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

项目总结/摘要 饮食失调是一种严重的精神疾病，死亡率最高， 精神障碍最广泛使用的经验支持治疗ED（认知 行为疗法）仅对约50%的个体有效。回复率低的原因是 事实上，ED是具有不同症状轨迹的异质性条件， 在当前的“一刀切”的心理治疗中得到充分解决。在我们确定 维持或加重个体症状，临床医生将继续有困难 准确预测预后，将没有经验指导，以制定有针对性的 促进康复的治疗计划。我们的科学前提，从我们过去的工作发展而来，是 网络理论的应用将使认知行为的识别 症状网络维持和“触发”个体之间和个体内部的ED。我们 研究目标是（1）识别个体艾德“触发”症状（认知，行为，情感， 和生理学）和（2）将触发症状与实时生理数据相关联， 预测艾德行为发作的算法。这些目标将最终确定症状 阻止完全缓解并导致复发。我们将使用多单元分析方法 结合了网络科学的新的、前沿的进展。我们将收集密集的真实的- 使用移动的和传感器技术获得关于认知、行为、情感和生理的时间数据 来自120名诊断为神经性厌食症（AN）、非典型AN和贪食症的个体 30天的神经。在1个月和6个月随访时，我们将评估艾德结局（例如， 缓解状态、艾德行为）以测试“触发”症状是否预测艾德结果。网络 科学和最先进的机器学习技术将首次让我们 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） 开发个性化的网络，以确定哪些认知，行为，情感， 生理症状维持ED并预测艾德结果，以及（2）利用传感器数据 识别与核心维护相关的人内和跨人的生理模式 症状和预测艾德行为。这项研究采用了高度创新的 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 技术来回答以前无法解决的问题， 症状会引发ED这项研究具有临床意义。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疗，并提供新的干预措施， 预防和治疗ED的目标。

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.