Personal Air Pollution Monitoring in the MADRES Cohort of Pregnant Women: Challenges and Lessons Learned

Abstract

Introduction: Increasing rates of maternal overweight and obesity before, during and after pregnancy pose serious health concerns for both mothers and children. In California, childbearing women from disadvantaged minority and low-income groups are disproportionately exposed to multiple environmental chemicals. Urban air pollution is a ubiquitous exposure that has been implicated in the pathogenesis of obesity, diabetes and related metabolic and cardiovascular disease. Most air pollution studies assign exposures based on residential location. Methods: Given that pregnant women may regularly travel to non-residential locations, we propose to use a personal exposure assessment approach that includes time-activity patterns in key microenvironments to reduce exposure measurement error in Project 2 of the recently established “Maternal and Developmental Risks from Environmental and Social Stressors” (MADRES) pregnancy cohort. Personal PM2.5 monitoring is currently being conducted using a microPEM, along with GPS, tri-axis accelerometry and Ecological Momentary Assessment (EMA) prompts on smartphones to capture geolocation, physical activity, and context-specific information, respectively, in 60 low income, predominantly Hispanic women living in East Los Angeles. Results: Data will be presented to illustrate and describe main challenges encountered in pilot testing, in terms of developing algorithms for evaluating participant compliance using a combination of sensors and self-report tools. Lessons learned for ensuring data quality while optimizing battery life and reducing participant burden will be presented. Conclusions: Recent advances in sensor technologies are making personal exposure assessment in health studies more feasible, presenting unique opportunities for capturing key contextual information and reducing exposure measurement error in personal exposure models.