随着移动设备已经成为我们日常生活的核心部分,它们在研究中也变得越来越重要。在医学方面,例如,智能手机用于使用生态瞬时评估(EMA)收集生态有效和纵向数据,这主要是通过通过智能通知发送问卷来实现的。这种类型的数据收集旨在实时和长期地捕获患者的状况。为了收集更客观和上下文的数据,更好地了解患者,研究人员不仅可以通过EMA使用患者输入,但也使用传感器作为移动人群感知(MCS)方法的一部分。在本文中,我们通过系统的文献综述研究了研究人员如何在EMA背景下接受MCS这一主题。这个PRISMA指导的审查是基于数据库PubMed,WebofScience,和EBSCOhost。通过结果表明,一般的EMA研究和传感器在EMA研究中的使用都在稳步增长。此外,审查的大多数研究都使用移动应用程序向参与者提供EMA,使用了固定时间的提示策略,并使用信号偶然性或间隔偶然性自我评估作为抽样/评估策略。EMA研究中最常用的传感器是加速度计和GPS。在大多数研究中,这些传感器用于简单的数据收集,但传感器数据也常用于验证研究参与者的反应,不太常见,触发EMA提示。仅在mHealthEMA出版物的一部分中解决了安全性和隐私方面的问题。此外,我们发现,EMA依从性与提示总数呈负相关,在使用基于微相互作用的EMA(μEMA)方法的研究以及使用传感器的研究中,EMA依从性较高.总的来说,我们设想未来可以更好地利用智能手机和传感器技术能力的潜力,更自动化的方法。
As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient\'s condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients\' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.