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

项目总结/摘要 饮食失调（ED）是严重的精神疾病。循证治疗的有效率 低（<50%反应）和复发率高（>35%治疗后复发）。低 治疗反应和高复发率部分是由于ED是异质性的 条件因此，具体的（即，一个人）的模型是需要的， 最终防止两种有问题的艾德行为（例如，净化，暴饮暴食）和 缓解/复发。当前的续订应用程序利用了我们现有的数据收集 （N=120艾德）以增加我们的样本量（N=140）并将数据收集延长至两年 随访我们的研究目标是：（1）表征和预测短期和长期复发 和缓解（2）使用实时生理数据算法来预测艾德行为的发作， 复发和缓解。我们将使用多个单元的分析方法结合新的， 在具体建模方面的前沿进展。在我们目前资助的提案中，我们收集了 使用移动的和传感器技术从120个诊断个体获得密集的实时数据 神经性厌食症（AN）、非典型AN和神经性贪食症在30天内的发生率，并在以下时间进行评估- 1个月和6个月时都有。在本次更新中，我们将收集18个月时的额外随访， 2-年，包括身体障碍和行为回避的行为评估。我们 还将收集一个新的参与者子样本（n=20），并包括额外的评估 全球定位系统（GPS）、睡眠-觉醒周期和昼夜节律紊乱（CR）。 这些额外的评估将改善复发的表征，捕获更大的 复发事件的百分比（2年内约35%），提高艾德预测的准确性 行为，复发和缓解，并确定哪些特征（例如，GPS，睡眠-觉醒周期） 有助于提高准确性。具体目标是：（1）充分表征长期（18个月） 和2年）现有ED样本中的复发/缓解，（2）检测是否同时存在特定EMA和 生理学（HR/HRV、EDA、ACC）特征预测长期复发/缓解，以及（3） 确定GPS和睡眠唤醒ACC & CR数据的增加是否提高了我们的准确性。 预测算法这项研究采用了高度创新的方法， 密集的纵向数据收集方法，所有远程程序， 具体的建模和传感器技术，以及最先进的机器学习技术。 这些数据将直接导致新的疗法，如即时移动的和传感器警报 系统可以为临床医生和患者提供关于如何预防问题的指导， 艾德行为，并最终增加缓解和降低复发率。

PROJECT SUMMARY/ABSTRACT Eating disorders (EDs) are severe mental illnesses. Efficacy rates of evidence-based treatments are low (<50% response) and relapse rates are high (>35% relapse after treatment). The low treatment response and high relapse rates are due, in part, to the fact that EDs are heterogeneous conditions. As such, idiographic (i.e., one person) models are needed that can predict and ultimately prevent, onset of both problematic ED behaviors (e.g., purging, binge eating) and remission/relapse. The current renewal application capitalizes on our existing data collection (N=120 ED) to both increase our sample size (N=140) and extend data collection to two years of follow-up. Our study goals are to: (1) characterize and predict shorter-and-longer-term relapse and remission (2) use real-time physiological data algorithms to predict onset of ED behaviors, relapse, and remission. We will use a multiple units of analysis approach combined with novel, cutting-edge advances in idiographic modeling. In our currently funded proposal, we collected intensive real-time data using mobile and sensor-technology from 120 individuals with a diagnosis of anorexia nervosa (AN), atypical AN, and bulimia nervosa across 30 days and assessed follow- up at 1-month and 6-months. In this renewal we will collect additional follow-ups at 18-month and 2-years and include behavioral assessments of body disturbances and behavioral avoidance. We will also collect a new subsample of participants (n=20) and include additional assessment of global positioning system (GPS), the sleep-wake cycle, and circadian rhythm disruption (CR). These additional assessments will improve characterization of relapse, capture a greater percentage of relapse events (~35% across two years), improve accuracy of prediction for ED behaviors, relapse, and remission, and identify which features (e.g., GPS, sleep-wake cycle) contribute to improved accuracy. Specific aims are to: (1) well-characterize longer-term (18 month and 2 years) relapse/remission in the existing sample of EDs, (2) test if both idiographic EMA and physiological (HR/HRV, EDA, ACC) features predict longer-term relapse/remission and (3) determine if the addition of GPS and sleep-wake ACC & CR data improve accuracy of our predictive algorithms. The proposed research uses highly innovative methods, combining intensive longitudinal data collection methods, all remote procedures, novel advances in idiographic modeling and sensor-technology, and state-of-the-art machine learning techniques. These data will lead directly to novel therapeutics such as just-in-time mobile and sensor alert systems that can provide guidance to both clinicians and patients on how to prevent problematic ED behaviors and ultimately increase remission and decrease relapse rates.