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

项目摘要/摘要 医学专业人士最近打消了这样一种观点，即有一种理想的减肥饮食可以 所有人。肥胖的一个原因是暴饮暴食，但我们不知道是什么模式和行为 助长这个有问题的习惯。定义导致能量消耗的有问题的饮食行为 失衡是治疗肥胖症的关键。研究通常集中在一种推定的因果关系上 暴饮暴食的机制，如压力或渴望，没有解决共同的多重特征 发生在吃得太多的时候。因此，预测暴饮暴食的因素仍然未知，因为 它们中的哪一个对个人暴饮暴食的一致性和可变性有贡献吗？ 鉴于最近在被动传感方面的进步，我们现在有可能检测到有问题的 使用无缝捕获的生理特征进行进食，例如进食手势和 燕子和心率变异性。收集可检测和可预测的特征以识别 暴饮暴食将磨练干预主义者可能最佳的帮助目标模式 肥胖人群了解他们的饮食习惯，并最终提高他们的自我保健能力 规范他们的饮食行为。区位尺度模型将绘制出影响最大的因素 在受试者中养成习惯，为干预者提供必要的目标来指导 行为。 第一个目标是收集基于传感器的和生态瞬时评估数据(评估 尚未通过感知检测到的因素)，并应用机器学习 识别检测暴饮暴食的特征子集的算法，并根据基本事实进行验证 录像带的进食情节和24小时的饮食回忆。参与者将佩戴被动感应器 传感器套件，并对有关每个进食事件的随机和事件触发提示做出反应。 然后，机器学习将确定检测暴饮暴食发作的最佳特征子集 使用梯度助推机。在第二个目标中，层次聚类技术将 将暴食发作归类为理论上有意义的和临床上已知的问题 与暴食有关的行为。最终的目标是建立统计模型来解释这种影响 关于新习惯形成的可检测到的和临床上已知的问题特征。这些型号将 为优化研究奠定基础，发现基于证据的决策规则，以指导 及时干预，防止暴饮暴食，保持健康饮食，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.