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 male body weight of U.S. adults aged 20 years and over from 1999 to 2016. According to the data, the average male body weight for those aged 40-59 years was ***** in 1999-2000 and increased to ***** as of 2015-2016.

In 2024, around 40 percent of U.S. men reported weighing 200 pounds or more. This statistic shows the average self-reported weight among U.S. men from 1990 to 2024.

In 2024, the mean average weight reported by men was 195 pounds, while the mean average weight for women was 164 pounds. This statistic shows the mean self-reported weight among U.S. adults from 1990 to 2024, by gender, in pounds.

In 2024, around 16 percent of U.S. women reported weighing 200 pounds or more. This statistic shows the average self-reported weight among U.S. women from 1990 to 2024.

This statistic depicts the average body weight of U.S. men aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average male body weight for those that identified as non-Hispanic white has increased from 192.3 in 1999-2000 to 202.2 in 2015-2016.

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.

BackgroundFew studies have examined weight transitions in contemporary multi-ethnic populations spanning early childhood through adulthood despite the ability of such research to inform obesity prevention, control, and disparities reduction.Methods and ResultsWe characterized the ages at which African American, Caucasian, and Mexican American populations transitioned to overweight and obesity using contemporary and nationally representative cross-sectional National Health and Nutrition Examination Survey data (n = 21,220; aged 2–80 years). Age-, sex-, and race/ethnic-specific one-year net transition probabilities between body mass index-classified normal weight, overweight, and obesity were estimated using calibrated and validated Markov-type models that accommodated complex sampling. At age two, the obesity prevalence ranged from 7.3% in Caucasian males to 16.1% in Mexican American males. For all populations, estimated one-year overweight to obesity net transition probabilities peaked at age two and were highest for Mexican American males and African American females, for whom a net 12.3% (95% CI: 7.6%-17.0%) and 11.9% (95% CI: 8.5%-15.3%) of the overweight populations transitioned to obesity by age three, respectively. However, extrapolation to the 2010 U.S. population demonstrated that Mexican American males were the only population for whom net increases in obesity peaked during early childhood; age-specific net increases in obesity were approximately constant through the second decade of life for African Americans and Mexican American females and peaked at age 20 for Caucasians.ConclusionsAfrican American and Mexican American populations shoulder elevated rates of many obesity-associated chronic diseases and disparities in early transitions to obesity could further increase these inequalities if left unaddressed.

This statistic depicts the average body weight of U.S. females aged 20 years and over from 1999 to 2016, by age. According to the data, the average female body weight for those aged 40-59 years was 169.4 in 1999-2000 and increased to 176.4 as of 2015-2016.

ObjectiveTo investigate the association of dynamic weight change in adulthood with leukocyte telomere length among U.S. adults.MethodsThis study included 3,886 subjects aged 36-75 years from the National Health and Nutrition Examination Survey (NHANES) 1999-2002 cycle. Survey-weighted multivariable linear regression with adjustments for potential confounders was utilized.Results3,386 individuals were finally included. People with stable obesity had a 0.130 kbp (95% CI: 0.061-0.198, P=1.97E-04) shorter leukocyte telomere length than those with stable normal weight (reference group) during the 10-year period, corresponding to approximately 8.7 years of aging. Weight gain from non-obesity to obesity shortened the leukocyte telomere length by 0.094 kbp (95% CI: 0.012-0.177, P=0.026), while normal weight to overweight or remaining overweight shortened the leukocyte telomere length by 0.074 kbp (95% CI: 0.014-0.134, P=0.016). The leukocyte telomere length has 0.003 kbp attrition on average for every 1 kg increase in weight from a mean age of 41 years to 51 years. Further stratified analysis showed that the associations generally varied across sex and race/ethnicity.ConclusionsThis study found that weight changes during a 10-year period was associated with leukocyte telomere length and supports the theory that weight gain promotes aging across adulthood.

Figure 7: Railroad Fuel Surcharges, North American Weight Average

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

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Weight Management Market Size 2025-2029

The weight management market size is forecast to increase by USD 114.79 billion at a CAGR of 10.9% between 2024 and 2029.

The market is driven by the growing obese population and rising demand for weight management services from developing economies. The increasing prevalence of obesity and related health issues globally presents a significant opportunity for market participants. However, marketing challenges associated with weight management products and services pose a significant hurdle. The stigma surrounding obesity and the perception that weight loss is a personal responsibility rather than a health issue create barriers to market penetration. Health insurance plays a pivotal role in covering costs, while fitness apps and mobile health apps enhance accessibility and tracking.
Companies seeking to capitalize on market opportunities must address these challenges through innovative marketing strategies, affordable pricing, and education initiatives to shift societal perceptions and increase accessibility to weight management services. By focusing on these areas, market participants can effectively navigate challenges and capitalize on the growing demand for weight management solutions. Innovative weight management solutions include waistline control, fitness equipment, surgical equipment, healthy dietary choices, and lifestyle changes.

What will be the Size of the Weight Management 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.
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The market for weight management solutions continues to evolve, reflecting the complex and multifaceted nature of weight management and its applications across various sectors. Sleeve gastrectomy and adjustable gastric banding are among the surgical interventions, while anti-obesity medications and pharmacological interventions offer alternative approaches. The prevalence of metabolic syndrome and its associated health risks, including cardiovascular disease and type 2 diabetes, underscores the urgency for sustainable weight loss solutions. Mindful eating, nutrition education, and meal planning are essential components of health behavior modification, while physical fitness and regular exercise routines contribute to weight regain prevention. Fitness and recreational sports centers are offering a wide range of HIIT classes, and HIIT fitness videos are flooding the market.

Hormonal imbalance and stress management are also crucial factors in weight management. The industry is expected to grow by 5.3% annually, driven by the increasing prevalence of obesity and related health issues. For instance, a study showed that patients who underwent bariatric surgery experienced an average weight loss of 30% within the first year. Social media and the young population's hectic lifestyles have led to increased fast food consumption and weight-related health issues, necessitating preventive measures and weight management programs. Additionally, the complexity and cost of weight management solutions can deter potential customers, particularly in developing economies with limited resources.

How is this Weight Management Industry segmented?

The weight management 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.

Type

  Diet
  Equipment
  Services


Distribution Channel

  Offline
  Online


End-user

  Fitness centers and health clubs
  Commercial weight loss centers
  Online weight loss programs
  Slimming centers
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The Diet segment is estimated to witness significant growth during the forecast period. The market is driven by the growing concern over health issues related to visceral fat, weight fluctuation, and obesity. Obesity, characterized by a body mass index (BMI) of 30 or higher, affects over one-third of the global population. This condition can lead to various health complications, including high blood pressure, joint problems, diabetes, and insulin sensitivity issues. To combat these health concerns, weight loss programs focusing on calorie expenditure through diet and physical activity have gained popularity. Diets, specifically, dominate the market, as they offer a more sustainable approach to weight management. Nutritional counseling and micronutrient intake are essential components of effective weight loss programs, ensuring a balanced macronutrient and micronutrient intake. Obesity rates continue to rise, fueling the demand for

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

This statistic depicts the average body weight of U.S. females aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average female body weight for those that identified as non-Hispanic white has increased from ***** in ********* to ***** in *********.

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).

Forecast: Chromite Ore Import Average Value, Gross Weight in the US 2023 - 2027 Discover more data with ReportLinker!

Objective and aimsIdentification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity.MethodsQuestionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017–2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis.ResultsThere were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034).ConclusionIn conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.

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?

Background: Obesity is a risk factor for many chronic diseases and the prevalence is increasing worldwide. Research suggests that sedentary behaviour (sitting) may be related to obesity. Aim: To examine the association between sitting time and obesity, while controlling for physical activity, in a large international sample. Subjects and methods: In total, 5338 adults from the UK, USA, Germany, Spain, Italy, France, Portugal, Austria and Switzerland self-reported their total daily sitting time, physical activity, age, height and weight. BMI (kg/m2), total physical activity (MET-minutes/week) and sitting time (hours/day) were derived. Participants were grouped into quartiles based on their daily sitting time (<4, 4–≤6, 6–≤8 and >8 hours/day) and logistic regression models explored the odds of being obese vs normal weight for each sitting time quartile. Results: Participants in the highest sitting time quartile (≥8 hours/day) had 62% higher odds of obesity compared to participants in the lowest quartile (<4 hours/day) after adjustment for physical activity and other confounding variables (OR = 1.62, 95% CI = 1.24–2.12, p < .01). Conclusion: Sitting time is associated with obesity in adults, independent of physical activity. Future research should clarify this association using objective measures of sitting time and physical activity to further inform health guidelines.