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

项目总结/摘要 医学专业人士最近提出休息的想法，有一个理想的减肥饮食， 大家肥胖的原因之一是暴饮暴食，但我们不知道什么样的模式和行为 助长了这种不良习惯。定义导致能量的有问题的饮食行为 失衡是治疗肥胖症的关键。研究通常集中在一个假定的因果关系 暴饮暴食的机制，如压力或渴望，而不是解决共同的多重特征， 发生在暴饮暴食。因此，预测暴饮暴食事件的因素仍然未知， 它们中的哪一个有助于个体暴饮暴食的一致性和可变性。 鉴于被动传感技术的最新进展，我们现在有可能发现有问题的 使用无缝捕获的生理特征进食，例如进食姿势的数量， 吞咽和心率变异性收集可检测和可预测的特征， 暴饮暴食将磨练出干预者可能最佳目标的模式， 肥胖人群了解他们的饮食习惯，并最终提高他们的自我调节能力， 规范自己的饮食行为。位置-规模模型将绘制出最具影响力的因素 在受试者中形成习惯，为干预者提供指导的基本目标 行为 第一个目标是收集基于传感器的生态瞬时评估数据（以评估 尚未通过传感检测到的因素），并应用机器学习 识别检测暴饮暴食的特征子集的算法，如对地面事实的验证 饮食录像和24小时饮食回忆。参与者将佩戴被动感应 传感器套件，并响应关于每个进食事件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事件的最佳特征子集 使用梯度推进器在第二个目标中，层次聚类技术将 将暴饮暴食事件归类为理论上有意义和临床上已知的问题 与暴饮暴食有关的行为。最终的目标是建立解释这种效应的统计模型 关于新习惯形成的可检测和临床已知的问题特征。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， 及时干预，通过防止暴饮暴食和保持健康饮食来治疗肥胖 行为。

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.