Christine M. Marx

Where is obesity prevention on the map?: Distribution and predictors of local health department prevention activities in relation to county-level obesity prevalence in the United States

CONTEXT The system of local health departments (LHDs) in the United States has the potential to advance a locally oriented public health response in obesity control and reduce geographic disparities. However, the extent to which obesity prevention programs correspond to local obesity levels is unknown. OBJECTIVE This study examines the extent to which LHDs across the United States have responded to local levels of obesity by examining the association between jurisdiction-level obesity prevalence and the existence of obesity prevention programs. DESIGN Data on LHD organizational characteristics from the Profile Study of Local Health Departments and county-level estimates of obesity from the Behavioral Risk Factor Surveillance System were analyzed (n = 2300). Since local public health systems are nested within state infrastructure, multilevel models were used to examine the relationship between county-level obesity prevalence and LHD obesity prevention programming and to assess the impact of state-level clustering. SETTING Two thousand three hundred local health department jurisdictions defined with respect to county boundaries. PARTICIPANTS Practitioners in local health departments who responded to the 2005 Profile Study of Local Health Departments. MAIN OUTCOME MEASURES Likelihood of having obesity prevention activities and association with area-level obesity prevalence. RESULTS The existence of obesity prevention activities was not associated with the prevalence of obesity in the jurisdiction. A substantial portion of the variance in LHD activities was explained by state-level clustering. CONCLUSIONS This article identified a gap in the local public health response to the obesity epidemic and underscores the importance of multilevel modeling in examining predictors of LHD performance.

Understanding the Independent and Joint Associations of the Home and Workplace Built Environments on Cardiorespiratory Fitness and Body Mass Index

This observational study examined the associations of built environment features around the home and workplace with cardiorespiratory fitness (CRF) based on a treadmill test and body mass index (BMI) (weight (kg)/height (m)(2)). The study included 8,857 adults aged 20-88 years who completed a preventive medical examination in 2000-2007 while living in 12 Texas counties. Analyses examining workplace neighborhood characteristics included a subset of 4,734 participants. Built environment variables were derived around addresses by using geographic information systems. Models were adjusted for individual-level and census block group-level demographics and socioeconomic status, smoking, BMI (in CRF models), and all other home or workplace built environment variables. CRF was associated with higher intersection density, higher number of private exercise facilities around the home and workplace, larger area of vegetation around the home, and shorter distance to the closest city center. Aside from vegetation, these same built environment features around the home were also associated with BMI. Participants who lived and worked in neighborhoods in the lowest tertiles for intersection density and the number of private exercise facilities had lower CRF and higher BMI values than participants who lived and worked in higher tertiles for these variables. This study contributes new evidence to suggest that built environment features around homes and workplaces may affect health.

Development and reliability testing of the Worksite and Energy Balance Survey.

CONTEXT Worksites represent important venues for health promotion. Development of psychometrically sound measures of worksite environments and policy supports for physical activity and healthy eating are needed for use in public health research and practice. OBJECTIVE Assess the test-retest reliability of the Worksite and Energy Balance Survey (WEBS), a self-report instrument for assessing perceptions of worksite supports for physical activity and healthy eating. DESIGN The WEBS included items adapted from existing surveys or new items on the basis of a review of the literature and expert review. Cognitive interviews among 12 individuals were used to test the clarity of items and further refine the instrument. A targeted random-digit-dial telephone survey was administered on 2 occasions to assess test-retest reliability (mean days between time periods = 8; minimum = 5; maximum = 14). SETTING Five Missouri census tracts that varied by racial-ethnic composition and walkability. PARTICIPANTS Respondents included 104 employed adults (67% white, 64% women, mean age = 48.6 years). Sixty-three percent were employed at worksites with less than 100 employees, approximately one-third supervised other people, and the majority worked a regular daytime shift (75%). MAIN OUTCOME MEASURES Test-retest reliability was assessed using Spearman correlations for continuous variables, Cohens κ statistics for nonordinal categorical variables, and 1-way random intraclass correlation coefficients for ordinal categorical variables. RESULTS Test-retest coefficients ranged from 0.41 to 0.97, with 80% of items having reliability coefficients of more than 0.6. Items that assessed participation in or use of worksite programs/facilities tended to have lower reliability. Reliability of some items varied by gender, obesity status, and worksite size. Test-retest reliability and internal consistency for the 5 scales ranged from 0.84 to 0.94 and 0.63 to 0.84, respectively. CONCLUSIONS The WEBS items and scales exhibited sound test-retest reliability and may be useful for research and surveillance. Further evaluation is needed to document the validity of the WEBS and associations with energy balance outcomes.

Workplace social and organizational environments and healthy-weight behaviors.

Background The workplace is an important setting for health promotion including nutrition and physical activity behaviors to prevent obesity. This paper explores the relationship between workplace social environment and cultural factors and diet and physical activity (PA) behaviors and obesity among employees. Methods Between 2012 and 2013, telephone interviews were conducted with participants residing in four Missouri metropolitan areas. Questions included demographic characteristics, workplace socio/organizational factors related to activity and diet, and individual diet and PA behaviors, and obesity. Multivariate logistic regression was used to examine associations between the workplace socio/organizational environment and nutrition, PA, and obesity. Results There were differences in reported health behaviors and socio/organizational environment by gender, race, age, income, and worksite size. For example, agreement with the statement the ‘company values my health’ was highest among Whites, older employees, and higher income workers. As worksite size increased, the frequency of reporting seeing co-workers doing several types of healthy behaviors (eat fruits and vegetables, doing PA, and doing PA on breaks at work) increased. In adjusted analyses, employees agreeing the company values my health were more likely to engage in higher PA levels (aOR=1.54, 95% CI: 1.09-2.16) and less likely to be obese (aOR=0.73, 95% CI: 0.54-0.98). Seeing co-workers eating fruits and vegetables was associated with increased reporting of eating at least one vegetable per day (aOR=1.43, 95% CI: 1.06-1.91) and seeing co-workers being active was associated with higher PA levels (aOR 1.56, 95% CI: 1.19-2.05). Conclusions This research suggests that social/organizational characteristics of the workplace environment, particularly feeling the company values the workers’ health and to seeing co-workers engaging in healthy behaviors, may be related to nutrition and PA behaviors and obesity. These findings point to the potential for intervention targets including environment and policy changes.

Occupational sitting and weight status in a diverse sample of employees in Midwest metropolitan cities, 2012–2013

Introduction Few studies have examined the association between occupational sitting and body mass index (BMI). There is a particular lack of evidence among diverse populations. The objective of this study was to quantify the association between self-reported occupational sitting time and BMI by sex and race, independent of levels of occupational and leisure-time physical activity. Methods In 2012 and 2013, participants residing in 4 Missouri metropolitan areas were interviewed via telephone. The interview included questions on sociodemographic characteristics and time spent sitting at work. Multinomial logistic regressions were used to examine the association between occupational sitting and BMI between men and women and between black and white women. Results Overall 1,891 participants (66.9% women, 29.5% black) provided complete data. Median daily time spent by both men and women in occupational sitting was 180 minutes (interquartile range, 30 to 360 minutes); most participants were overweight (32.3%) or obese (33.6%). After adjusting for potential confounders, we found that black women in 3 categories of sitting time (31–180 minutes, 181–360 minutes, and >360 minutes) were approximately 2.5 times as likely (P for trend, .02) to be obese as black women who reported sitting for 30 minutes or less, independent of occupational and leisure-time physical activity. This association was not seen among white women. No significant associations were found among men. Conclusion Occupational sitting is associated with an increased likelihood of obesity among black women, independent of occupational and leisure-time physical activity. Areas of future research include evaluating associations among various occupations and industries, assessing the association in prospective cohorts, and exploring the feasibility of worksite interventions that target sitting.

Spatial Energetics: Integrating Data From GPS, Accelerometry, and GIS to Address Obesity and Inactivity.

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward.

Review of measures of worksite environmental and policy supports for physical activity and healthy eating

Introduction Obesity prevention strategies are needed that target multiple settings, including the worksite. The objective of this study was to assess the state of science concerning available measures of worksite environmental and policy supports for physical activity (PA) and healthy eating (HE). Methods We searched multiple databases for instruments used to assess worksite environments and policies. Two commonly cited instruments developed by state public health departments were also included. Studies that were published from 1991 through 2013 in peer-reviewed publications and gray literature that discussed the development or use of these instruments were analyzed. Instrument administration mode and measurement properties were documented. Items were classified by general health topic, 5 domains of general worksite strategy, and 19 subdomains of worksite strategy specific to PA or HE. Characteristics of worksite measures were described including measurement properties, length, and administration mode, as well as frequencies of items by domain and subdomain. Results Seventeen instruments met inclusion criteria (9 employee surveys, 5 manager surveys, 1 observational assessment, and 2 studies that used multiple administration modes). Fourteen instruments included reliability testing. More items were related to PA than HE. Most instruments (n = 10) lacked items in the internal social environment domain. The most common PA subdomains were exercise facilities and lockers/showers; the most common HE subdomain was healthy options/vending. Conclusion This review highlights gaps in measurement of the worksite social environment. The findings provide a useful resource for researchers and practitioners and should inform future instrument development.

Does availability of worksite supports for physical activity differ by industry and occupation

Purpose: To explore combinations of worksite supports (WSS) for physical activity (PA) that may assist employees in meeting PA recommendations and to investigate how availability of WSS differs across industries and occupations. Design: Cross-sectional. Setting: Several Missouri metropolitan areas. Participants: Adults employed >20 h/wk outside the home. Measures: Survey utilized existing self-reported measures (eg, presence of WSS for PA) and the International Physical Activity Questionnaire. Analysis: Logistic regression was conducted for 2 outcome variables: leisure and transportation PA. Independent variables included 16 WSS. Of particular interest were interaction effects between WSS variables. Analyses were stratified by 5 occupation and 7 industry types. Results: Overall, 2013 people completed the survey (46% response rate). Often, availability of 1 WSS did not increase the likelihood of meeting PA recommendations, but several pairs of WSS did. For example, in business occupations, the odds of meeting PA recommendations through transportation PA increased when employees had access to showers and incentives to bike/walk (odds ratio [OR] = 1.6; 95% confidence interval [CI] = 1.16-2.22); showers and maps (OR = 1.25; 1.02-1.55); maps and incentives to bike/walk (OR = 1.48; 1.04-2.12). Conclusion: Various combinations of WSS may increase the likelihood that employees will meet PA recommendations. Many are of low or no cost, including flexible time for exercise and maps of worksite-adjacent walk/bike routes. Findings may be instructive for employers seeking to improve employee health through worksite PA.

Work-related correlates of occupational sitting in a diverse sample of employees in Midwest metropolitan cities

The worksite serves as an ideal setting to reduce sedentary time. Yet little research has focused on occupational sitting, and few have considered factors beyond the personal or socio-demographic level. The current study i) examined variation in occupational sitting across different occupations, ii) explored whether worksite level factors (e.g., employer size, worksite supports and policies) may be associated with occupational sitting. Between 2012 and 2013, participants residing in four Missouri metropolitan areas were interviewed via telephone and provided information on socio-demographic characteristics, schedule flexibility, occupation, work related factors, and worksite supports and policies. Occupational sitting was self-reported (daily minutes spent sitting at work), and dichotomized. Occupation-stratified analyses were conducted to identify correlates of occupational sitting using multiple logistic regressions. A total of 1668 participants provided completed data. Those employed in business and office/administrative support spent more daily occupational sitting time (median 330 min) compared to service and blue collar employees (median 30 min). Few worksite supports and policies were sitting specific, yet factors such as having a full-time job, larger employer size, schedule flexibility, and stair prompt signage were associated with occupational sitting. For example, larger employer size was associated with higher occupational sitting in health care, education/professional, and service occupations. Work-related factors, worksite supports and policies are associated with occupational sitting. The pattern of association varies among different occupation groups. This exploratory work adds to the body of research on worksite level correlates of occupational sitting. This may provide information on priority venues for targeting highly sedentary occupation groups.

Availability and Use of Workplace Supports for Health Promotion Among Employees of Small and Large Businesses

Purpose: To explore the availability and utilization of workplace health supports by employees of small and large-sized employers. Design: Cross-sectional, telephone-based interviews collected on 16 workplace health supports for physical activity and diet. Setting: Participants selected by random-digit-dialing from 4 metropolitan areas of Missouri employees from 2012 to 2013. Participants: Two thousand fifteen working adults. Methods: We explored the availability and use of supports by employer size (<100 employees vs ≥100 employees), accounting for industry and personal factors. Analysis: We examined distributions and Poisson regression models of availability for supports by employer size and by industry and use of supports by employer size and personal factors. Results: One-fifth of the 1796 employees were employed by small-sized employers. Large employers offered more supports than small (mean: 6 vs 3), but a higher proportion of employees of small-sized employers used supports when available (59% vs 47%). The differences in offered supports between industries were not due to size alone. In regard to the determinants of participation, the personal factors of gender, age, weight, and income were associated with participation in 10 of the supports. Employer size was also associated with participation in 10 supports. No associations were found between personal factors or workplace size and participation for 3 supports. Conclusion: A higher proportion of employees working for smaller businesses use available supports than employees of larger businesses. Supports offered by employers should target the needs and interests of the workforce, particularly for the higher risk low-income employees.