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: 10652078
• 负责人: Cheri Alicia Levinson
• 金额: $ 46.95万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-06-14
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652078
• 关键词: Accelerometer; Adult; Affective; Aftercare; Algorithms; Anorexia Nervosa; Behavior Disorders; Behavior assessment; Behavioral; Binge Eating; Bulimia; Chronic; Circadian Dysregulation; Circadian Rhythms; Cognitive; Data; Data Collection; Diagnosis; Diagnostic; Dimensions; Disease remission; Eating Disorders; Ecological momentary assessment; Event; Evidence based treatment; Feeling; Fright; Funding; Future; Global Positioning System; Goals; Health Personnel; Heart Rate; Hyperphagia; Individual; Lead; Machine Learning; Maintenance; Measures; Methods; Modeling; Outcome; Participant; Patient Self-Report; Patients; Persons; Physiological; Physiology; Precision Medicine Initiative; Procedures; Publishing; Recovery; Relapse; Reporting; Research; Sample Size; Sampling; Severities; Signal Transduction; Sleep; Sleep Wake Cycle; Sleep disturbances; Symptoms; System; Techniques; Testing; Thinking; Thinness; Time; United States National Institutes of Health; Weight Gain; Work; dieting; effective therapy; feature detection; follow up assessment; follow-up; heart rate variability; improved; innovation; mobile computing; novel; novel therapeutics; personalized medicine; prediction algorithm; predictive modeling; prevent; public health relevance; purge; recruit; relapse prevention; response; sensor; sensor technology; severe mental illness; standard care; treatment response

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.

项目摘要/摘要 饮食失调（ED）是严重的精神疾病。循证治疗的功效率 低（<50％的响应），复发率很高（治疗后> 35％复发）。低 治疗反应和高复发率部分是由于ED是异质的事实 状况。因此，需要进行印度（即一个人）模型，以预测和 最终阻止了有问题的ED行为（例如，清除，暴饮暴食）和 缓解/复发。当前的续订应用程序大写了我们现有的数据收集 （n = 120 ED）既增加了我们的样本量（n = 140），并将数据收集扩展到两年 后续。我们的研究目标是：（1）表征和预测较短的较长期复发 （2）使用实时生理数据算法来预测ED行为的发作， 复发和缓解。我们将使用多个分析方法与新颖的分析方法相结合 独特建模的尖端进步。在我们目前的资助建议中，我们收集了 使用来自120名有诊断的人的移动和传感器技术的密集实时数据 在30天内神经性厌食（AN），非典型AN和神经性神经性神经性的 在1个月和6个月的时间内。在此续约中，我们将在18个月收集其他随访，然后 2年，包括对身体障碍和避免行为的行为评估。我们 还将收集一个新的参与者子样本（n = 20），并包括 全球定位系统（GPS），睡眠效果周期和昼夜节律破坏（CR）。 这些额外的评估将改善复发的特征，捕获更大的 复发事件的百分比（两年内约35％），提高了ED的预测准确性 行为，复发和缓解，并识别哪些特征（例如GPS，睡眠效果周期） 有助于提高准确性。具体目的是：（1）长期表征良好（18个月 和2年）在现有的ED样本中复发/缓解，（2）测试Indographic EMA和是否同时 生理（HR/HRV，EDA，ACC）特征可预测长期复发/缓解和（3） 确定添加GPS和Sleep-Wake ACC＆CR数据是否提高了我们的准确性 预测算法。拟议的研究使用了高度创新的方法，结合了 密集的纵向数据收集方法，所有远程程序，新的进步 印度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颖的治疗剂，例如即时移动和传感器警报 可以为临床医生和患者提供指导的系统，以防止有问题 ED行为并最终增加缓解并降低复发率。

项目成果

Not niche: eating disorders as an example in the dangers of overspecialisation.

不是利基市场：饮食失调是过度专业化危险的一个例子。

• DOI: 10.1192/bjp.2023.160
• 发表时间: 2024
• 期刊: The British journal of psychiatry : the journal of mental science
• 作者: Haynos,AnnF;Egbert,AmyH;Fitzsimmons-Craft,EllenE;Levinson,CheriA;Schleider,JessicaL
• 通讯作者: Schleider,JessicaL

Are central eating disorder network symptoms sensitive to item selection and sample? Implications for conceptualization of eating disorder psychopathology from a network perspective.

中枢性饮食失调网络症状对项目选择和样本敏感吗？

• DOI: 10.1037/abn0000865
• 发表时间: 2024
• 期刊: Journal of psychopathology and clinical science
• 作者: Cusack,ClaireE;Vanzhula,IrinaA;Sandoval-Araujo,LuisE;Pennesi,Jamie-Lee;Kelley,SeanW;Levinson,CheriA
• 通讯作者: Levinson,CheriA