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Obesity is a serious health concern in the United States, affecting more than one in six children (1) and putting their long-term health and quality of life at risk.* During the COVID-19 pandemic, children and adolescents spent more time than usual away from structured school settings, and families who were already disproportionally affected by obesity risk factors might have had additional disruptions in income, food, and other social determinants of health. † As a result, children and adolescents might have experienced circumstances that accelerated weight gain, including increased stress, irregular mealtimes, less access to nutritious foods, increased screen time, and fewer opportunities for physical activity (e.g., no recreational sports) (2,3). CDC used data from IQVIA's Ambulatory Electronic Medical Records database to compare longitudinal trends in body mass index (BMI, kg/m 2 ) among a cohort of 432,302 persons aged 2-19 years before and during the COVID-19 pandemic (January 1, 2018-February 29, 2020 and March 1, 2020-November 30, 2020, respectively). Between the prepandemic and pandemic periods, the rate of BMI increase approximately doubled, from 0.052 (95% confidence interval [CI] = 0.051-0.052 to 0.100 (95% CI = 0.098-0.101) kg/m 2 /month (ratio = 1.93 [95% CI = 1.90-1.96]). Persons aged 2-19 years with overweight or obesity during the prepandemic period experienced significantly higher rates of BMI increase during the pandemic period than did those with healthy weight. These findings underscore the importance of efforts to prevent excess weight gain during and following the COVID-19 pandemic, as well as during future public health emergencies, including increased access to efforts that promote healthy behaviors. These efforts could include screening by health care providers for BMI, food security, and social determinants of health, increased access to evidence-based pediatric weight management programs and food assistance resources, and state, community, and school resources to facilitate healthy eating, physical activity, and chronic disease prevention., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Obesity* is a recognized risk factor for severe COVID-19 (1,2), possibly related to chronic inflammation that disrupts immune and thrombogenic responses to pathogens (3) as well as to impaired lung function from excess weight (4). Obesity is a common metabolic disease, affecting 42.4% of U.S. adults (5), and is a risk factor for other chronic diseases, including type 2 diabetes, heart disease, and some cancers. † The Advisory Committee on Immunization Practices considers obesity to be a high-risk medical condition for COVID-19 vaccine prioritization (6). Using data from the Premier Healthcare Database Special COVID-19 Release (PHD-SR), § CDC assessed the association between body mass index (BMI) and risk for severe COVID-19 outcomes (i.e., hospitalization, intensive care unit [ICU] or stepdown unit admission, invasive mechanical ventilation, and death). Among 148,494 adults who received a COVID-19 diagnosis during an emergency department (ED) or inpatient visit at 238 U.S. hospitals during March-December 2020, 28.3% had overweight and 50.8% had obesity. Overweight and obesity were risk factors for invasive mechanical ventilation, and obesity was a risk factor for hospitalization and death, particularly among adults aged <65 years. Risks for hospitalization, ICU admission, and death were lowest among patients with BMIs of 24.2 kg/m 2 , 25.9 kg/m 2 , and 23.7 kg/m 2 , respectively, and then increased sharply with higher BMIs. Risk for invasive mechanical ventilation increased over the full range of BMIs, from 15 kg/m 2 to 60 kg/m 2 . As clinicians develop care plans for COVID-19 patients, they should consider the risk for severe outcomes in patients with higher BMIs, especially for those with severe obesity. These findings highlight the clinical and public health implications of higher BMIs, including the need for intensive COVID-19 illness management as obesity severity increases, promotion of COVID-19 prevention strategies including continued vaccine prioritization (6) and masking, and policies to ensure community access to nutrition and physical activities that promote and support a healthy BMI., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. John House reports employment with Premier, Inc. No other potential conflicts of interest were disclosed.

Background: BMI z-scores (BMIz) based on the Centers for Disease Control and Prevention (CDC) growth charts among children do not accurately characterise BMI levels among children with very high BMIs. These limitations may be particularly relevant in longitudinal and intervention studies, as the large changes in the L (normality) and S (dispersion) parameters with age can influence BMIz., Aim: To compare longitudinal changes in BMIz with BMI expressed as a percentage of the 95th percentile (%BMIp95) and a modified z-score calculated as log(BMI/M)/S., Subjects and Methods: A total of 45 414 2-4-year-olds with severe obesity (%BMIp95 ≥ 120)., Results: Changes in very high BMIz levels differed from the other metrics. Among severely obese 2-year-old girls, for example, the mean BMIz decreased by 0.6 SD between examinations, but there were only small changes in BMIp95 and modified BMIz. Some 2-year-old girls had BMIz decreases of >1 SD, even though they had large increases in BMI, %BMIp95 and modified BMIz., Conclusions: Among children with severe obesity, BMIz changes may be due to differences in the transformations used to estimate levels of BMIz rather than to changes in body size. The BMIs of these children could be expressed relative to the 95th percentile or as modified z-scores.

Objective: To examine the associations among several body mass index (BMI) metrics (z-scores, percent of the 95th percentile (%BMI p95 ) and BMI minus 95th percentile (ΔBMI p95 ) as calculated in the growth charts from the Centers for Disease Control and Prevention (CDC). It is known that the widely used BMI z-scores (BMIz) and percentiles calculated from the growth charts can differ substantially from those that directly observed in the data for BMIs above the 97th percentile (z = 1.88)., Study Design: Cross-sectional analyses of 8.7 million 2- to 4-year-old children who were examined from 2008 through 2011 in the CDC's Pediatric Nutrition Surveillance System., Results: Because of the transformation used to calculate z-scores, the theoretical maximum BMIz varied by >3-fold across ages. This results in the conversion of very high BMIs into a narrow range of z-scores that varied by sex and age. Among children with severe obesity, levels of BMIz were only moderately correlated (r ~ 0.5) with %BMI p95 and ΔBMI p95 . Among these children with severe obesity, BMIz levels could differ by more than 1 SD among children who had very similar levels of BMI, %BMI p95 and ΔBMI p95 due to differences in age or sex., Conclusions: The effective upper limit of BMIz values calculated from the CDC growth charts, which varies by sex and age, strongly influences the calculation of z-scores for children with severe obesity. Expressing these very high BMIs relative to the CDC 95th percentile, either as a difference or percentage, would be preferable to using BMI-for-age, particularly when assessing the effectiveness of interventions., (Published by Elsevier Inc.)

Objective: Although the Centers for Disease Control and Prevention (CDC) growth charts are widely used, BMI-for-age z-Scores (BMIz) are known to be uninformative above the 97th percentile. This study compared the relations of BMIz and other BMI metrics (%BMI p95 , percent of 95th percentile, and ΔBMI p95 , BMI minus 95th percentile) to circumferences, skinfolds, and fat mass. We were particularly interested in the differences among children with severe obesity (%BMI p95  ≥ 120)., Methods: Data was used from 30,003 2- to 19-year-olds who were examined from 1999-2000 through 2013-2014 in the National Health and Nutrition Examination Survey (NHANES)., Results: The theoretical maximum BMIz based on the growth charts varied by more than threefold across ages. The BMI metrics were strongly intercorrelated, but BMIz was less strongly related to the adiposity measures than were ΔBMI p95 and %BMI p95 . Among children with severe obesity, circumferences and triceps skinfold showed almost no association with BMIz (r ≤ 0.10), whereas associations with %BMI p95 and ΔBMI p95 ranged from r = 0.32 to 0.79. Corresponding associations with fat mass ÷ height 2 ranged from r = 0.40 (BMIz) to r =0.82 (%BMI p95 ) among 8- to 19-year-olds., Conclusions: Among children with severe obesity, BMIz is only weakly associated with other measures of body fatness. Very high BMIs should be expressed relative to the CDC 95th percentile, particularly in studies that evaluate obesity interventions., (© 2017 The Obesity Society.)

Objective: This study assessed the prevalence and consistency of high values of weight, height, and BMI considered to be biologically implausible (BIV) using cut points proposed by WHO among 8.8 million low-income children (13.7 million observations)., Methods: Cross-sectional and longitudinal analyses were performed among 2- to 4-year-olds who were examined from 2008 through 2011., Results: Overall, 2.7% of the body size measurements were classified as BIVs; 95% of these BIVs were very high. Among the subset of children (3.6 million) examined more than once, most of those who initially had a high weight or BMI BIV also had a high BIV at the second examination; odds ratios were >250. Based on several alternative classifications of BIVs, the current cut points likely underestimate the prevalence of obesity by about 1%., Conclusions: Many of the extremely high values of body size currently flagged as BIVs are unlikely to be errors. Increasing the z-score cut points or using a percentage of the maximum values in the National Health and Nutrition Examination Survey, could improve the balance between removing probable errors and retaining those that are likely correct., (© 2016 The Obesity Society.)

Objective: To examine the accuracies of body mass index (BMI) and skinfold thicknesses in classifying the body fatness of 7365 8- to 19-year-old subjects in a national sample., Study Design: We used percent body fat determined by dual-energy x-ray absorptiometry (PBFDXA) between 1999 and 2004. Categories of PBFDXA and the skinfold sum (triceps plus subscapular) were constructed so that that numbers of children in each category were similar to the number in each of 5 BMI categories based on the Centers for Disease Control and Prevention growth charts., Results: Approximately 75% of the children and adolescents who had a BMI-for-age ≥ 95th percentile (considered obese) had elevated body fatness, but PBFDXA levels were more variable at lower BMIs. For example, only 41% of the boys who had a BMI < 25th percentile, had a similarly low PBFDXA. The use of the skinfold sum, rather than BMI, slightly improved the identification of elevated levels of body fatness among boys (P = .03), but not among girls (P > .10). A low sum of the triceps and subscapular skinfold thicknesses was a better indicator of low PBFDXA than was a low BMI, but differences were smaller among children with greater levels of body fatness. Among girls who had a PBFDXA above the median, for example, BMI and the skinfold sum were correlated similarly (r = 0.77-0.79) with body fatness., Conclusions: Both BMI and skinfold thicknesses are fairly accurate in identifying children who have excess body fatness. In contrast, if the goal is to identify children who have low body fatness, skinfold thicknesses would be preferred., (Copyright © 2013 Mosby, Inc. All rights reserved.)

Objective: A recent, cross-sectional analysis of adults found that the hip circumference divided by height(1.5) minus 18 (the body adiposity index, BAI) was strongly correlated (r = 0.79) with percent body fat determined by dual energy X-ray absorptiometry. The BAI was proposed as a more accurate index of body fatness than BMI. We examined whether BAI was more strongly related, than was BMI and waist circumference, to skinfold thicknesses and levels of various risk factors for coronary heart disease., Design and Methods: Cross-sectional analyses of adults (n = 14,263 for skinfold thickness; n=6291 for fasting lipid levels) in the National Health and Nutrition Examination Survey (NHANES) III, 1988-1994., Results: As compared with BMI and waist circumference, we found that BAI was less strongly associated with the skinfold sum and with risk factor levels. For example, correlations with the skinfold sum were r = 0.79 (BMI) vs. r = 0.70 (BAI) among men, and r = 0.86 (BMI) vs. r = 0.79 (BAI) among women; p < 0.001 for the difference between each pair of correlations. An overall index of the 7 risk factors was also more strongly associated with BMI and waist circumference than BAI in analyses stratified by sex, race-ethnicity and age. Multivariable analyses indicated that if BMI was known, BAI provided little additional information on risk factor levels., Conclusions: Based on the observed associations with risk factor levels and skinfold thicknesses, we conclude that BAI is unlikely to be a better index of adiposity than BMI., (Copyright © 2012 The Obesity Society.)

Based on cross-sectional analyses, it was suggested that hip circumference divided by height(1.5) -18 (the body adiposity index (BAI)), could directly estimate percent body fat without the need for further correction for sex or age. We compared the prediction of percent body fat, as assessed by dual-energy X-ray absorptiometry (PBF(DXA)), by BAI, BMI, and circumference (waist and hip) measurements among 1,151 adults who had a total body scan by DXA and circumference measurements from 1993 through 2005. After accounting for sex, we found that PBF(DXA) was related similarly to BAI, BMI, waist circumference, and hip circumference. In general, BAI underestimated PBF(DXA) among men (2.5%) and overestimated PBF(DXA) among women (4%), but the magnitudes of these biases varied with the level of body fatness. The addition of covariates and quadratic terms for the body size measures in regression models substantially improved the prediction of PBF(DXA), but none of the models based on BAI could more accurately predict PBF(DXA) than could those based on BMI or circumferences. We conclude that the use of BAI as an indicator of adiposity is likely to produce biased estimates of percent body fat, with the errors varying by sex and level of body fatness. Although regression models that account for the nonlinear association, as well as the influence of sex, age, and race, can yield more accurate estimates of PBF(DXA), estimates based on BAI are not more accurate than those based on BMI, waist circumference, or hip circumference.

Introduction: Parents can set household practices that influence children's behaviors. The objective of this study was to determine whether children (children and adolescents aged 9-18 y) who live in a household that has healthful practices related to behaviors associated with obesity have a lower body mass index (BMI)., Methods: We analyzed data from the 2005 Styles mail panel survey (N = 1,685 parents and children). We used multiple logistic regression to assess associations between 4 household practices and 3 children's behaviors: watching television, participating in vigorous physical activity, and purchasing sodas and snacks at school., Results: Children watched more television if they had a television in their bedrooms, were less active as a family, and had no junk food restrictions at home. Children in less active families participated in about half as much VPA as children in more active families. Children purchased more sodas and snacks at school if they had a television in their bedrooms and their family consumed more meals at fast-food restaurants. Children whose families were less active were more likely to have a self-reported BMI at or above the 85th percentile. In addition, children who watched more television were more likely to have a self-reported BMI at or above the 85th percentile., Conclusion: Household practices were associated with children's behaviors and self-reported BMI. A household profile that includes being active as a family may counteract the increase in childhood obesity.

Objectives: To describe the relationship between BMI and perceived weight status and to determine how underassessment of weight status is associated with demographic characteristics, self-reported general health, and perceived health risk in relation to one's body weight., Methods and Procedures: In the 2004 Styles surveys, 3,888 US adult participants described their current weight status (underweight, about right, slightly overweight, very overweight), which we compared with self-reported BMI in order to determine concordance. We used multivariable logistic regression to evaluate associations between underassessment of body weight and characteristics of interest., Results: Among persons with a BMI>or=25, women were more likely than men to recognize their overweight status (slightly or very overweight; 93.0% of women vs. 73.5% of men) and the extent to which they were overweight: 70.4% of obese women vs. 49.5% of obese men described themselves as very overweight. Among the overweight and obese of both sexes, disagreement with regard to current weight as a health risk was associated with underassessment of weight. Additional factors associated with underassessment were education and race/ethnicity among overweight women; race/ethnicity among overweight men; household income and self-rated health among obese women; and self-rated health among obese men (P<0.05)., Discussion: While most of the obese participants recognized that they were overweight, many of them, particularly among the men, did not realize the extent to which they were overweight. Public health messages may be more effective if they are specifically tailored to target audiences, besides emphasizing the health risks associated with excess body weight.

The Mind, Exercise, Nutrition, Do It! 7–13 (MEND 7–13) program was adapted in 2016 by 5 Denver Health federally qualified health centers (DH FQHC) into MEND+, integrating clinician medical visits into the curriculum and tracking health measures within an electronic health record (EHR). We examined trajectories of body mass index (BMI, kg/m2) percentile, and systolic and diastolic blood pressures (SBP and DBP) among MEND+ attendees in an expanded age range of 4 to 17 years, and comparable nonattendees. Data from April 2015 to May 2018 were extracted from DH FQHC EHR for children eligible for MEND+ referral (BMI ≥85th percentile). The sample included 347 MEND+ attendees and 21,061 nonattendees. Mixed-effects models examined average rate of change for BMI percent of the 95th percentile (%BMIp95), SBP and DBP (mm Hg), after completion of the study period. Most children were ages 7 to 13 years, half were male, and most were Hispanic. An average of 4.2 MEND+ clinical sessions were attended. Before MEND+, %BMIp95 increased by 0.247 units/month among MEND+ attendees. After attending, %BMIp95 decreased by 0.087 units/month (P <.001). Eligible nonattendees had an increase of 0.084/month in %BMIp95. Before MEND+ attendance, SBP and DBP increased by 0.041 and 0.022/month, respectively. After MEND+ attendance, SBP and DBP decreased by 0.254/month (P <.001) and 0.114/month (P <.01), respectively. SBP and DBP increased by 0.033 and 0.032/month in eligible nonattendees, respectively. %BMIp95, SBP, and DBP significantly decreased among MEND+ attendees when implemented in community-based clinical practice settings at DH FQHC. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To examine the prevalence and trends in severe obesity among 16.6 million children aged 2 to 4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) from 2010 to 2020. METHODS: Severe obesity was defined as a sex-specific BMI for age ≥120% of the 95th percentile on the Centers for Disease Control and Prevention growth charts or BMI ≥35 kg/m°. Joinpoint regression was used to identify when changes occurred in the overall trend. Logistic regression was used to compute the adjusted prevalence differences between years controlling for sex, age, and race and ethnicity. RESULTS: The prevalence of severe obesity significantly decreased from 2.1% in 2010 to 1.8% in 2016 and then increased to 2.0% in 2020. From 2010 to 2016, the prevalence decreased significantly among all sociodemographic subgroups except for American Indian/Alaska Native (AI/AN) children. The largest decreases were among 4-year-olds, Asian/Pacific Islander and Hispanic children, and children from higher-income households. However, from 2016 to 2020, the prevalence increased significantly overall and among sociodemographic subgroups, except for AI/AN and non-Hispanic white children. The largest increases occurred in 4-year-olds and Hispanic children. Among 56 WIC agencies, the prevalence significantly declined in 17 agencies, and 1 agency (Mississippi) showed a significant increase from 2010 to 2016. In contrast, 21 agencies had significant increases, and only Alaska had a significant decrease from 2016 to 2020. CONCLUSIONS: Although severe obesity prevalence in toddlers declined from 2010 to 2016, recent trends are upward. Early identification and access to evidence-based family healthy weight programs for at-risk children can support families and child health. [ABSTRACT FROM AUTHOR]

Background: The prevalence of obesity among U.S. preschoolers has doubled in recent decades. Childhood obesity increases the risk for adult obesity and is associated with negative health consequences. Trends in the state-specific prevalence of obesity among low-income U.S. preschool children have not been examined since 2008. State-specific obesity prevalence surveillance helps determine the need for and impact of state and local obesity prevention strategies. Methods: Measured weight and height data from approximately 11.6 million low-income children aged 2-4 years from 40 states, the District of Columbia, and two U.S. territories who participated in the Pediatric Nutrition Surveillance System during 2008-2011 were used to estimate state obesity prevalence. Obesity was defined as having an age- and sex-specific body mass index ≥95th percentile, according to the 2000 CDC growth charts. Logistic regression models adjusted for age, sex, and race/ethnicity were used to examine trends in the state-specific obesity prevalence. Results: During 2008-2011, statistically significant downward trends in obesity prevalence were observed in 18 states and the U.S. Virgin Islands. Florida, Georgia, Missouri, New Jersey, South Dakota, and the U.S. Virgin Islands had the largest absolute decreases in obesity prevalence, each with a decrease of ≥1 percentage point. Twenty states and Puerto Rico experienced no significant change, and obesity prevalence increased significantly in three states. Conclusions and Implications for Public Health Practice: Small but significant declines in obesity among low-income preschoolers were observed in 19 of 43 states/territories examined. Continued prevention efforts are needed to sustain and expand the implementation and evaluation of population-level interventions to prevent childhood obesity. [ABSTRACT FROM AUTHOR]

Objective: To examine associations of problem behaviors and victimization with nondiet soda intake among a na­tional sample of 16,188 US high school students. Methods: We used the 2009 national Youth Risk Behavior Survey. The outcome measure was daily nondiet soda intake. Results: Smoking, having any sex partners, not always wearing a seat belt, being bullied/threatened/ injured on school property, and being physically hurt by their boyfriend/girl­friend were significantly associated with daily nondiet soda intake after adjust­ment for age, sex, race/ethnicity, and weight status. Conclusions: Our findings suggest a need to examine why nondiet soda intake is associated with these be­haviors to understand potential mecha­nisms. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: School attendance can impact academic performance. Childhood obesity-related physical and psychosocial consequences are potentially associated with school absenteeism. Thus, we examined the association between school absenteeism attributed to illness or injury and obesity among adolescents aged 12–17 years. Methods: We used a weighted sample of 3,470 U.S. adolescents from the 2009 National Health Interview Survey. School absenteeism was measured from the parent-reported number of sick days taken in the preceding 12 months. Body mass index was calculated from parent-reported weight and height. Weight status was classified based on the sex-specific body mass index-for-age percentile defined by the CDC growth charts. Poisson regression was conducted to examine the association between school absenteeism and weight status, controlling for selected sociodemographic characteristics and disease status. Results: The mean number of annual sick days was 3.9 days overall; 3.4 days among normal-weight, 4.4 days among overweight, and 4.5 days among obese adolescents. Obese adolescents had a higher proportion of missing ≥4 days of school per year than adolescents of normal weight. Our multivariate analyses found that compared with adolescents of normal weight, overweight and obese adolescents had greater than one-third more sick days annually (rate ratio = 1.36 for overweight and 1.37 for obese adolescents). Conclusions: Overweight and obese adolescents had 36% and 37% more sick days, respectively, than adolescents of normal weight. The results suggest another potential aspect of obesity prevention and reduction efforts among children and families is to improve children''s school attendance. [Copyright &y& Elsevier]

Based on cross-sectional analyses, it was suggested that hip circumference divided by height1.5 −18 (the body adiposity index (BAI)), could directly estimate percent body fat without the need for further correction for sex or age. We compared the prediction of percent body fat, as assessed by dual-energy X-ray absorptiometry (PBFDXA), by BAI, BMI, and circumference (waist and hip) measurements among 1,151 adults who had a total body scan by DXA and circumference measurements from 1993 through 2005. After accounting for sex, we found that PBFDXA was related similarly to BAI, BMI, waist circumference, and hip circumference. In general, BAI underestimated PBFDXA among men (2.5%) and overestimated PBFDXA among women (4%), but the magnitudes of these biases varied with the level of body fatness. The addition of covariates and quadratic terms for the body size measures in regression models substantially improved the prediction of PBFDXA, but none of the models based on BAI could more accurately predict PBFDXA than could those based on BMI or circumferences. We conclude that the use of BAI as an indicator of adiposity is likely to produce biased estimates of percent body fat, with the errors varying by sex and level of body fatness. Although regression models that account for the nonlinear association, as well as the influence of sex, age, and race, can yield more accurate estimates of PBFDXA, estimates based on BAI are not more accurate than those based on BMI, waist circumference, or hip circumference. [ABSTRACT FROM AUTHOR]

Purpose: Determine prevalence of overweight and obesity as reported in Head Start Program Information Reports. Design: Serial cross-sectional census reports from 2012–2018. Setting: Head Start programs countrywide, aggregated from program level to state and national level. Subjects: Population of children enrolled in Head Start with reported weight status data. Measures: Prevalence of overweight (body mass index [BMI] ≥85th percentile to <95th percentile) and obesity (BMI ≥95th percentile). Analysis: Used descriptive statistics to present the prevalence of overweight and obesity by state. Performed unadjusted regression analysis to examine annual trends or average annual changes in prevalence. Results: In 2018, the prevalence of overweight was 13.7% (range: 8.9% in Alabama to 20.4% in Alaska). The prevalence of obesity was 16.6% (range: 12.5% in South Carolina to 27.1% in Alaska). In the unadjusted regression model, 34 states and the District of Columbia did not have a linear trend significantly different from zero. There was a statistically significant positive trend in obesity prevalence for 13 states and a negative trend for 3 states. Conclusion: The prevalence of obesity and overweight in Head Start children remained stable but continues to be high. Head Start reports may be an additional source of surveillance data to understand obesity prevalence in low-income young children. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: We examined whether the reported prevalence of trying to lose weight among overweight and obese individuals has changed over time, and whether those trying to lose weight report using recommended weight-loss strategies. Methods: We used Behavioral Risk Factor Surveillance System data from 50 states and the District of Columbia during 1996, 1998, 2000, and 2003. The sample included participants with a self-reported Body Mass Index (BMI) of ≥25.0 kg/m2 (N =333,378). The prevalence of trying to lose weight and eating fewer calories, using physical activity, or both, were examined for endpoint change and linear trends. Results: Between 1996 and 2003, the prevalence of trying to lose weight among obese individuals increased significantly, while it remained stable among overweight individuals. The prevalence of eating fewer calories, using physical activity and using a combination of both increased significantly over time among the overweight and obese individuals trying to lose weight. Conclusion: Despite a rise in the number of overweight and obese people, there was little change among overweight adults in trying to lose weight over time, and a modest—but significant—change among obese adults in trying to lose weight over time. Among those who reported trying to lose weight, there were significant increases in their efforts to use recommended strategies. [Copyright &y& Elsevier]