<?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%">Riley, Kylie W</style></author><author><style face="normal" font="default" size="100%">Burke, Kimberly</style></author><author><style face="normal" font="default" size="100%">Cole, Anabel</style></author><author><style face="normal" font="default" size="100%">Ureno, Marciela</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">Lehyla Calero</style></author><author><style face="normal" font="default" size="100%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Factors that influence environmental health literacy from returning polycyclic aromatic hydrocarbon exposure results</style></title><secondary-title><style face="normal" font="default" size="100%">International Public Health Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">In Press</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">317-331</style></pages><isbn><style face="normal" font="default" size="100%">23741023</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Reporting personal environmental exposure data back from researchers to study participants is becoming more common, however there are few tools to assess whether report back increases environmental health literacy (EHL). This study assessed whether sociodemographic or environmental characteristics were associated with changes in EHL after receiving personal air monitoring results. This study was conducted in a New York City based pregnancy cohort wherein participants were assessed for exposure to polycyclic aromatic hydrocarbons during the third trimester of pregnancy. Participants (n = 168) received their results two to five years after participation and a subset (n = 47) completed a survey evaluating perspectives on their results and subsequent behaviors. Using these results, we created a quantitative scale of EHL, with higher scores indicative of higher EHL. We found that participants with a college degree were significantly more likely to be surprised by their results than those with less than a high school degree (OR = 5.60, p &amp;lt; 0.05) and that higher naphthalene levels were associated with decreased odds of being surprised about receiving the results (OR = 0.37, p = 0.02). There were no observed associations between demographic or exposure characteristics and our dichotomous EHL indicator; however, those with more education and higher income tended to have higher EHL scores. Additionally, participants who reported being surprised by or glad to receive their results had higher EHL scores. Open-ended text responses indicated that while some participants felt worried after receiving their results, participants reported being glad to have received the report.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">317</style></section></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%">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%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Richard P Scott</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</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%">PM Is Insufficient to Explain Personal PAH Exposure.</style></title><secondary-title><style face="normal" font="default" size="100%">Geohealth</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Geohealth</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e2023GH000937</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM AQI only, HMS only, and a multivariate feature set including PM AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></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%">McLarnan, Sarah M</style></author><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%">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%">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%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting personal PAH exposure using high dimensional questionnaire and wristband data.</style></title><secondary-title><style face="normal" font="default" size="100%">J Expo Sci Environ Epidemiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Expo Sci Environ Epidemiol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Jan 05</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;&lt;strong&gt;BACKGROUND: &lt;/strong&gt;Polycyclic aromatic hydrocarbons (PAHs) are a class of pervasive environmental pollutants with a variety of known health effects. While significant work has been completed to estimate personal exposure to PAHs, less has been done to identify sources of these exposures. Comprehensive characterization of reported sources of personal PAH exposure is a critical step to more easily identify individuals at risk of high levels of exposure and for developing targeted interventions based on source of exposure.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;OBJECTIVE: &lt;/strong&gt;In this study, we leverage data from a New York (NY)-based birth cohort to identify personal characteristics or behaviors associated with personal PAH exposure and develop models for the prediction of PAH exposure.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;METHODS: &lt;/strong&gt;We quantified 61 PAHs measured using silicone wristband samplers in association with 75 questionnaire variables from 177 pregnant individuals. We evaluated univariate associations between each compound and questionnaire variable, conducted regression tree analysis for each PAH compound and completed a principal component analysis of for each participant&#039;s entire PAH exposure profile to determine the predictors of PAH levels.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;RESULTS: &lt;/strong&gt;Regression tree analyses of individual compounds and exposure mixture identified income, time spent outdoors, maternal age, country of birth, transportation type, and season as the variables most frequently predictive of exposure.&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%">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%">Data to Accompany: Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2023</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%">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;
</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%">Evoy, Richard</style></author><author><style face="normal" font="default" size="100%">Laurel D Kincl</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><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%">Hystad, Perry</style></author><author><style face="normal" font="default" size="100%">Bae, Harold</style></author><author><style face="normal" font="default" size="100%">Michael L Barton</style></author><author><style face="normal" font="default" size="100%">Phillips, Aaron</style></author><author><style face="normal" font="default" size="100%">Miller, Rachel L</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%">Impact of acute temperature and air pollution exposures on adult lung function: A panel study of asthmatics.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution</style></keyword><keyword><style  face="normal" font="default" size="100%">Asthma</style></keyword><keyword><style  face="normal" font="default" size="100%">Bronchodilator Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Exposure</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Lung</style></keyword><keyword><style  face="normal" font="default" size="100%">Temperature</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e0270412</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;strong&gt;BACKGROUND: &lt;/strong&gt;Individuals with respiratory conditions, such as asthma, are particularly susceptible to adverse health effects associated with higher levels of ambient air pollution and temperature. This study evaluates whether hourly levels of fine particulate matter (PM2.5) and dry bulb globe temperature (DBGT) are associated with the lung function of adult participants with asthma.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;METHODS AND FINDINGS: &lt;/strong&gt;Global positioning system (GPS) location, respiratory function (measured as forced expiratory volume at 1 second (FEV1)), and self-reports of asthma medication usage and symptoms were collected as part of the Exposure, Location, and Lung Function (ELF) study. Hourly ambient PM2.5 and DBGT exposures were estimated by integrating air quality and temperature public records with time-activity patterns using GPS coordinates for each participant (n = 35). The relationships between acute PM2.5, DBGT, rescue bronchodilator use, and lung function collected in one week periods and over two seasons (summer/winter) were analyzed by multivariate regression, using different exposure time frames. In separate models, increasing levels in PM2.5, but not DBGT, were associated with rescue bronchodilator use. Conversely DBGT, but not PM2.5, had a significant association with FEV1. When DBGT and PM2.5 exposures were placed in the same model, the strongest association between cumulative PM2.5 exposures and the use of rescue bronchodilator was identified at the 0-24 hours (OR = 1.030; 95% CI = 1.012-1.049; p-value = 0.001) and 0-48 hours (OR = 1.030; 95% CI = 1.013-1.057; p-value = 0.001) prior to lung function measure. Conversely, DBGT exposure at 0 hours (β = 3.257; SE = 0.879; p-value&amp;gt;0.001) and 0-6 hours (β = 2.885; SE = 0.903; p-value = 0.001) hours before a reading were associated with FEV1. No significant interactions between DBGT and PM2.5 were observed for rescue bronchodilator use or FEV1.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;CONCLUSIONS: &lt;/strong&gt;Short-term increases in PM2.5 were associated with increased rescue bronchodilator use, while DBGT was associated with higher lung function (i.e. FEV1). Further studies are needed to continue to elucidate the mechanisms of acute exposure to PM2.5 and DBGT on lung function in asthmatics.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></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%">Messier, K P</style></author><author><style face="normal" font="default" size="100%">Lane G Tidwell</style></author><author><style face="normal" font="default" size="100%">Christine C Ghetu</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Richard P Scott</style></author><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%">Katrina M Waters</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%">Indoor versus Outdoor Air Quality during Wildfires.</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Sci Technol Lett</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Environ Sci Technol Lett</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Dec 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">696-701</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The human behavioral modification recommendations during wildfire events are based on particulate matter and may be confounded by the potential risks of gas-phase pollutants such as polycyclic aromatic hydrocarbons (PAHs). Moreover, the majority of adults spend over 90 percent of their time indoors where there is an increased concern of indoor air quality during wildfire events. We address these timely concerns by evaluating paired indoor and outdoor PAH concentrations in residential locations and their relationship with satellite model-based categorization of wildfire smoke intensity. Low-density polyethylene passive air samplers were deployed at six urban sites for 1 week in Eugene, Oregon with matched indoor and outdoor samples and 24 h time resolution. Samples were then quantitatively analyzed for 63 PAH concentrations using gas-chromatography-tandem mass spectrometry. A probabilistic principal components analysis was used to reduce all 63 PAHs into an aggregate measure. Linear regression of the first principal component against indoor versus outdoor shows that indoor gas-phase PAH concentrations are consistently equal to or greater than outdoor concentrations. Regression against a satellite-based model for wildfire smoke shows that outdoor, but not indoor gas-phase PAH concentrations are likely associated with wildfire events. These results point toward the need to include gas-phase pollutants such as PAHs in air pollution risk assessment.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue></record></records></xml>