<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">David J Degnan</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.</style></title><secondary-title><style face="normal" font="default" size="100%">bioRxiv</style></secondary-title><alt-title><style face="normal" font="default" size="100%">bioRxiv</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Oct 02</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Richard P Scott</style></author><author><style face="normal" font="default" size="100%">Lehyla Calero</style></author><author><style face="normal" font="default" size="100%">Darrell Holmes</style></author><author><style face="normal" font="default" size="100%">Gibson, Elizabeth A</style></author><author><style face="normal" font="default" size="100%">Cavalier, Haleigh M</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Antonia M Calafat</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating predictive relationships between wristbands and urine for assessment of personal PAH exposure.</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Int</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Environ Int</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Apr 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">163</style></volume><pages><style face="normal" font="default" size="100%">107226</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;During events like the COVID-19 pandemic or a disaster, researchers may need to switch from collecting biological samples to personal exposure samplers that are easy and safe to transport and wear, such as silicone wristbands. Previous studies have demonstrated significant correlations between urine biomarker concentrations and chemical levels in wristbands. We build upon those studies and use a novel combination of descriptive statistics and supervised statistical learning to evaluate the relationship between polycyclic aromatic hydrocarbon (PAH) concentrations in silicone wristbands and hydroxy-PAH (OH-PAH) concentrations in urine. In New York City, 109 participants in a longitudinal birth cohort wore one wristband for 48&amp;nbsp;h and provided a spot urine sample at the end of the 48-hour period during their third trimester of pregnancy. We compared four PAHs with the corresponding seven OH-PAHs using descriptive statistics, a linear regression model, and a linear discriminant analysis model. Five of the seven PAH and OH-PAH pairs had significant correlations (Pearson&#039;s r&amp;nbsp;=&amp;nbsp;0.35-0.64, p&amp;nbsp;≤&amp;nbsp;0.003) and significant chi-square tests of independence for exposure categories (p&amp;nbsp;≤&amp;nbsp;0.009). For these five comparisons, the observed PAH or OH-PAH concentration could predict the other concentration within a factor of 1.47 for 50-80% of the measurements (depending on the pair). Prediction accuracies for high exposure categories were at least 1.5 times higher compared to accuracies based on random chance. These results demonstrate that wristbands and urine provide similar PAH exposure assessment information, which is critical for environmental health researchers looking for the flexibility to switch between biological sample and wristband collection.&lt;/p&gt;
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