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OBJECTIVE We sought to evaluate the use of behavioral economics approaches to promote the carrying of epinephrine auto-injectors (EAIs) among adolescents with food allergies. We hypothesized that adolescents who receive frequent text message nudges (Intervention 1) or frequent text message nudges plus modest financial incentives (Intervention 2) would be more likely to carry their epinephrine than members of the usual care control group. METHODS We recruited 131 adolescents ages 15 to 19 with a food allergy and a current prescription for epinephrine to participate in a cohort multiple randomized controlled trial. Participants were randomly assigned to participate in Intervention 1, Intervention 2, or to receive usual care. The primary outcome was consistency of epinephrine-carrying, measured as the proportion of checkpoints at which a participant could successfully demonstrate they were carrying their EAI, with photo-documentation of the device. RESULTS During Intervention 1, participants who received the intervention carried their EAI 28% of the time versus 38% for control group participants (P = .06). During Intervention 2, participations who received the intervention carried their EAI 45% of the time versus 23% for control group participants (P = .002). CONCLUSIONS Text message nudges alone were unsuccessful at promoting EAI-carrying but text message nudges combined with modest financial incentives almost doubled EAI-carriage rates among those who received the intervention compared with the control group. However, even with the intervention, adolescents with food allergies carried their EAI

Objective: To quantify the potential population-wide costs, number of individuals reached, and impact on obesity of five effective interventions to reduce children's television viewing if implemented nationally. Study Design: Utilizing evidence from systematic reviews, the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES) microsimulation model estimated the cost, population reach, and impact on childhood obesity from 2020 to 2030 of five hypothetical policy strategies to reduce the negative impact of children's TV exposure: (1) eliminating the tax deductibility of food and beverage advertising; (2) targeting TV reduction during home visiting programs; (3) motivational interviewing to reduce home television time at Women, Infants, and Children (WIC) clinic visits; (4) adoption of a television-reduction curriculum in child care; and (5) limiting noneducational television in licensed child care settings. Results: Eliminating the tax deductibility of food advertising could reach the most children [106 million, 95% uncertainty interval (UI): 105–107 million], prevent the most cases of obesity (78,700, 95% UI: 30,200–130,000), and save more in health care costs than it costs to implement. Strategies targeting young children in child care and WIC also cost little to implement (between $0.19 and $32.73 per child reached), and, although reaching fewer children because of the restricted age range, were estimated to prevent between 25,500 (95% UI: 4600–59,300) and 35,400 (95% UI: 13,200–62,100) cases of obesity. Home visiting to reduce television viewing had high costs and a low reach. Conclusions: Interventions to reduce television exposure across a range of settings, if implemented widely, could help prevent childhood obesity in the population at relatively low cost.

Introduction The 2010 Affordable Care Act required chain retail food establishments, including supermarkets, to post calorie information for prepared (i.e., ready to eat) foods. Implementation of calorie labeling could spur companies to reduce the calorie content of prepared foods, but few studies have explored this. This study evaluates the changes in the calorie content of prepared foods at 2 large U.S. supermarket chains after they implemented calorie labels in April 2017. Methods The chains (≈1,200 stores) provided data on the calorie content and labeling status of all items sold between July 2015 and January 2019. In 2021, analyses used a difference-in-differences approach to examine the changes in the calorie content of prepared bakery, entree, and deli items introduced before calorie labeling to those introduced after the labeling compared with changes in similar foods not subject to the new labeling requirement. Primary analyses examined continuously available items; exploratory analyses examined items newly introduced to the marketplace. Results Relative to changes in comparison foods not subject to the labeling requirement, continuously available prepared bakery items decreased by 7.7 calories per item after calorie labels were implemented (95% CI= −12.9, −2.5, p=0.004, ≈0.5% reduction). In exploratory analyses, prepared bakery items introduced after calorie labeling contained 440 fewer calories per item than those introduced before calorie labeling (95% CI= −773.9, −106.1, p=0.01, ≈27% reduction), driven by reductions in product size. No changes were observed in the calorie content of continuously available or newly introduced prepared entrees or deli items. Conclusions Implementing calorie labels could encourage product reformulation among some types of prepared supermarket foods. These supply-side changes could lead to reductions in caloric intake.

To describe the grocery shopping patterns of people who shopped both online and in-store and evaluate whether shoppers purchased fewer unhealthy, impulse-sensitive items online.Secondary analysis of 44 weeks of grocery transaction data collected for fruit and vegetable incentive trials in 2 Maine supermarkets.A total of 137 primary household shoppers who shopped at least once in-store and online (curbside pickup) for 5,573 total transactions MAIN OUTCOME MEASURES AND ANALYSIS: Paired t tests and descriptive analyses compared online and in-store transactions with respect to frequency, total spending, number of items purchased, and spending on 10 food groups and 34 subgroups. Mixed-effects regression models estimated differences in online vs in-store spending on 5 unhealthy, impulse-sensitive subgroups.When shopping online, participants spent 44% more per transaction ($113.58 vs $78.88, P0.001) and purchased more items (38.3 vs 26.6 items/transaction, P0.001). Compared with in-store, shopping online was associated with reduced spending per transaction on candy (-$0.65, P0.001), cold or frozen desserts (-$0.52, P0.001), and grain-based desserts (-$1.29, P0.001).Online shopping was associated with lower spending on certain unhealthy, impulse-sensitive foods. Grocery-based healthy eating initiatives might leverage online ordering platforms to increase their reach and effectiveness.

Objective:Online grocery shopping could improve access to healthy food, but it may not be equally accessible to all populations – especially those at higher risk for food insecurity. The current study aimed to compare the socio-demographic characteristics of families who ordered groceries online v. those who only shopped in-store.Design:We analysed enrollment survey and 44 weeks of individually linked grocery transaction data. We used univariate χ2 and t-tests and logistic regression to assess differences in socio-demographic characteristics between households that only shopped in-store and those that shopped online with curbside pickup (online only or online and in-store).Setting:Two Maine supermarkets.Participants:863 parents or caregivers of children under 18 years old enrolled in two fruit and vegetable incentive trials.Results:Participants had a total of 32 757 transactions. In univariate assessments, online shoppers had higher incomes (P < 0 0001), were less likely to participate in Special Supplemental Nutrition Program for Women, Infants, and Children or Supplemental Nutrition Assistance Program (SNAP; P < 0 0001) and were more likely to be female (P = 0·04). Most online shoppers were 30–39 years old, and few were 50 years or older (P = 0·003). After controlling for age, gender, race/ethnicity, number of children, number of adults, income and SNAP participation, female primary shoppers (OR = 2·75, P = 0·003), number of children (OR = 1·27, P = 0·04) and income (OR = 3·91 for 186–300 % federal poverty line (FPL) and OR = 6·92 for >300 % FPL, P < 0·0001) were significantly associated with likelihood of shopping online.Conclusions:In the current study of Maine families, low-income shoppers were significantly less likely to utilise online grocery ordering with curbside pickup. Future studies could focus on elucidating barriers and developing strategies to improve access.

The Healthy, Hunger-Free Kids Act of 2010 strengthened nutrition standards for meals and beverages provided through the National School Lunch, Breakfast, and Smart Snacks Programs, affecting fifty ...

The Healthy, Hunger-Free Kids Act of 2010 strengthened nutrition standards for meals and beverages provided through the National School Lunch, Breakfast, and Smart Snacks Programs, affecting fifty million children daily at 99,000 schools. The legislation's impact on childhood obesity is unknown. We tested whether the legislation was associated with reductions in child obesity risk over time using an interrupted time series design for 2003-18 among 173,013 youth in the National Survey of Children's Health. We found no significant association between the legislation and childhood obesity trends overall. For children in poverty, however, the risk of obesity declined substantially each year after the act's implementation, translating to a 47 percent reduction in obesity prevalence in 2018 from what would have been expected without the legislation. These results suggest that the Healthy, Hunger-Free Kids Act's science-based nutritional standards should be maintained to support healthy growth, especially among children living in poverty.

Although the national obesity epidemic has been well documented, less is known about obesity at the U.S. state level. Current estimates are based on body measures reported by persons themselves that underestimate the prevalence of obesity, especially severe obesity.We developed methods to correct for self-reporting bias and to estimate state-specific and demographic subgroup-specific trends and projections of the prevalence of categories of body-mass index (BMI). BMI data reported by 6,264,226 adults (18 years of age or older) who participated in the Behavioral Risk Factor Surveillance System Survey (1993-1994 and 1999-2016) were obtained and corrected for quantile-specific self-reporting bias with the use of measured data from 57,131 adults who participated in the National Health and Nutrition Examination Survey. We fitted multinomial regressions for each state and subgroup to estimate the prevalence of four BMI categories from 1990 through 2030: underweight or normal weight (BMI [the weight in kilograms divided by the square of the height in meters],25), overweight (25 to30), moderate obesity (30 to35), and severe obesity (≥35). We evaluated the accuracy of our approach using data from 1990 through 2010 to predict 2016 outcomes.The findings from our approach suggest with high predictive accuracy that by 2030 nearly 1 in 2 adults will have obesity (48.9%; 95% confidence interval [CI], 47.7 to 50.1), and the prevalence will be higher than 50% in 29 states and not below 35% in any state. Nearly 1 in 4 adults is projected to have severe obesity by 2030 (24.2%; 95% CI, 22.9 to 25.5), and the prevalence will be higher than 25% in 25 states. We predict that, nationally, severe obesity is likely to become the most common BMI category among women (27.6%; 95% CI, 26.1 to 29.2), non-Hispanic black adults (31.7%; 95% CI, 29.9 to 33.4), and low-income adults (31.7%; 95% CI, 30.2 to 33.2).Our analysis indicates that the prevalence of adult obesity and severe obesity will continue to increase nationwide, with large disparities across states and demographic subgroups. (Funded by the JPB Foundation.).

An excise tax of 1 peso per liter on sugar-sweetened beverages was implemented in Mexico in 2014. We estimated the cost-effectiveness of this tax and an alternative tax scenario of 2 pesos per liter. We developed a cohort simulation model calibrated for Mexico to project the impact of the tax over ten years. The current tax is projected to prevent 239,900 cases of obesity, 39 percent of which would be among children. It could also prevent 61,340 cases of diabetes, lead to gains of 55,300 quality-adjusted life-years, and avert 5,840 disability-adjusted life-years. The tax is estimated to save $3.98 per dollar spent on its implementation. Doubling the tax to 2 pesos per liter would nearly double the cost savings and health impact. Countries with comparable conditions could benefit from implementing a similar tax.

In 2019, Ward et al. proposed a method to adjust BMI calculated from self-reported weight and height for bias relative to measured data. They did not evaluate the adjusted values relative to measured BMI values for the same individuals.A large data set (n = 37,439) with both measured and self-reported weight and height was randomly divided into two groups. The proposed method was used to adjust the BMI values in one group to the measured data from the other group. The adjusted values were then compared with the measured values for the same individuals.Before adjustment, 24.9% were incorrectly classified relative to measured BMI categories, including 7.9% in too high a category; after adjustment, 24.3% were incorrectly classified, with 12.8% in too high a category. The variance of the difference was unchanged. The adjustments reduced some errors and introduced new errors. At an individual level, results were unpredictable.The suggested method has little effect on misclassification, can introduce new errors, and could magnify errors associated with factors, such as age, race, educational level, or other characteristics. State-level estimates and projections of obesity prevalence from values adjusted by this method may be incorrect.

OBJECTIVES The objective was to evaluate if 2 pediatric weight management interventions delivered to Hispanic, low-income children (one in a health center the other in a Young Men’s Christian Association) were effective in reducing BMI. We hypothesized that they would be equally effective. METHODS A total 407 children aged 6 to 12 years with BMI ≥ 85th percentile receiving care at 2 health centers were randomly assigned to a healthy weight clinic (HWC) at the health center or to a modified Healthy Weight and Your Child (M-HWYC) intervention delivered in Young Men’s Christian Associations. A total of 4037 children served as the comparison group. We completed a noninferiority test comparing the M-HWYC with the HWC, which was supported if the bounds of the 90% confidence interval (CI) for the difference in percentage of the 95th percentile (%BMIp95) change did not contain what we considered a minimally clinically important difference, on the basis of previous data (0.87). Then, using linear mixed models, we assessed yearly changes in BMI among intervention participants compared with the comparison sites. RESULTS The mean difference in %BMIp95 between the M-HWYC and the HWC was 0.75 (90% CI: 0.07 to 1.43), which did not support noninferiority. Compared with the comparison sites, per year, children in the HWC had a −0.23 (95% CI: −0.36 to −0.10) decrease in BMI and a −1.03 (95% CI −1.61 to −0.45) %BMIp95 decrease. There was no BMI effect in the M-HWYC. CONCLUSIONS We were unable to establish noninferiority of the M-HWYC. The HWC improved BMI, offering an effective treatment of those disproportionately affected.

Background: Despite evidence that offering multidisciplinary treatment for children with obesity is effective, access to evidence-based pediatric weight management interventions (PWMIs) is limited. The Healthy Weight Clinic PWMI is a multidisciplinary approach in primary care that improves BMI among children with a BMI ≥ 85th percentile. Objective: To describe the method by which we will evaluate the adoption, acceptability, and feasibility of integrating and implementing a multidisciplinary Healthy Weight Clinic (HWC) into primary care. Design/Methods: We used the Consolidated Framework for Implementation Research (CFIR) domains and constructs to inform our implementation strategies. We will use a Type III hybrid effectiveness-implementation design to test our implementation strategies and improvement in BMI. Sources of data collection will include qualitative interviews with patient caregivers, HWC staff and surveys with HWC staff, patient caregivers, and electronic health record data. Our outcomes are guided by the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework. Results: We identified all five CFIR domains as integral for successful implementation. Some strategies to address barriers within these domains include online self-paced training modules for the HWC staff, a virtual learning collaborative, and engagement of site leadership. Outcomes will be measured at the patient and pilot site levels, and they will include patients reached, patient health outcomes such as BMI and quality of life, level of adoption, acceptability, feasibility, and sustainability of the PWMI. Conclusion: Our use of implementation science frameworks in the planning of Healthy Weight Clinic PWMI could create a sustainable and effective program for dissemination.

Alexandra Chung and colleagues call for governments to prioritise child obesity as they implement measures to recover from the pandemic

Calorie labels for prepared (ie, ready-to-eat) foods are required in large chain food establishments in the US. Large evaluations in restaurants suggest small declines in purchases of prepared foods after labeling, but to the authors' knowledge, no studies have examined how this policy influences supermarket purchases.To estimate changes in calories purchased from prepared foods and potential packaged substitutes compared with control foods after calorie labeling of prepared foods in supermarkets.This controlled interrupted time series compared sales 2 years before labeling implementation (April 2015-April 2017) with sales 7 months after labeling implementation (May 2017-December 2017). Data from 173 supermarkets from a supermarket chain with locations in Maine, Massachusetts, New Hampshire, New York, and Vermont were analyzed from March 2020 to May 2022.Implementation of calorie labeling of prepared foods in April 2017.Purchased items were classified as prepared foods, potential packaged substitutes for prepared foods, or all other (ie, control) foods. The primary outcome was mean weekly calories per transaction purchased from prepared foods, and the secondary outcome was mean weekly calories per transaction purchased from similar packaged items (for substitution analyses). Analyses of prepared and packaged foods were stratified by food category (bakery, entrées and sides, or deli meats and cheeses).Among the included 173 supermarkets, calorie labeling was associated with a mean 5.1% decrease (95% CI, -5.8% to -4.4%) in calories per transaction purchased from prepared bakery items and an 11.0% decrease (95% CI, -11.9% to -10.1%) from prepared deli items, adjusted for changes in control foods; no changes were observed for prepared entrées and sides (change = 0.3%; 95% CI, -2.5% to 3.0%). Labeling was also associated with decreased calories per transaction purchased from packaged bakery items (change = -3.9%; 95% CI, -4.3% to -3.6%), packaged entrées and sides (change = -1.2%; 95% CI, -1.4% to -0.9%), and packaged deli items (change = -2.1%; 95% CI, -2.4% to -1.7%).In this longitudinal study of supermarkets, calorie labeling of prepared foods was associated with small to moderate decreases in calories purchased from prepared bakery and deli items without evidence of substitution to similar packaged foods.

Background Estimates of health care costs associated with excess weight are needed to inform the development of cost-effective obesity prevention efforts. However, commonly used cost estimates are not sensitive to changes in weight across the entire body mass index (BMI) distribution as they are often based on discrete BMI categories. Methods We estimated continuous BMI-related health care expenditures using data from the Medical Expenditure Panel Survey (MEPS) 2011–2016 for 175,726 respondents. We adjusted BMI for self-report bias using data from the National Health and Nutrition Examination Survey (NHANES) 2011–2016, and controlled for potential confounding between BMI and medical expenditures using a two-part model. Costs are reported in $US 2019. Results We found a J-shaped curve of medical expenditures by BMI, with higher costs for females and the lowest expenditures occurring at a BMI of 20.5 for adult females and 23.5 for adult males. Over 30 units of BMI, each one-unit BMI increase was associated with an additional cost of $253 (95% CI $167-$347) per person. Among adults, obesity was associated with $1,861 (95% CI $1,656-$2,053) excess annual medical costs per person, accounting for $172.74 billion (95% CI $153.70-$190.61) of annual expenditures. Severe obesity was associated with excess costs of $3,097 (95% CI $2,777-$3,413) per adult. Among children, obesity was associated with $116 (95% CI $14-$201) excess costs per person and $1.32 billion (95% CI $0.16-$2.29) of medical spending, with severe obesity associated with $310 (95% CI $124-$474) excess costs per child. Conclusions Higher health care costs are associated with excess body weight across a broad range of ages and BMI levels, and are especially high for people with severe obesity. These findings highlight the importance of promoting a healthy weight for the entire population while also targeting efforts to prevent extreme weight gain over the life course.

Background Calorie menu labeling is a policy that requires food establishments to post the calories on menu offerings to encourage healthy food choice. Calorie labeling has been implemented in the United States since May 2018 per the Affordable Care Act, but to the best of our knowledge, no studies have evaluated the relationship between calorie labeling and meal purchases since nationwide implementation of this policy. Our objective was to investigate the relationship between calorie labeling and the calorie and nutrient content of purchased meals after a fast food franchise began labeling in April 2017, prior to the required nationwide implementation, and after nationwide implementation of labeling in May 2018, when all large US chain restaurants were required to label their menus. Methods and findings We obtained weekly aggregated sales data from 104 restaurants that are part of a fast food franchise for 3 national chains in 3 US states: Louisiana, Mississippi, and Texas. The franchise provided all sales data from April 2015 until April 2019. The franchise labeled menus in April 2017, 1 year prior to the required nationwide implementation date of May 2018 set by the US Food and Drug Administration. We obtained nutrition information for items sold (calories, fat, carbohydrates, protein, saturated fat, sugar, dietary fiber, and sodium) from Menustat, a publicly available database with nutrition information for items offered at the top revenue-generating US restaurant chains. We used an interrupted time series to find level and trend changes in mean weekly calorie and nutrient content per transaction after franchise and nationwide labeling. The analytic sample represented 331,776,445 items purchased across 67,112,342 transactions. Franchise labeling was associated with a level change of −54 calories/transaction (95% confidence interval [CI]: −67, −42, p < 0.0001) and a subsequent 3.3 calories/transaction increase per 4-week period (95% CI: 2.5, 4.1, p < 0.0001). Nationwide implementation was associated with a level decrease of −82 calories/transaction (95% CI: −88, −76, p < 0.0001) and a subsequent −2.1 calories/transaction decrease per 4-week period (95% CI: −2.9, −1.3, p < 0.0001). At the end of the study, the model-based predicted mean calories/transaction was 4.7% lower (change = −73 calories/transaction, 95% CI: −81, −65), and nutrients/transaction ranged from 1.8% lower (saturated fat) to 7.0% lower (sugar) than what we would expect had labeling not been implemented. The main limitations were potential residual time-varying confounding and lack of individual-level transaction data. Conclusions In this study, we observed that calorie labeling was associated with small decreases in mean calorie and nutrient content of fast food meals 2 years after franchise labeling and nearly 1 year after implementation of labeling nationwide. These changes imply that calorie labeling was associated with small improvements in purchased meal quality in US chain restaurants., In an interrupted time series analysis, Joshua Petimar and colleagues investigate the relationship between the implementation of calorie content labelling on fast food restaurant menus and changes in calorie and nutrition content of meal purchases from a restaurant franchise in the US., Author summary Why was this study done? The 2010 Affordable Care Act required that all chain food establishments with ≥20 locations in the United States label their menus with calorie content of prepared foods to encourage healthier eating. There is little evidence on how the nutritional quality of purchased meals changed after nationwide implementation of this policy. What did the researchers do and find? We analyzed sales data from a franchise of 104 fast food restaurants (representing 3 large nationwide chains in the US) to determine how nutrient purchases changed after voluntary franchise labeling in April 2017 and nationwide implementation of labeling in May 2018. At the end of the study, nearly 1 year after nationwide implementation of calorie labeling, the average calorie content of meals was 4.7% lower than what would be expected had labeling not occurred. Other nutrients ranged from 1.8% lower (saturated fat) to 7.0% lower (sugar) at the end of the study, but the nutrient density of purchases still generally exceeded US dietary guidelines. What do these findings mean? Calorie menu labeling is a promising strategy for improving purchased meal quality in fast food restaurants, but the effect is small. Future food retail interventions may be necessary to make further improvements in meal quality in restaurants.

Although the current obesity epidemic has been well documented in children and adults, less is known about long-term risks of adult obesity for a given child at his or her present age and weight. We developed a simulation model to estimate the risk of adult obesity at the age of 35 years for the current population of children in the United States.We pooled height and weight data from five nationally representative longitudinal studies totaling 176,720 observations from 41,567 children and adults. We simulated growth trajectories across the life course and adjusted for secular trends. We created 1000 virtual populations of 1 million children through the age of 19 years that were representative of the 2016 population of the United States and projected their trajectories in height and weight up to the age of 35 years. Severe obesity was defined as a body-mass index (BMI, the weight in kilograms divided by the square of the height in meters) of 35 or higher in adults and 120% or more of the 95th percentile in children.Given the current level of childhood obesity, the models predicted that a majority of today's children (57.3%; 95% uncertainly interval [UI], 55.2 to 60.0) will be obese at the age of 35 years, and roughly half of the projected prevalence will occur during childhood. Our simulations indicated that the relative risk of adult obesity increased with age and BMI, from 1.17 (95% UI, 1.09 to 1.29) for overweight 2-year-olds to 3.10 (95% UI, 2.43 to 3.65) for 19-year-olds with severe obesity. For children with severe obesity, the chance they will no longer be obese at the age of 35 years fell from 21.0% (95% UI, 7.3 to 47.3) at the age of 2 years to 6.1% (95% UI, 2.1 to 9.9) at the age of 19 years.On the basis of our simulation models, childhood obesity and overweight will continue to be a major health problem in the United States. Early development of obesity predicted obesity in adulthood, especially for children who were severely obese. (Funded by the JPB Foundation and others.).

Background: Although proven effective interventions for childhood obesity exist, there remains a substantial gap in the adoption of recommended practices by clinicians. Objective: The aims are to: (1) package implementation and training supports to facilitate the adoption of the evidence-based Healthy Weight Clinic Pediatric Weight Management Intervention (PWMI) (based on three previous effectiveness trials); (2) pilot and evaluate the packaged Healthy Weight Clinic PWMI; and (3) develop a sustainability and dissemination plan. Design/Methods: We used the Consolidated Framework of Implementation Research constructs to create an Implementation Research Logic Model that defined the facilitators and barriers of the Healthy Weight Clinic PWMI. We linked these constructs to implementation strategies and mechanisms. Packaging and design will be informed by the core essential components and functions of the PWMI along with stakeholder engagement. Once the package is complete, we will pilot the PWMI by using a Type III effectiveness-implementation hybrid design. Implementation outcomes will be evaluated by using the RE-AIM framework. Results: We will create an integrated, multisystems level package for national dissemination. The package will include training and a suite of resources for primary care physicians and healthy weight clinic staff, including: patient and caregiver facing videos, patient and caregiver handouts, group curriculum guide, online provider trainings, and access to a virtual learning collaborative. Conclusion: The results will highlight the extent to which the package of the Healthy Weight Clinic PWMI facilitates the adoption of effective strategies for treating childhood obesity. Lessons learned will inform modifications to the Healthy Weight Clinic PWMI and strategies for future scaling.

Physical activity engagement during childhood is associated with positive health outcomes in adulthood. Exercise and sport science research links physical activity enjoyment with physical activity adoption and maintenance, among other positive health behaviors. However, public health researchers rarely measure enjoyment or discuss its role in interventions or theory. In this paper, we present the rationale for bringing enjoyment to the forefront of public health dialogue and action to increase physical activity in children and across the life course. We outline five potential explanations for the lack of physical activity enjoyment research in public health, and offer solutions and action steps for each. Enjoyment research has the potential to improve people's health by working on multiple levels, from individuals to schools to public sectors, and could have positive implications for various health behaviors.

Objectives. To evaluate whether differences in tap water and other beverage intake explain differences in inadequate hydration among US adults by race/ethnicity and income. Methods. We estimated the prevalence of inadequate hydration (urine osmolality ≥ 800 mOsm/kg) by race/ethnicity and income of 8258 participants aged 20 to 74 years in the 2009 to 2012 National Health and Nutrition Examination Survey. Using multivariable regression models, we estimated associations between demographic variables, tap water intake, and inadequate hydration. Results. The prevalence of inadequate hydration among US adults was 29.5%. Non-Hispanic Blacks (adjusted odds ratio [AOR] = 1.44; 95% confidence interval [CI] = 1.17, 1.76) and Hispanics (AOR = 1.42; 95% CI = 1.21, 1.67) had a higher risk of inadequate hydration than did non-Hispanic Whites. Lower-income adults had a higher risk of inadequate hydration than did higher-income adults (AOR = 1.23; 95% CI = 1.04, 1.45). Differences in tap water intake partially attenuated racial/ethnic differences in hydration status. Differences in total beverage and other fluid intake further attenuated sociodemographic disparities. Conclusions. Racial/ethnic and socioeconomic disparities in inadequate hydration among US adults are related to differences in tap water and other beverage intake. Policy action is needed to ensure equitable access to healthy beverages.

Objective To examine changes in prevalence of obesity and target health behaviors (fruit, vegetable, and beverage consumption; physical activity; screen time; sleep duration) among students from communities that participated in the Massachusetts Childhood Obesity Research Demonstration (MA-CORD) project compared to controls. Methods MA-CORD was implemented in two low-income communities. School-level prevalence of obesity among students in first, fourth, and seventh grades was calculated for the intervention communities and nine matched control communities pre and post intervention. Fourth- and seventh-grade students’ self-reported health behaviors were measured in intervention communities at baseline and post intervention. Results Among seventh-graders (the student group with greatest intervention exposure), a statistically significant decrease in prevalence of obesity from baseline to post intervention in Community 2 (−2.68%, P = 0.049) and a similar but nonsignificant decrease in Community 1 (−2.24%, P = 0.099) was observed. Fourth- and seventh-grade students in both communities were more likely to meet behavioral targets post intervention for sugar-sweetened beverages (both communities: P

Objective To examine the extent to which a clinical intervention resulted in reduced BMI z scores among 2- to 12-year-old children compared to routine practice (treatment as usual [TAU]). Methods The Massachusetts Childhood Obesity Research Demonstration (MA-CORD) project is a multifaceted initiative to prevent childhood obesity among low-income children. At the federally qualified community health centers (FQHCs) of two communities (Intervention Site #1 and #2), the following were implemented: (1) pediatric weight management training, (2) electronic decision supports for clinicians, (3) on-site Healthy Weight Clinics, (4) community health worker integration, and (5) healthful clinical environment changes. One FQHC in a demographically matched community served as the TAU site. Using electronic health records, we assessed BMI z scores and used linear mixed models to examine BMI z score change over 2 years in each intervention site compared to a TAU site. Results Compared to children in the TAU site (n = 2,286), children in Intervention Site #2 (n = 1,368) had a significant decline in BMI z scores following the start of the intervention (−0.16 units/y; 95% confidence interval: −0.21 to −0.12). No evidence of an effect was found in Intervention Site #1 (n = 111). Conclusions The MA-CORD clinical interventions were associated with modest improvement in BMI z scores in one of two intervention communities compared to a TAU community.

Objective To quantify the relationships between youth use of television (TV) and other screen devices, including smartphones and tablets, and obesity risk factors. Study design TV and other screen device use, including smartphones, tablets, computers, and/or videogames, was self-reported by a nationally representative, cross-sectional sample of 24 800 US high school students (2013-2015 Youth Risk Behavior Surveys). Students also reported on health behaviors including sugar-sweetened beverage (SSB) intake, physical activity, sleep, and weight and height. Sex-stratified logistic regression models, adjusting for the sampling design, estimated associations between TV and other screen device use and SSB intake, physical activity, sleep, and obesity. Results Approximately 20% of participants used other screen devices for ≥5 hours daily. Watching TV ≥5 hours daily was associated with daily SSB consumption (aOR = 2.72, 95% CI: 2.23, 3.32) and obesity (aOR = 1.78, 95% CI: 1.40, 2.27). Using other screen devices ≥5 hours daily was associated with daily SSB consumption (aOR = 1.98, 95% CI: 1.69, 2.32), inadequate physical activity (aOR = 1.94, 95% CI: 1.69, 2.25), and inadequate sleep (aOR = 1.79, 95% CI: 1.54, 2.08). Conclusions Using smartphones, tablets, computers, and videogames is associated with several obesity risk factors. Although further study is needed, families should be encouraged to limit both TV viewing and newer screen devices.

Participation in recommended levels of physical activity promotes a healthy body weight and reduced chronic disease risk. To inform investment in prevention initiatives, we simulate the national implementation, impact on physical activity and childhood obesity and associated cost-effectiveness (versus the status quo) of six recommended strategies that can be applied throughout childhood to increase physical activity in US school, afterschool and childcare settings. In 2016, the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES) systematic review process identified six interventions for study. A microsimulation model estimated intervention outcomes 2015-2025 including changes in mean MET-hours/day, intervention reach and cost per person, cost per MET-hour change, ten-year net costs to society and cases of childhood obesity prevented. First year reach of the interventions ranged from 90,000 youth attending a Healthy Afterschool Program to 31.3 million youth reached by Active School Day policies. Mean MET-hour/day/person increases ranged from 0.05 MET-hour/day/person for Active PE and Healthy Afterschool to 1.29 MET-hour/day/person for the implementation of New Afterschool Programs. Cost per MET-hour change ranged from cost saving to $3.14. Approximately 2500 to 110,000 cases of children with obesity could be prevented depending on the intervention implemented. All of the six interventions are estimated to increase physical activity levels among children and adolescents in the US population and prevent cases of childhood obesity. Results do not include other impacts of increased physical activity, including cognitive and behavioral effects. Decision-makers can use these methods to inform prioritization of physical activity promotion and obesity prevention on policy agendas.

Interventions for obesity have not often been based on considerations that could predict their effectiveness. However, advances in research provide several new approaches that can inform priorities for public health interventions directed at nutrition, physical activity, and obesity. These approaches include estimation of the effect size, comparison of the calorie gap with the caloric deficit induced by the intervention, population reach and impact, cost and cost effectiveness of the intervention, time required to evaluate the effect of the intervention on weight change, and feasibility of the intervention. Incorporation of these considerations by policymakers and public health practitioners will help identify those interventions most likely to achieve changes in the prevalence of obesity.

OBJECTIVES: To evaluate the association of the 2009 changes to the US Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food package and childhood obesity trends. We hypothesized that the food package change reduced obesity among children participating in WIC, a population that has been especially vulnerable to the childhood obesity epidemic. METHODS: We used an interrupted time-series design with repeated cross-sectional measurements of state-specific obesity prevalence among WIC-participating 2- to 4-year-old children from 2000 to 2014. We used multilevel linear regression models to estimate the trend in obesity prevalence for states before the WIC package revision and to test whether the trend in obesity prevalence changed after the 2009 WIC package revision, adjusting for changes in demographics. In a secondary analysis, we adjusted for changes in macrosomia and high prepregnancy BMI. RESULTS: Before the 2009 WIC food package change, the prevalence of obesity across states among 2- to 4-year-old WIC participants was increasing by 0.23 percentage points annually (95% confidence interval: 0.17 to 0.29; P < .001). After 2009, the trend was reversed (−0.34 percentage points per year; 95% confidence interval: −0.42 to −0.25; P < .001). Changes in sociodemographic and other obesity risk factors did not account for this change in the trend in obesity prevalence. CONCLUSIONS: The 2009 WIC food package change may have helped to reverse the rapid increase in obesity prevalence among WIC participants observed before the food package change.

Objective This study aimed to estimate the cost-effectiveness and impact on childhood obesity of installation of chilled water dispensers ("water jets") on school lunch lines and to compare water jets' cost, reach, and impact on water consumption with three additional strategies. Methods The Childhood Obesity Intervention Cost Effectiveness Study(CHOICES) microsimulation model estimated the cost-effectiveness of water jets on US childhood obesity cases prevented in 2025. Also estimated were the cost, number of children reached, and impact on water consumption of the installation of water jets and three other strategies. Results Installing water jets on school lunch lines was projected to reach 29.6 million children (95% uncertainty interval [UI]: 29.4 million-29.8 million), cost $4.25 (95% UI: $2.74-$5.69) per child, prevent 179,550 cases of childhood obesity in 2025 (95% UI: 101,970-257,870), and save $0.31 in health care costs per dollar invested (95% UI: $0.15-$0.55). In the secondary analysis, installing cup dispensers next to existing water fountains was the least costly but also had the lowest population reach. Conclusions Installating water jet dispensers on school lunch lines could also save almost half of the dollars needed for implementation via a reduction in obesity-related health care costs. School-based interventions to promote drinking water may be relatively inexpensive strategies for improving child health.

Many children are not sufficiently physically active. This study uses a quasi-experimental design to evaluate whether participation in a before-school physical activity program called Build Our Kids' Success (BOKS) increases physical activity. Participants (n = 426) were students in Fall, 2016 enrolled in BOKS programming and matched non-BOKS control students from the same grades (Kindergarten-6) and schools in Massachusetts and Rhode Island. Analyses conducted in 2017 examined differences between children in BOKS versus controls in total daily steps, minutes of moderate-to-vigorous (MVPA), vigorous (VPA), and total physical activity (TPA) assessed via Fitbit Charge HR™ monitors. Additional analyses compared physical activity on program days and non-program days. Students (mean age = 8.6 y; 47% female, 58% White, Non-Hispanic) wore monitors an average of 21.7 h/day on 3.2 days during the school week. Compared with controls, on BOKS days, BOKS participants accumulated more steps (1147, 95% confidence interval (CI): 583–1712, P, Highlights • Activity programs before school may increase physical activity levels. • Program participants were more active than non-participants. • Program participants accumulated more physical activity on days they participated.

BACKGROUND/OBJECTIVES: State-specific obesity prevalence data are critical to public health efforts to address the childhood obesity epidemic. However, few states administer objectively-measured BMI surveillance programs. This study reports state-specific childhood obesity prevalence by age and sex correcting for parent-reported child height and weight bias. SUBJECTS/METHODS: As part of the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES), we developed childhood obesity prevalence estimates for states for the period 2005–2010 using data from the 2010 U.S. Census and American Community Survey (ACS), 2003–2004 and 2007–2008 National Survey of Children’s Health (NSCH) (n=133,213), and 2005–2010 National Health and Nutrition Examination Surveys (NHANES) (n=9,377; ages 2–17). Measured height and weight data from NHANES were used to correct parent-report bias in NSCH using a non-parametric statistical matching algorithm. Model estimates were validated against surveillance data from five states (AR, FL, MA, PA, and TN) that conduct censuses of children across a range of grades. RESULTS: Parent-reported height and weight resulted in the largest overestimation of childhood obesity in males ages 2–5 years (NSCH: 42.36% vs. NHANES: 11.44%). The CHOICES model estimates for this group (12.81%) and for all age and sex categories were not statistically different from NHANES. Our modeled obesity prevalence aligned closely with measured data from five validation states, with a 0.64 percentage point mean difference (range: 0.23–1.39) and a high correlation coefficient (r=0.96, p=0.009). Estimated state-specific childhood obesity prevalence ranged from 11.0% to 20.4%. CONCLUSION: Uncorrected estimates of childhood obesity prevalence from NSCH vary widely from measured national data, from a 278% overestimate among males aged 2–5 years to a 44% underestimate among females aged 14–17 years. This study demonstrates the validity of the CHOICES matching methods to correct the bias of parent-reported BMI data and highlights the need for public release of more recent data from the 2011–2012 NSCH.

SummaryBackground The obesity epidemic has spared no age group, even young infants. Most childhood obesity is incident by the age of 5 years, making prevention in preschool years a priority. Objective To examine 2-year changes in age- and sex-specific BMI z-scores and obesity-related behaviours among 441 of the 475 originally recruited participants in High Five for Kids, a cluster randomized controlled trial in 10 paediatric practices. Methods The intervention included a more intensive 1-year intervention period (four in-person visits and two phone calls) followed by a less intensive 1-year maintenance period (two in-person visits) among children who were overweight or obese and age 2–6 years at enrolment. The five intervention practices restructured care to manage these children including motivational interviewing and educational modules targeting television viewing and intakes of fast food and sugar-sweetened beverages. Results After 2 years, compared with usual care, intervention participants had similar changes in BMI z-scores (−0.04 units; 95% CI −0.14, 0.06), television viewing (−0.20 h/d; −0.49 to 0.09) and intakes of fast food (−0.09 servings/week; −0.34 to 0.17) and sugar-sweetened beverages (−0.26 servings/day; −0.67 to 0.14). Conclusion High Five for Kids, a primarily clinical-based intervention, did not affect BMI z-scores or obesity-related behaviours after 2 years.

An intervention study showed that promoting water consumption in schoolchildren prevented overweight, but a mechanism linking water consumption to overweight was not substantiated. We investigated whether increased water consumption replaced sugar-containing beverages and whether changes in water or sugar-containing beverages influenced body weight outcomes. In a secondary analysis of the intervention study in Germany, we analysed combined longitudinal data from the intervention and control groups. Body weight and height were measured and beverage consumption was self-reported by a 24-h recall questionnaire at the beginning and end of the school year 2006/2007. The effect of a change in water consumption on change in sugar-containing beverage (soft drinks and juices) consumption, change in BMI (kg/m2) and prevalence of overweight and obesity at follow-up was analysed using regression analyses. Of 3220 enroled children, 1987 children (mean age 8·3 (sd 0·7) years) from thirty-two schools were analysed. Increased water consumption by 1 glass/d was associated with a reduced consumption of sugar-containing beverages by 0·12 glasses/d (95 % CI −0·16, −0·08) but was not associated with changes in BMI (P=0·63). Increased consumption of sugar-containing beverages by 1 glass/d was associated with an increased BMI by 0·02 (95 % CI 0·00, 0·03) kg/m2 and increased prevalence of obesity (OR 1·22; 95 % CI 1·04, 1·44) but not with overweight (P=0·83). In conclusion, an increase in water consumption can replace sugar-containing beverages. As sugar-containing beverages were associated with weight gain, this replacement might explain the prevention of obesity through the promotion of water consumption.

Efforts to expand Medicaid while controlling spending must be informed by a deeper understanding of the extent to which the high medical costs associated with severe obesity (having a body mass index of [Formula: see text] or higher) determine spending at the state level. Our analysis of population-representative data indicates that in 2013, severe obesity cost the nation approximately $69 billion, which accounted for 60 percent of total obesity-related costs. Approximately 11 percent of the cost of severe obesity was paid for by Medicaid, 30 percent by Medicare and other federal health programs, 27 percent by private health plans, and 30 percent out of pocket. Overall, severe obesity cost state Medicaid programs almost $8 billion a year, ranging from $5 million in Wyoming to $1.3 billion in California. These costs are likely to increase following Medicaid expansion and enhanced coverage of weight loss therapies in the form of nutrition consultation, drug therapy, and bariatric surgery. Ensuring and expanding Medicaid-eligible populations' access to cost-effective treatment for severe obesity should be part of each state's strategy to mitigate rising obesity-related health care costs.

Policy makers seeking to reduce childhood obesity must prioritize investment in treatment and primary prevention. We estimated the cost-effectiveness of seven interventions high on the obesity policy agenda: a sugar-sweetened beverage excise tax; elimination of the tax subsidy for advertising unhealthy food to children; restaurant menu calorie labeling; nutrition standards for school meals; nutrition standards for all other food and beverages sold in schools; improved early care and education; and increased access to adolescent bariatric surgery. We used systematic reviews and a microsimulation model of national implementation of the interventions over the period 2015-25 to estimate their impact on obesity prevalence and their cost-effectiveness for reducing the body mass index of individuals. In our model, three of the seven interventions--excise tax, elimination of the tax deduction, and nutrition standards for food and beverages sold in schools outside of meals--saved more in health care costs than they cost to implement. Each of the three interventions prevented 129,000-576,000 cases of childhood obesity in 2025. Adolescent bariatric surgery had a negligible impact on obesity prevalence. Our results highlight the importance of primary prevention for policy makers aiming to reduce childhood obesity.

Ensuring students&rsquo, access to safe drinking water at school is essential. However, many schools struggle with aging infrastructure and subsequent water safety problems and have turned to bottled water delivery systems. Little is known about whether such systems are feasible and effective in providing adequate student water access. This study was a mixed-methods investigation among six schools in an urban district in the U.S. with two types of water delivery systems: (1) tap water infrastructure, with updated water fountains and bottle fillers, and (2) bottled water coolers. We measured students&rsquo, water consumption and collected qualitative data from students and teachers about their perceptions of school drinking water. Student water consumption was low&mdash, between 2.0 (SD: 1.4) ounces per student and 2.4 (SD: 1.1) ounces per student during lunch. Students and teachers reported substantial operational hurdles for relying on bottled water as a school&rsquo, s primary source of drinking water, including difficulties in stocking, cleaning, and maintaining the units. While students and teachers perceived newer bottle filler units positively, they also reported a distrust of tap water. Bottled water delivery systems may not be effective long-term solutions for providing adequate school drinking water access and robust efforts are needed to restore trust in tap water.

Objective To evaluate the potential cost-effectiveness of and stakeholder perspectives on a sugar-sweetened beverage (SSB) excise tax and a Supplemental Nutrition Assistance Program (SNAP) policy that would not allow SSB purchases in Maine, US. Design A cost-effectiveness simulation model combined with stakeholder interviews. Setting Maine, US. Participants Microsimulation of the Maine population in 2015 and interviews with stakeholders (n = 14). Study conducted from 2013 to 2017. Main Outcome Measures Health care cost savings, net costs, and quality-adjusted life-years (QALYs) from 2017 to 2027. Stakeholder positions on policies. Retail SSB cost and implementation cost data were collected. Analysis Childhood Obesity Intervention Cost-Effectiveness Study project microsimulation model with uncertainty analysis to estimate cost-effectiveness. Thematic stakeholder interview coding. Results Over 10 years, the SSB and SNAP policies were projected to reduce health care costs by $78.3 million (95% uncertainty interval [UI], $31.7 million–$185 million) and $15.3 million (95% UI, $8.32 million–$23.9 million), respectively. The SSB and SNAP policies were projected to save 3,560 QALYs (95% UI, 1,447–8,361) and 749 QALYs (95% UI, 415–1,168), respectively. Stakeholders were more supportive of SSB taxes than the SNAP policy because of equity concerns associated with the SNAP policy. Conclusions and Implications Cost-effectiveness analysis provided evidence of potential health improvement and cost savings to state-level stakeholders weighing broader implementation considerations.

OBJECTIVES: To estimate the cost-effectiveness and population impact of the national implementation of the Study of Technology to Accelerate Research (STAR) intervention for childhood obesity. METHODS: In the STAR cluster-randomized trial, 6- to 12-year-old children with obesity seen at pediatric practices with electronic health record (EHR)-based decision support for primary care providers and self-guided behavior-change support for parents had significantly smaller increases in BMI than children who received usual care. We used a microsimulation model of a national implementation of STAR from 2015 to 2025 among all pediatric primary care providers in the United States with fully functional EHRs to estimate cost, impact on obesity prevalence, and cost-effectiveness. RESULTS: The expected population reach of a 10-year national implementation is ∼2 million children, with intervention costs of $119 per child and $237 per BMI unit reduced. At 10 years, assuming maintenance of effect, the intervention is expected to avert 43 000 cases and 226 000 life-years with obesity at a net cost of $4085 per case and $774 per life-year with obesity averted. Limiting implementation to large practices and using higher estimates of EHR adoption improved both cost-effectiveness and reach, whereas decreasing the maintenance of the intervention’s effect worsened the former. CONCLUSIONS: A childhood obesity intervention with electronic decision support for clinicians and self-guided behavior-change support for parents may be more cost-effective than previous clinical interventions. Effective and efficient interventions that target children with obesity are necessary and could work in synergy with population-level prevention strategies to accelerate progress in reducing obesity prevalence.

ObjectiveFinancial conflicts of interest involving the food industry have been reported to bias nutrition studies. However, some have hypothesized that independently funded studies may be biased if the authors have strong a priori beliefs about the healthfulness of a food product (‘white hat bias’). The extent to which each source of bias may affect the scientific literature has not been examined. We aimed to explore this question with research involving sugar-sweetened beverages (SSB) as a test case, focusing on a period during which scientific consensus about the adverse health effects of SSB emerged from uncertainty.DesignPubMed search of worldwide literature was used to identify articles related to SSB and health risks published between 2001 and 2013. Financial relationships and article conclusions were classified by independent groups of co-investigators. Associations were explored by Fischer’s exact tests and regression analyses, controlling for covariates.ResultsA total of 133 articles published in English met inclusion criteria. The proportion of industry-related scientific studies decreased significantly with time, from approximately 30 % at the beginning of the study period to P=0·003). A ‘strong’ or ‘qualified’ scientific conclusion was reached in 82 % of independent v. 7 % of industry-related SSB studies (PConclusionIndustry-related research during a critical period appears biased to underestimate the adverse health effects of SSB, potentially delaying corrective public health action.