Perspectives From Extension Staff on Building and Sustaining Community Wellness Coalitions.

BACKGROUND: Community wellness coalitions are an important part of functioning program infrastructure and have been effective in promoting policy, systems, and environmental (PSE) change, especially when paired with technical support by a community champion or Extension staff. PSE strategies are critical to support long-lasting behavior change but can be challenging to implement. Extension is an established and equipped organization that has potential to help community overcome those challenges. The aim of this article was to identify and describe experiences of Extension staff working as community coaches. METHODS: A mixed-methods design to assess the impact of Extension staff working with Community Champions included a quantitative Extension Coaching Confidence Scorecard and an Extension Key Informant Interview. RESULTS: The total Extension Coaching Confidence score significantly increased from pre- to post-intervention (55.1 ± 35.3 vs. 81.7 ± 37.7, p = .03). Five facilitators and two barriers to wellness coalition development were identified by Extension staff. DISCUSSION: The results of this study indicate that the model of community coaching used was effective at addressing the foundational components of the Component Model of Infrastructure (CMI). However, comprehensive training for Extension staff in the CMI and technical assistance are needed in order to build capacity, achieve outcomes, and promote sustainability. IMPLICATIONS FOR PRACTICE: Individuals seeking to transition to PSE work should be provided with a foundation of specific, targeted training in the CMI and evidence-based technical assistance methods. Practitioners should recognize the essential role of community champions in PSE work. Completing the Extension Coaching Confidence Scorecard periodically can inform evolving training needs.

Facilitating Nutrition and Physical Activity-Focused Policy, Systems, and Environmental Change in Rural Areas: A Methodological Approach Using Community Wellness Coalitions and Cooperative Extension.

Obesity prevalence is higher among rural populations than urban, which may be related to differences in environments. Rural counties face barriers to accessing healthy foods and physical activity opportunities including isolation, transportation distances, and lack of facilities. As part of the Centers for Disease Control and Prevention Division of Nutrition, Physical Activity, and Obesity High Obesity Program, community-based wellness coalitions were established in six South Dakota counties with adult obesity prevalence >40%. The community coalitions were charged with improving access to healthy foods and safe and accessible places to participate in physical activity within their rural, underserved communities. Coalitions were created and members were recruited by Cooperative Extension staff who had existing relationships with key stakeholders within the community. Within these coalitions, champions were identified to provide leadership and ensure project implementation. Ongoing support and technical assistance were provided to the community coalitions by Cooperative Extension staff as they completed a community needs assessment, disseminated results of the needs assessment with the community, created action plans based on needs assessment data, implemented evidence-based interventions to support nutrition and physical activity policy, system and environmental changes, and assessed impact within their community. Overall, the purpose of this article is to share the project methodology, which capitalized on using Cooperative Extension, to build capacity to improve the nutrition and physical activity environment in rural, unserved communities. Sustainability of this work, along with lessons learned, is also discussed.

Comparison of VO2peak from the Progressive Aerobic Cardiovascular Endurance Run (PACER) and treadmill in children.

Background/Objectives: The purpose of this study was to investigate the difference in peak oxygen consumption (VO2peak) during a graded treadmill test and the Progressive Aerobic Cardiovascular Endurance Run (PACER) in a sample of 7-14 year old children. Methods: Forty-four participants (25 boys, 19 girls) had VO2 assessed during a peak treadmill test and the PACER by a portable indirect calorimeter on non-consecutive days. Exercise parameters were compared between exercise tests by paired t-tests. Results: The PACER elicited a greater measured VO2peak (49.4 ± 9.4 vs. 46.7 ± 7.5 ml. kg-1·min-1) and maximum respiratory exchange ratio (1.14 ± 0.08 vs. 1.07 ± 0.08) than the treadmill test (p < 0.05). Rating of perceived exertion was higher (8.1 ± 3.5 vs 7.6 ± 3.8) during the treadmill test compared to the PACER test (p < 0.05). There was no difference in maximum heart rate between treadmill test and PACER test (196.9 ± 9.3 vs. 198.6 ± 8.8, p > 0.05). Conclusions: The PACER provides an acceptable measure of cardiorespiratory fitness in children but the finding that children elicit a higher measured VO2peak during the PACER compared to a graded treadmill test warrants continual refinement in future aerobic fitness prediction equations from the PACER.

Identifying Food Parenting Practices From Comprehensive Home Environment Survey.

OBJECTIVE: Conducting exploratory factor analysis (EFA) with the existing home environment assessment-the Comprehensive Home Environment Survey (CHES), to identify scales related to food parenting practices. METHODS: Parents of 3- to 5-year-old children (n = 172) completed the CHES surveys. After selected items from CHES were categorized into food parenting practice constructs, EFA was used to identify potential subconstructs. Internal consistency and Spearman correlation analysis were also conducted. RESULTS: The EFAs identified 4 factors within the structure, 4 within coercive control, and 2 within autonomy support. CONCLUSIONS AND IMPLICATIONS: The results provided preliminary evidence and support that the CHES can be used to measure food parenting practices. The study was limited to a small sample of non-Hispanic White and highly educated participants, less identified items within autonomy support, and lower internal consistency for several identified factors. Confirmatory factor analysis in a larger and more diverse sample is needed for future research.

Quantifying the Food and Physical Activity Environments in Rural, High Obesity Communities.

The built environment contributes to an individual's health, and rural geographies face unique challenges for healthy eating and active living. The purpose of this descriptive study was to assess the nutrition and physical activity environments in rural communities with high obesity prevalence. One community within each of six high obesity prevalence counties in a rural Midwest state completed the Nutrition Environment Measures Survey for Stores (NEMS-S) and the Rural Active Living Assessment (RALA). Data were collected by trained community members and study staff. All communities had at least one grocery store and five had at least one convenience store. Grocery stores had higher mean total NEMS-S scores than convenience stores (26.6 vs. 6.0, p < 0.001), and higher scores for availability (18.7 vs. 5.3, p < 0.001) and quality (5.4 vs. 0, p < 0.001) of healthful foods (higher scores are preferable). The mean RALA town-wide assessment score across communities was 56.5 + 15.6 out of a possible 100 points. The mean RALA program and policy assessment score was 40.8 + 20.4 out of a possible 100 points. While grocery stores and schools are important for enhancing food and physical environments in rural areas, many opportunities exist for improvements to impact behaviors and address obesity.

Parenting Styles, Food Parenting Practices and Dietary Intakes of Preschoolers.

Previous evidence suggests that children's eating behaviors were largely influenced by the parent and home eating structure. This study examined the relationship between parenting styles (including authoritative, authoritarian, indulgent, and uninvolved), food parenting practices (within Structure, Coercive Control, and Autonomy Support constructs) and dietary intakes of preschoolers. Children aged 3-5 years and their parents were recruited from preschools/daycare centers and parents completed the surveys (n = 166). Dietary intakes were collected using the Harvard Service Food Frequency Questionnaire (HSFFQ), parenting style was assessed using the Parenting Dimensions Inventory-Short Version (PDI-S), and food parenting practices were measured using Comprehensive Home Environment Survey (CHES). The results showed that food parenting practices had a higher number of specific significant findings on children's nutrient and food group intakes than parenting styles. Correlation analyses showed positive parenting practices within Structure were significantly related to healthier children's intakes (e.g., vegetables, iron, and folate) and less unhealthy dietary intakes (e.g., sweets and total fats). Regression models show that children with authoritative parents consumed more fruits compared to children with authoritarian parents and indulgent parents. The results addressed the importance of parental influences for preschoolers' healthy dietary intakes, which suggested that future interventions and educational programs could enhance parenting practices to impact child diet.

Influence of Parenting Style on Body Mass Index, Physical Activity, and Sedentary Time.

The purpose of the current study was to determine the influence of parenting style on body mass index (BMI) percentile, physical activity (PA), and sedentary time (ST) in children. Accelerometers were used to assess PA and ST in 152 fifth-grade children. Parenting style was assessed by the child participants' responses to modified questions from the Parenting Style Inventory II and dichotomized as authoritative or non-authoritative. Multiple linear regression analyses were utilized to identify significant predictors of outcomes of interest. Parenting style did not predict ST or any intensity of PA; however, BMI percentile and gender were significant predictors of moderate-intensity PA, vigorous-intensity PA, and moderate-to-vigorous intensity PA (P < .01). BMI percentile was predicted to be lower in females with authoritative mothers (P < .01). While authoritative and non-authoritative parenting style did not predict objectively measured PA or ST in early adolescents, authoritative parenting style did predict BMI percentile in female participants.

A Generalized Equation for Prediction of VO2peak from a Step Test.

This study aimed to develop an equation to reduce variability of VO2peak prediction from a step test and compare VO2peak prediction from the new equation to the Queen's College Step Test (QCST). The development group (n=86; 21.7±2 years) was utilized to develop the SDState step test equation to predict relative VO2peak. The cross-validation group (n=99; 21.6±2 years) was used to determine the validity of the SDState step test VO2peak prediction equation. A regression analysis was used to identify the best model to predict VO2peak. Analysis of variance (ANOVA) was further used to determine differences among predicted and measured VO2peak values. Forward stepwise multiple regression identified age, sex, abdominal circumference, and active heart rate at the 3-min mark of the step test to be significant predictors of VO2peak (mL·kg-1·min-1). No differences among measured VO2peak (47.3±7.1 mL·kg-1·min-1) and predicted VO2peak (QCST, 46.9±9.3 mL·kg-1·min-1; SDState 48.3±5.7 mL·kg-1·min-1) were found. Pearson correlations, ICC, SEE, TEE, Bland-Altman plots, and Mountain plots indicate the SDState step test equation provides less variation in the prediction of VO2peak compared to the QCST. The SDState step test equation is effective for predicting VO2peak from the YMCA step test in young, healthy adults.

Model School-District Wellness Policies Warrant Improvements in Comprehensiveness and Strength.

BACKGROUND: School districts participating in the US Child Nutrition program are required to have a wellness policy. Many state agencies provide model policies to aid districts in policy creation. However, use of model wellness policies has not been associated with higher quality policies. Therefore, the purpose of the present study was to assess the quality of model wellness policies and to determine if federal regulations are more likely to be included than evidence-based best practices. METHODS: Model wellness polices available through state agency websites were analyzed for comprehensiveness and strength using the WellSAT 3.0 and item status as a federal regulation or best practice was assigned. We used linear regression to determine if federal regulation status was associated with inclusion in model wellness policies. RESULTS: Overall, 34 states had model wellness policies available online. The total comprehensiveness and strength of model wellness policies was 59.3 ± 17.5 and 21.4 ± 17.6, respectively, out of 100 possible points. Among policy sections, comprehensiveness was highest within Nutrition Education (73.2 ± 31.6) and lowest in Wellness Promotion and Marketing (49.8 ± 27.2). On average, WellSAT items that were federal regulations were covered in 71% of model policies, while best practices were only covered in 54% of model policies (p = .008). CONCLUSIONS: There is a need to improve the quality of model wellness policies. The development of a uniform model policy may be warranted to provide a comprehensive list of federal regulations and best practices, written with strong language, for inclusion within school wellness policies.

Relationship of Weight-Related Eating Behaviors and Weight Loss of Participants Enrolled in a Proprietary Weight Loss Program.

Background: There is a need for improving long-term success in meal replacement programs and identifying the variables that affect weight loss and maintenance in a proprietary weight loss program that includes health coaching. Objective: The aim of this study is to evaluate weight-related eating behaviors of participants with clinically significant weight loss (CSWL) in a proprietary weight loss program. Study Design, Setting, and Participants: A cross-sectional sample of participants (n=1,454) enrolled in a proprietary weight-loss program that includes meal replacements and health coaching were queried via an on-line survey for weight-related eating behaviors and weight history. Main Outcome Measures and Analysis: Weight-related eating behaviors of routine restraint (RR), compensatory restraint (CR), susceptibility to external cues (SEC), and emotional eating (EE) were assessed using the Weight Related Eating Questionnaire. CSWL was defined as having achieved a weight loss greater than 10% of starting weight. Participants were dichotomized into those with CSWL (n=973) and with no CSWL (n=481). The relationship between CSWL (controlling for age and sex) as the dependent variable and weight-related eating behaviors (RR, CR, SEC, and EE) as the independent variables was assessed using logistic regression (Stata/SE 14). Results: Those with CSWL have higher odds of having RR (OR: 1.3, p<0.05) and CR (OR: 1.1, p<0.05) and lower odds of SEC (OR: 0.7, p<0.05) and EE (OR: 0.8, p<0.05) eating behaviors than those without CSWL. Conclusions: Weight-related eating behaviors of participants in proprietary meal replacement weight-loss programs who have successfully lost weight differ compared to those who have not. Knowledge of the relationship between CSWL and weight-related eating behaviors can be used by coaches to assist participants in reinforcing those behaviors that support weight-loss. These results are limited to participants who self-select for proprietary meal-replacement weight-loss programs and cannot be generalized to other weight-loss or maintenance programs.

Differences in Overweight/Obesity Among Youth in a Midwest State by Rural-Urban Continuum Codes.

BACKGROUND: A disparity in overweight/obesity prevalence exists between rural and urban youth; however, definitions of 'rural' vary widely and the degree to which rurality impacts overweight/obesity prevalence is unclear. Therefore, the purpose of this study was to examine the school height and weight data in a rural Midwest state to determine differences in overweight and obesity prevalence among youth by using Rural-Urban Continuum (RUC) codes to define county-level degree of urbanization. METHODS: De-identified statewide data were obtained in electronic format from the state Department of Health. Height, weight, sex and age were used to calculate body mass index (BMI) z-scores, which were used to determine BMI percentile and categories. The county variable was used to assign a RUC code to each individual. Logistic regression was used to examine binary weight classifications by rural status while controlling for age, sex and race/ethnicity. RESULTS: Odds of obesity and of overweight/obesity were higher among rural youth compared to non-rural. Odds of overweight/obesity increased with increasing rurality. Compared to youth who lived in counties with a RUC code of 3, youth who lived in counties with RUC codes of 5, 7, 8 and 9 had greater odds of overweight/obesity. The number of youth classified as 'rural' ranged from 11-48 percent, depending on how 'rural' was defined. Likewise, overweight/obesity prevalence differed by 4.6 percent depending on how 'rural' was defined. CONCLUSIONS: Consistently defining 'rural' and determining degree of rurality is important in understanding how geographic location plays a role in overweight/obesity among youth. Future research should work to assess the physical and social environments of these different types of rural areas to better understand the role that rurality plays in contributing to overweight/obesity among youth. Assessing social determinants of health and its impact on health in rural youth is essential for designing effective public health interventions that can be implemented to address the issue.

Comparison of Physical Activity by Lifestyle Between Two Rural Pediatric Population Groups.

BACKGROUND: Obesity prevalence is higher among rural populations than urban, including youth. Reduced physical activity levels are associated with childhood obesity. It could be assumed that the obesity disparity between rural and urban children is attributable, in part, to differences in physical activity levels; however, previous research quantifying and comparing physical activity levels between rural and urban youth are mixed. Lifestyle may be more important than geographic location in determining physical activity levels. Therefore, the objective of this study was to compare sex and lifestyle group (Hutterite vs. non-Hutterite) differences in physical activity in a free-living, rural pediatric population. METHODS: Youth (n=58) were instructed to wear accelerometers for seven days. Mean percent time in light, moderate or vigorous activity during waking hours was calculated. Two-way ANOVAs and multiple regression models were used for analyses. RESULTS: Percent time in vigorous activity was significantly greater for Hutterite males than Hutterite females, and Hutterite males had greater percent time in vigorous activity and moderate plus vigorous activity than non-Hutterite males. CONCLUSIONS: There is evidence to support differences in rural lifestyles to be associated with differences in physical activity levels between children living in the same geographic location, particularly among males. Active transportation and having a safe environment for unstructured outdoor play may account for activity and lifestyle differences between the two rural groups.

Barriers to and Facilitators of Weight Management in Adults Using a Meal Replacement Program That Includes Health Coaching.

PURPOSE: The Academy of Nutrition and Dietetics supports meal replacement (MR) programs as an effective diet-related weight management strategy. While MR programs have been successful promoting initial weight loss, weight regain has been as high as 50% 1 year following MR program participation. The purpose of this article is to identify barriers to and facilitators of weight loss (WL) and weight loss maintenance (WM) among individuals participating in a MR program. METHODS: Sixty-one MR program clients participated in focus groups (WL = 29, WM = 32). Barriers and facilitators were discussed until saturation of themes was reached. Focus group transcriptions were coded into themes to identify the barriers to and facilitators of weight management that emerged within each phase. Queries were run to assess frequencies of references to each theme. RESULTS: The primary barriers within the WL phase included program products, physical activity, and social settings. WM phase participants referenced nutrition, lack of health coach knowledge, and physical activity as barriers. Personal benfits, ability to adhere to the program, and family support emerged as leading facilitators for WL phase participants. Personal benefits, health coach support, and physical activity emerged as facilitators by WM phase participants. CONCLUSIONS: Health coaches have the unique opportunity to use perceived facilitators to improve participant success, and help participants address their personal barriers in order to progress through successful, long-term weight management. Current health coaching models used in MRP should aim to identify participants' specific barriers and develop steps to overcome them.

The iCook 4-H Study: Report on Physical Activity and Sedentary Time in Youth Participating in a Multicomponent Program Promoting Family Cooking, Eating, and Playing Together.

OBJECTIVE: To report physical activity and sedentary time outcomes of youth in iCook 4-H. STUDY DESIGN AND SETTING: iCook 4-H was a 5-state, randomized, control-treatment, family-based childhood obesity prevention intervention promoting cooking, eating, and playing together. PARTICIPANTS AND INTERVENTION: Youth aged 9-10 years and the main preparer of their meals participated in the 12-week program followed by monthly newsletters and biyearly booster sessions until 24 months. MAIN OUTCOME MEASURE(S): A total of 155 youth were fitted with an Actigraph GT3X+ accelerometer, which they wore for 7 days at baseline and 4, 12, and 24 months to measure mean daily minutes per hour of waking wear time for sedentary time (ST), light physical activity (PA) (LPA), moderate PA, vigorous PA, and moderate to vigorous PA. Self-reported PA was assessed using the Block Kids Physical Activity Screener and additional questions querying for the program goal of the frequency of family actively playing together. Linear mixed models were used to determine differences from baseline to 24 months. Significance was set at P ≤ .05. RESULTS: There was a significant (P < .05) group × time interaction for LPA (adjusted interaction B estimate, 95% confidence interval; 0.18 [0.05, 0.30]) and ST (-0.15 [-0.26, -0.04]); ST increased and LPA decreased in the treatment group. There were no differences in other accelerometer-derived PA measures, self-report Block Kids Physical Activity Screener measures, or frequency of family actively playing together at any time point. CONCLUSIONS AND IMPLICATIONS: iCook 4-H was a multicomponent program observing youth aged 9-10 years for 24 months that focused on enhancing cooking skills, mealtime behavior and conversation, and PA through daily family activities. Greater emphasis on developing PA skills, changing environmental factors, and increasing PA both in and after school may be needed.

Differences in Physical Activity and Diet Patterns between Non-Rural and Rural Adults.

BACKGROUND: It is unclear how rural occupations and lifestyles may play a role in shaping physical activity and diet behaviors that contribute to the rural⁻urban obesity disparity. METHODS: Data come from the prospective and observational South Dakota Rural Bone Health Study, which included adults aged 20⁻66 years in three groups: (1) non-rural non-Hutterite, (2) rural non-Hutterite, and (3) rural Hutterite. Physical activity data were collected using 7-day physical activity questionnaires, and hours per day in physical activity categories are reported. Diet data were collected using food frequency questionnaires, and food group servings per day (svg/day) are reported. Mixed models were generated to determine group differences in physical activity and diet outcomes, and marginal group means are presented. RESULTS: Among females, both rural groups spent more time in moderate activity (4.8 ± 0.13 h/day and 4.7 ± 0.09 h/day vs. 3.5 ± 0.11 h/day, both p < 0.001) and vigorous activity (0.58 ± 0.03 h/day and 0.53 ± 0.02 h/day vs. 0.43 ± 0.03 h/day, both p < 0.01) and less time sitting (4.4 ± 0.13 h/day and 4.3 ± 0.09 h/day vs. 5.0 ± 0.11 h/day, both p < 0.001) on weekdays than non-rural groups. Hutterite females spent fewer hours in moderate activity (2.6 ± 0.08 h/day vs. 4.5 ± 0.11 h/day, p < 0.001) and vigorous activity (0.18 ± 0.02 h/day vs. 0.46 ± 0.02 h/day, p < 0.001) on weekend days compared to rural females. Hutterite females consumed more fruits (2.2 ± 0.06 svg/day vs. 1.7 ± 0.10 svg/day, p < 0.001) and vegetables (3.6 ± 0.08 svg/day vs. 2.7 ± 0.12 svg/day, p < 0.001) than rural females. Among males, both rural groups spent more time in moderate activity (4.9 ± 0.13 h/day and 6.1 ± 0.12 h/day vs. 3.0 ± 0.16 h/day, both p < 0.001) and less time sitting (4.1 ± 0.13 h/day and 3.4 ± 0.12 h/day vs. 6.0 ± 0.15 h/day, both p < 0.001) on weekdays compared to non-rural groups. Hutterite males spent less time in moderate activity (2.1 ± 0.10 h/day vs. 4.1 ± 0.11 h/day, p < 0.001) and vigorous activity (0.15 ± 0.04 h/day vs. 0.74 ± 0.04 h/day, p < 0.001) on weekend days compared to rural males. Hutterite males consumed more vegetables (3.0 ± 0.10 svg/day vs. 2.0 ± 0.11 svg/day, p < 0.001) than rural males. CONCLUSIONS: A rural occupation and lifestyle appear to contribute to differences in physical activity, while traditional rural lifestyle practices contribute to differences in diet.

Use of a Model Wellness Policy May Not Increase the Strength and Comprehensiveness of Written School Wellness Policies.

BACKGROUND: Many state agencies have developed model wellness policies (MWPs) to serve as examples for schools when writing their own school wellness policy (SWP). The purpose of this study was to evaluate if a MWP aids schools in writing stronger, more comprehensive SWPs. METHODS: For this cross-sectional study, 91 school districts submitted their current SWP and completed a survey that classified districts into either districts that utilized the state MWP (N = 56; 61.5%) or those that did not (NMWP, N =35; 38.5%). The Wellness School Assessment Tool (WellSAT) was used to assess the strength, comprehensiveness, total overall score, and subsection scores of each policy. Dependent variables were compared between groups using t tests. Statistical significance was set at p ≤ .05. Data are presented as mean ±SD. RESULTS: No significant differences were found between groups in total overall (MWP 76.8 ± 37.9; NMWP 62.1 ± 34.3), strength (MWP 25.3 ± 17.6; NMWP 19.1 ± 12.8), or comprehensiveness scores (MWP 51.5 ± 21.2; NMWP 43.0 ± 22.1). The only subsection score difference identified between groups was the Nutrition Standards comprehension score (p = .02). CONCLUSIONS: These data suggest MWPs may not improve the quality of written SWPs. Further research is needed to better understand the needs of school districts in SWP development.

Bigger ≠ Better: The Comprehensiveness and Strength of School Wellness Policies Varies by School District Size.

BACKGROUND: District size has been shown to impact the anticipated barriers to wellness policy creation and implementation. Therefore, the purpose of the present study was to determine if strength and comprehensiveness of wellness policies differs among school districts of varying size. METHODS: Wellness policies were collected from 10 large, 29 medium, and 31 small school districts in a rural Midwest state. District size was categorized by the average daily membership in grades 9-11. Polices were coded using the Wellness School Assessment Tool (WellSAT). Strength and comprehensiveness of the full policy and policy sections were compared among small, medium, and large districts using 1-way analyses of variance (ANOVAs). Data are presented as mean ± SD. Statistical significance was set at p ≤ .05. RESULTS: There was a difference in the total combined (p = .041), total comprehensiveness (p = .043), and total strength scores (p = .031) based on school district size, such that small districts had stronger, more comprehensive wellness policies than large districts. Section comparisons revealed the section focused on Standards for United States Department of Agriculture School Meals was primarily responsible for these differences. CONCLUSIONS: These data suggest smaller districts write policies that are more comprehensive to governmental standards and use more definitive language than larger districts.

Associations Between Sedentary Time, Physical Activity, and Dual-Energy X-ray Absorptiometry Measures of Total Body, Android, and Gynoid Fat Mass in Children.

Negative health outcomes are associated with excess body fat, low levels of physical activity (PA), and high sedentary time (ST). Relationships between PA, ST, and body fat distribution, including android and gynoid fat, assessed using dual-energy X-ray absorptiometry (DXA) have not been measured in children. The purpose of this study was to test associations between levels of activity and body composition in children and to evaluate if levels of activity predict body composition by DXA and by body mass index percentile in a similar manner. PA, ST, and body composition from 87 children (8.8-11.8 yr, grades 3-5, 44 boys) were used to test the association among study variables. Accelerometers measured PA and ST. Body composition measured by DXA included bone mineral content (BMC) and fat and lean mass of the total body (TB, less head), android, and gynoid regions. ST (range: 409-685 min/wk) was positively associated with TB percent fat (0.03, 95% confidence interval [CI]: 0.00-0.05) and android fat mass (1.5 g, 95% CI: 0.4-3.0), and inversely associated with the lean mass of the TB (-10.7 g, 95% CI: -20.8 to -0.63) and gynoid regions (-2.2 g, 95% CI: -4.3 to -0.2), and with BMC (-0.43 g, 95% CI: 0.77-0.09). Moderate-to-vigorous PA was associated with lower TB (-53 g, 95% CI: -87 to -18), android (-5 g, 95% CI: -8 to -2]), and gynoid fat (-6 g, 95% CI: -11 to -0.5). Vigorous activity results were similar. Light PA was associated with increased TB (17.1 g, 95% CI: 3.0-31.3) and gynoid lean mass (3.9 g, 95% CI: 1.0-6.8) and BMC (0.59 g, 95% CI: 0.10-1.07). In boys, there were significant associations between activity and DXA percent body fat measures that were not found with the body mass index percentile. Objective measures of PA were inversely associated with TB, android, and gynoid fat, whereas ST was directly associated with TB percent fat and, in particular, android fat. Activity levels predict body composition measures by DXA and, in particular, android fat distribution.

Diet and Physical Activity in Rural vs Urban Children and Adolescents in the United States: A Narrative Review.

Current research suggests that the prevalence of obesity is higher among rural youth than urban youth. Due to the health implications that are associated with child and adolescent obesity, it is critical to understand systematic differences in diet and physical activity (PA) behaviors that may be contributing to this disparity in weight. However, varying definitions of rural and inconsistencies in study tools and methodologies may limit the generalizability of findings from research in this area. The objective of this narrative review was to synthesize and critically evaluate existing literature comparing diet and PA behaviors between rural and urban children and adolescents, providing recommendations for future research. Only five studies were found that reported on measures of diet in rural vs urban youth, whereas 16 were found that reported on measures of PA. Dietary assessment tools were generally standard and acceptable; however, differences existed in how dietary outcomes were defined. Few studies used assessment tools that objectively measured PA, and definitions for meeting PA recommendations varied among studies. Very few studies defined rural using the same criteria. Future research on the rural youth obesity disparity should focus on including a high-quality assessment of both diet and PA (as opposed to one or the other) and on using an appropriate and consistent definition of rural.