Objective

In response to recent national efforts to increase the availability of healthy food in small stores, we sought to understand the extent to which small food stores could implement the newly published Healthy Small Store Minimum Stocking Recommendations and reflect on the new US Department of Agriculture Food and Nutrition Service's final rule for stocking of staple foods for Supplemental Nutrition Assistance Program–approved retailers.

Design

We collected qualitative and quantitative data from 57 small stores in four states (Arizona, Delaware, Minnesota, and North Carolina) that accepted Supplemental Nutrition Assistance Program but not Special Supplemental Nutrition Assistance Program for Women, Infants, and Children benefits. Data from semistructured, in-depth interviews with managers/owners were transcribed, coded, and analyzed. We collected quantitative store inventory data onsite and later performed descriptive analyses.

Results

Store interviews revealed a reluctant willingness to stock healthy food and meet new recommendations. No stores met recommended fruit and vegetable stocking, although 79% carried at least one qualifying fruit and 74% carried at least one qualifying vegetable. Few stores met requirements for other food categories (ie, whole grains and low-fat dairy) with the exception of lean proteins, where stores carrying nuts or nut butter were more likely to meet the protein recommendation. Water and 100% juice were widely available and 68% met basic healthy beverage criteria.

Conclusions

In contrast to the inventory observed, most owners believed store stock met basic recommendations. Further, findings indicate that small stores are capable of stocking healthy products; however, technical and infrastructure support, as well as incentives, would facilitate shifts from staple to healthier staple foods. Retailers may need support to understand healthier product criteria and to drive consumer demand for new products.

Frequent consumption of fruits and vegetables has been linked to better dietary quality and positive health outcomes. Unfortunately, fruit and vegetable consumption among elementary school children falls far short of the recommendations. Therefore, finding strategies to promote fruit and vegetable consumption in children is a public health priority. One such strategy is the United States Department of Agriculture’s (USDA) Fresh Fruit and Vegetable Program (FFVP), which provides fresh fruits and vegetables as snacks, at least twice per week, in elementary schools with high student enrollment from low-income households. The program aims to expand the variety of fruits and vegetables children experience, impacting their present and future health outcomes.  Another USDA initiative, the Supplemental Nutrition Assistance Program Education (SNAP-Ed), offered in community and school settings, aims to improve the likelihood that SNAP eligible individuals will make healthy food choices consistent with the Dietary Guidelines for Americans.  SNAP-Ed is a potential resource for FFVP schools, providing nutrition education, staff training, and promotional materials.

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: 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: 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.