Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Oct 3, 2024
Date Accepted: Jul 31, 2025
Understanding longitudinal ecological momentary assessment completion: Results from 12 months of burst sampling in the TIME study
ABSTRACT
Background:
Ecological Momentary Assessment (EMA) is a valuable method for capturing real-time data on behaviors and experiences in naturalistic settings. However, maintaining participant engagement in longitudinal (i.e., multi-burst) EMA studies remains challenging.
Objective:
This study investigated factors influencing EMA completion rates in a 12-month intensive longitudinal study among young adults (N=246, ages 18-29) in the United States.
Methods:
Participants responded to smartphone-based EMA prompts during bi-weekly measurement bursts, with continuous passive data collection via smartwatches. Multilevel logistic regression models examined effects of time-varying temporal, contextual, behavioral, and psychological factors (e.g., time of day, phone status, location, sleep, physical activity, affect, stress) and time-invariant demographic characteristics (e.g., sex, race, ethnicity) on EMA completion.
Results:
Time-varying contextual factors, such as having the phone screen on (OR=3.39, p<.001) and being at home, were associated with higher completion odds. Short sleep duration (OR=0.92, p=.019) and higher stress levels (OR=0.85,p<.001) were associated with lower completion odds. Demographic factors, including race, ethnicity, and employment status, also influenced completion odds. Notably, completion odds declined over the course of the 12-month study (OR=0.95, p<.001), with significant interactions between time in study and various predictors.
Conclusions:
Findings highlight the dynamic nature of EMA engagement in longitudinal multi-burst studies and underscore the importance of considering time-varying and time-invariant factors in study design and analysis. This study provides valuable insights for researchers designing intensive longitudinal studies in behavioral science and digital health. Potential strategies for optimizing EMA protocols could include tailoring prompt schedules to individual contexts and developing adaptive sampling techniques.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.