的移动医疗干预的初步效果大小。 目标，我们将采用计算机化自适应测试与机器学习算法相结合，交付 当他们的客户面临不良结果和复发的风险时，在我们的应用程序中向人群发出信号特定目标。 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移动医疗信息； 2) 对我们的综合评估和移动医疗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。 AN 急性治疗后缺乏专业护理，但 95% 的青少年拥有智能手机， 拟议的研究具有创新性和意义，因为它具有减少复发和减少复发的未来潜力。 在急性后治疗窗口期优化现有社区提供的 AN 干预措施。