Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 28, 2022
Open Peer Review Period: Jul 28, 2022 - Sep 22, 2022
Date Accepted: Mar 21, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Who participates in ecological momentary assessment (EMA) studies? Predicting participation in a general population EMA study
ABSTRACT
Background:
Ecological momentary assessment (EMA) is widely used in health research to capture individuals’ experiences in the flow of daily life. The majority of EMA studies; however, rely on non-probability sampling approaches, leaving open the possibility of non-random participation with respect to the individual characteristics of interest in EMA research. Knowledge of the factors that predict participation in EMA research is required to evaluate this possibility and can also inform optimal recruitment strategies.
Objective:
The present study aimed to examine the extent to which being willing to participate in EMA research is related to respondent characteristics and to identify the most critical predictors of participation.
Methods:
We leveraged the availability of comprehensive data on a general young adult population pool of potential EMA participants and used and compared logistic regression, classification and regression trees (CART), and random forest approaches to evaluate respondent characteristic predictors of willingness to participate in the Decades-to-Minutes (D2M) EMA study .
Results:
In unadjusted logistic regression models, gender, migration background, anxiety, ADHD symptoms, stress, and prosociality were significant predictors of participation willingness and in logistic regression models mutually adjusting for all predictors, migration background, tobacco use, and social exclusion were significant predictors. Tree-based approaches also identified migration status, tobacco use, and prosociality as prominent predictors. However, overall, willingness to participate in the D2M EMA study was only weakly predictable from respondent characteristics. Cross-validation areas under the curve (AUCs) for the best models were in the range of .56-.57.
Conclusions:
Results suggest that migration background is the single most promising target for improving EMA participation and sample representativeness; however, more research is needed to improve prediction of participation in EMA studies in health.
Citation
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Copyright
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