Using Wearable Passive Sensing to Predict Engagement in Binge Eating in Response to Negative Affect: A Multimethod Investigation of Predictive Utility, Feasibility, and Acceptability

项目摘要/摘要 暴饮暴食，特征是在短时间内吃下大量食物，并伴有 对饮食失去控制的感觉，是一种公共健康危机。负面情绪是一个公认的前因。 为了暴饮暴食。暴饮暴食的情绪调节模型假设负性情绪升高 暴饮暴食的短暂风险，因为暴饮暴食可以缓解负面影响，并增强 行为。该领域现有的识别负面情绪升高时刻的能力，因此有狂欢的风险 进食，在很大程度上依赖于生态瞬时评估(EMA)。EMA涉及完成 在智能手机上进行实时调查，以报告整个过程中的行为、认知和情绪症状 这一天。尽管EMA提供了关于日常经历的生态有效信息，但EMA调查通常是 每天只提供5-6次，涉及情感强度的自我报告，并且无法评估生理 伴随着情感的唤醒。可穿戴的心理生理传感器，可测量情感的标记 包括心率、心率变异性和皮肤电活动在内的唤醒可能会增加EMA检查 提高我们实时准确检测暴饮暴食风险的能力。这些传感器客观上可以， 连续和被动地测量神经系统唤醒的生物标记物，这些生物标记物与情感一致，因此 使他们能够在连续的时间尺度上测量情感轨迹，检测负面情绪的变化 在个人意识到它们之前，减轻用户负担，提高数据的完整性。 尽管它们有潜力提高现场检测暴饮暴食风险的能力，但可行性和 这些传感器在暴饮暴食的个体中的可接受性尚未确定。此外，它还 尚不清楚从这些传感器提取的特征是否能充分区分阳性和 负面情绪状态，因为生理唤醒可能发生在负面情绪和正面情绪中 各州。本研究的目的是：1)检验传感器特征将区分阳性的假设 在暴饮暴食的个体中的负面情绪状态具有60%的准确率；2)检验假设 使用传感器数据和EMA报告的负面情绪数据来预测发生的机器学习算法 与使用EMA的算法相比，暴饮暴食事件预测暴饮暴食的准确性更高 3)使用混合方法评估可穿戴设备的可接受性和可行性 暴饮暴食的人身上的传感器。为此，本研究将招募30名具有 临床上有意义的暴饮暴食的人将佩戴Empatica E4腕带被动测量心率和 在四周的EMA调查中，皮肤电活动和报告影响和暴饮暴食。参与者具有 暴饮暴食(N=30)和社区饮食障碍临床医生(N=10)也将完成自我报告 评估有关使用的可行性、可接受性和用户偏好的措施和重点小组 为暴食提供动力的传感器改进了对暴饮暴食的瞬时干预。

PROJECT SUMMARY/ABSTRACT Binge eating, characterized by eating a large amount of food in a short period of time accompanied by a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent for binge eating. The affect regulation model of binge eating posits that elevated negative affect increases momentary risk for binge eating, as engaging in binge eating alleviates negative affect and reinforces the behavior. The field’s existing capacity to identify moments of elevated negative affect, and thus risk for binge eating, has largely relied on ecological momentary assessment (EMA). EMA involves the completion of surveys in real time on one’s smartphone to report behavioral, cognitive, and emotional symptoms throughout the day. Although EMA provides ecologically valid information about daily experiences, EMA surveys are often delivered only 5-6 times per day, involve self-report of affect intensity, and are unable to assess physiological arousal that accompanies affect. Wearable, psychophysiological sensors that measure markers of affect arousal including heart rate, heart rate variability, and electrodermal activity, may augment EMA surveys to improve our capacity to accurately detect risk for binge eating in real time. These sensors can objectively, continuous, and passively measure biomarkers of nervous system arousal that coincide with affect, thus allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect before the individual is consciously aware of them, and reduce user burden to improve data completeness. Despite their potential to improve the field’s capacity to detect risk for binge eating, the feasibility and acceptability of these sensors among individuals with binge eating has not yet been established. Additionally, it is unknown whether features extracted from these sensors can adequately distinguish between positive and negative affect states, given that physiological arousal may occur during both negative and positive affect states. The aims of the present study are: 1) test the hypothesis that sensor features will distinguish positive and negative affect states in individuals with binge eating with > 60% accuracy; 2) test the hypothesis that a machine learning algorithm using sensor data and EMA-reported negative affect data to predict the occurrence of binge eating episodes will predict binge eating with greater accuracy than an algorithm using EMA-reported negative affect alone; 3) use a mixed methods approach to evaluate acceptability and feasibility of wearable sensors among individuals with binge eating. To do so, the present study will recruit 30 individuals with clinically-significant binge eating who will wear Empatica E4 wristbands to passively measure heart rate and electrodermal activity and report affect and binge eating on EMA surveys for four weeks. Participants with binge eating (N = 30) and community eating disorder clinicians (N = 10) will also complete self-report measures and focus groups to assess the feasibility, acceptability, and user preferences regarding the use of sensors to power improved momentary interventions for binge eating.