Designing opportune stress intervention delivery timing using multi-modal data
Designing opportune stress intervention delivery timing using multi-modal data
复制标题
使用多模式数据设计适当的压力干预实施时机
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Czerwinski
中科院分区：
文献类型：
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作者：
Akane Sano;Paul Johns;M. Czerwinski
本文描述了一个针对工作场所信息办公室工作人员的微压力干预系统，他们对干预的反应以及预测提供干预的最佳时机的机器学习模型。我们对30名上班族进行了为期10天的研究，通过监测他们的电脑和应用程序使用情况、睡眠、活动、心率及其可变性，以及通过我们的桌面软件提供的微压力干预的历史，来检查他们的工作模式。我们分析了压力干预接受/拒绝的时间模式，以及他们对干预的主客观反应与感知工作投入、挑战和压力水平之间的关系。然后，我们开发了机器学习模型，以基于这些多模态数据预测更好的压力干预交付时间。我们发现，使用多核支持向量机算法预测干预时机的准确性高达80.0%，这些特征来自计算机和应用程序的使用情况、活动、心率变异性和压力干预历史。这些发现可以帮助从业者设计出最有效、及时、闭环的压力干预措施。据我们所知，这是第一批回顾适时压力干预的交付时间研究的论文之一，这可能对设计压力干预技术产生重大影响。
This paper describes a micro-stress intervention system for information office workers in the workplace, their responses to the interventions and machine learning models to predict the most opportune timing for providing the interventions. We studied 30 office workers for 10 days and examined their work patterns by monitoring their computer and application usage, sleep, activity, heart rate and its variability, as well as the history of micro-stress interventions provided through our desktop software. We analyzed temporal patterns of stress intervention acceptance/rejection and the relationships between their subjective and objective responses to the interventions and perceived work engagement, challenge and stress levels. We then developed machine learning models to predict better stress intervention delivery timing based on this multi-modal data. We found that features from computer and application usage, activity, heart rate variability and stress intervention history showed up to 80.0% accuracy in predicting good or bad intervention timing using a multi-kernel support vector machine algorithm. These findings could help practitioners design the most effective, just-in-time, closed-loop, stress interventions. To our knowledge, this is one of the first papers to review opportune stress interventions' delivery timing research, which could have a big influence in designing stress intervention technologies.
午餐后表现下降的昼夜节律决定因素。
影响因子：
2.8
作者：
Monk,TH;Buysse,DJ;Reynolds3rd,CF;Kupfer,DJ
通讯作者：
Kupfer,DJ
影响因子：
5.6
作者：
R. Leproult;G. Copinschi;O. Buxton;E. Cauter
通讯作者：
R. Leproult;G. Copinschi;O. Buxton;E. Cauter