Ponnada Aditya, Wang Shirlene, Chu Daniel, Do Bridgette, Dunton Genevieve, Intille Stephen
Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
Bouve College of Health Sciences, Northeastern University, Boston, MA, United States.
JMIR Form Res. 2022 Feb 9;6(2):e32772. doi: 10.2196/32772.
Ecological momentary assessment (EMA) uses mobile technology to enable in situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. Although the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in situ self-report data that are less burdensome yet provide comprehensive information on an individual's behaviors and states. A new approach, microinteraction EMA (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated.
This paper describes the μEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the μEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the μEMA app, changes made to the main TIME Study μEMA protocol and app based on the pilot feedback, and preliminary μEMA results from a subset of active participants in the TIME Study.
The TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. Every day, participants also answer a nightly EMA survey. On non-EMA burst days, participants answer μEMA questions on the smartwatch, assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants describe their experience with EMA and μEMA in a semistructured interview. A pilot study was used to test and refine the μEMA protocol before the main study.
Changes made to the μEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%).
The TIME Study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that μEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report data as people go about their lives.
生态瞬时评估(EMA)利用移动技术实现对行为和状态的现场自我报告数据收集。在典型的EMA研究中,参与者每天会被多次提示回答多组多项选择题。尽管EMA的重复性质减少了回忆偏差,但可能会带来参与负担。有必要探索补充方法来收集现场自我报告数据,这些方法负担较小,但能提供有关个人行为和状态的全面信息。一种新方法,即微交互EMA(μEMA),将EMA项目限制为在智能手表上通过单次点击评估回答的单个、认知简单的问题。然而,在大规模纵向研究中使用μEMA来捕捉行为和状态的可行性尚未得到证实。
本文描述了目前在针对年轻人开展的“时间对运动与锻炼的影响(TIME)研究”中使用的μEMA方案、用于在智能手表上收集自我报告回复的μEMA应用程序界面、μEMA应用程序试点研究后参与者的定性反馈、根据试点反馈对主要TIME研究μEMA方案和应用程序所做的更改,以及TIME研究中一部分活跃参与者的μEMA初步结果。
TIME研究涉及收集246人的行为和状态数据;测量包括来自智能手表和智能手机的被动传感以及基于智能手机的每小时密集EMA,每2周有4天的EMA爆发期。参与者每天还需回答一次夜间EMA调查。在非EMA爆发日,参与者在智能手表上回答μEMA问题,评估诸如身体活动、久坐行为和情绪等瞬时状态。在研究结束时,参与者在半结构化访谈中描述他们对EMA和μEMA的体验。在主要研究之前,通过一项试点研究来测试和完善μEMA方案。
根据试点反馈对μEMA研究方案所做的更改包括调整单问题选择方法和智能手表震动触觉提示。我们还添加了针对身体活动和久坐行为的传感器触发问题。截至2021年6月,共有81名参与者在主要研究中完成了至少6个月的数据收集。对于发送的662397个μEMA问题,依从率为67.6%(标准差24.4%),完成率为79%(标准差22.2%)。
TIME研究提供了机会,以可持续的方式探索一种收集时间密集型纵向自我报告数据的新方法。数据表明,μEMA对于在个体层面理解行为和状态可能有价值,从而可能支持未来需要人们在日常生活中获取日内时间密集型自我报告数据的纵向干预。