Evidence map›Paper›PMID 35138253›Full record

ArticleJMIR formative research2022

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results.

Aditya Ponnada, Shirlene Wang, Daniel Chu, Bridgette Do, Genevieve Dunton, Stephen Intille

Abstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA).Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
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  8. Review
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  13. Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
  14. Observational
  15. Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
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  16. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Aditya PonnadaKhoury College of Computer Sciences, Northeastern University, Boston, MA, United States.ORCID https://orcid.org/0000-0003-0072-1089
Shirlene WangDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-9132-4427
Daniel ChuDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-8401-6418
Bridgette DoDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-1601-9139
Genevieve DuntonDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-4129-3829
Stephen IntilleKhoury College of Computer Sciences, Northeastern University, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0287-2553

Funding

Microtemporal Processes Underlying Health Behavior Adoption and MaintenanceU01HL146327 · NHLBI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI DUNTON, GENEVIEVE FRIDLUND, INTILLE, STEPHEN S · 2018 to 2022
$2.9M
NHLBI NIH HHS U01 HL146327
6 · The paper itself

Abstract

backgroundEcological 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.

objectiveThis 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.

methodsThe 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.

resultsChanges 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%).

conclusionsThe 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.

Indexed as

ecological momentary assessmentexperience samplinghealth behavior researchintensive longitudinal datamicrointeractionsmobile phonesmartwatch

Identifiers

PMID35138253
PMCPMC8867293

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.