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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要饮食失调（ED）是严重的精神疾病。循证治疗的有效率疗效低（<50%），复发率高（治疗后复发>35%）。低治疗反应和高复发率部分是由于 ED 具有异质性状况。因此，需要具体的（即一个人）模型来预测和最终预防有问题的 ED 行为（例如，排泄、暴饮暴食）的发生，以及缓解/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 ED) 既增加了样本量 (N=140)，又将数据收集延长至两年后续行动。我们的研究目标是：(1) 描述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 ED 行为的发生，复发、缓解。我们将使用多单元分析方法与新颖的、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和传感器技术收集来自 120 名诊断患者的密集实时数据神经性厌食症 (AN)、非典型 AN 和神经性贪食症的 30 天，并进行随访评估 1个月和6个月上涨。在本次更新中，我们将在 18 个月和 2 年，包括对身体障碍和行为回避的行为评估。我们还将收集新的参与者子样本（n = 20）并包括对全球定位系统 (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特征，捕获更大的复发事件的百分比（两年内约 35%），提高 ED 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) 充分描述长期（18 个月）和 2 年）现有 ED 样本中的复发/缓解，（2）测试具体的 EMA 和生理（HR/HRV、EDA、ACC）特征可预测长期复发/缓解以及 (3) 确定添加 GPS 和睡眠唤醒 ACC 和 CR 数据是否可以提高我们的准确性预测算法。拟议的研究采用高度创新的方法，结合密集的纵向数据收集方法、所有远程程序、新进展具体建模和传感器技术，以及最先进的机器学习技术。这些数据将直接导致新的治疗方法，例如即时移动和传感器警报可以为临床医生和患者提供有关如何预防问题的指导的系统 ED 行为并最终提高缓解率并降低复发率。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

使用机器学习区分非典型神经性厌食症和神经性厌食症。

DOI：
发表时间：
2024-04
期刊：
影响因子：
0
作者：
Sandoval;Cusack, Claire E;Ralph;Glatt, Sofie;Han, Yuchen;Bryan, Jeffrey;Hooper, Madison A;Karem, Andrew;Levinson, Cheri A
通讯作者：
Levinson, Cheri A

Comparisons between atypical anorexia nervosa and anorexia nervosa: Psychological and comorbidity patterns.

非典型神经性厌食症和神经性厌食症之间的比较：心理和合并症模式。

DOI：
发表时间：
2024-04
期刊：
影响因子：
0
作者：
Fitterman;Han, Yuchen;Osborn, Kimberly D;Faulkner, Loie M;Williams, Brenna M;Pennesi, Jamie;Levinson, Cheri A
通讯作者：
Levinson, Cheri A

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？