Javascript must be enabled to continue!
Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results (Preprint)
View through CrossRef
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 (μEMA), restricts EMA items to <i>single, cognitively simple questions</i> 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.
OBJECTIVE
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.
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 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.
RESULTS
Changes made to the μEMA study protocol based on pilot feedback included adjusting the <i>single</i>-<i>question</i> 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%).
CONCLUSIONS
The TIME Study provides opportunities to explore a novel approach for collecting <i>temporally dense</i> 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.
JMIR Publications Inc.
Title: Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results (Preprint)
Description:
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 (μEMA), restricts EMA items to <i>single, cognitively simple questions</i> 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.
OBJECTIVE
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.
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 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.
RESULTS
Changes made to the μEMA study protocol based on pilot feedback included adjusting the <i>single</i>-<i>question</i> 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%).
CONCLUSIONS
The TIME Study provides opportunities to explore a novel approach for collecting <i>temporally dense</i> 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.
Related Results
Measuring Loneliness in Everyday Life
Measuring Loneliness in Everyday Life
Evidence of the public health significance of loneliness is accumulating. Lonely individuals are at greater risk for diverse physical and mental health conditions, and the prevalen...
Just-in-time adaptive ecological momentary assessment (JITA-EMA)
Just-in-time adaptive ecological momentary assessment (JITA-EMA)
AbstractInterest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tail...
IOR Pilot Evaluation in a Brown-Field Fractured Reservoir Using Data Analytics of Reservoir Simulation Results
IOR Pilot Evaluation in a Brown-Field Fractured Reservoir Using Data Analytics of Reservoir Simulation Results
Abstract
A well-designed pilot is instrumental in reducing uncertainty for the full-field implementation of improved oil recovery (IOR) operations. Traditional model...
Study on the Ecological Carrying Capacity and Driving Factors of the Source Region of the Yellow River in China in the Past 30 Years
Study on the Ecological Carrying Capacity and Driving Factors of the Source Region of the Yellow River in China in the Past 30 Years
Abstract
Under the influence of natural factors and human activities, the ecological environment functions in the source region of the Yellow River in China have been degra...
Spatio-Temporal Evolution of Key Areas of Territorial Ecological Restoration in Resource-Exhausted Cities: A Case Study of Jiawang District, China
Spatio-Temporal Evolution of Key Areas of Territorial Ecological Restoration in Resource-Exhausted Cities: A Case Study of Jiawang District, China
Resource-exhausted cities usually face problems of environmental degradation, landscape fragmentation, and impeded ecological mobility. By clarifying the spatial heterogeneity of e...
Fuzzy logic‐based detection scheme for pilot fatigue
Fuzzy logic‐based detection scheme for pilot fatigue
PurposeThe paper aims to present the development of a detection scheme for pilot fatigue using fuzzy logic. Evaluation parameters based on the dynamic response of the pilot/aircraf...
Realization and Prediction of Ecological Restoration Potential of Vegetation in Karst Areas
Realization and Prediction of Ecological Restoration Potential of Vegetation in Karst Areas
Based on the vegetation ecological quality index retrieved by satellite remote sensing in the karst areas of Guangxi in 2000–2019, the status of the ecological restoration of the v...
Integrating Ecological Importance and Risk for Restoration Zoning and Ecological Water Demand in the Shiyang River Basin
Integrating Ecological Importance and Risk for Restoration Zoning and Ecological Water Demand in the Shiyang River Basin
Abstract
Effective ecological protection and restoration in arid inland river basins requires a holistic perspective of territorial spatial planning that balances conservat...

