Disparities in healthy food access are well documented in cross-sectional studies in communities across the United States. However, longitudinal studies examining changes in food environments within various neighborhood contexts are scarce. In a sample of 142 census tracts in four low-income, high-minority cities in New Jersey, United States, we examined the availability of different types of food stores by census tract characteristics over time (2009–2017). Outlets were classified as supermarkets, small grocery stores, convenience stores, and pharmacies using multiple sources of data and a rigorous protocol. Census tracts were categorized by median household income and race/ethnicity of the population each year. Significant declines were observed in convenience store prevalence in lower- and medium-income and majority black tracts (p for trend: 0.004, 0.031, and 0.006 respectively), while a slight increase was observed in the prevalence of supermarkets in medium-income tracts (p for trend: 0.059). The decline in prevalence of convenience stores in lower-income and minority neighborhoods is likely attributable to declining incomes in these already poor communities. Compared to non-Hispanic neighborhoods, Hispanic communities had a higher prevalence of small groceries and convenience stores. This higher prevalence of smaller stores, coupled with shopping practices of Hispanic consumers, suggests that efforts to upgrade smaller stores in Hispanic communities may be more sustainable.
Introduction
Children consume much of their daily energy intake at school. School district policies, state laws, and national policies, such as revisions to the US Department of Agriculture’s school meals standards, may affect the types of foods and beverages offered in school lunches over time.
Methods
This study evaluated changes and disparities in school lunch characteristics from 2006–2007 to 2013–2014. Data were obtained from annual cross-sectional surveys at 4,630 public elementary schools participating in the National School Lunch Program. Multivariate logistic regressions were conducted to examine lunch characteristics.
Results
The percentage of schools regularly offering healthful items such as vegetables (other than potatoes), fresh fruit, salad bars, whole grains, and more healthful pizzas increased significantly from 2006–2007 to 2013–2014, and the percentage of schools offering less healthful items such as fried potatoes, regular pizza, and high-fat milks decreased significantly. Nevertheless, disparities were evident in 2013–2014. Schools in the West were significantly more likely to offer salad bars than were schools in the Northeast, Midwest, or South (adjusted prevalence: West, 66.3%; Northeast, 22.3%; Midwest, 20.8%; South, 18.3%). Majority-black or majority-Latino schools were significantly less likely to offer fresh fruit than were predominantly white schools (adjusted prevalence: majority black, 61.3%; majority Latino, 73.0%; predominantly white, 87.8%). Schools with low socioeconomic status were significantly less likely to offer salads regularly than were schools with middle or high socioeconomic status (adjusted prevalence: low, 38.5%; middle, 47.4%; high, 59.3%).
Conclusion
Much progress has been made in improving the quality of school lunches in US public elementary schools, but additional opportunities for improvement remain.
Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children's weight status.
Design: Cross-sectional survey of randomly selected households with children living in low-income diverse communities.
Setting: A telephone survey conducted in 2009-2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child's residence to food and physical activity outlets.
Subjects: Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights.
Results: Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children's weight status.
Conclusions: The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.
The epidemic of overweight and obesity and its multiple causes have captured the attention of researchers, program administrators, politicians, and the public alike. Recently, many stakeholder groups have started investigating the role that food and nutrition assistance programs play in the etiology of the problem and in identifying possible solutions. As a result, policy changes have been recommended and implemented for programs such as the National School Lunch Program (NSLP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) to improve the nutritional quality of foods they offer to their participants. The Supplemental Nutrition Assistance Program (SNAP) is also attracting attention as a potential vehicle to reduce the burden of obesity among its users. Because of the tough economic and political climate in which all federal programs currently operate, the need for making nutrition assistance programs more efficient and effective in addressing health and nutrition related problems affecting the country has never been greater.
This document proposes a set of strategies to improve the effectiveness and efficiency of SNAP. These strategies are based on a review of research literature, recommendations from expert groups, and the experiences of other communities and states. We include information that pertains to potential stakeholder arguments for and against each strategy, as well as the political feasibility, financial impact, and logistical requirements for implementation. We drew candidate strategies from the range of options that have been tested through research and from policies that have been implemented around the country. The order of strategies in this document is based on overall strength of supportive research, as well as political and implementation feasibility. The four proposed strategies are improving access to healthy foods to provide better choices, incentivizing the purchase of healthy foods, restricting access to unhealthy foods, and maximizing education to more effectively reach a larger population of SNAP participants.
Background: Understanding determinants of high consumption of sugar-sweetened beverages (SSBs), a highly prevalent obesogenic behavior, will help build effective customized public health interventions.
Objective: Our aim was to identify child and parent lifestyle and household demographic factors predictive of high SSB consumption frequency in children from low-income, ethnically diverse communities that may help inform public health interventions.
Design: We used a cross-sectional telephone household survey.
Participants/setting: Participants were 717 boys and 686 girls aged 3 to 18 years old from the New Jersey Childhood Obesity Study living in five low-income cities (Camden, New Brunswick, Newark, Trenton, and Vineland). The adult most knowledgeable about household food shopping completed a questionnaire over the telephone inquiring about their and their child's dietary and physical activity habits, and household-, parent-, and child-level demographics.
Main outcome measures: Child's SSB consumption frequency was measured.
Statistical analysis performed: Multivariate ordered logit models were designed to investigate a variety of variables hypothesized to affect the frequency of SSB consumption. Exploratory stratified analyses by race, sex, and age were also conducted.
Results: Eight percent of our study participants never consumed SSBs, 45% consumed SSBs at least once per day, and 23% consumed twice or more per day. SSB consumption was higher among children 12 to 18 years vs 3 to 5 years (P<0.0001), of non-Hispanic black vs non-Hispanic white race/ethnicity (P=0.010), who were moderate fast food consumers vs never consumers (P=0.003), and those whose parents were high vs low SSB consumers (P<0.0001). Living in a non-English-speaking household (P=0.030), having a parent with a college or higher education vs less than high school (P=0.003), and having breakfast 6 to 7 days/wk vs never to 2 days/wk or less were associated with lower SSB consumption (P=0.001).
Conclusions: We identified a number of household-, parent-, and child-level predictors of SSB consumption, which varied by race, sex, and age, useful for building customized interventions targeting certain behaviors in ethnically diverse, low-income children.
Objectives: Conflicting findings on associations between food and physical activity (PA) environments and children's weight status demand attention in order to inform effective interventions. We assess relationships between the food and PA environments in inner-city neighborhoods and children's weight status and address sources of conflicting results of prior research.
Methods: Weight status of children ages 3-18 was assessed using parent-measured heights and weights. Data were collected from 702 children living in four low-income cities in New Jersey between 2009 and 2010. Proximity of a child's residence to a variety of food and PA outlets was measured in multiple ways using geo-coded data. Multivariate analyses assessed the association between measures of proximity and weight status.
Results: Significant associations were observed between children's weight status and proximity to convenience stores in the 1/4 mile radius (OR = 1.9) and with presence of a large park in the 1/2 mile radius (OR = 0.41). No associations were observed for other types of food and PA outlets.
Conclusions: Specific aspects of the food and PA environments are predictors of overweight and obese status among children, but the relationships and their detection are dependent upon aspects of the geospatial landscape of each community.
Background: The Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously validated SenseWear Armband (SWA).
Methods: Twenty-four participants (n = 24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike + Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion).
Results: The Nike + Fuelband (ICC = 0.77) and SWA (ICC = 0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike + Fuelband (246 ± 67 kcal) and SWA (238 ± 57 kcal) were not statistically different than OM (243 ± 67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike + Fuelband = 16 ± 13 %; SWA = 18 ± 18 %). Test-retest reliability for PAEE indicated good stability for Nike + Fuelband (ICC = 0.96) and SWA (ICC = 0.90).
Conclusion: The Nike + Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.
Background: Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists.
Methods: We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression.
Results: D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts.
Conclusion: Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.
Background: To identify social ecological correlates of objectively measured workplace sedentary behavior.
Methods: Participants from 24 worksites - across academic, industrial, and government sectors - wore an activPAL-micro accelerometer for 7-days (Jan-Nov 2016). Work time was segmented using daily logs. Sedentary behavior outcomes included time spent sitting, standing, in light intensity physical activity (LPA, stepping cadence <100 steps/min), and in prolonged sitting bouts (>30 min). Outcomes were standardized to an 8 h work day. Two electronic surveys were completed to derive individual (job type and work engagement), cultural (lunch away from the desk, walking at lunch and face-to-face interaction), physical (personal printer and office type) and organizational (sector) factors. Mixed-model analyses with worksite-level clustering were performed to examine multi-level associations. Secondary analyses examined job type and sector as moderators of these associations. All models were adjusted for age, race/ethnicity and gender.
Results: Participants (N = 478; 72% female; age: 45.0 ± 11.3 years; 77.8% non-Hispanic white) wore the activPAL-micro for 90.2 ± 15.5% of the reported workday. Walking at lunch was positively associated with LPA (5.0 ± 0.5 min/8 h, P < 0.001). Regular face-to-face interaction was negatively associated with prolonged sitting (−11.3 ± 4.8 min/8 h, P < 0.05). Individuals in private offices sat more (20.1 ± 9.1 min/8 h, P < 0.05), stood less (−21.5 ± 8.8 min/8 h, P < 0.05), and engaged in more prolonged sitting (40.9 ± 11.2 min/8 h, P < 0.001) than those in public office space. These associations were further modified by job type and sector.
Conclusions: Work-specific individual, cultural, physical and organizational factors are associated with workplace sedentary behavior. Associations vary by job type and sector and should be considered in the design of workplace interventions to reduce sedentary behavior.
Background: Emerging interventions that rely on and harness variability in behavior to adapt to individual performance over time may outperform interventions that prescribe static goals (e.g., 10,000 steps/day). The purpose of this factorial trial was to compare adaptive vs. static goal setting and immediate vs. delayed, non-contingent financial rewards for increasing free-living physical activity (PA).
Methods: A 4-month 2 × 2 factorial randomized controlled trial tested main effects for goal setting (adaptive vs. static goals) and rewards (immediate vs. delayed) and interactions between factors to increase steps/day as measured by a Fitbit Zip. Moderate-to-vigorous PA (MVPA) minutes/day was examined as a secondary outcome.
Results: Participants (N = 96) were mainly female (77%), aged 41 ± 9.5 years, and all were insufficiently active and overweight/obese (mean BMI = 34.1 ± 6.2). Participants across all groups increased by 2389 steps/day on average from baseline to intervention phase (p < .001). Participants receiving static goals showed a stronger increase in steps per day from baseline phase to intervention phase (2630 steps/day) than those receiving adaptive goals (2149 steps/day; difference = 482 steps/day, p = .095). Participants receiving immediate rewards showed stronger improvement (2762 step/day increase) from baseline to intervention phase than those receiving delayed rewards (2016 steps/day increase; difference = 746 steps/day, p = .009). However, the adaptive goals group showed a slower decrease in steps/day from the beginning of the intervention phase to the end of the intervention phase (i.e. less than half the rate) compared to the static goals group (−7.7 steps vs. -18.3 steps each day; difference = 10.7 steps/day, p < .001) resulting in better improvements for the adaptive goals group by study end. Rate of change over the intervention phase did not differ between reward groups. Significant goal phase x goal setting x reward interactions were observed.
Conclusions: Adaptive goals outperformed static goals (i.e., 10,000 steps) over a 4-month period. Small immediate rewards outperformed larger, delayed rewards. Adaptive goals with either immediate or delayed rewards should be preferred for promoting PA.