This statistic depicts the average body mass index (BMI) of U.S. adults aged 20 years and over as of 2016, by gender. According to the data, the average male BMI has increased from 27.8 in 1999-2000 to 29.1 as of 2015-2016.

Surveys in which U.S. adults report their current weight have shown that the share of those reporting they weigh 200 pounds or more has increased over the past few decades. In 2024, around 28 percent of respondents reported their weight as 200 pounds or more, compared to 15 percent in 1990. However, the same surveys show the share of respondents who report they are overweight has decreased compared to figures from 1990. What percentage of the U.S. population is obese? Obesity is an increasing problem in the United States that is expected to become worse in the coming decades. As of 2023, around one third of adults in the United States were considered obese. Obesity is slightly more prevalent among women in the United States, and rates of obesity differ greatly by region and state. For example, in West Virginia, around 41 percent of adults are obese, compared to 25 percent in Colorado. However, although Colorado is the state with the lowest prevalence of obesity among adults, a quarter of the adult population being obese is still shockingly high. The health impacts of being obese Obesity increases the risk of developing a number of health conditions including high blood pressure, heart disease, type 2 diabetes, and certain types of cancer. It is no coincidence that the states with the highest rates of hypertension are also among the states with the highest prevalence of obesity. West Virginia currently has the third highest rate of hypertension in the U.S. with 45 percent of adults with the condition. It is also no coincidence that as rates of obesity in the United States have increased so have rates of diabetes. As of 2022, around 8.4 percent of adults in the United States had been diagnosed with diabetes, compared to six percent in the year 2000. Obesity can be prevented through a healthy diet and regular exercise, which also increases overall health and longevity.

This statistic depicts the average body mass index (BMI) of U.S. females aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average female BMI for those that identified as white was **** in 1999-2000 and increased to **** as of 2015-2016.

Data on normal weight, overweight, and obesity among adults aged 20 and over by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

United States Prevalence of Overweight: % of Adults data was reported at 67.900 % in 2016. This records an increase from the previous number of 67.400 % for 2015. United States Prevalence of Overweight: % of Adults data is updated yearly, averaging 55.200 % from Dec 1975 (Median) to 2016, with 42 observations. The data reached an all-time high of 67.900 % in 2016 and a record low of 41.000 % in 1975. United States Prevalence of Overweight: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;;

West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 6.900 % in 2012. This records an increase from the previous number of 6.400 % for 2009. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.900 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 8.700 % in 2005 and a record low of 5.100 % in 1991. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Data from a clustered randomized controlled trial of 12 Head Start Centers in San Antonio, Texas: !Míranos! Look at Us, We Are Healthy! (¡Míranos!)The prevalence of obesity remains high in American children aged 2-5 while one in three Head Start children is overweight or obese. The !Míranos! study was designed to test the efficacy of !Míranos!, an early childhood obesity prevention program, which promoted healthy growth in predominantly Latino children in Head Start. The Míranos! included center-based (policy changes, staff development, gross motor program, and nutrition education) and home-based (parent engagement/ education and home visits) interventions to address key enablers and barriers in obesity prevention in young children. In partnership with Head Start, the study team demonstrated the feasibility and acceptability of the proposed interventions to influence energy-balance-related behaviors favorably in Head Start children. Using a three-arm cluster randomized design, 21 Head Start centers in equal numbers wiere randomly assigned to one of three conditions: 1) a combined center- and home-based intervention, 2) center-based intervention only, or 3) control. The interventions were delivered during the academic year (an 8-month period). A total of 526 3-year-old children were enrolled in the study and followed prospectively one year post-intervention. Outcome data collection was conducted at baseline, immediate post-intervention, and 1-year follow-up and included height, weight, physical activity (PA), and sedentary behaviors by accelerometry, parent reports of sleep duration and TV watching time, gross motor development, dietary intakes, and food and activity preferences. Information on family background, parental weight, PA- and nutrition-related practices and behaviors, PA and nutrition policy and environment at center and home, intervention program costs, and treatment fidelity will also be collected. The study had three specific aims: 1) to test the efficacy of the !Míranos! intervention on healthy weight growth (primary outcome) in normal weight, overweight and obese children, 2) to test the impact of the !Míranos! intervention on children’s PA, sedentary behavior, sleep, and dietary behaviors (secondary outcomes), and 3) to evaluate the cost-effectiveness of the !Míranos! intervention. By targeting different levels of influence and in multiple settings, the !Míranos! showed great promise of developing long-term health habits that reduce the energy imbalance gap by targeting multiple energy-balance-related behaviors. The !Míranos! can be disseminated to various organized childcare settings since it is built on the Head Start program and its infrastructure—a gold standard in early childhood education, as well as current PA and nutrition recommendations for preschool children.

These data are from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS). These surveys have been discontinued. Adults, adolescents, and children (with parental assistance) were asked for their current height and weight, from which, body mass index (BMI) was calculated. For adults, a BMI of 30.0 and above is considered obese. For adolescents and children, obesity is defined as having a BMI at or above the 95th percentile, according to CDC growth charts.

The California Dietary Practices Surveys (CDPS), the California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and the California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS) (now discontinued) were the most extensive dietary and physical activity assessments of adults 18 years and older, adolescents 12 to 17, and children 6 to 11, respectively, in the state of California. CDPS and CalCHEEPS were administered biennially in odd years up through 2013 and CalTEENS was administered biennially in even years through 2014. The surveys were designed to monitor dietary trends, especially fruit and vegetable consumption, among Californias for evaluating their progress toward meeting the Dietary Guidelines for Americans and the Healthy People 2020 Objectives. All three surveys were conducted via telephone. Adult and adolescent data were collected using a list of participating CalFresh households and random digit dial, and child data were collected using only the list of CalFresh households. Older children (9-11) were the primary respondents with some parental assistance. For younger children (6-8), the primary respondent was parents. Data were oversampled for low-income and African American to provide greater sensitivity for analyzing trends among the target population. Wording of the question used for these analyses varied by survey (age group). The questions were worded are as follows: Adult:1) How tall are you without shoes?2) How much do you weigh?Adolescent:1) About how much do you weigh without shoes?2) About how tall are you without shoes? Child:1) How tall is [child's name] now without shoes on?2) How much does [child's name] weigh now without shoes on?

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.

In Mississippi, over ***** out of ten adults were reported to be either overweight or obese in 2018, making it the leading U.S. state that year. Other prominent states, in terms of overweight and obesity, included Arkansas in ******, Oklahoma in *******, and Louisiana in ***** place.

Corpulence per state

When it comes to obesity, specifically, percentages were still very high for certain states. Almost forty percent of West Virginia’s population was obese in 2018. Colorado, Hawaii, and California were some of the healthier states that year, with obesity rates between ** and ** percent. The average for the country itself stood at just over ** percent.

Obesity-related health problems

Being obese can lead to various health-related complications, such as diabetes and diseases of the heart. In 2017, almost ** people per 100,000 died of diabetes mellitus in the United States. In the same year, roughly *** per 100,000 Americans died of heart disease. While the number of deaths caused by heart disease has decreased significantly over the past sixty to seventy years, it is still one of the leading causes of death in the country.

Average BMI by genotype for rs12255372 in MESA and WHI Hispanics. (XLSX 39 kb)

United States US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2012. This records a decrease from the previous number of 7.800 % for 2009. United States US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 7.000 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 8.100 % in 2005 and a record low of 5.400 % in 1991. United States US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

For more recent aggregated data reports on childhood obesity in NM, visit NM Healthy Kids Healthy Communities Program, NMDOH: https://www.nmhealth.org/about/phd/pchb/hknm/TitleChildhood Obese and Overweight Estimates, NM Counties 2016 - NMCHILDOBESITY2017SummaryCounty level childhood overweight and obese estimates for 2016 in New Mexico. *Most recent data known to be available on childhood obesity*NotesThis map shows NM County estimated rates of childhood overweight and obesity. US data is available upon request. Published in May, 2022. Data is most recent known sub-national obesity data set. If you know of another resource or more recent, please reach out. emcrae@chi-phi.orgSourceData set produced from the American Journal of Epidemiology and with authors and contributors out of the University of South Carolina, using data from the National Survey of Children's Health. Journal SourceZgodic, A., Eberth, J. M., Breneman, C. B., Wende, M. E., Kaczynski, A. T., Liese, A. D., & McLain, A. C. (2021). Estimates of childhood overweight and obesity at the region, state, and county levels: A multilevel small-area estimation approach. American Journal of Epidemiology, 190(12), 2618–2629. https://doi.org/10.1093/aje/kwab176 Journal article uses data fromThe United States Census Bureau, Associate Director of Demographic Programs, National Survey of Children’s Health 2020 National Survey of Children's Health Frequently Asked Questions. October 2021. Available from:https://www.census.gov/programs-surveys/nsch/data/datasets.htmlGIS Data Layer prepared byEMcRae_NMCDCFeature Servicehttps://nmcdc.maps.arcgis.com/home/item.html?id=80da398a71c14539bfb7810b5d9d5a99AliasDefinitionregionRegion NationallystateState (data set is NM only but national data is available upon request)fips_numCounty FIPScountyCounty NamerateRate of Obesitylower_ciLower Confidence Intervalupper_ciUpper Confidence IntervalfipstxtCounty FIPS text

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Smart Weight, Body Composition, And BMI Scales Market Size 2025-2029

The smart weight, body composition, and bmi scales market size is forecast to increase by USD 115.7 million, at a CAGR of 5.3% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising health consciousness among individuals. This trend is fueled by the increasing awareness of the importance of maintaining a healthy weight and body composition. Another key factor propelling market expansion is the innovative features offered by smart scales, such as pregnancy mode, which cater to specific user needs. However, the market faces challenges as well. The proliferation of alternative smart wearable devices and applications poses a threat to the market, as consumers have an abundance of choices for tracking their health metrics. Companies in this market must differentiate themselves by offering unique features and integrating seamlessly with other health and fitness platforms to attract and retain customers. To capitalize on opportunities and navigate challenges effectively, market players should focus on continuous innovation, user-centric design, and strategic partnerships.

What will be the Size of the Smart Weight, Body Composition, And BMI Scales Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe smart weight and body composition scale market continues to evolve, driven by advancements in technology and shifting consumer preferences. These devices offer more than just basic weight measurement, providing insights into body composition, muscle mass, body water, bone density, and visceral fat. The market caters to various sectors, including disease prevention, fitness tracking, and health data management. Smart scales integrate user-friendly interfaces and Bluetooth connectivity for seamless data synchronization with mobile apps, allowing for real-time health monitoring and analysis. Marketing strategies focus on personalized feedback, privacy compliance, and user experience (UX) to attract and retain customers. Differentiation comes from features like segmental body composition analysis, dietary analysis, health coaching, and wellness programs. Regulatory compliance, safety standards, and data security are essential considerations, ensuring the protection of sensitive health information. The market's growth potential is significant, with retail sales and online sales contributing to its expansion. Wellness improvement and weight management remain key applications, while pricing strategies and product differentiation influence market penetration. Manufacturing costs, distribution channels, and software updates impact the competitive landscape. As technology advances, smart scales continue to offer more comprehensive health assessments, integrating with smartphones, wearables, and cloud storage for enhanced functionality and convenience.

How is this Smart Weight, Body Composition, And BMI Scales Industry segmented?

The smart weight, body composition, and bmi scales industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Distribution ChannelOfflineOnlinePriceLess than USD100More than USD100TypeWi-FiBluetoothApplicationHouseholdGymOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyRussiaAPACChinaJapanSouth KoreaRest of World (ROW)

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period.The market for smart weight, body composition, and BMI scales has seen substantial growth in recent years, with both online and offline channels experiencing significant demand. Online sales enable consumers to purchase these advanced scales from the comfort of their homes, while offline retail outlets provide an opportunity for customers to physically assess the product before making a purchase. Offline channels, including specialty health stores, department stores, hypermarkets, and fitness equipment stores, are particularly effective in reaching a broad consumer base. These retailers often have dedicated sections for health and wellness products, showcasing smart scales alongside other related items. User interface and experience, marketing strategies, data synchronization, Bluetooth connectivity, and sensor technology are integral features of these devices, catering to consumers seeking health risk assessments, muscle mass measurement, segmental body composition analysis, and health data management. Wellness programs, health coaching, body water monitoring, smartphone integration, and personalized feedback are additional features that attract consumers. Regulatory

BMI, body mass index; N, unweighted number; IQR, interquartile range.

In 2023, Black adults had the highest obesity rates of any race or ethnicity in the United States, followed by American Indians/Alaska Natives and Hispanics. As of that time, around ** percent of all Black adults were obese. Asians/Pacific Islanders had by far the lowest obesity rates. Obesity in the United States Obesity is a present and growing problem in the United States. An astonishing ** percent of the adult population in the U.S. is now considered obese. Obesity rates can vary substantially by state, with around ** percent of the adult population in West Virginia reportedly obese, compared to ** percent of adults in Colorado. The states with the highest rates of obesity include West Virginia, Mississippi, and Arkansas. Diabetes Being overweight and obese can lead to a number of health problems, including heart disease, cancer, and diabetes. Being overweight or obese is one of the most common causes of type 2 diabetes, a condition in which the body does not use insulin properly, causing blood sugar levels to rise. It is estimated that just over ***** percent of adults in the U.S. have been diagnosed with diabetes. Diabetes is now the seventh leading cause of death in the United States, accounting for ***** percent of all deaths.

Sample characteristicsof non-frail older Mexican Americans by BMI categories at baseline (N = 1,648).

Background and objectiveMore research is required to understand associations of body mass index (BMI) and sarcopenia with cognition, especially in Latin America. The objective of this study was to investigate associations of BMI and sarcopenia with mild cognitive impairment in Colombia.Design, setting, and participantsData were from the National Survey of Health, Wellbeing and Aging in Colombia (SABE Colombia, in Spanish). Community-dwelling adults aged 60 years or older were invited to participate.MethodsTrained interviewers administered a shorter version of the mini-mental state examination and mild cognitive impairment was defined as a score of 12 or less out of 19. Body mass index was defined using standard cut-offs. Sarcopenia was defined as low grip strength or slow chair stands. Logistic regression models were adjusted for age, sex, height, education, income, civil status, smoking, and alcohol drinking.ResultsThe prevalence of mild cognitive impairment was 20% in 23,694 participants in SABE Colombia and 17% in 5,760 participants in the sub-sample in which sarcopenia was assessed. Overweight and obesity were associated with decreased risk of mild cognitive impairment and sarcopenia was associated with increased risk. Sarcopenia was a risk factor for mild cognitive impairment in those with normal BMI (adjusted model included 4,911 men and women). Compared with those with normal BMI and without sarcopenia, the odds ratio for mild cognitive impairment was 1.84 in those with normal BMI and sarcopenia (95% confidence interval: 1.25, 2.71). Sarcopenia was also a risk factor in those with obesity but did not present a greater risk than sarcopenia alone. Compared with those with normal BMI and without sarcopenia, the odds ratio was 1.62 in those with obesity and sarcopenia (95% confidence interval: 1.07, 2.48). Sarcopenia was not a risk factor for mild cognitive impairment in those with overweight. Similar results were observed when reference values from Colombia were used to set cut-offs for grip strength. Similar results were also observed in cross-validation models, which suggests the results are robust.ConclusionThis is the first study of the combined associations of sarcopenia and obesity with cognition in Colombia. The results suggest that sarcopenia is the major predictor of screen-detected mild cognitive impairment in older adults, not overweight or obesity.

Generalized estimating equation models for frailty as a function of BMI categories over 18-years of follow up among non-frail older Mexican Americans at baseline (N = 1,648).