TY  - JOUR
AU  - Murray, Aja
AU  - Ushakova, Anastasia
AU  - Zhu, Xinxin
AU  - Yang, Yi
AU  - Xiao, Zhuoni
AU  - Brown, Ruth
AU  - Speyer, Lydia
AU  - Ribeaud, Denis
AU  - Eisner, Manuel
PY  - 2023
DA  - 2023/8/2
TI  - Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study
JO  - J Med Internet Res
SP  - e41412
VL  - 25
KW  - ecological momentary assessment
KW  - experience sampling
KW  - machine learning
KW  - recruitment
KW  - sampling
AB  - 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 nonprobability sampling approaches, leaving open the possibility of nonrandom participation concerning 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: This 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, and random forest approaches to evaluate respondents’ characteristic predictors of willingness to participate in the Decades-to-Minutes EMA study. Results: In unadjusted logistic regression models, gender, migration background, anxiety, attention deficit hyperactivity disorder symptoms, stress, and prosociality were significant predictors of participation willingness; 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 Decades-to-Minutes EMA study was only weakly predictable from respondent characteristics. Cross-validation areas under the curve for the best models were only in the range of 0.56 to 0.57. 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e41412
UR  - https://doi.org/10.2196/41412
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37531181
DO  - 10.2196/41412
ID  - info:doi/10.2196/41412
ER  -