PURPOSE: The aim of this study was to determine if the linear and nonlinear components of the energy expenditure-walking speed relationship are influenced by body mass index (BMI; kg/m2). The secondary aims were to determine if the relationship was influenced by age, height, and sex. METHODS: Subjects (n=182) walked at 2, 3, and 4 mph for six minutes each with oxygen consumption (V̇O2; ml/kg/min) and measured via indirect calorimetry and converted to energy expenditure (EE; W/kg). Because of the curvilinear change in metabolic rate with increase in walking speed, polynomial random coefficient regression (PRCR) was employed to produce a model which captures the slope of change. Individual level linear and quadratic coefficients were analyzed for relationships with BMI, age, height, and sex. RESULTS: The net V̇O2 regression formula for walking was 1.79(x-3)2+4.97(x-3)+9.32 where x is speed in mph. BMI was modestly correlated with the quadratic coefficients (r = 0.15 to 0.17, p = 0.02 to 0.04) but not the linear coefficients (r =0.02- 0.07, p = 0.36-0.78) for V̇O2 and EE. There was no difference in coefficients between normal BMI (18.5-<25.0 kg/m2), overweight (25-<30.0 kg/m2) and obese (>30.0 kg/m2) groups (H = 1.5-4.0, p = 0.13-0.48). Delta V̇O2 for 2-3 mph, 3-4 mph, and 2-4 mph were not correlated with BMI (r = -0.02 - 0.13, p = 0.11 - 0.41). Height was inversely correlated with the linear and quadratic coefficients (r = -0.32 to -0.14, p = 0.09). Age was not correlated to coefficients (r = -0.16 to 0.32, p = 0.06-0.44). The coefficients for sex were not different after controlling for height in ANCOVA (F(1,179)=0.3-2.9, p >0.09). Age was not correlated to coefficients (r = -0.16 to –0.32, p = 0.06-0.44). CONCLUSION: Although BMI had a modest relationship with the quadratic coefficient, it explained less than 3% of the variance in V̇O2 or EE. Combined with the absence of a delta V̇O2 or a linear component, BMI does not influence the energy expenditure-walking speed relationship. Height explained up to 9% of the variance in the coefficients and eliminated apparent sex differences. Age was not related to the coefficients.

Background: Children in the United States have low diet quality scores and consume less than the recommended amounts of fruits, vegetables, whole grains, and dairy. The National School Lunch Program serves nearly 30 million children daily, and has the potential to improve the diet quality of children. However, there are high levels of food waste, particularly of fruits and vegetables. Purpose: The purpose of this study is to determine which menu items students are throwing away untouched most frequently. A secondary purpose of this study is to determine which menu items students are fully consuming most frequently. Methods: Student participants (n=2,881) in Arizona elementary, middle, and high schools who participated in school lunch were randomly selected to participate in the study. Student lunch trays were photographed before and after the student ate. Visual estimation was used to determine if menu items were untouched or fully consumed. Menu item names were standardized and categorized into menu categories. The frequency menu items were untouched or fully consumed were summarized in percentages by menu category, and stratified by school level. Results: Findings show that menu items within each menu category are untouched and fully consumed with different frequencies. Cold vegetable items were untouched with the greatest frequency, with 39% of all servings untouched. Some menu items were both untouched and fully consumed with high frequency. Conclusion: Food service managers can use these results to plan menus with food items that are more popular among their students to help increase consumption and decrease waste. Future research should explore the relationship between packaging and preparation with student consumption and waste. Researchers should also examine aspects of the high school food environment that may lead to increased student consumption.

Relationships between food and physical activity (PA) environments and children's related behaviors are complex.

Latent class analyses derived patterns from proximity to healthy and unhealthy food outlets, PA facilities and parks, and counts of residential dwellings and intersections. Regression analyses examined whether derived classes were related to food consumption, PA, and overweight among 404 low-income children.

Compared to children living in Low PA-Low Food environments, children in High Intersection&Parks-Moderate Density&Food, and High Density-Low Parks-High Food environments, had significantly greater sugar-sweetened beverage consumption (ps<0.01) and overweight/obesity (ps<0.001). Children in the High Density-Low Parks-High Food environments were more likely to walk to destinations (p = 0.01)

Recognizing and leveraging beneficial aspects of neighborhood patterns may be more effective at positively influencing children's eating and PA behaviors compared to isolating individual aspects of the built environment.

Background

The United States Department of Agriculture’s Supplemental Nutrition Assistance Program (SNAP) is the country’s largest nutrition assistance program for low-income populations. Although SNAP has been shown to reduce food insecurity, research findings on the diet quality of program participants are inconsistent.

Objective

This study evaluated whether the community food environment is a potential moderator of the association between SNAP participation and eating behaviors.

Design

This cross-sectional study used participant data from a telephone survey of 2,211 households in four cities in New Jersey. Data were collected from two cross-sectional panels from 2009 to 2010 and 2014. Food outlet data were purchased from commercial sources and classified as supermarkets, small grocery stores, convenience stores, or limited service restaurants.

Participants/setting

Analysis is limited to 983 respondents (588 SNAP participants) with household incomes below 130% of the federal poverty level.

Main outcome measures

Eating behaviors were assessed as frequency of consumption of fruit, vegetables, salad, and sugar-sweetened beverages.

Statistical analyses performed

Interaction and stratified analyses using gamma regression determined the differences in the association between SNAP participation and eating behaviors by the presence or absence of food outlets adjusted for covariates.

Results

SNAP participation was associated with a higher frequency of consuming sugar-sweetened beverages (P<0.05) when respondents lived within ¼ to ½ mile of a small grocery store, supermarket, and limited service restaurant. SNAP participants who did not live close to a convenience store reported a lower frequency of sugar-sweetened beverage consumption (P=0.01), and those living more than ½ mile away from a supermarket reported a lower frequency of fruit consumption (P=0.03).

Conclusions

The findings from this study suggest that the community food environment may play a role in moderating the association between SNAP participation and eating behaviors. Although SNAP participation is associated with some unhealthy behaviors, this association may only hold true when respondents live in certain food environments.

Features of the built environment (BE) are related to a wide range of health factors, including leisure-time physical activity (PA) and active forms of transportation. For working adults, worksite neighborhood is likely an important BE to better understand the impact of various factors on PA patterns. Compared to home neighborhood walkability research, worksite walkability has received relatively less attention. The objective of this project was to identify if worksite walkability was significantly associated with PA behavior.

Aims: to evaluate 1) the PA variation explained by work walkability, 2) the moderating effects of person-level characteristics to the relationship between PA and work walkability, and 3) the differences in the rate of change in PA over time by worksite walkability.

Methods: self-report and accelerometer measured PA at baseline (aim 1, 2); longitudinal accelerometer PA during the initial 56 days of a behavioral intervention (aim 3). Adults were generally healthy and reported part- or full-time employment with a geocodeable address outside the home. Geographic Information Systems (GIS) measured walkability followed established techniques (i.e., residential, intersection, and transit densities, and land-use-mix).

Results: On average, worksite walkability did not show direct relationships with PA (aim 1); yet certain person-level characteristics moderated the relationships: sex, race, and not having young children in the household (aim 2). During 56 days of intervention, the PA rate of change over time showed no evidence of a moderating effect by worksite walkability.

Discussion: Worksite walkability was generally not shown to relate to the overall PA. However, specific subgroups (women, those without young children) appeared more responsive to their worksite neighborhood walkability. Prior literature shows certain demographics respond differently with various BE exposures, and this study adds a potentially novel moderator of interest regarding young children at home. Understanding who benefits from access to walkable BE may inform targeted interventions and policy to improve PA levels and foster health equity.

Physical activity is critical for optimal health and has emerged as a viable option to improve sleep. Moderate- and vigorous-intensity physical activity comparisons to improve sleep in non-exercising adults with sleep problems is limited. The purpose was to determine the effects of moderate- or vigorous-intensity exercise on sleep outcomes and peripheral skin temperature compared to a no-exercise control. The exercise intensity preference also was determined.

Eleven women (46.9±7.0 years) not participating in regular exercise and self-reporting insomnia completed a graded maximal exercise test followed by a crossover trial of three randomly assigned conditions separated by a 1-week washout. Participants performed moderate-intensity [MIC, 30 minutes, 65-70% maximum heart rate (HRmax)] or high-intensity (HIT, 20 minutes, 1-minute bouts at 90-95% HRmax alternating with 1-minute active recovery) treadmill walking or a no-exercise control (NEC) on two consecutive weekdays 4-6 hours prior to typical bed time. A dual-function wrist-worn accelerometer/temperature monitor recorded movement and skin temperature from which sleep-onset latency (SOL), sleep maintenance, sleep efficiency, total sleep time (TST), and peripheral skin temperature changes were calculated. Participants self-reported sleep outcomes weekly, enjoyment of exercise the morning after HIT and MIC, and exercise intensity preference upon completing all conditions. Mixed models analysis of variance examined differences between and within conditions controlling for demographic characteristics and habitual physical activity.

HIT resulted in up to a 90-minute TST increase on night four (448 minutes, 95% CI 422.4-474.2) compared to nights one-three. MIC nights three (43.5 minutes, 95% CI 30.4-56.6) and four (42.1 minutes 95% CI 29.0-55.2) showed nearly a 30-minute SOL worsening compared to nights one-two. No other actigraphy-measured sleep parameters differenced within or between conditions. Self-reported sleep outcomes, peripheral skin temperature change, and exercise enjoyment between conditions were similar (p>0.05). More participants preferred lower (n=3) to higher (n=1) intensity activities.

Early evening high-intensity and moderate-intensity exercise had no effect on sleep outcomes compared to a control in non-exercising adults reporting sleep complaints. Sleep benefits from HIT may require exercise on successive days. Participants indicated partiality for lower intensity exercise. More information on timing and mode of physical activity to improve sleep in this population is warranted.