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.

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.

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.

Dataset

This dataset was created by Johar M. Ashfaque

Contents

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.

Figure 7: Railroad Fuel Surcharges, North American Weight Average

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. 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.

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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

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

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 *********.

Dataset Description: AI-Generated Person Data

This dataset contains 1,001,000 synthetic records representing demographic and physical attributes of individuals. The data is AI-generated and designed to simulate realistic human characteristics without using personally identifiable information (PII).

Structure

Rows: 1,001,000

Columns: 6

Features

id – Unique identifier for each individual (1 to 1,001,000).

dob – Date of birth (ranging across ~32,000 unique values).

age – Age of the person (from -1 to 95, mostly realistic ages but may include outliers like -1).

gender – Binary category (Male, Female), nearly evenly distributed.

height_cm – Height in centimeters (ranging from 50 cm to 209.7 cm).

weight_kg – Weight in kilograms (ranging from 3 kg to 198.9 kg).

Statistical Highlights

Average age: ~38.1 years (with some anomalies).

Average height: ~159.3 cm (std dev ~24.7 cm).

Average weight: ~65.7 kg (std dev ~27.4 kg).

Gender distribution: ~50% Male, ~50% Female.

Applications

This dataset can be used for:

Testing and benchmarking machine learning models.

Simulating healthcare, biometric, or demographic analytics.

Data visualization and statistical analysis practice.

Building and validating data pipelines without real PII.

Maternal obesity has been associated with a higher risk of pregnancy-related complications in mothers and offspring; however, effective interventions have not yet been developed. We tested two common interventions, calorie restriction and pravastatin administration, during pregnancy in a rhesus macaque model with the hypothesis that these interventions would normalize metabolic dysregulation in pregnant mothers leading to an improvement in infant metabolic and cognitive/social development. A total of 19 obese mothers were assigned to either one of the two intervention groups (n=5 for calorie restriction; n=7 for pravastatin) or an obese control group (n=7) with no intervention, and maternal gestational samples and postnatal infant samples were compared with lean control mothers (n=6). Gestational calorie restriction normalized one-carbon metabolism dysregulation in obese mothers but altered energy metabolism in their offspring. Although administration of pravastatin during pregnancy tended to normalize blood cholesterol in the mothers, it potentially impacted the gut microbiome and kidney function of their offspring. In the offspring, both calorie restriction and pravastatin administration during pregnancy tended to normalize the activity of AMPK in the brain at 6 months, and while results of the Visual Paired-Comparison test, which measures infant recognition memory, were not significantly impacted by either of the interventions, gestational pravastatin administration, but not calorie restriction, tended to normalize anxiety assessed by the Human Intruder test. Although the two interventions tested in a non-human primate model led to some improvements in metabolism and/or infant brain development, negative impacts were also found in both mothers and infants. Our study emphasizes the importance of assessing gestational interventions for maternal obesity on both maternal and offspring long-term outcomes. Methods Study population Pregnant female rhesus macaques (Macaca mulatta) from an indoor breeding colony at the California National Primate Research Center with appropriate social behavior and previous successful pregnancies were enrolled. Animal handling was approved by the UC Davis Institutional Animal Care and Use Committee (IACUC) (#19299). A qualitative real-time PCR assay (Jimenez & Tarantal, 2003) was used to identify mothers with male fetuses to include in this study. Since obesity is defined as subjects with body fat above 30% for women, according to guidelines from the American Society of Bariatric Physicians, American Medical Association, and in some publications (Okorodudu et al., 2010; Shah & Braverman, 2012), a Body Condition Score (BCS) of 3.5 (32.8 % body fat on average (Summers et al., 2012)) was used as the cutoff. Therefore, mothers with BCS of 3.5 and above were categorized as obese. Obese mothers were randomly assigned to the Obese Control (OC) group, OR group (received calorie Restriction), or OP group (received Pravastatin). Mothers with BCS of 2.5 and below were assigned to the Lean Control (LC) group. The unbalanced sample size was because some mothers were removed from the analyses due to fetal deaths for unknown reasons, misidentification of a female fetus, different timing for study enrollment, or technical issues upon collecting samples. The number of animals was six for the LC, seven for the OC, five for the OR, and seven for the OP groups. Feeding, rearing, and interventions Adult female animals were provided monkey diet (High Protein Primate Diet Jumbo #5047; LabDiet, St. Louis, MO, USA) twice a day between 6–9 am and 1–3 pm. The calories were provided as 56% from carbohydrates, 30% from protein, and 13% from. Mothers in the LC, OC, and OP groups were fed nine biscuits twice a day once pregnancy was confirmed. Mothers in the OR group received a restricted supply of food once the pregnancy was detected and was maintained throughout pregnancy. The food restriction was set such that the average total weight increase would be 8% body weight from the last day before conception because the recommended total weight gain in the 2nd and 3rd trimesters is 5-9 kg for the average US woman with obesity who weighs 80 kg and is 1.6 m in height (Body Mass Index of 30), according to the Institute of Medicine 2009 guidelines (Institute of Medicine and National Research Council, 2009). During nursing of infants older than 4 months, all mothers were provided twelve biscuits. Fresh produce was provided biweekly, and water was provided ad libitum for all mothers. Mothers in the OP group were given pravastatin sodium (ApexBio Technology, Houston, TX, USA) at 20 mg/kg body weight prepared in a neutralized syrup (20 mg/mL sodium bicarbonate dissolved in a fruit-flavored syrup (Torani, San Leandro, CA, USA)) once a day from the time pregnancy was confirmed until delivery. The caloric value of the administration was made so as not to influence body weight or skew nutritional value of the diet among all treatment groups. Both interventions were applied only during gestation. Although most mothers were allowed to deliver naturally, cesarean delivery was performed for fetal indications when recommended by veterinarians (2 for each of the LC and OC groups, and 1 for the OP group). These mothers did not accept their infant following birth, so foster mothers were provided. Sample Collection and pre-processing prior to sample storage The animal caretakers and researchers who collected samples were blinded for group assignment by coding all animals by IDs. The collected biological samples were randomized by using random numbers and the group assignment was blinded during the data collection. Both mothers (during pregnancy) and infants were weighed every week. One day before sample collection, food was removed 30 min after the afternoon feeding, and biological samples were collected prior to the morning feeding. To collect biological samples, animals were anesthetized using 5–30 mg/kg ketamine or 5–8 mg/kg telazol. Both maternal and infant blood was collected using 5 mL lavender top (EDTA) tubes (Monoject, Cardinal Health, Dublin, OH, USA) and urine was collected from the bladder by ultrasound-guided transabdominal cystotomy using a 22-gauge needle and stored in a 15 mL Falcon tube. A placental sample was collected at GD150 transabdominally under ultrasound guidance using an 18-gauge needle attached to a sterile syringe. Sample processing was as previously described in (Hasegawa et al., 2022). Necropsy was conducted between 9:30 am–1:30 pm. First, infants at the age of PD180 were fasted and anesthetized with ketamine, and plasma and urine were collected. Then, euthanasia was performed with 120 mg/kg pentobarbital, followed by heparin injection, clamping of the descending aorta, and flushing with saline until clear. The kidney and brain (amygdala, hippocampus, hypothalamus, and prefrontal cortex) were collected, weighed, and immediately frozen on dry ice or liquid nitrogen to store at -80 °C until further analyses. Metabolite extraction and analysis by 1H NMR, and measurement of insulin, cholesterol, cytokine, and cortisol Detailed procedures were previously described (Hasegawa et al., 2022). Briefly, plasma and urine samples were filtered using Amicon Ultra Centrifugal Filter (3k molecular weight cutoff; Millipore, Billerica, MA, USA), and the supernatant was used for analysis. For both the placental and brain tissue samples, polar metabolites were extracted using our previously reported method (Hasegawa et al., 2020). A total of 180 μL of sample (tissue extract or filtered urine or serum) was transferred to 3 mm Bruker NMR tubes (Bruker, Billerica, MA, USA). Within 24 h of sample preparation, all 1H NMR spectra were acquired using the noesypr1d pulse sequence on a Bruker Avance 600 MHz NMR spectrometer (Bruker, Billerica, MA, USA) (O’Sullivan et al., 2013). Chenomx NMRSuite (version 8.1, Chenomx Inc., Edmonton, Canada) (Weljie et al., 2006) was used to identify and quantify metabolites. Heparin-treated plasma samples were used to measure insulin and 17 cytokines and chemokines (hs-CRP, Granulocyte-macrophage colony-stimulating factor, IFN-γ, TNF-α, transforming growth factor-α, monocyte chemoattractant protein-1, macrophage inflammatory protein-1β (MIP-1β), and interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-2, IL-6, IL-8, IL-10, IL-12/23 p40, IL-13, IL-15, and IL-17A) using a multiplex Bead-Based Kit (Millipore) on a Bio-Plex 100 (Bio-rad, Hercules, CA) following the manufacturer’s protocol. For each sample, a minimum of fifty beads per region were collected and analyzed with Bio-Plex Manager software using a 5-point standard curve with immune marker quantities extrapolated based on the standard curve. Two samples were removed for analysis of TNF-α and IL-1ra as technical errors (both from Animal ID 1132103: 895.2 and 1115.1 pg/mL at gestational days (GD) 90; 510.8 and 617.2 pg/mL at GD120, respectively). Plasma cholesterol level was measured by Clinical Laboratory Diagnostic Product (OSR6116) on Beckman Coulter AU480 (Beckman Coulter, Brea, CA). Infant plasma cortisol level at PD110 was assessed as previously described (Vandeleest et al., 2019; Walker et al., 2018). In short, infants were transferred to a test room at 9 am and blood was drawn at 11 am (Sample 1), followed by another blood collection at 4 pm (Sample 2) and intramuscular injection of 500 μg/kg dexamethasone (Dex) (American Regent Laboratories, Inc., Shirley, NY). On the next day, a blood sample was collected at 8:30 am (Sample 3), and then 2.5 IU of adrenocorticotropic hormone (Amphastar Pharmaceuticals, Inc., Rancho Cucamonga, CA) was injected intramuscularly. The last blood was collected (Sample 4) 30 min after adrenocorticotropic hormone injection. The collected blood samples were processed and stored, and cortisol concentration was assessed by a chemiluminescent assay on the ADVIA Centaur CP platform

Bolded when p-value < 0.05a. Adjusted for all the variables except for waist circumference in column 2 and all of the variables, including waist circumference in column 3.Unadjusted and multivariable-adjusted prevalence ratios of the metabolic unhealthy profile associated with socio-demographic and behaviors variables among normal weight individuals (N = 889).

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

Bolded when p-value < 0.05a Adjusted for all the variables except for waist circumference in column 2 and all of the variables, including waist circumference in column 3.Unadjusted and multivariable-adjusted prevalence ratios of metabolic healthy profile associated with socio-demographic and behaviors variables among overweight and obese individuals (N = 2198).

In the fall of 2024, around 53 percent of U.S. college students described their weight as “about the right weight”. Around 10 percent of respondents stated that they were slightly underweight, while 29 percent described themselves as slightly overweight. This statistic shows the percentage of college students in the United States who described themselves as underweight or overweight as of fall 2024.

This statistic represents the curb weight of North American light vehicles between 2008 and 2020. It is expected that the average North American vehicle is required to weigh ***** pounds to meet the 2020 fuel economy standards.