Kosuke Tamura

Relationships Between the Built Environment and Walking and Weight Status Among Older Women in Three U.S. States

There are few studies of built environment associations with physical activity and weight status among older women in large geographic areas that use individual residential buffers to define environmental exposures. Among 23,434 women (70.0 ± 6.9 yr; range = 57-85) in 3 states, relationships between objective built environment variables and meeting physical activity recommendations via walking and weight status were examined. Differences in associations by population density and state were explored in stratified models. Population density (odds ratio [OR] =1.04 [1.02, 1.07]), intersection density (ORs = 1.18-1.28), and facility density (ORs = 1.01-1.53) were positively associated with walking. Density of physical activity facilities was inversely associated with overweight/obesity (OR = 0.69 [0.49, 0.96]). The strongest associations between facility density variables and both outcomes were found among women from higher population density areas. There was no clear pattern of differences in associations across states. Among older women, relationships between accessible facilities and walking may be most important in more densely populated settings.

Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states

BackgroundIdentifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study’s aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters.MethodsIn 2004, Nurses’ Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared.ResultsSeven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband’s education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters.ConclusionsSpatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.

Factorial validity of an abbreviated Neighborhood Environment Walkability Scale for seniors in the Nurses’ Health Study

BackgroundUsing validated measures of individuals’ perceptions of their neighborhood built environment is important for accurately estimating effects on physical activity. However, no studies to date have examined the factorial validity of a measure of perceived neighborhood environment among older adults in the United States. The purpose of this measurement study was to test the factorial validity of a version of the Abbreviated Neighborhood Environment Walkability Scale (NEWS-A) modified for seniors in the Nurses’ Health Study (NHS).FindingsA random sample of 2,920 female nurses (mean age = 73 ± 7 years) in the NHS cohort from California, Massachusetts, and Pennsylvania completed a 36-item modified NEWS-A for seniors. Confirmatory factor analyses were conducted to test measurement models for both the modified NEWS-A for seniors and the original NEWS-A. Internal consistency within factors was examined using Cronbach’s alpha. The hypothesized 7-factor measurement model was a poor fit for the modified NEWS-A for seniors. Overall, the best-fitting measurement model was the original 6-factor solution to the NEWS-A. Factors were correlated and internally consistent.ConclusionsThis study provided support for the construct validity of the original NEWS-A for assessing perceptions of neighborhood environments in older women in the United States.

Characteristics of Adults Who Switched From Cigarette Smoking to E-cigarettes

INTRODUCTION Because of the rapidly increasing use of electronic cigarettes (e-cigarettes), this study aimed to investigate the individual characteristics and state-level prevalence of U.S. adults who have switched to e-cigarettes from traditional cigarettes. METHODS Data from the 2012-2013 and 2013-2014 National Adult Tobacco Surveys were analyzed in 2016. Relative percent change in switching was estimated, and the state-specific prevalence of adults who switched to e-cigarettes from traditional cigarettes was calculated and mapped. Multivariate logistic regression was conducted to examine how switching varied by sociodemographic subgroups and region. RESULTS Overall, the number of individuals who switched from traditional cigarettes to e-cigarettes increased by approximately 100% over the 1-year interval. Significant increases were found among a number of sociodemographics and regions. Multivariate logistic regression analyses showed that young adults and those living in the South and West were more likely to switch to e-cigarettes, compared to former smokers who did not switch. Compared with current dual users, those with higher education and those who were not single were more likely to switch to e-cigarettes. The state with the highest prevalence of switching was New Mexico (7.3%), whereas Connecticut had the lowest prevalence (0.8 %) among former smokers. CONCLUSIONS There is an increase in the progression from traditional cigarette use to e-cigarette use. Further research is warranted to determine whether this change continues and facilitates cigarette smoking cessation as a possible public health benefit and opportunity to save lives rather than constitutes a potential threat to public health.

Analysis of state-specific prevalence, regional differences, and correlates of hookah use in U.S. adults, 2012-2013

Objective To investigate the state-specific prevalence, regional differences, and correlates of hookah use among U.S. adults. Methods We analyzed the most recent nationally representative data of adults from the National Adult Tobacco Survey (NATS) 2012-2013 (n = 60192). State-specific prevalence of lifetime and current hookah use was calculated and mapped. Multivariate logistic regression was performed to determine the association between sociodemographic characteristics, regional differences, and hookah use. Results Among U.S. adults (≥18 years), overall prevalence of lifetime hookah use was 12.3%, while current use was 3.9%. Mapping of state-specific prevalence revealed that the West tended to have higher rates of use, while the South tended to have lower ones. In the adjusted model, we observed that current hookah use was positively associated with younger adults, males, non-Hispanic adults, those with higher education and income statuses, being single, those living in the West, and current cigarette use. Conclusion The prevalence of hookah use varies by state, region, and sociodemographic characteristics among adults. Future research, including longitudinal studies, are needed to identify geographic and sociodemographic characteristics and trends among hookah users, investigate hookah-related health outcomes, and evaluate targeted public health efforts aimed at this emerging threat. Implications This study investigates state-level prevalence, regional differences, and sociodemographic characteristics of hookah use among U.S. adults, using the most recent NATS. Hookah use was positively associated with younger adults and those living in the West. This study adds to the understanding of the geographic and sociodemographic factors underlying hookah use, which can be used to develop much needed evidence-based regulations and programs that are responsive to the needs of different risk groups.

Geospatial clustering in sugar-sweetened beverage consumption among Boston youth

Abstract The objective was to detect geospatial clustering of sugar-sweetened beverage (SSB) intake in Boston adolescents (age = 16.3 ± 1.3 years [range: 13–19]; female = 56.1%; White = 10.4%, Black = 42.6%, Hispanics = 32.4%, and others = 14.6%) using spatial scan statistics. We used data on self-reported SSB intake from the 2008 Boston Youth Survey Geospatial Dataset (n = 1292). Two binary variables were created: consumption of SSB (never versus any) on (1) soda and (2) other sugary drinks (e.g., lemonade). A Bernoulli spatial scan statistic was used to identify geospatial clusters of soda and other sugary drinks in unadjusted models and models adjusted for age, gender, and race/ethnicity. There was no statistically significant clustering of soda consumption in the unadjusted model. In contrast, a cluster of non-soda SSB consumption emerged in the middle of Boston (relative risk = 1.20, p = .005), indicating that adolescents within the cluster had a 20% higher probability of reporting non-soda SSB intake than outside the cluster. The cluster was no longer significant in the adjusted model, suggesting spatial variation in non-soda SSB drink intake correlates with the geographic distribution of students by race/ethnicity, age, and gender.

Accelerometer and GPS Analysis of Trail Use and Associations With Physical Activity

BACKGROUND Concurrent use of accelerometers and global positioning system (GPS) data can be used to quantify physical activity (PA) occurring on trails. This study examined associations of trail use with PA and sedentary behavior (SB) and quantified on trail PA using a combination of accelerometer and GPS data. METHODS Adults (N = 142) wore accelerometer and GPS units for 1-4 days. Trail use was defined as a minimum of 2 consecutive minutes occurring on a trail, based on GPS data. We examined associations between trail use and PA and SB. On trail minutes of light-intensity, moderate-intensity, and vigorous-intensity PA, and SB were quantified in 2 ways, using accelerometer counts only and with a combination of GPS speed and accelerometer data. RESULTS Trail use was positively associated with total PA, moderate-intensity PA, and light-intensity PA (P < .05). On trail vigorous-intensity PA minutes were 346% higher when classified with the combination versus accelerometer only. Light-intensity PA, moderate-intensity PA, and SB minutes were 15%, 91%, and 85% lower with the combination, respectively. CONCLUSIONS Adult trail users accumulated more PA on trail use days than on nontrail use days, indicating the importance of these facilities for supporting regular PA. The combination of GPS and accelerometer data for quantifying on trail activity may be more accurate than accelerometer data alone and is useful for classifying intensity of activities such as bicycling.