Completion rates for ecological momentary assessments of dietary behaviors during pregnancy and postpartum (Preprint)

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Abstract
BACKGROUND Background: Collection of dietary behavior data is crucial in childbearing populations. In addition to observed inequities in perinatal dietary intake and quality, burdensome assessment methods used (e.g., 24-hour dietary recall) may limit research participation for some groups. Ecological momentary assessment (EMA) is associated with reduced recall bias and participant convenience, but there is a dearth of studies with diverse cohorts. OBJECTIVE Objective: Our aim is to describe participant completion of dietary intake items in EMA surveys, overall and across individual characteristics (e.g., pre-pregnancy body mass index). METHODS Methods: Using EMA data from participants in a longitudinal study, we report average completion rates of survey items regarding dietary behavior (e.g., number of meals eaten in a day) across demographic variables (e.g., age) and intersectional strata (e.g., race + age) during late pregnancy and throughout 12 months’ postpartum. RESULTS Results: In our analytic sample (N=310), average completion rates were 59.1% during pregnancy, dropping to 52.4% after giving birth. Participants who were older (>30 years), overweight before pregnancy, self-identified as white, working, or earning higher annual income (>$50,000) had higher average completion rates than their counterparts. Examining intersectional strata, we found some variation in survey completion within racial groups. Black participants using a study phone had higher average completion rates during pregnancy and postpartum, but this relationship was reversed for white participants. CONCLUSIONS Discussion: We showed relatively stable engagement with EMA surveys in a childbearing cohort across 15 months. Increased completion rates among privileged groups (e.g., white, higher income) may demonstrate the impact of socioeconomic advantages on individual health behaviors. Investigators should consider how intersections between race and other factors (e.g., employment) may impact participation and data collection.

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