Personalized Networks and Sensor Technology Algorithms of Eating Disorder Symptoms Predicting Eating Disorder Outcomes

个性化网络和传感器技术饮食失调症状的算法预测饮食失调的结果

• 批准号: 10044077
• 负责人: Cheri Alicia Levinson
• 金额: $ 46.32万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2023-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10044077
• 关键词: Acceleration; Accelerometer; Address; Adult; Affect; Affective; Algorithms; Anorexia Nervosa; Anxiety; Behavior; Behavior Disorders; Behavioral; Behavioral Symptoms; Binge Eating; Bulimia; Cellular Phone; Chronic; Clinical; Cognition; Cognitive; Cognitive Therapy; Data; Data Collection; Development; Diagnosis; Disease remission; Eating Disorders; Epilepsy; Evidence based treatment; Fright; Future; Goals; Gold; Health Personnel; Heart Rate; Individual; Individual Differences; Interruption; Intervention; Lead; Machine Learning; Maintenance; Mental disorders; Methods; Modeling; Outcome; Pathway Analysis; Pathway interactions; Patient Self-Report; Patients; Pattern; Persons; Physiological; Physiology; Precision Medicine Initiative; Procedures; Psychiatry; Psychopathology; Psychotherapy; Recovery; Relapse; Research; Science; Seizures; Signal Transduction; Structure; Symptoms; System; Techniques; Technology; Testing; Time; United States National Institutes of Health; Variant; Weight Gain; Work; base; effective therapy; follow-up; food restriction; innovation; mobile computing; mortality; novel; outcome forecast; personalized intervention; personalized medicine; prediction algorithm; prevent; psychologic; public health relevance; purge; recruit; response; sensor; sensor technology; severe mental illness; smartphone Application; standard care; targeted treatment; theories; treatment planning; wearable sensor technology

PROJECT SUMMARY/ABSTRACT Eating disorders (EDs) are severe mental illnesses with the highest mortality rate of any psychiatric disorder. The most widely used empirically supported treatment for EDs (cognitive behavior therapy) is only efficacious for ~50% of individuals. This low response rate is due to the fact that EDs are heterogeneous conditions with diverse symptom trajectories that are not adequately addressed in current “one-size-fits-all” psychotherapies. Until we can identify what maintains or exacerbates individual symptoms, clinicians will continue to have difficulty accurately predicting prognosis and will have no empirical guidance to develop targeted treatment plans to promote recovery. Our scientific premise, developed from our past work, is that the application of network theory will enable the identification of cognitive-behavioral symptom networks that maintain and ‘trigger’ EDs both between and within individuals. Our study goals are to (1) identify individual ED ‘trigger’ symptoms (cognitions, behaviors, affect, and physiology) and (2) correlate trigger symptoms with real-time physiological data to create an algorithm predicting onset of ED behaviors. These goals will ultimately identify symptoms that prevent full remission and lead to relapse. We will use a multiple units of analysis approach combined with novel, cutting-edge advances in network science. We will collect intensive real- time data on cognitions, behavior, affect, and physiology using mobile and sensor-technology from 120 individuals with a diagnosis of anorexia nervosa (AN), atypical AN, and bulimia nervosa across 30 days. At 1-month and 6-month follow ups we will assess ED outcomes (e.g., remission status, ED behaviors) to test if ‘trigger’ symptoms predict ED outcomes. Network science and state-of-the-art machine learning techniques will allow us, for the first time, to discover whether certain trigger symptoms predict worse outcomes. Specific aims are to (1) develop personalized networks to identify which cognitive, behavioral, affective, and physiological symptoms maintain EDs and predict ED outcomes and (2) utilize sensor data to identify physiological patterns both within and across people that correlate with core maintaining symptoms and that predict ED behaviors. The proposed research uses highly innovative methods, combining intensive longitudinal data collection methods, all remote procedures, novel advances in network science and sensor-technology, and state-of-the-art machine learning techniques to answer previously unresolvable questions pinpointing which personalized symptoms trigger EDs. The proposed research has clinical impact. If we identify patterns that contribute to symptom network variation within individuals, these data will provide a model of personalized medicine for the entire field of psychiatry, as well as providing novel intervention targets to prevent and treat EDs.

项目摘要/摘要 饮食失调(EDs)是死亡率最高的严重精神疾病。 精神障碍。最广泛使用的经验性支持的EDS治疗(认知 行为疗法)只对50%的人有效。这一低应答率是由于 事实上，ED是具有不同症状轨迹的异质性疾病，而不是 在目前的“一刀切”心理疗法中得到了充分的解决。直到我们能确定 维持或加剧个别症状，临床医生将继续存在困难 准确预测预后，将没有经验指导制定有针对性的 治疗计划是为了促进康复。我们从过去的工作中发展出来的科学前提是 网络理论的应用将使认知行为的识别成为可能 维持和“触发”个体之间和个体内部的ED的症状网络。我们的 研究目标是：(1)确定个体的ED‘触发’症状(认知、行为、情感、 和生理)和(2)将触发症状与实时生理数据相关联以创建 一种预测ED行为开始的算法。这些目标最终将识别症状 这会阻止完全缓解并导致复发。我们将使用多单元分析方法 与网络科学中新颖、前沿的进步相结合。我们将收集密集的真实- 使用移动和传感器技术获得认知、行为、情感和生理方面的时间数据 来自120名被诊断为神经性厌食症、不典型神经性厌食症和暴食症的人 神经官能症持续30天。在1个月和6个月的随访中，我们将评估ED的结果(例如， 缓解状态、勃起功能障碍行为)，以测试“触发”症状是否预示着勃起功能障碍的结果。网络 科学和最先进的机器学习技术将使我们第一次能够 发现某些触发症状是否预示着更糟糕的结果。具体目标是(1) 开发个性化网络，以确定哪些认知、行为、情感和 生理症状维持ED并预测ED结果，以及(2)利用传感器数据 确定与核心维护相关的人内和人之间的生理模式 症状和预测勃起功能障碍的行为。拟议的研究使用了高度创新的 方法，结合集约化纵向数据采集方法，全远程操作，新颖 网络科学和传感器技术的进步，以及最先进的机器学习 回答以前无法解决的问题的技术，以确定哪些个性化 症状会引发急救。这项拟议的研究具有临床影响。如果我们识别出 有助于个体内的症状网络变化，这些数据将提供一个模型 为整个精神病学领域提供个性化医学，以及提供新的干预 预防和治疗EDS的目标。