SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing.

SenseWhy：从被动感知的角度看肥胖症的暴饮暴食。

基本信息

批准号：
10406434
负责人：
Nabil Alshurafa
金额：
$ 5.38万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2022-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10406434
关键词：
Adult Affect Alcohol consumption Algorithms American Appetitive Behavior Award Behavior Behavioral Medicine Behavioral Sciences Body Weight decreased Calories Chronic Disease Clinical Computers Cues Custom Data Data Analytics Deglutition Detection Diet Diet Habits Dietitian Eating Eating Behavior Ecological momentary assessment Energy Intake Etiology Event Family Feeding behaviors Food Access Fostering Foundations Friends Gestures Goals Habits Health Care Costs Healthy Eating Heart Rate Hour Hyperphagia Impulsivity Individual Intake Intervention Knowledge Lead Learning Life Location Machine Learning Maintenance Maps Mastication Measures Medical Methods Modeling Monitor Neck Obesity Participant Patient Recruitments Patient Self-Report Pattern Phenotype Physiological Population Predictive Factor Public Health Records Regimen Research Personnel Rest Role Running Scientist Sensitivity and Specificity Statistical Methods Statistical Models Stress Techniques Technology Time Training Videotape Wrist adaptive intervention adult obesity algorithmic methodologies base behavioral phenotyping computer science craving dietary emotional eating evidence base experience feeding heart rate variability hedonic improved lens machine learning algorithm machine learning method novel obese person obesity prevention obesity treatment personalized care predictive modeling psychologic response sensor social substance abuse treatment theories wearable device wearable sensor technology

项目摘要

PROJECT SUMMARY/ABSTRACT Medical professionals have recently put to rest the idea that there is an ideal weight loss diet for everyone. One cause for obesity is overeating, but we do not know what patterns and behaviors contribute to this problematic habit. Defining problematic eating behaviors that lead to energy imbalance is essential for treating obesity. Studies typically focus on a single putative causal mechanism of overeating such as stress or craving, not addressing the multiple features that co- occur with overeating. Hence, the factors that predict overeating episodes remain unknown, as do which of them contribute to an individual's consistency and variability of overeating. Given recent advancements in passive sensing, we now have the potential to detect problematic eating using seamlessly captured physiological features such as number of feeding gestures and swallows, and heart rate variability. Collecting detectable and predictable features that identify overeating will hone in on the patterns that interventionists may optimally target to help populations with obesity understand their eating habits and ultimately improve their ability to self- regulate their eating behaviors. Location-scale models will map the factors that most contribute to habit formation within subjects, providing interventionists with essential targets to guide behavior. The first aim is to collect sensor-based and ecological momentary assessment data (to assess factors not yet detectable through sensing) from adults with obesity and apply machine learning algorithms to identify a subset of features that detect overeating, as validated against ground truth of videotaped eating episodes and 24 hour dietary recall. Participants will wear a passive sensing sensor suite and respond to random and event-triggered prompts regarding each eating episode. Then, machine learning will determine the optimal feature subset that detect overeating episodes using Gradient Boosting Machines. In the second aim, hierarchical clustering techniques will cluster overeating episodes into theoretically meaningful and clinically known problematic behaviors related to overeating. The final aim is to build statistical models that explain the effect of detectable and clinically-known problematic features on new habit formation. These models will lay a foundation for optimization studies to discover evidence-based decision rules that can guide timely interventions to treat obesity by preventing overeating, and maintaining healthy eating behaviors.

项目摘要/摘要医疗专业人员最近提出了一个理想的减肥饮食的想法每个人。肥胖的原因之一是暴饮暴食，但我们不知道哪些模式和行为有助于这个有问题的习惯。定义导致能量的有问题的饮食行为失衡对于治疗肥胖至关重要。研究通常集中于单个推定因果强调或渴望之类的暴饮暴食机制，而不是解决共同的多种特征暴饮暴食发生。因此，预测暴饮暴事的因素仍然未知，因为他们当中的哪一个有助于个人的一致性和暴饮暴食的可变性。鉴于被动传感的最新进展，我们现在有可能发现有问题的使用无缝捕获的生理特征饮食，例如喂养手势的数量和燕子和心率变异性。收集可检测和可预测的功能来识别暴饮暴食将磨练干预主义者可能最佳针对的模式以帮助肥胖的人群了解他们的饮食习惯，并最终提高他们自我自我的能力调节他们的饮食行为。位置尺度模型将绘制最大的因素为受试者内部的习惯形成，为干预者提供基本目标来指导行为。第一个目的是收集基于传感器的生态瞬时评估数据（评估来自肥胖的成年人并应用机器学习的因素尚未检测到的因素算法以确定检测暴饮暴食的特征子集，以验证地面真相录像的饮食情节和24小时饮食召回。参与者将佩戴被动感传感器套件并响应每个饮食情节的随机和事件触发的提示。然后，机器学习将确定检测暴饮暴食的最佳特征子集使用梯度提升机。在第二个目标中，分层聚类技术将聚集过多的情节中的理论上有意义且临床上已知的有问题与暴饮暴食有关的行为。最终目的是建立统计模型来解释效果关于新习惯形成的可检测和临床上有问题的特征。这些模型将为优化研究奠定基础，以发现基于证据的决策规则，以指导及时的干预措施通过防止暴饮暴食和保持健康饮食来治疗肥胖行为。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

To Mask or Not to Mask? Balancing Privacy with Visual Confirmation Utility in Activity-Oriented Wearable Cameras.

DOI：
10.1145/3351230
发表时间：
2019-09-01
期刊：
Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
影响因子：
0
作者：
Alharbi, Rawan;Tolba, Mariam;Alshurafa, Nabil
通讯作者：
Alshurafa, Nabil

micro-Stress EMA: A Passive Sensing Framework for Detecting in-the-wild Stress in Pregnant Mothers.

DOI：
10.1145/3351249
发表时间：
2019-09-01
期刊：
Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
影响因子：
0
作者：
King, Zachary D;Moskowitz, Judith;Alshurafa, Nabil
通讯作者：
Alshurafa, Nabil

Association of number of bites and eating speed with energy intake: Wearable technology results under free-living conditions.