ArticleJMIR human factors2025
Completion Rates for Ecological Momentary Assessments of Food Intake During Pregnancy and Post Partum: Descriptive Study.
Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The collection of dietary behavior data is crucial in childbearing populations. In addition to observed inequities in perinatal dietary intake and quality, burdensome assessment methods (eg, 24-h 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: Our aim is to describe participant completion of food intake items in EMA surveys, overall and across individual characteristics (eg, prepregnancy BMI). Methods: Using secondary EMA data from participants in a longitudinal study, we report average completion rates of survey items regarding dietary behavior (eg, number of meals eaten in a day) across individual demographic variables (eg, age) and combined strata (eg, race+age) during late pregnancy and throughout 12 months post partum. Results: In our analytic sample (N=310), the average completion rate was 52.4% (SD 27.8%) during pregnancy, rising to 59.1% (SD 22.0%) after giving birth. Participants who were older (>30 y), overweight before pregnancy, self-identified as White, working, or earning higher annual income (>US $50,000) had higher average completion rates than their counterparts. Examining combined 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 post partum, but this relationship was reversed for White participants. Conclusions: Our secondary analysis showed relatively stable engagement with EMA surveys in a childbearing cohort across 15 months. Increased completion rates among privileged groups (eg, White, higher income) may demonstrate the impact of socioeconomic advantages on individual health behaviors. Investigators should consider how intersections between race and other factors (eg, employment) may impact participation and data collection.
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