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BACKGROUND: Interventions are needed to help people reduce exposure to harmful chemicals from everyday products and lifestyle habits. Report-back of individual exposures is a potential pathway to increasing environmental health literacy (EHL) and readiness to reduce exposures. OBJECTIVES: Our objective was to determine if report-back of endocrine-disrupting chemicals (EDCs) can reduce EDC exposure, increase EHL, and increase readiness to change (i.e., to implement EDC exposure-reduction behaviors). METHODS: Participants in the Healthy Nevada Project completed EHL and readiness-to-change surveys before (n = 424) and after (n = 174) a report-back intervention. Participants used mail-in kits to measure urinary biomarkers of EDCs. The report-back of results included urinary levels, information about health effects, sources of exposure, and personalized recommendations to reduce exposure. RESULTS: EHL was generally very high at baseline, especially for questions related to the general pollution. For questions related to chemical exposures, responses varied across several demographics. Statistically reliable improvements in EHL responses were seen after report-back. For readiness to change, 72% were already or planning to change their behaviors. Post-intervention, women increased their readiness (p = 0.053), while men decreased (p = 0.007). When asked what challenges they faced in reducing exposure, 79% cited not knowing what to do. This dropped to 35% after report-back. Participants with higher propylparaben were younger (p = 0.03) and women and participants who rated themselves in better health had higher levels of some phthalates (p = 0.02-0.003 and p = 0.001-0.003, respectively). After report-back, monobutyl phthalate decreased among the 48 participants who had valid urine tests before and after the intervention (p < 0.001). CONCLUSIONS: The report-back intervention was successful as evidenced by increased EHL behaviors, increased readiness to change among women, and a decrease in monobutyl phthalate. An EHL questionnaire more sensitive to chemical exposures would help differentiate high and low literacy. Future research will focus on understanding why men decreased their readiness to change and how the intervention can be improved for all participants.

BackgroundSmartphone diet-tracking apps may help individuals lose weight, manage chronic conditions, and understand dietary patterns; however, the usabilities and functionalities of these apps have not been well studied. ObjectiveThe aim of this study was to review the usability of current iPhone operating system (iOS) and Android diet-tracking apps, the degree to which app features align with behavior change constructs, and to assess variations between apps in nutrient coding. MethodsThe top 7 diet-tracking apps were identified from the iOS iTunes and Android Play online stores, downloaded and used over a 2-week period. Each app was independently scored by researchers using the System Usability Scale (SUS), and features were compared with the domains in an integrated behavior change theory framework: the Theoretical Domains Framework. An estimated 3-day food diary was completed using each app, and food items were entered into the United States Department of Agriculture (USDA) Food Composition Databases to evaluate their differences in nutrient data against the USDA reference. ResultsOf the apps that were reviewed, LifeSum had the highest average SUS score of 89.2, whereas MyDietCoach had the lowest SUS score of 46.7. Some variations in features were noted between Android and iOS versions of the same apps, mainly for MyDietCoach, which affected the SUS score. App features varied considerably, yet all of the apps had features consistent with Beliefs about Capabilities and thus have the potential to promote self-efficacy by helping individuals track their diet and progress toward goals. None of the apps allowed for tracking of emotional factors that may be associated with diet patterns. The presence of behavior change domain features tended to be weakly correlated with greater usability, with R2 ranging from 0 to .396. The exception to this was features related to the Reinforcement domain, which were correlated with less usability. Comparing the apps with the USDA reference for a 3-day diet, the average differences were 1.4% for calories, 1.0% for carbohydrates, 10.4% for protein, and −6.5% for fat. ConclusionsAlmost all reviewed diet-tracking apps scored well with respect to usability, used a variety of behavior change constructs, and accurately coded calories and carbohydrates, allowing them to play a potential role in dietary intervention studies.

(1) Background: Findings from observational studies of relations between neighborhood environments and health outcomes underscore the importance of both objective and perceived experiences of those environments. A clearer understanding of the factors associated with discrepancies between these two assessment approaches is needed to tailor public health interventions to specific populations. This study examined how individual and neighborhood characteristics affect perceptions of supermarket distance, particularly when perceptions do not match objective measures. (2) Methods: Participants were older adults (n = 880) participating in the Senior Neighborhood Quality of Life Study in the Seattle/King County, WA or Baltimore/Washington, DC regions. Two main analyses were conducted. The primary outcome for Analysis I was participants' geographic information systems (GIS)-based objective network distance to the closest supermarket. Generalized linear mixed models with block group-level random effects were used to assess associations between objective supermarket distance and individual/neighborhood characteristics. The primary outcome for Analysis II was a categorical "accuracy" variable, based on participants' perceived distance to the nearest supermarket/grocery store relative to the objective distance, assuming a walking speed of 1.0 m/s. Multivariate log-linear models fit neural networks were used to assess influential covariates. (3) Results: Several significant associations with objective distance to the nearest supermarket were observed, including a negative relationship with body mass index (BMI) (95% CI = -45.56, -0.23), having walked to the supermarket in the last 30 days (-174.86, -59.42), living in a high-walkability neighborhood, and residing in Seattle/King County (-707.69, -353.22). In terms of participants' distance accuracy, 29% were classified as accurate, 33.9% were "Underestimators", 24.0% "Overestimators", and 13.2% responded "Don't Know". Compared to Accurate participants, Overestimators were significantly less likely to have walked to the supermarket in the last 30 days, and lived objectively closer to a supermarket; Underestimators perceived significantly higher pedestrian safety and lived objectively further from a supermarket; and Don't Know were more likely to be women, older, not living independently, and not having recently walked to the supermarket. (4) Conclusions: Both modifiable and nonmodifiable factors influence the accuracy of older adults' perceptions of their proximity to the nearest supermarket. Recent experience in walking to the closest supermarket, along with personal safety, represent potentially modifiable perceived environmental factors that were related to older adults' accuracy of perceptions of their neighborhood food environment.

Inorganic arsenic (iAs) is carcinogenic and highly concentrated in rice. Dietary exposure to iAs is concerning among adolescents due to their developmental stage and iAs's long-latency effects. This paper aimed to assess iAs exposure from rice and related lifetime cancer risks (LCR) among adolescents in Kunming, China. A comprehensive literature review of iAs levels in rice and LCR in humans was also conducted. Average daily consumption of rice (ADC) was estimated from 267 adolescents (15⁻18 years). Rice samples obtained from 6 markets were analyzed for iAs concentration (AC). Estimated daily intake (EDI) of iAs was calculated using ADC, AC, and average body weight (BW). Lifetime Cancer Risk (LCR) was calculated using EDI and U.S. EPA derived iAs oral slope factor. The AC was 0.058 mg/kg and the average BW and ADC were 67.5 kg and 410 g/day for males and 55.5 kg and 337 g/day for females. The EDI and LCR were 3.52 × 10-4 mg/kg-BW/day and 5.28 × 10-4 for both males and females, with LCR 5 times above the U.S. LCR upper limit of 1.0 × 10-4. While the AC was below the Chinese maximum contaminant level of 0.2 mg/kg, study results indicated that Kunming adolescents may be at increased risk for iAs-related cancers.

BackgroundGiven the rapid pace of urbanization and Westernization and the increasing prevalence of obesity, there is a need for research to better understand the influence of the built environment on overweight and obesity in world’s developing regions. Culturally-specific food environment survey instruments are important tools for studying changing food availability and pricing. Here, we present findings from an effort to develop and evaluate food environment survey instruments for use in a rapidly developing city in southwest China.MethodsWe developed two survey instruments (for stores and restaurants), each designed to be completed within 10 minutes. Two pairs of researchers surveyed a pre-selected 1-km stretch of street in each of three socio-demographically different neighborhoods to assess inter-rater reliability. Construct validity was assessed by comparing the food environments of the neighborhoods to cross-sectional height and weight data obtained on 575 adolescents in the corresponding regions of the city.Results273 food establishments (163 restaurants and 110 stores) were surveyed. Sit-down, take-out, and fast food restaurants accounted for 40%, 21% and 19% of all restaurants surveyed. Tobacco and alcohol shops, convenience stores and supermarkets accounted for 25%, 12% and 11%, respectively, of all stores surveyed. We found a high percentage of agreement between teams (>75%) for all categorical variables with moderate kappa scores (0.4-0.6), and no statistically significant differences between teams for any of the continuous variables. More developed inner city neighborhoods had a higher number of fast food restaurants and convenience stores than surrounding neighborhoods. Adolescents who lived in the more developed inner neighborhoods also had a higher percentage of overweight, indicating well-founded construct validity. Depending on the cutoff used, 19% to 36% of male and 10% to 22% of female 16-year old adolescents were found to be overweight.ConclusionsThe prevalence of overweight Chinese adolescents, and the food environments they are exposed to, deserve immediate attention. To our knowledge, these are the first food environment surveys developed specifically to assess changing food availability, accessibility, and pricing in China. These instruments may be useful in future systematic longitudinal assessments of the changing food environment and its health impact in China.

This paper presents the project outcomes and lessons learned from the San Francisco PedSafe, a comprehensive pedestrian safety planning and engineering project funded by the Federal Highway Administration. It assesses the effectiveness of the Phase I pedestrian safety plan targeted to higher-injury areas by evaluating the Phase II implementation of a range of mostly low-to-moderate-cost innovative safety improvements.A total of 13 countermeasures (comprised of nine general engineering countermeasures and four Intelligent Transportation Systems (ITS) countermeasures) were implemented by the San Francisco Municipal Transportation Agency (SFMTA) and evaluated by the University of California- Berkeley, Traffic Safety Center (TSC) in a three-year period (2004-2007). Regarding the effectiveness of the 13 countermeasures, six were considered generally successful; three were considered less successful and four were considered inconclusive. The six most successful countermeasures included: flashing beacons (with automated and push button actuation), in- street pedestrian signs, video detection to adjust signal timing, pedestrian head starts (leading pedestrian intervals), portable changeable message speed limit signs, and “Turning Traffic Must Yield to Pedestrians” signsA summary of countermeasure evaluation result highlights is also reported.This paper describes the methodology of countermeasure evaluation, including video data, tools used in data analysis, the advantages and limitations of this method, and possible improvements. In addition, general lessons learned of countermeasure implementation, as well as recommendations for further research are also described.

This work demonstrates high-throughput screening of personal care products to provide an overview of potential exposure. Sixty-seven products from five categories (body/fragrance oil, cleaning product, hair care, hand/body wash, lotion, sunscreen) were rapidly extracted and then analyzed using suspect screening by two-dimensional gas chromatography (GCxGC) high-resolution mass spectrometry (GCxGC-HRT). Initial peak finding and integration were performed using commercial software, followed by batch processing using the machine learning program Highlight. Highlight automatically performs background subtraction, chromatographic alignment, signal quality review, multidilution aggregation, peak grouping, and iterative integration. This data set resulted in 2,195 compound groups and 43,713 individual detections. Compounds of concern (101) were downselected and classified as mild irritants (29%), environmental toxicants/severe irritants (51%) and endocrine disrupting chemicals/carcinogens (20%). High risk compounds such as phthalates, parabens, and avobenzone were detected in 46 out of the 67 products (69%), and only 5 out of the 67 products (7%) listed these compounds on their ingredient labels. The Highlight results for the compounds of concern were compared to commercial software results (ChromaTOF) and 5.3% of the individual detections were discerned only by Highlight, demonstrating the strength of the iterative algorithm to effectively discover low-level signatures. Highlight provides a significant labor advantage, requiring only 2.6% of the time estimated for a largely manual workflow using commercial software. In order to address significant time needed for postprocessing assignment of identification confidence, a new machine-learning-based algorithm was developed to assess the quality of assigned library matches, and a balanced accuracy of 79% was achieved. [ABSTRACT FROM AUTHOR]

Human behavioral interventions aimed at improving health can benefit from objective wearable sensor data and mathematical models. Smartphone-based sensing is particularly practical for monitoring behavioral patterns because smartphones are fairly common, are carried by individuals throughout their daily lives, offer a variety of sensing modalities, and can facilitate various forms of user feedback for intervention studies. We describe our findings from a smartphone-based study, in which an Android-based application we developed called CalFit was used to collect information related to young adults' dietary behaviors. In addition to monitoring dietary patterns, we were interested in understanding contextual factors related to when and where an individual eats, as well as how their dietary intake relates to physical activity (which creates energy demand) and psychosocial stress. 12 participants were asked to use CalFit to record videos of their meals over two 1-week periods, which were translated into nutrient intake by trained dietitians. During this same period, triaxial accelerometry was used to assess each subject's energy expenditure, and GPS was used to record time-location patterns. Ecological momentary assessment was also used to prompt subjects to respond to questions on their phone about their psychological state. The GPS data were processed through a web service we developed called Foodscoremap that is based on the Google Places API to characterize food environments that subjects were exposed to, which may explain and influence dietary patterns. Furthermore, we describe a modeling framework that incorporates all of these information to dynamically infer behavioral patterns that may be used for future intervention studies.