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.

项目概要/摘要 饮食失调 (ED) 是一种严重的精神疾病，其死亡率是所有疾病中最高的。 精神疾病最广泛使用的经验支持治疗（认知障碍）。 行为疗法）仅对约 50% 的个体有效。 事实上，ED 是一种异质性疾病，具有不同的症状轨迹， 在当前的“一刀切”心理治疗中得到充分解决，直到我们能够确定什么。 个体症状持续或恶化，蜂群将继续面临困难 准确预测预后，并且没有经验指导来制定有针对性的 我们的科学前提是根据我们过去的工作制定的。 网络理论的应用将能够识别认知行为 维持和“触发”个体之间和个体内部的 ED 的症状网络。 研究目标是 (1) 识别个体 ED“触发”症状（认知、行为、情感、 (2) 将触发症状与实时生理数据相关联，以创建 预测 ED 行为发作的算法最终将识别症状。 我们将使用多单元分析方法 结合网络科学的新颖、前沿进展，我们将收集大量的真实信息。 使用移动和传感器技术获取有关认知、行为、情感和生理的时间数据 来自 120 名被诊断为神经性厌食症 (AN)、非典型 AN 和贪食症的个体 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、ED 行为）来测试“触发”症状是否可以预测 ED 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) 开发个性化网络来识别哪些认知、行为、情感和 生理症状维持 ED 并预测 ED 结果，并且 (2) 利用传感器数据 识别与核心维持相关的人体内和人之间的生理模式 所提出的研究采用了高度创新的方法。 方法，结合密集的纵向数据收集方法，所有远程程序，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，确定哪些是个性化的 如果我们确定了症状触发 ED 的模式，那么拟议的研究就会产生临床影响。 有助于个体内的症状网络变异，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疗，并提供新颖的干预措施 预防和治疗 ED 的目标。