Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study.
Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study.
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DOI：
10.2196/jmir.5505
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发表时间：
2016-03-29
影响因子：
7.4
通讯作者：
Riper H
中科院分区：
文献类型：
--
作者：
Asselbergs J;Ruwaard J;Ejdys M;Schrader N;Sijbrandij M;Riper H
生态瞬时评估（EMA）是一种有用的方法，以挖掘在现实世界中的心理和行为现象的动态。然而，（自我报告）EMA的应答负担限制了其临床效用。其目的是探索基于移动的电话的不显眼的EMA，其中移动的电话使用日志被认为是临床相关的用户状态和上下文的代理措施。这是一项非对照探索性初步研究。我们的研究包括在荷兰学生群体（N=33）中进行为期6周的EMA/非侵入性EMA数据收集，然后进行回归建模分析。参与者在他们的移动的手机（EMA）上用一维情绪测量（1到10）和二维环测量（唤醒/效价，-2到2）自我监测他们的情绪。与此同时，在参与者的同意下，移动的手机应用程序从六个智能手机传感器日志（不引人注目的EMA：通话/短信服务（SMS）文本消息，屏幕时间，应用程序使用，加速度计和手机摄像头事件）中不引人注目地收集（Meta）数据。通过前向逐步回归（FSR），我们从不引人注目的EMA变量中建立了个性化的回归模型，以预测EMA情绪评分的日常变化。将这些模型的预测性能（即交叉验证的均方误差和正确预测百分比）与朴素基准回归模型（平均值模型和滞后2历史模型）进行比较。共有27名参与者（81%）提供了平均35.5天（SD 3.8）的有效EMA/非干扰性EMA数据。FSR模型准确预测了55%至76%的EMA情绪评分。然而，这些模型的预测性能显着劣于天真的基准模型。基于移动的电话的不显眼的EMA是技术上可行的并且潜在地强大的EMA变体。该方法是年轻的，阳性结果可能不会复制。目前，我们不建议在现实世界的临床环境中应用基于FSR的情绪预测。需要进一步的心理测量学研究和更先进的数据挖掘技术来释放不引人注目的EMA的真正潜力。
Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility. The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts. This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, –2 to 2). Meanwhile, with participants’ consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model). A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models. Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA’s true potential.