Emotion dysregulation is a known risk factor for a variety of maladaptive eating behaviors, including emotional eating (Crockett, Myhre, & Rokke, 2015; Evers et al., 2010; Lavender et al., 2015). New passive sensing technologies offer the prospect of detecting emotion dysregulation in real-time through measurement of heart rate variability (HRV), a transdiagnostic bio-signal of emotion regulation, which may in turn signal risk of engaging in a maladaptive eating behavior. In the current study, our primary aim was to test the hypothesis that momentary changes in HRV can be used to detect risk of experiencing an emotional eating episode in an ecologically valid setting using a wrist worn sensor with acceptable classification accuracy. Participants were 21 adults with clinically significant emotional eating behaviors. Participants wore the Empatica E4 wrist-sensor and tracked all emotional eating episodes using ecological momentary assessment for four weeks. Time and frequency domain features of HRV were extracted in the 30-min period preceding emotional eating episodes and control cases (defined as the 30 min prior to an EMA survey that did not contain an emotional eating episode). Support vector machine (SVM) learning models were implemented using time domain and frequency domain features. SVM models using frequency domain features achieved the highest classification accuracy (77.99%), sensitivity (78.75%), and specificity (75.00%), consistent with standards deemed acceptable for the prediction of event-level health behavior. SVM models using time domain features still performed above chance, though were less accurate at classifying episodes (accuracy 63.48%, sensitivity 62.68%, and specificity 70.00%) and did not meet acceptable classification accuracy. Wearable sensors that assess HRV show promise as a tool for capturing risk of engaging in emotional eating episodes.

中文翻译：

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心率变异性的瞬时变化可以检测情绪化进食事件的风险

情绪失调是各种适应不良饮食行为的已知风险因素，包括情绪化饮食（Crockett、Myhre 和 Rokke，2015 年；Evers 等人，2010 年；Lavender 等人，2015 年）。新的被动传感技术提供了通过测量心率变异性 (HRV) 实时检测情绪失调的前景，心率变异性是情绪调节的一种跨诊断生物信号，这可能反过来表明发生适应不良饮食行为的风险。在当前的研究中，我们的主要目的是检验这样一个假设，即 HRV 的瞬时变化可用于使用具有可接受分类精度的腕戴传感器检测在生态有效环境中经历情绪化进食事件的风险。参与者是 21 名具有临床显着情绪饮食行为的成年人。参与者佩戴 Empatica E4 腕式传感器并使用生态瞬时评估跟踪所有情绪化进食事件持续四个星期。在情绪化进食事件和对照病例之前的 30 分钟内（定义为不包含情绪化进食事件的 EMA 调查之前的 30 分钟）提取 HRV 的时域和频域特征。支持向量机 (SVM) 学习模型是使用时域和频域特征实现的。使用频域特征的 SVM 模型实现了最高的分类准确度 (77.99%)、灵敏度 (78.75%) 和特异性 (75.00%)，与事件级健康行为预测可接受的标准一致。使用时域特征的 SVM 模型的表现仍然高于机会，但在对情节进行分类时不太准确（准确率 63.48%，敏感性 62.68%，特异性 70.00%），但未达到可接受的分类准确度。评估 HRV 的可穿戴传感器有望成为捕捉情绪化进食风险的工具。