Background Household PM 2.5 exposures have adverse health effects, and cooking behaviors are an important source of PM 2.5 in the home. There is a need for accurate measures of cooking activity to better understand it...
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Background Household PM 2.5 exposures have adverse health effects, and cooking behaviors are an important source of PM 2.5 in the home. There is a need for accurate measures of cooking activity to better understand its associations with household PM 2.5 since self-reported surveys are often subject to recall bias and misreporting of cooking events. Objective In this study, we aimed to address limitations associated with a self-reported cooking metric, by using temperature data to estimate cooking activity. Methods We developed an algorithm to identify cooking events at the 5-minute level using real-time temperature data measured near the stove and in the living room, across 148 households in Chelsea and Dorchester, MA. We compared the number of cooking events identified by this algorithm with cooking events self-reported by participants in daily activity logs and survey responses, and further assessed how these metrics differed with respect to their associations with occurrence of peak PM2.5, in mixed effects logistic regression models. Results We found that 65 % of the cooking events identified by the algorithm were not reported by participants. Furthermore, households classified as frequent vs infrequent cooking households using the algorithm had a larger difference in indoor PM 2.5 levels, compared to households classified by self-report. In mixed effects logistic regression models for elevated household PM 2.5 levels, we observed much stronger associations between household PM 2.5 and algorithm-derived cooking activity (OR: 2.85 [95 % CI: 2.76, 2.95]) as compared to the association between household PM 2.5 and self-reported cooking activity (OR: 1.22 [95 % CI: 1.17, 1.27] for stove use and OR: 1.67 [95 % CI: 1.58, 1.76] for grill use/frying/broiling/sauteing). Significance Overall, the algorithm developed in this study presents a data-driven approach to collecting cooking activity data in U.S. households, that may be more indicative of actual cooking events and
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