Mishra Varun, Caine Kelly, Lowens Byron, Kotz David, Lord Sarah
Dartmouth College Hanover, NH.
Clemson University Clemson, SC.
Proc ACM Int Conf Ubiquitous Comput. 2017 Sep;2017:935-940. doi: 10.1145/3123024.3124571.
In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) trigger. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant's activity, conversation status, audio, and location, we can predict if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.
在这项工作中，我们试图确定参与者的上下文信息是否可用于预测该参与者是否会对特定的生态瞬时评估（EMA）触发做出反应。我们在工作中使用了一个公开可用的数据集，并发现通过使用有关参与者活动、对话状态、音频和位置的基本上下文特征，我们可以预测在特定时间触发的EMA是否会得到回复，其精度为0.647，这显著高于0.41的基线精度。利用这一知识，进行实地研究的研究人员可以有效地安排EMA并实现更高的回复率。
