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Understanding the Relationship Between Ecological Momentary Assessment Methods, Sensed Behavior, and Responsiveness:
Diane Cook1, Aiden Walker1, Bryan Minor1
1Department of Psychology, College of Arts and Sciences, Washington State University, 3160 Folsom Blvd, Sacramento, WA, 95816, United States, 1 5093354985.
JMIR Mhealth and Uhealth
|April 10, 2025
Summary
Ecological momentary assessment (EMA) response rates and quality are influenced by participant factors and study design. Optimizing prompt timing and duration can enhance data collection for well-being research.
Area of Science:
- Digital Health
- Behavioral Science
- Data Science
Background:
- Ecological momentary assessment (EMA) collects real-time well-being data but faces challenges in response consistency and accuracy.
- Sensor data integration offers a method to contextualize EMA responses and analyze patterns.
Purpose of the Study:
- To examine EMA response patterns and their relationship with sensed behavior across diverse studies.
- To investigate factors influencing EMA response rate (RR) and quality, including prompt timing, number of questions, and behavior context.
- To explore the impact of participant demographics and study design on EMA data collection.
Main Methods:
- Analysis of 146,753 EMA prompts from 454 participants across 9 clinical studies.
- Collection of concurrent sensor data (smartwatch/smart home) to extract digital markers of behavior.
- Examination of response rate, completeness, quality, alignment with sensor data, and modeling of response series.
Main Results:
- Average EMA response rate (RR) was 79.95%, with 88.37% of responses being fully completed.
- Higher responsiveness was observed in the evening and on weekdays, with variations across age groups.
- RR correlated negatively with the number of EMA questions and positively with sensor-detected activity and time at home; response quality declined over time.
Conclusions:
- EMA response patterns are significantly influenced by participant demographics and study parameters.
- Tailoring EMA strategies, such as prompt timing near activity transitions and minimizing interaction duration, can improve RR and data quality.
- Strategies like gamification may enhance participant engagement and maintain response variance for more robust data.
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