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; 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.

项目摘要/摘要 厌食症神经（AN）的死亡率最高。 尽管基于家庭的干预措施对于多达75％的青少年有效，但大约30％ 恢复后将缓解。迫切需要优化治疗和防止后收力后继电器 急性治疗以改善An的青少年的结局。为了满足这一关键需求，我们的团队 开发了一套数字工具，可以推进评估，风险预测和临床决策的科学 支持在急性后治疗窗口中使用，称为“厌食症恢复（星星）的智能治疗”。 Star使用尖端评估技术来缩短测试管理和机器学习来预测 恢复的可能性。然后，通过易于使用的临床向临床提供此信息 当用户输入的数据表明患者没有进展时，决策支持工具以提醒临床。在 当前的应用程序，我们建议扩展星星以测试在 放电后窗口。我们的科学前提是转诊评估和临床决策 星套内提供的支持工具将优化面对面的临床服务，并增加 自适应MHealth干预将改善门诊治疗反应并减少青少年的缓解 从强化治疗中排出。我们以前的工作支持我们的科学前提。具体来说，我们的 研究为我们的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相关的精神障碍和康复。但是，我们基于纸的项目数量 评估工具为144，常规使用过于漫长。为了克服这一挑战，我们开发了 使用计算机化自适应测试将评估长度降低多达50％的手机应用 保留原始纸笔测量的可靠性和有效性。我们建议利用这一点 创新以优化面对面和MHealth治疗的创新。我们的目标是：1）开发 MHealth干预措施（使用临床和利益相关者的意见）和2）建立可行性，可接受性和 使用临床和患者数据同时使用MHealth干预的初步效果大小。完成我们的 目标，我们将采用计算机化自适应测试，加上机器学习算法，交付 在我们的应用程序中向临床医生发出信号时，他们的客户处于危险中，以表现出糟糕的结果和救济。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信息； 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）测试导致符号变化的初步机制。鉴于有一个 在以下急性治疗中缺乏专业护理，但有95％的青少年有智能手机， 拟议的研究具有创新性和重要意义，因为它具有减少继电器和的未来潜力 优化现有的社区交付干预措施，以备后急性治疗窗口。