micro-Stress EMA: A Passive Sensing Framework for Detecting in-the-wild Stress in Pregnant Mothers.
micro-Stress EMA: A Passive Sensing Framework for Detecting in-the-wild Stress in Pregnant Mothers.
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DOI：
10.1145/3351249
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发表时间：
2019-09-01
影响因子：
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通讯作者：
Alshurafa, Nabil
中科院分区：
文献类型：
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作者：
King, Zachary D;Moskowitz, Judith;Alshurafa, Nabil
怀孕期间的高度压力会增加早产或低出生体重婴儿的机会。自我报告的压力感知通常不会捕捉或与生理和行为反应保持一致。但是，如果有一种自我报告的方法可以更好地捕捉生理反应呢？目前的感知压力自我报告评估要求用户在一天中的不同时间点回答多项量表。将其简化为一个问题，使用基于微相互作用的生态瞬时评估（micro-EMA，收集单个原位自我报告以评估行为）使我们能够识别更小或更微妙的生理变化。它还允许更频繁的响应，以捕捉感知到的压力，同时减轻参与者的负担。我们提出了一个框架，选择最佳的微EMA，结合无偏特征选择和无监督聚集聚类。我们测试我们的框架在18名妇女执行16个活动在实验室穿着生物邮票，NeuLog，和极地胸带。我们在现实世界中的17名孕妇中验证了我们的结果。我们的框架表明，问题“你有多担心？“在使用生理模型时会导致最高的准确性。我们的研究结果提供了进一步深入的暴露在现实世界的情况下评估压力模型的挑战。
High levels of stress during pregnancy increase the chances of having a premature or low-birthweight baby. Perceived self-reported stress does not often capture or align with the physiological and behavioral response. But what if there was a self-report measure that could better capture the physiological response? Current perceived stress self-report assessments require users to answer multi-item scales at different time points of the day. Reducing it to one question, using microinteraction-based ecological momentary assessment (micro-EMA, collecting a single in situ self-report to assess behaviors) allows us to identify smaller or more subtle changes in physiology. It also allows for more frequent responses to capture perceived stress while at the same time reducing burden on the participant. We propose a framework for selecting the optimal micro-EMA that combines unbiased feature selection and unsupervised Agglomerative clustering. We test our framework in 18 women performing 16 activities in-lab wearing a Biostamp, a NeuLog, and a Polar chest strap. We validated our results in 17 pregnant women in real-world settings. Our framework shows that the question "How worried were you?" results in the highest accuracy when using a physiological model. Our results provide further in-depth exposure to the challenges of evaluating stress models in real-world situations.