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Introduction This study examines the associations of neighborhood environments with BMI, HbA1c, and diabetes across 6 years in Hispanic/Latino adults. Methods Participants from the Hispanic Community Health Study/Study of Latinos San Diego site (n=3,851, mean age=39.4 years, 53.3% women, 94.0% Mexican heritage) underwent assessment of metabolic risk factors and diabetes status (categorized as normoglycemia, prediabetes, and diabetes) at baseline (2008–2011) and approximately 6 years later (2014–2017). In the Study of Latinos Community and Surrounding Areas Study ancillary study (2015–2020), participant baseline addresses were geocoded, and neighborhoods were defined using 800-meter circular buffers. Neighborhood variables representing socioeconomic deprivation, residential stability, social disorder, walkability, and greenness were created using Census and other public databases. Analyses were conducted in 2020–2021. Results Complex survey regression analyses revealed that greater neighborhood socioeconomic deprivation was associated with higher BMI (β=0.14, p

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Purpose It remains unclear why individuals living in disadvantaged neighborhoods have shorter non-small cell lung cancer (NSCLC) survival. It is possible that living in these deprived areas is linked with increased risk of developing aggressive NSCLC biology. Here, we explored the association of somatic KRAS mutations, which are associated with shorter survival in NSCLC patients, and 11 definitions of neighborhood disadvantage spanning socioeconomic and structural environmental elements. Methods We analyzed data from 429 NSCLC patients treated at a Comprehensive Cancer Center from 2015 to 2018. Data were abstracted from medical records and each patient’s home address was used to assign publicly available indices of neighborhood disadvantage. Prevalence Ratios (PRs) for the presence of somatic KRAS mutations were estimated using modified Poisson regression models adjusted for age, sex, smoking status, race/ethnicity, educational attainment, cancer stage, and histology. Results In the NSCLC cohort, 29% had KRAS mutation-positive tumors. We found that five deprivation indices of socioeconomic disadvantage were associated with KRAS mutation. A one decile increase in several of these socioeconomic disadvantage indices was associated with a 1.06 to 1.14 increased risk of KRAS mutation. Measures of built structural environment were not associated with KRAS mutation status. Conclusion Socioeconomic disadvantage at the neighborhood level is associated with higher risk of KRAS mutation while disadvantage related to built environmental structural measures was inversely associated. Our results indicate not only that neighborhood disadvantage may contribute to aggressive NSCLC biology, but the pathways linking biology to disadvantage are likely operating through socioeconomic-related stress.

Limited previous work has identified a relationship between exposure to ambient air pollution and aggressive somatic lung tumor mutations. More work is needed to confirm this relationship, especially using spatially resolved air pollution. We aimed to quantify the association between different air pollution metrics and aggressive tumor biology. Among patients treated at City of Hope Comprehensive Cancer Center in Duarte, CA (2013–2018), three non-small cell lung cancer somatic tumor mutations, TP53, KRAS, and KRAS G12C/V, were documented. PM2.5 exposure was assessed using state-of-the art ensemble models five and ten years before lung cancer diagnosis. We also explored the role of NO2 using inverse-distance-weighting approaches. We fitted logistic regression models to estimate odds ratio (OR) and their 95% confidence intervals (CIs). Among 435 participants (median age: 67, female: 51%), an IQR increase in NO2 exposure (3.5 μg/m3) five years before cancer diagnosis was associated with an increased risk in TP53 mutation (OR, 95% CI: 1.30, 0.99–1.71). We found an association between highly-exposed participants to PM2.5 (>12 μg/m3) five and ten years before cancer diagnosis and TP53 mutation (OR, 95% CI: 1.61, 0.95–2.73; 1.57, 0.93–2.64, respectively). Future studies are needed to confirm this association and better understand how air pollution impacts somatic profiles and the molecular mechanisms through which they operate.

Supplemental digital content is available in the text., Introduction Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method. Methods CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification). Results For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%–83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP’s positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min). Conclusion CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Background Risk of nonrelapse mortality (NRM) after hematopoietic cell transplantation (HCT) is high. Patient-level clinical prediction models such as the HCT–comorbidity index (HCT-CI) help identify those at increased risk for NRM, but the independent contribution of social determinants of health on HCT outcomes is not well characterized. Methods This study included 1602 patients who underwent allogeneic HCT between 2013 and 2019 at City of Hope. Census tract–level social vulnerability was measured using the social vulnerability index (SVI). Fine-Gray multivariable regression evaluated the association between SVI and 1-year NRM. Subgroup analysis examined risk of NRM across combined SVI and HCT-CI categories and by race and ethnicity. Results Cumulative incidence of 1-year NRM after HCT was 15.3% (95% confidence interval [CI] = 13.6% to 17.1%). In multivariable analysis, patients in the highest SVI tertile (highest social vulnerability) had a 1.4-fold risk (subdistribution hazard ratio [sHR] = 1.36, 95% CI = 1.04 to 1.78) of NRM compared with individuals in the lower tertiles; patients in the highest SVI tertile who also had elevated (≥3) HCT-CI scores had the highest risk (sHR = 1.81, 95% CI = 1.26 to 2.58) of 1-year NRM (reference: lower SVI tertiles and HCT-CI < 3). High social vulnerability was associated with risk of 1-year NRM in Asian (sHR = 2.03, 95% CI = 1.09 to 3.78) and Hispanic (sHR = 1.63, 95% CI = 1.04 to 2.55) but not non-Hispanic White patients. Conclusions High social vulnerability independently associated with 1-year NRM after HCT, specifically among minority populations and those with a high comorbidity burden at HCT. These findings may inform targeted approaches for needs assessment during and after HCT, allowing for timely interventions to improve health outcomes in at-risk patients.

We examined associations of micro-scale environment attributes (e.g., sidewalks, street crossings) with three physical activity (PA) measures among Hispanic/Latino adults (n = 1776) living in San Diego County, CA. Systematic observation was used to quantify micro-scale environment attributes near each participant's home. Total PA was assessed with accelerometers, and PA for transportation and recreation were assessed by validated self-report. Although several statistically significant interactions between individual and neighborhood characteristics were identified, there was little evidence micro-scale attributes were related to PA. An important limitation was restricted environmental variability for this sample which lived in a small area of a single county.

Neighborhood-level socioeconomic deprivation can increase risk for higher blood pressure or hypertension, while greater neighborhood safety and walkability may protect against hypertension. Large-scale prospective research, particularly among Hispanics/Latinos, is lacking. We examined cross-sectional and prospective associations between neighborhood environments and blood pressure and hypertension among 3851 Hispanic/Latinos enrolled in the Hispanic Community Health Study/Study of Latinos San Diego, CA cohort. Addresses from Visit 1 (2008-2011) were geocoded and neighborhood characteristics were determined as part of the SOL CASAS ancillary study. Home addresses were geocoded and home areas created using 800m circular radial buffers. Neighborhood indices socioeconomic deprivation, residential stability, and social disorder were created using Census and other publicly available data. Walkability was computed as density of intersections, retail spaces, and residences. Greenness was measured via satellite imagery using the Normalized Difference Vegetation Index. Visit 1 and Visit 2 (2014-2017) clinical outcomes included systolic (SBP) and diastolic (DBP) blood pressure, as well as prevalent and 6-year incident hypertension, defined as SBP/DBP ≥140/90mmHg or antihypertensive medication use. Complex survey regression models adjusted for covariates revealed cross-sectional associations between greater walkability and lower SBP (B=-0.05; 95% CI: -0.09, -0.003). In prospective analyses, greater neighborhood social disorder was related to increasing SBP (B=0.05; 95% CI: 0.01, 0.09) and DBP (B=0.07; 95% CI: 0.02, 0.12) over time. Greater socioeconomic deprivation (OR=1.47; 95% CI: 1.06, 2.04) and greater social disorder (OR=1.25; 95% CI: 1.02, 1.54) were associated with higher odds of incident hypertension. All other associations were not significant. Beyond individual-level characteristics, greater neighborhood social disorder and socioeconomic deprivation were related to adverse changes in blood pressure over 6 years among Hispanics/Latinos. Neighborhood social environment may help identify, or be an area for future intervention for, cardiovascular risk among Hispanics/Latinos.

Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. Methods Participants were 278, 8–11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). Results Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. Conclusions There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

As cannabis use is being legalized in an increasing number of states, it is important to understand the changing dynamic of the risk in cannabis use disorder (CUD). Shape-based time-series clustering was used to identify ZIP Code Tabulation Areas (ZCTAs) with similar changing pattern in CUD over time. We conducted a cross-sectional logistic regression analysis to investigate the most recent ZCTA socio-demographic characteristics in relation to the changing CUD rates. The emergency discharge rates generally increased during 2010-2016. Increase during 2017-2019 was found in Sacramento and Santa Barbara County. Approximately 13% of ZCTAs showed an increasing trend of hospitalization discharge during 2017-2019. Males and non-Hispanic Black had larger increase than other groups during 2017-2019. The recent growing trend was found associated with greater racial diversity and rural ZCTAs. The findings from this study hold promise for local public health officials to adjust the cannabis intervention strategies in target districts and improve overall health outcomes.

Objective: Older adults are the least active population group in the U.S. Low income and ethnically diverse communities have fewer physical activity (PA) related resources and facilities, which contributes to lower PA levels and disparities in cardiovascular (CVD) risk factors and disease outcomes. This study assessed the hypothesis that low income and diverse older adults participating in the multilevel Peer Empowerment Program 4 Physical Activity (PEP4PA) would increase moderate-to-vigorous PA (MVPA), improve blood pressure (BP), perceived quality of life (PQoL), and depressive symptoms to a greater extent than older adults receiving usual senior center programming. Methods: In a cluster-randomized controlled trial (RCT) in 12 senior centers, 476 older adults (mean age 71.4 years, 76% female, 60% low income, 38% minority) were assigned to a PA intervention (n=267) or control (n=209) condition. The peer-led intervention included individual self-monitoring and counseling, group walks and social support, and community advocacy to improve walking conditions. Outcomes included minutes of MVPA per day (7-day accelerometer), systolic and diastolic BP (automatic cuff), PQoL (PQoL-20) and depressive symptoms (CES-D 10) at baseline, 6, 12, 18 and 24 months. To account for multiple measurement days and clustering of participants within senior centers, mixed effects regression models with random effects estimated the intervention effects on all outcomes between groups over time. Models were adjusted for imbalanced baseline covariates. A three-way interaction term assessed whether intervention effects differed by income status. Results: Compared to the control group, intervention participants significantly increased MVPA from baseline at 6, 12, 18 and 24 months by 8, 11, 9 and 9 minutes/day respectively (p Conclusions: The multilevel RCT achieved sustained increases in MVPA and QoL in a diverse cohort of older adults across 2 years of follow up. The peer-led, community-based intervention provides a sustainable model to improve health behaviors related to CVD in an at-risk and often difficult to reach aging population. Further exploration is needed to understand what components of the intervention may be modified to address the differential effects by income status.

Background: Targeting daily increases in physical activity (PA) is a common lifestyle strategy for cardiovascular disease (CVD) risk reduction. While less targeted in lifestyle interventions, poor sleep is also consistently linked to CVD. Emerging evidence connects changes in daily PA to beneficial changes in sleep; however, little is known about the impact of community-based PA interventions on habitual sleep among older adults. We leveraged accelerometer data from the Peer Empowerment Program for Physical Activity in Low Income & Minority Seniors (PEP4PA) randomized controlled trial to examine the effect of a PA intervention on accelerometer-measured sleep characteristics among older adults in low income and diverse senior center communities. Methods: PEP4PA participants (N=476; mean age=71.0 ± 9 years, 75% female, 60% low income, 36% non-white) were recruited from senior centers and randomized to a healthy control condition(n=209) or a peer-lead walking intervention (N=267). The peer-led PA intervention included self-monitoring techniques, health coach counseling, group walks, and community advocacy to improve walking conditions. Participants wore Actigraph GT3X+ accelerometers on the wrist at baseline, 6, 12, and 24 months. Data were processed for sleep characteristics including weekly average sleep duration and sleep efficiency. Mixed effects regression models provided estimates of the intervention effect on sleep characteristics at each time point. Models were adjusted for age, sex, and race. Three way interaction terms (time*treatment*sleep duration status) were used to assess differential intervention effects by short (8 hours) sleep duration status at baseline. Results: At baseline, the average sleep duration of participants was 7 hours (SD: 1.07 hours) and the average sleep efficiency was 87.0% (SD: 6.2%). Compared to participants in the control group, participants in the intervention group had shorter sleep durations (7.1 hours vs 7.0 hours; p value: 0.08) and poorer sleep efficiency 86.0% vs 88.2%; p Conclusions: Over the 2-year period, the PA intervention did not appear to have an impact on participants’ average sleep duration or quality. More investigation is needed into the temporal and daily relations of PA and sleep among older adults in interventions to understand if increases in daily PA are associated with better sleep characteristics at night.

Active travel (AT) provides an opportunity to alleviate the physical inactivity and climate crises contributing to the global chronic disease burden, including cardiovascular diseases (CVD). Though AT shows promising links to reduced CVD risk, prior studies relied on self-reported AT assessment. In the present study, device-measured and self-reported AT were compared across population subgroups and relationships with CVD risk biomarkers were evaluated for both measures. The study recruited an ethnically diverse sample (N = 602, mean age 59 years, 42% Hispanic/Latino ethnicity) from neighborhoods that varied by walkability and food access. AT was assessed using concurrently collected accelerometer and GPS data and self-report data from a validated survey. Relationships with body mass index (BMI), triglycerides, high-density lipoprotein (HDL) cholesterol, blood pressure (BP), and moderate-to-vigorous physical activity (MVPA) were modeled using multivariable linear regression. Devices captured more AT than did self-report. We found differences in AT measures by population subgroups, including race, ethnicity, education, income, vehicle access, and walkability. Men had more accelerometer-measured MVPA, though women self-reported more daily minutes. Both device and survey AT measures were positively associated with total accelerometer-measured MVPA, though the relationship was stronger with device-measured AT. Device-measured AT was associated with lower BMI. No other CVD risk biomarker was associated with either AT measure. No effect modification by Hispanic/Latino ethnicity was detected. Further studies with device-based measures are warranted to better understand the relationship between AT and cardiovascular health.

Physical inactivity and unhealthy diet are modifiable behaviors that lead to several cancers. Biologically, these behaviors are linked to cancer through obesity-related insulin resistance, inflammation, and oxidative stress. Individual strategies to change physical activity and diet are often short lived with limited effects. Interventions are expected to be more successful when guided by multi-level frameworks that include environmental components for supporting lifestyle changes. Understanding the role of environment in the pathways between behavior and cancer can help identify what environmental conditions are needed for individual behavioral change approaches to be successful, and better recognize how environments may be fueling underlying racial and ethnic cancer disparities. This cross-sectional study was designed to select participants (n = 602 adults, 40% Hispanic, in San Diego County) from a range of neighborhoods ensuring environmental variability in walkability and food access. Biomarkers measuring cancer risk were measured with fasting blood draw including insulin resistance (fasting plasma insulin and glucose levels), systemic inflammation (levels of CRP), and oxidative stress measured from urine samples. Objective physical activity, sedentary behavior, and sleep were measured by participants wearing a GT3X+ ActiGraph on the hip and wrist. Objective measures of locations were obtained through participants wearing a Qstarz Global Positioning System (GPS) device on the waist. Dietary measures were based on a 24-h food recall collected on two days (weekday and weekend). Environmental exposure will be calculated using static measures around the home and work, and dynamic measures of mobility derived from GPS traces. Associations of environment with physical activity, obesity, diet, and biomarkers will be measured using generalized estimating equation models. Our study is the largest study of objectively measured physical activity, dietary behaviors, environmental context/exposure, and cancer-related biomarkers in a Hispanic population. It is the first to perform high quality measures of physical activity, sedentary behavior, sleep, diet and locations in which these behaviors occur in relation to cancer-associated biomarkers including insulin resistance, inflammation, impaired lipid metabolism, and oxidative stress. Results will add to the evidence-base of how behaviors and the built environment interact to influence biomarkers that increase cancer risk. ClinicalTrials.gov NCT02094170 , 03/21/2014.

Introduction: Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures. Methods: Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data. Results: Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07–1.28; RR 1.21, 95% CI: 1.12–1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity. Conclusion: These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.

Socioeconomic factors appear to impact mental health conditions such as depression, but little is known about the relative and combined role of neighborhood and personal socioeconomic deprivation among Hispanics/Latinos. This study examined cross-sectional associations of neighborhood and personal socioeconomic deprivation with depression symptoms in a US Hispanic/Latino population from the San Diego Field Center of the Hispanic Community Health Study/Study of Latinos (n=3,851). Depression symptoms were assessed with the ten-item Centers for Epidemiological Studies in Depression Scale. Neighborhood socioeconomic deprivation was a composite of eleven variables (e.g., neighborhood income, education, employment, household crowding). Greater personal socioeconomic deprivation based on education, income, and employment was generally associated with higher depression symptoms, including after adjusting for neighborhood socioeconomic deprivation. Greater neighborhood socioeconomic deprivation was associated with higher depression symptoms in females but not males, but the association in females became non-significant when adjusting for personal socioeconomic deprivation. Neighborhood socioeconomic deprivation did not significantly interact with personal socioeconomic deprivation in relation to depression symptoms. The present findings support the association of personal socioeconomic status with mental health (indicated by depression symptoms) among Hispanic/Latino populations, whereas neighborhood socioeconomic deprivation did not relate to depression beyond the impact of personal indicators.

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

Time spent sitting is positively correlated with endothelial dysfunction and cardiovascular disease risk. The underlying molecular mechanisms are unknown. MicroRNAs contained in extracellular vesicles (EVs) reflect cell/tissue status and mediate intercellular communication. We explored the association between sitting patterns and microRNAs isolated from endothelial cell (EC)-derived EVs. Using extant actigraphy based sitting behavior data on a cohort of 518 postmenopausal overweight/obese women, we grouped the woman as Interrupted Sitters (IS; N = 18) or Super Sitters (SS; N = 53) if they were in the shortest or longest sitting pattern quartile, respectively. The cargo microRNA in EC-EVs from the IS and SS women were compared. MicroRNA data were weighted by age, physical functioning, MVPA, device wear days, device wear time, waist circumference, and body mass index. Screening of CVD-related microRNAs demonstrated that miR-199a-5p, let-7d-5p, miR-140-5p, miR-142-3p, miR-133b level were significantly elevated in SS compared to IS groups. Group differences in let-7d-5p, miR-133b, and miR-142-3p were validated in expanded groups. Pathway enrichment analyses show that mucin-type O-glycan biosynthesis and cardiomyocyte adrenergic signaling (P

Introduction: Social connectedness (SC) is an important determinant of maintaining overall health, and the strength of connections with others may influence cardiovascular health (CVH) in part through improvement in cardiovascular disease risk factors. However, there is a lack of research on the role of SC in promoting CVH, as defined by the American Heart Association’s 2020 Impact Goals. The objective of this study is to determine the effect of SC on CVH among adults. Hypothesis: Higher SC will be associated with higher odds of optimal CVH, relative to inadequate CVH. Methods: Data are from the Community of Mine study, a cross-sectional study of 602 participants (42% Hispanic, 56% female, mean age 59 years) living in San Diego County. CVH was based on Life’s Simple 7 using six metrics including smoking, physical activity, body mass index, total cholesterol, blood pressure, and fasting glucose. A 12-point summary score was used to classify participants as having inadequate (0-6), average (7-8), or optimal (9-12) CVH. SC was assessed using a 7-item scale designed to assess social relationships and strength of connections with others (Cronbach’s alpha=0.92). A mean score across all items was generated. Multinomial logistic regression was used to estimate the association between SC and CVH. We adjusted for age, race/ethnicity, governmental cash assistance, income, education, sex, history of medical comorbidities, family history of heart disease, and depressive symptoms. Results: SC was independently associated with CVH (p=0.04). More specifically, a 1-unit increase in mean SC score was associated with a 46% increase in relative odds of having optimal CVH compared to inadequate CVH (OR=1.46, 95% CI: 1.07-1.98), and a 21% increase in relative odds of having average CVH compared to inadequate CVH (OR=1.21, 95% CI: 0.96-1.52). Conclusions: Higher social connectedness is associated with a greater likelihood of having optimal CVH. Individuals’ perceptions of the strength of their relationships could have cardioprotective benefits through promotion of favorable cardiovascular health behaviors and factors.

Objectives To assess 1) the validity of online crowdsourcing platforms in enumerating licensed brick-and-mortar marijuana dispensaries and 2) the validity of state licensing directory and online crowdsourcing platforms in enumerating active brick-and-mortar marijuana dispensaries in California. Methods We obtained business lists from California Bureau of Cannabis Control (BCC) licensing directory and three online crowdsourcing platforms (Weedmaps, Leafly, and Yelp) in May 2019. Calls were made to verify street address, operation status, dispensary category (recreational-only, medical-only, recreational & medical), and presence of storefronts in May-July 2019. Validity measures, including sensitivity, specificity, positive predictive value, and negative predictive value, were calculated when applicable. Results In identifying licensed dispensaries in BCC, Leafly had the highest sensitivity (.66) and Yelp had the highest specificity (.87). The dispensary category posted on online crowdsourcing platforms in over 25 % licensed dispensaries and the dispensary category claimed in call verification in over 10 % licensed dispensaries disagreed with the approved category in BCC. There were 2121 businesses combined from BCC and online crowdsourcing platforms, among which 826 were verified to be active brick-and-mortar dispensaries. Weedmaps had the highest sensitivity (.80) and Yelp had the highest negative predictive value (.74) in identifying verified dispensaries. Weedmaps overall had the highest sensitivity in all three dispensary categories. Weedmaps had the highest sensitivity in more populated counties whereas BCC had the highest sensitivity in less populated counties. Conclusions Each secondary data source has strengths and limitations. The findings inform surveillance and research regarding how to best strategize data use when resources are limited.

Background Sedentary behavior is pervasive, especially in older adults, and is associated with cardiometabolic disease and mortality. Relationships between cardiometabolic biomarkers and sitting time are unexplored in older women, as are possible ethnic differences. Methods and Results Ethnic differences in sitting behavior and associations with cardiometabolic risk were explored in overweight/obese postmenopausal women (n=518; mean± SD age 63±6 years; mean body mass index 31.4±4.8 kg/m 2 ). Accelerometer data were processed using validated machine‐learned algorithms to measure total daily sitting time and mean sitting bout duration (an indicator of sitting behavior pattern). Multivariable linear regression was used to compare sitting among Hispanic women (n=102) and non‐Hispanic women (n=416) and tested associations with cardiometabolic risk biomarkers. Hispanic women sat, on average, 50.3 minutes less/day than non‐Hispanic women ( P P =0.02) mean sitting bout duration. Among all women, longer total sitting time was deleteriously associated with fasting insulin and triglyceride concentrations, insulin resistance, body mass index and waist circumference; longer mean sitting bout duration was deleteriously associated with fasting glucose and insulin concentrations, insulin resistance, body mass index and waist circumference. Exploratory interaction analysis showed that the association between mean sitting bout duration and fasting glucose concentration was significantly stronger among Hispanic women than non‐Hispanic women ( P ‐interaction=0.03). Conclusions Ethnic differences in 2 objectively measured parameters of sitting behavior, as well as detrimental associations between parameters and cardiometabolic biomarkers were observed in overweight/obese older women. The detrimental association between mean sitting bout duration and fasting glucose may be greater in Hispanic women than in non‐Hispanic women. Corroboration in larger studies is warranted.

During puberty, a woman’s breasts are vulnerable to environmental damage (“window of vulnerability”). Early exposure to environmental carcinogens, endocrine disruptors, and unhealthy foods (refined sugar, processed fats, food additives) are hypothesized to promote molecular damage that increases breast cancer risk. However, prospective human studies are difficult to perform and effective interventions to prevent these early exposures are lacking. It is difficult to prevent environmental exposures during puberty. Specifically, young women are repeatedly exposed to media messaging that promotes unhealthy foods. Young women living in disadvantaged neighborhoods experience additional challenges including a lack of access to healthy food and exposure to contaminated air, water, and soil. The purpose of this review is to gather information on potential exposures during puberty. In future directions, this information will be used to help elementary/middle-school girls to identify and quantitate environmental exposures and develop cost-effective strategies to reduce exposures.

This article explores the role of the work environment in determining physical activity gained within and around the workplace. With most adults spending more than half of their waking day at work, the workplace is a promising venue for promoting physical activity. We used a sample of 147 employed women—median age = 53 years old; 42% meeting Centers for Disease Control and Prevention (CDC) physical activity recommendations—wearing a GPS device and accelerometer on the hip for 7 days to assess location and physical activity at minute-level epochs. We analyzed the association between geographic information systems (GIS) measures of walkability and greenness around the workplace and the amount of physical activity gained while in the work neighborhood. Our results showed that working in high walkable environments was associated with higher levels of moderate to vigorous physical activity while at work, and with higher moderate to vigorous physical activity gained within the work neighborhood. Increasing walkability levels around workplaces can contribute to increasing physical activity of employees.

Disparities in prevalence of obesity in the USA continue to increase. Here, we review progress and highlight gaps in understanding disparities in obesity with a focus on the Hispanic/Latino population from a systems epidemiology framework. We review seven domains: environment, behavior, biomarkers, nutrition, microbiome, genomics, and epigenomics/transcriptomics. We focus on recent advances that integrate at least two or more of these domains, and then provide a real-world example of data collection efforts that encompass these domains. Research into discrimination-related DNA methylation patterns and how microbiome profiles are related to eating and physical activity behaviors is furthering understanding of why disparities in obesity persist. Environmental and neighborhood level research is uncovering the importance of exposures such as air and noise pollution and systematic or structural racism for obesity and related outcomes through behaviors such as sleep. Obesity disparities and the biological processes associated with them must be better contextualized within the social, economic, and political environments that contribute to them. One avenue for accomplishing this is by modeling relationships between within-body mechanisms and omics and beyond-body mechanisms and exposures. However, data integration across the various domains and data collection are significant challenges for generating a comprehensive systems model for obesity disparities.