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Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA).

Aditya Ponnada1, Shirlene D Wang2, Jixin Li1

  • 1Northeastern University, USA.

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|September 18, 2025
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Summary
This summary is machine-generated.

Microinteraction ecological momentary assessment (μEMA) significantly boosts engagement in long-term studies compared to traditional ecological momentary assessment (EMA). Participants were more likely to respond to μEMA prompts, finding it less burdensome.

Keywords:
Ecological momentary assessmentlongitudinal data collectionmicrointeractionssmartwatch

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Psychological Measurement

Background:

  • Ecological momentary assessment (EMA) is crucial for real-world data but faces engagement challenges.
  • Microinteraction ecological momentary assessment (μEMA), using single-prompt smartwatch notifications, shows promise for higher response rates in shorter durations.
  • Longitudinal engagement of μEMA over extended periods remains under-evaluated.

Purpose of the Study:

  • To evaluate the longitudinal engagement and perceived burden of μEMA compared to traditional EMA over a 12-month study.
  • To assess μEMA's viability for intensive longitudinal data collection in diverse participant engagement scenarios.

Main Methods:

  • A 12-month study involving 177 participants comparing EMA (smartphone prompts) and μEMA (smartwatch prompts).
  • Data analyzed across three groups: completed 12 months of EMA, withdrew after 6 months, and unenrolled due to poor EMA response.
  • Engagement metrics (response rates) and perceived burden were compared between EMA and μEMA.

Main Results:

  • Participants were significantly more likely to respond to μEMA prompts compared to EMA across all groups (Unenrolled: 2.25x, Withdrew: 1.65x, Completed: 1.53x; p < 0.001).
  • μEMA was consistently perceived as less burdensome than EMA, irrespective of participant response rates (p < 0.001).
  • 1.37 million μEMA surveys and 14.9K EMA surveys were collected.

Conclusions:

  • μEMA is a highly effective and less burdensome method for intensive longitudinal data collection.
  • μEMA demonstrates superior engagement over extended periods, making it suitable for participants who struggle with traditional EMA.
  • This study supports μEMA as a viable tool for enhancing data capture in long-term digital health research.