@Article{info:doi/10.2196/32772,
author="Ponnada, Aditya
and Wang, Shirlene
and Chu, Daniel
and Do, Bridgette
and Dunton, Genevieve
and Intille, Stephen",
title="Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results",
journal="JMIR Form Res",
year="2022",
month="Feb",
day="9",
volume="6",
number="2",
pages="e32772",
keywords="intensive longitudinal data; ecological momentary assessment; experience sampling; microinteractions; smartwatch; health behavior research; mobile phone",
abstract="Background: 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 ($\mu$EMA), 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 $\mu$EMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated. Objective: This paper describes the $\mu$EMA protocol currently used in the Temporal Influences on Movement {\&} Exercise (TIME) Study conducted with young adults, the interface of the $\mu$EMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the $\mu$EMA app, changes made to the main TIME Study $\mu$EMA protocol and app based on the pilot feedback, and preliminary $\mu$EMA results from a subset of active participants in the TIME Study. Methods: 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 $\mu$EMA 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 $\mu$EMA in a semistructured interview. A pilot study was used to test and refine the $\mu$EMA protocol before the main study. Results: Changes made to the $\mu$EMA 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 $\mu$EMA questions delivered, the compliance rate was 67.6{\%} (SD 24.4{\%}) and the completion rate was 79{\%} (SD 22.2{\%}). Conclusions: 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 $\mu$EMA 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. ",
issn="2561-326X",
doi="10.2196/32772",
url="https://formative.jmir.org/2022/2/e32772",
url="https://doi.org/10.2196/32772",
url="http://www.ncbi.nlm.nih.gov/pubmed/35138253"
}