Differences In Prevalence Of Obesity Research Articles

The Relationship between Age, Gender, and Physical Inactivity on the Incidence of Obesity at Puskesmas Johar Baru, Central Jakarta

Background: Obesity is an increasing global health challenge, including in Indonesia. Objectives: This study was intended to explore the correlation between age, gender, and physical activity level with the incidence of obesity. Research Methods: This study used secondary data from Puskesmas Johar Baru, Central Jakarta, in 2021, with a sample size of 72,680 patients; the number of patients who were obese was 22,297 people. Data processing was performed using SPSS 25 software, involving bivariate analysis using the Chi-Square test and multivariate analysis using multiple logistic regression with the enter method at a significance level 0.05. Results: The results showed that the majority of obese respondents were of productive age, as many as 17,521 people (p-value 0.000), female gender as many as 9,441 people (p-value 0.000), and stated that they lacked physical activity as many as 17,207 people (p-value 0.000). Statistically, through the Chi-Square test, it was found that there was a significant association between the incidence of obesity and the three variables, with a p-value ≤ 0.05. Multivariate analysis using logistic regression reinforced that gender emerged as the dominant factor, with females having a 12.925 times higher risk of being obese compared to males. Conclusion: These findings underscore the importance of gender variables in the context of obesity in Puskesmas Johar Baru. This study highlights the differences in obesity prevalence by age and gender. Middle-aged adults, particularly women, are more prone to obesity. Physical inactivity was also identified as a significant factor in the increased prevalence of obesity. These results have implications for developing more specific obesity prevention interventions, especially for women.

Female obesity: clinical and psychological assessment toward the best treatment.

Obesity is a heterogeneous condition which results from complex interactions among sex/gender, sociocultural, environmental, and biological factors. Obesity is more prevalent in women in most developed countries, and several clinical and psychological obesity complications show sex-specific patterns. Females differ regarding fat distribution, with males tending to store more visceral fat, which is highly correlated to increased cardiovascular risk. Although women are more likely to be diagnosed with obesity and appear more motivated to lose weight, as confirmed by their greater representation in clinical trials, males show better outcomes in terms of body weight and intra-abdominal fat loss and improvements in the metabolic risk profile. However, only a few relatively recent studies have investigated gender differences in obesity, and sex/gender is rarely considered in the assessment and management of the disease. This review summarizes the evidence of gender differences in obesity prevalence, contributing factors, clinical complications, and psychological challenges. In addition, we explored gender differences in response to obesity treatments in the specific context of new anti-obesity drugs.

The relationships between obesity and epilepsy: A systematic review with meta-analysis.

There is ongoing debate regarding the association between epilepsy and obesity. Thus, the aim of this study was to examine the correlation between epilepsy and obesity. This study adhered to the PRISMA guidelines for systematic reviews and meta-analyses. On The Prospero website, this study has been successfully registered (CRD42023439530), searching electronic databases from the Cochr-ane Library, PubMed, Web of Sciences and Embase until February 10, 2024.The search keywords included "Epilepsy", "Obesity", "Case-Control Studies", "cohort studies", "Randomized Controlled Trial" and "Cross-Sectional Studies". The medical subject headings(MeSH) of PubMed was utilized to search for relevant subject words and free words, and a comprehensive search strategy was developed. Two reviewers conducted article screening, data extraction and bias risk assessment in strict accordance with the predefined criteria for including and excluding studies. The predefined inclusion criteria were as follows: 1) Inclusion of case-control, cohort, randomized controlled trial, and cross-sectional studies; 2) Segregation of subjects into epileptic patients and healthy controls; 3)Obesity as the outcome measure; 4) Availability of comprehensive data; 5) Publication in English. The exclusion criteria were as follows: 1) Exclusion of animal experiments, reviews, and other types of studies; 2) Absence of a healthy control group; 3) Incomplete data; 4) Unextractable or unconvertible data; 5) Low quality, indicated by an Agency for Healthcare Research and Quality(AHRQ) score of 5 or lower,or a Newcastle-Ottawa Scale (NOS) score less than 3. The subjects included in the study included adults and children, and the diagnostic criteria for obesity were used at different ages. In this study, obesity was defined as having a body mass index(BMI) of 25 kg/m2 or higher in adults and being above the 85th percentile of BMI for age in children. We used obesity as an outcome measure for meta-analysis using RevMan, version 5.3. A meta-analysis was conducted on a total of 17 clinical studies, which involved 5329 patients with epilepsy and 480837 healthy controls. These studies were selected from a pool of 1497 articles obtained from four electronic databases mentioned earlier. Duplicate studies were removed based on the search strategies employed. No significant heterogeneity was observed in the outcome measure of obesity in epileptic patients compared with healthy controls(p = 0.01,I2 = 49%). Therefore, a fixed effects model was utilized in this study. The findings revealed a significant difference in obesity prevalence between patients with epilepsy and healthy controls(OR = 1.28, 95%CI: 1.20-1.38, p<0.01). The results of this meta-analysis indicate that epilepsy patients are more prone to obesity than healthy people, so we need to pay attention to the problem of post-epilepsy obesity clinically. Currently, there is a scarcity of largescale prospective studies. Additional clinical investigations are warranted to delve deeper into whether obesity is a comorbidity of epilepsy and whether obesity can potentially trigger epilepsy.

The association of age, gender, and physical inactivity with obesity

Obesity is a growing global health concern, including in Indonesia. This study aimed to determine the relationship between obesity and age, sex, and physical inactivity. This study used secondary data from Puskesmas Johar Baru in Central Jakarta in 2021. The total initial data were 102,647 patients, the data eligible for sampling was 72,680 patients, and 22, 297 (24 %) patients were obese. Data processing was performed using SPSS 25 software, bivariate analysis (chi-square test), and multivariate analysis (multiple logistic regression with the enter method), at a significance level of 0.05. The Chi-Square test showed that age, sex, and lack of physical activity had a significant relationship with ob with (p ≤ 0.05). In multivariate analysis, the gender variable had the highest OR value of 12.925, where the majority of respondents who suffered from obesity were female, so they had a greater risk of suffering from obesity. These findings underscore the importance of sex variables in the context of obesity in Puskesmas Johar Baru and highlight the differences in obesity prevalence by age and sex. Middle-aged adults, especially women, are more prone to obesity, which is supported by a lack of physical activity.

Abstract 12637: Sociodemographic Measures and Disparities in Obesity in US Rural vs Nonrural Counties, 2004-2019

Introduction: US rural, compared with nonrural, counties have a higher burden of CVD. Obesity is a major CVD risk factor with 40% prevalence in adults. However, the rural-nonrural disparities in obesity are incompletely described. We aimed to evaluate differences in prevalence of obesity by county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined obesity (body mass index ≥30 kg/m 2 ) using self-reported weight and height. Rurality was based on six levels from large central metro (most urban) to noncore (most rural). We compared prevalence by rurality level using weighted least squares regression, adjusting for 13 county-level sociodemographic measures, and reported prevalence ratios (95% CI). Results: Counties with increasing rurality reflected older populations, as indicated by higher median age (from 35.6 years to 42.4 years). Also, increasing rurality was associated with differences in income and proportion uninsured. In age and sex ratio adjusted analysis (model 1), obesity prevalence was 1.05- to 1.06-fold higher in medium metro, small metro, and micropolitan counties compared with large central metro counties (global test, P &lt;.001). With further adjustment for sociodemographic measures (model 2; model 1 covariates plus race, state, income, proportion uninsured) these relative differences were preserved but attenuated (each 1.03-fold higher), while a greater difference was revealed for large fringe metro (1.06-fold higher) counties. In both models, differences in obesity prevalence between noncore and large central metro counties were minimal. (Table) Conclusion: Counties with intermediate rurality were associated with higher obesity prevalence. The association was partly accounted for by nonmodifiable and modifiable sociodemographic measures, which may be targeted to reduce rural-nonrural disparities in obesity.

Associations between traditional Chinese medicine body constitution and obesity risk among US adults.

Traditional Chinese medicine (TCM) body constitution (BC), primarily determined by physiological and clinical characteristics, is an important process for clinical diagnosis and treatment and play a critical role in precision medicine in TCM. The purpose of the study was to explore whether the distributions of BC types differed by obesity status. We conducted a study to evaluate BC type in US population during 2012-2016. A total of 191 White participants from Personalized Prevention of Colorectal Cancer Trial (PPCCT) completed a self-administered Traditional Chinese Medicine Questionnaire (TCMQ, English version). In this study, we further compared the distribution of major types of TCM BC in the PPCCT to those Chinese populations stratified by obesity status. We found the Blood-stasis frequency was higher in US White adults, 22.6% for individuals with BMI <30 and 11.2% for obese individuals, compared to 1.4% and 1.8%, respectively, in Chinese populations. We also found the percentages Inherited-special and Qi-stagnation were higher in US White adults than those in Chinese populations regardless of obesity status. However, the proportions of Yang-deficiency were higher in Chinese populations than those in our study conducted in US White adults regardless of obesity status. These new findings indicate the difference in distribution of BC types we observed between US and Chinese populations cannot be explained by the differences in prevalence of obesity. Further studies are needed to confirm our findings and understand the potential mechanism including genetic background and/or environmental factors.

Prevalence and risk factors of diabetes mellitus and hypertension in North East Tunisia calling for efficient and effective actions

Diabetes and hypertension are a serious public health problem worldwide. In the last decades, prevalence of these two metabolic diseases has dramatically increased in the Middle East and North Africa region, especially in Tunisia. This study aimed to determine the prevalence of type 2 diabetes (T2D) and High Blood Pressure (HBP) in Zaghouan, a North-East region of Tunisia. To this end, an exploratory study with stratified random sampling of 420 participants has been carried out. Various data were collected. Blood samples and urine were drawn for biochemical assay. Then, all data were analyzed using the statistical R software. Results showed an alarming situation with an inter-regional difference in prevalence of obesity (50.0%, CI 95.0%), HBP (39.0%, CI 95.0%) and T2D (32.0%, CI 95.0%). This study allowed the discovery of 24, 17 and 2 new cases of T2D, HBP and T2D&HBP respectively. The association of some socio-economic factors and biochemical parameters with these chronic diseases has been highlighted. To conclude, the health situation in the governorate of Zaghouan requires urgent interventions to better manage the growing epidemic of non-communicable diseases (NCD) in the region. This study demonstrated the importance of engaging health policy makers in road mapping and implementing national NCD prevention programs.

Prevalence of Long COVID Symptoms Related to SARS-CoV-2 Strains.

Few studies have assessed the differences of patterns of Long COVID (L-COVID) with regards to the pathogenetic SARS-CoV-2 strains. To investigate the relationship between demographic and clinical characteristics of acute phase of infection and the persistence of L-COVID symptoms and clinical presentation across different SARS-CoV-2 strains. In this observational-multicenter study we recorded all demographic and clinical characteristics, severity of infection, presence/persistence of symptoms of fatigue, dyspnoea and altered quality of life (QoL) at baseline and after 6 months, in a sample of Italian patients from Liguria between March 2020 and March 2022. 308 patients (mean age 63.2 years; 55.5% men) with previous COVID were enrolled. Obese patients were 21.2% with a significant difference in obesity prevalence across the second and third wave (p = 0.012). Treatment strategies differed between waves (p < 0.001): more patients required invasive mechanical ventilation in the first wave, more patients were treated with high-flow nasal cannula/non-invasive ventilation in the in the second and more patients were treated with oxygen-therapy in the fourth wave. At baseline, a high proportion of patients were symptomatic (dyspnoea and fatigue), with impairment in some QoL indicators. A higher prevalence of patients with pain, were seen in the first wave compared to later infections (p = 0.01). At follow-up, we observed improvement of dyspnoea, fatigue and some dimensions of QoL scale evaluation such as mobility, usual activities, pain evaluations; instead there was no improvement in remaining QoL scale indicators (usual care and anxiety-depression). There were no significant differences in the prevalence of the most frequent L-COVID symptoms, except for QoL pain domain that was especially associated with classical variant. Our results show substantial impact on social and professional life and usual care activities. These findings highlight the importance of multidisciplinary post COVID follow-up care including mental health support and rehabilitation program.

School entrance examinations as a small-scale data source for health monitoring of children using obesity as an example.

In the scope of the nationwide obligatory school entrance examinations (SEE), a standardised assessment of the preparedness for school of preschool children takes place in the federal states of Germany. For this purpose, height and weight of the children are determined. These data are available in aggregated form at county level, but are not yet being regularly compiled and processed at national level for use in policy and research. In a pilot project, the indexing and merging of SEE data from 2015-2019 was tested in collaboration with six federal states. This was done using obesity prevalence at the time of the school entrance examination. In addition, prevalences were linked to small-scale indicators on settlement structure and sociodemographics from public databases, differences in obesity prevalence at county level were identified, and correlations to regional influencing factors were visualised. It was feasible to merge SEE data from the federal states with little effort. The majority of the selected indicators were freely available in public databases. In an interactive, easily comprehensible and user-friendly Tableau dashboard for visualising the SEE data, it can be seen that obesity prevalences differ significantly between counties that are similar in terms of settlement structure or sociodemographics. Providing federal state SEE data and linking them to small-scale indicators enables region-based analyses and cross-state comparisons of similar counties and provides a data basis for continuous monitoring of the prevalence of obesity in early childhood.

The Burden of Obesity in Type 1 Diabetic Subjects: A Sex-specific Analysis From the AMD Annals Initiative.

Obesity is a growing emergency in type 1 diabetes (T1D). Sex differences in obesity prevalence and its clinical consequences in adult T1D subjects have been poorly investigated. The aim of this study was to investigate the prevalence of obesity and severe obesity, clinical correlates, and potential sex differences in a large cohort of T1D subjects participating to the AMD (Associazione Medici Diabetologi) Annals Initiative in Italy. The prevalence of obesity [body mass index(BMI) ≥30 kg/m2] and severe obesity (BMI ≥ 35 kg/m2) according to sex and age, as well as obesity-associated clinical variables, long-term diabetes complications, pharmacological treatment, process indicators and outcomes, and overall quality of care (Q-score) were evaluated in 37 436 T1D subjects (45.3% women) attending 282 Italian diabetes clinics during 2019. Overall, the prevalence of obesity was similar in the 2 sexes (13.0% in men and 13.9% in women; mean age 50 years), and it increased with age, affecting 1 out of 6 subjects ages >65 years. Only severe obesity (BMI >35 kg/m2) was more prevalent among women, who showed a 45% higher risk of severe obesity, compared with men at multivariate analysis. Cardiovascular disease risk factors (lipid profile, glucose, and blood pressure control), and the overall quality of diabetes care were worse in obese subjects, with no major sex-related differences. Also, micro- and macrovascular complications were more frequent among obese than nonobese T1D men and women. Obesity is a frequent finding in T1D adult subjects, and it is associated with a higher burden of cardiovascular disease risk factors, micro- and macrovascular complications, and a lower quality of care, with no major sex differences. T1D women are at higher risk of severe obesity.

An urban versus rural comparison of obesity between youth with and without autism spectrum disorder.

To examine obesity prevalence in youth with autism spectrum disorder (ASD) and neurotypical (NT) youth living in rural and urban areas. Data is from the 2019 National Survey of Children's Health, a national dataset that collected information on child health and well-being. Overweight/obesity status was based on parent/caregiver report of child height and weight. Urban and rural status was determined by whether children lived in/near a city. Chi-square tests were conducted to examine differences in obesity prevalence in youth with ASD and NT youth living in rural and urban areas. Overall, 43.9% of youth with ASD were overweight/obese compared to 30.6% of NT youth (p < 0.001). There was a significantly higher proportion of NT youth living in rural areas (36.44%) who were overweight/obese compared to urban NT youth (30.35%, p=0.002). There were no significant differences in the proportion of overweight/obese youth with ASD living in rural areas (44.02%) compared to urban areas (44.44%, p=0.96). Urban residence reduced the odds of overweight/obese compared to rural residence among NT youth (aOR=0.77, 95%CI=0.66-0.90) but urban/rural residence was not a significant factor in models for ASD youth (aOR=1.25, 95%CI=0.63-2.48). In contrast to NT youth, there were no differences in levels of overweight/obesity in youth with ASD living in rural areas compared to urban areas. Further research on how sociodemographic factors and geographic location affect obesity in youth with ASD is warranted.

Trends and Factors Associated with Obesity Prevalence in Rural Australian Adults-Comparative Analysis of the Crossroads Studies in Victoria over 15 Years.

This study examined the changes in the prevalence of obesity and associated lifestyle factors using data from repeated cross-sectional, self-reported surveys (Crossroads I: 2001-2003 and Crossroads II: 2016-2018, studies) and clinic anthropometric measurements collected from regional and rural towns in the Goulburn Valley, Victoria. Given that past community studies have only focused categorically on dietary intake, or assessed caloric energy intake, we examined the difference in broad dietary practices at two different times. Clinical assessments from randomly selected household participants aged ≥18 years were analyzed. Differences in obesity prevalence were calculated for each individual variable. Logistic regression was used to determine the odds ratios (95% confidence intervals (CI)) with and without adjustment for key lifestyle factors. There were 5258 participants in Crossroads I and 2649 in Crossroads II surveys. Obesity prevalence increased from 28.2% to 30.8% over 15 years, more among those who ate fried food, but decreased significantly among rural dwellers (31.7: 27.0, 36.8% versus 25.1: 22.9, 27.5%) and those who had adequate fruit intake (28.5: 25.0, 32.3% to 23.9: 21.8, 26.2%). Obesity was associated with older age (≥35 years), use of fat-based spreads for bread (adjusted odds ratio, aOR:1.26: 1.07, 1.48) and physical inactivity. The increase in obesity prevalence especially in the rural towns, was associated with unhealthy dietary behaviour which persisted over 15 years. Understanding and addressing the upstream determinants of dietary intake and choices would assist in the development of future health promotion Programs.

The Characteristics of Obesogenic Environment and Their Association With BMI Among the Elderly Population in Rural South Korea

ObjectivesThe purpose of this research is to analyze the environmental factors affecting obesity in the elderly in rural South Korea. MethodsA survey and Geographical Information System(GIS) data analysis were conducted on 300 elderly people aged 65 and over living in four rural Counties in two Provinces. Formative research was conducted to develop the survey questionnaire. The perception of food environment was measured with five questions, mainly on availability, affordability, and accessibility. The perception of walking environment was measured with eleven questions on esthetics, safety, traffic and community. Social cohesion and the sense of community were asked for measuring social environment. T-test, chi-squared test and stepwise backward regression modeling were performed using Stata/SE 17.0 version. ResultsWe found that the participants from Gangwon-do had higher obesity prevalence than the ones from Jeollanam-do. There was no significant difference in the physical environment between the two Provinces using GIS data analysis; however, there were differences in the perception of the physical environment and the social environment. It was confirmed that the perceptions of the physical environment and social environment were more positive among participants from Jeollanam-do, where obesity prevalence was lower. In stratified stepwise regression models, better perception of food environment was associated with lower BMI in both Provinces. ConclusionsIn this study, various obesity-related environmental factors were explored in rural South Korea. The differences in obesity prevalence in four rural Counties may be explained by different perception on food, walking, and social environments, despite of the similar background factors, such as age distribution, sex ratio, educational level, and financial environment. These findings confirmed that even areas with similar physical settings have different environmental characteristics, which should be considered in developing effective obesity prevention programs. Future research should focus on the development of environmental evaluation tools and the effective measures to enhance the perception of social environment in rural South Korea. Funding SourcesMinistry of Health and Welfare, South Korea NRF of Korea (No. 2021R1A6A1A03044501, No. 2021R1I1A306024811).

Prevalence of obesity and elevated body mass index along a progression of rurality: A cross-sectional study - The Canadian Longitudinal Study on Aging.

Obesity is an important public health concern, and large studies of rural-urban differences in prevalence of obesity are lacking. Our purpose is to compare body mass index (BMI) and obesity in Canada using an expanded definition of rurality. A cross-sectional analysis of self-reported BMI across diverse communities of Canadians aged 45-85 years was conducted using data from the Canadian Longitudinal Study on Aging (CLSA), a national sample representative of community-dwelling residents. Rurality was identified in the CLSA based on residential postal codes, which were divided into 4 categories: urban, peri-urban, mixed and rural. Logistic regression models were constructed to calculate adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs) between obesity (BMI ≥30 kg/m2 from self-reported weight and height) and rurality, adjusting for age, sex, province, marital status, number of residents in household and household income. Twenty-one thousand one hundred and twenty-six Canadian residents aged 45-85 years, surveyed during 2010-2015, were included. 26.8% were obese. Obesity was less prevalent amongst urban (25.2%) than rural (30.3%, P < 0.0001), mixed (28.7%, P < 0.0001) or peri-urban communities (28.1%, P < 0.0001). When compared to urban areas, the aOR (95% CI) for obesity was 1.09 (1.00-1.20) in rural regions and 1.20 (1.08-1.35) in peri-urban settings. In areas of mixed urban and rural residence, the aOR was 1.12 (0.99-1.27). One in four Canadian adults were obese. Living in a non-urban setting is an independent risk factor for obesity. Rural-urban health disparities could underlie rural-urban differences, but further research is needed.

Do outcomes from a behavioral weight loss intervention differ in Alabama versus Colorado?

BackgroundThere are well‐established regional differences in obesity prevalence in the United States but relatively little is known about why or whether success in weight loss differs regionally.ObjectiveThe objective of this study was to determine whether changes in body weight, engagement in physical activity (PA), and psychosocial factors differed in Alabama (AL) versus Colorado (CO) in response to a 16‐week behavioral weight loss program.DesignThis is an ancillary study to a weight loss intervention being conducted simultaneously in AL and CO with identical intervention content and delivery in 70 participants (n = 31 AL and n = 39 CO). Body weight, objective (accelerometry) PA, and responses to psychosocial questionnaires (reward‐based eating, stress, social support) were collected at baseline and at Week 16.ResultsThere were no differences in percent weight loss between states (AL: 10.98%; CO: 11.675%, p = 0.70), and weights at Week 16 were not different for participants in AL and CO (AL: 101.54 ± 4.39 kg, CO: 100.42 ± 3.67 kg, p = 0.84). Accelerometry‐derived step count, stepping time, and activity score were all greater at Week 16 for participants in AL compared to participants in CO. Hedonic eating scores were more favorable for participants in AL at baseline (AL: 24.08 ± 2.42; CO: 34.99 ± 2.12, p = 0.0023) and at Week 16 (AL: 18.62 ± 2.70; CO: 29.11 ± 2.19, p = 0.0023). Finally, participants in AL presented more favorable social support scores at Week 16 compared to participants in CO.ConclusionsWeight loss did not differ between states, suggesting that factors contributing to higher obesity rates in some regions of the United States may not be barriers to weight loss. Further, participants in AL experienced greater improvements in some factors associated with weight maintenance, indicating the need to study regional differences in weight loss maintenance. National Clinical Trial 03832933.

Sex/Gender Differences in Obesity Prevalence, Comorbidities, and Treatment.

Obesity is a heterogeneous condition, yet sex/gender is rarely considered in the prevention or clinical care of this disease. This review examined and evaluated recent literature regarding the influence of sex and gender on obesity prevalence, comorbidities, and treatment in adults. Obesity is more prevalent in women than men in most countries, but in some countries and population subgroups, this gap is more pronounced. Several obesity-related comorbidities, including type 2 diabetes and hypertension, demonstrate sex-specific pathways. Women, compared to men, are more likely to be diagnosed with obesity and seek and obtain all types of obesity treatment including behavioral, pharmacological, and bariatric surgery. Men tend to have greater absolute weight loss, but this difference is attenuated once accounting for baseline weight. Obesity is a multifactorial condition with complex interactions among sex/gender, sociocultural, environmental, and physiological factors. More sex/gender research is needed to investigate mechanisms underlying sex/gender differences in prevalence, comorbidities, and treatment, identify ways to increase men's interest and participation in obesity treatment, and examine differences in obesity prevalence and treatments for transgender and gender non-conforming individuals.

Gender Difference in Obesity Prevalence Among General Population of Lahore, Pakistan

Obesity has become a pandemic problem and it is infamous for causing both physical and psychological disorders. Even though obesity is influenced by many factors which include fast food, menopause, socioeconomic status, and genetics, however, gender has paramount importance as a factor that affects obesity when we do not consider other factors. Thus, our current research study was aimed to evaluate the prevalence of obesity and influence of gender on it. Our current cross-sectional descriptive study was finished in almost 6 months from August 2020 to January 2021 among general population of Pak Aran Society Lahore, Punjab, Pakistan. 183 people were recruited in study in accordance to set criteria of exclusion and inclusion. Only those people whose age was from above 20 years to below 61 years, and who had no physical or mental illness and were willing to participate were enrolled in study. Data collection was done through one self-structured proforma and WHO Classification of Weight Status established on BMI. SPSS version 25 was applied to perform data analysis. Various statistical tests like Chi-square test, Independent Sample t-test, and One-Way ANOVA test were used to evaluate the study variables and their relationship. Overall, our current study indicates high prevalence (19.10%) of obesity among all participants, nevertheless, mean of BMI value was higher among women (26.26 SD of ±3.70) as compared to men (25.06 with SD of ±4.27) which shows that women had higher risk of development of obesity than men. However, relationship between obesity and gender was statistically insignificant (p=0.262). The difference of BMI mean value between women and men was significant statistically (p=0.046). Significance difference was also observed in BMI mean value of across four grades of weight status (p=0.0001). In summary, in general, prevalence of obesity is higher among women than among men which means female gender raises the obesity incidence among women.

Do Outcomes From a Behavioral Weight Loss Intervention Differ in Alabama vs Colorado

ObjectivesThere are well-established regional differences in obesity prevalence in the U.S., but relatively little is known about whether these differences impact efforts for weight loss. The objective of the study was to determine whether changes in body weight, engagement in physical activity (PA), and psychosocial factors differed in Colorado (CO) vs Alabama (AL) in response to a 16-week standardized behavioral weight management program. We hypothesized that weight loss would be greater in Colorado due to a more favorable physical and social environment. MethodsThis is an ancillary study to a weight loss intervention being conducted simultaneously in AL and CO with identical intervention content and delivery. Study participants (n = 70, 39 CO, 31 AL) were randomized to either a high protein (HP) or normal protein (NP) diet for 16 weeks and attended weekly group classes led by a trained coach targeting diet, mindset, and physical activity. Body weight, objective (accelerometry) and self-reported (International Physical Activity Questionnaire) PA, and responses to psychosocial questionnaires were collected at baseline and week 16. Psychosocial constructs included executive function, hedonic eating, stress, and social support. ResultsBoth states lost a significant amount of weight (CO 13.2 ± 4.9 kg P = 0.0067; AL 12.5 ± 5.6 kg P = 0.0262) with no differences between states (P = 0.9315). Both states improved in all PA outcomes over time, with AL increasing significantly more in objective PA measures when compared to CO. AL had more favorable scores for hedonic eating at baseline (23.2 ± 2.4 vs 32.5 ± 1.8, P = 0.0017), which persisted to week 16 (19.0 ± 2.7 vs 29.7 ± 2.2, P = 0.0021). Finally, AL improved in several social support factors while CO did not. ConclusionsWhile weight loss did not differ between states, AL experienced greater improvements in some factors known to improve long-term weight loss maintenance. Results from this study provide a strong rationale for investigating potential regional differences in the maintenance of lost weight that may not be apparent during the active weight loss phase of interventions. Future research in this area will require effective methods for tracking participants beyond the conclusion of most clinical trials. Funding SourcesThe parent clinical trial is supported by The Beef Checkoff.

Increasing trends in obesity prevalence from 2013 to 2019 and associated factors in Brazil.

To investigate the variation of anthropometric indicators from 2013 to 2019 and the factors associated with obesity in Brazil, using information from the National Health Survey. Cross-sectional study with cluster sampling and simple random sampling in the three stages. Measurements of weight and height among participants in 2013 (n=59,592) and in 2019 (n=6,672) were used. Differences in obesity prevalence were tested by Student's t test for independent samples. To identify the sociodemographic factors and health problems associated with obesity, we used Poisson regression models with robust variance and crude and age-adjusted prevalence ratios to test the associations. From 2013 to 2019, prevalence of obesity increased significantly, from 20.8 to 25.9%. Among men, the greatest increases were found in the 40-59 age group (9.1%) and in the median income category (8.3%). Among women, the greatest rises were found among those with low education (8.7%) and non-white ones (6.0%). For both males and females, factors associated with obesity were age, to live with a partner, level of instruction directly associated among men, and inversely associated among women. In 2019, for males, the crude and adjusted prevalence ratios were significant for high cholesterol, high blood pressure and at least one chronic non-communicable disease and, for females, for poor self-rated health, high blood pressure, diabetes, and at least one chronic non-communicable. It is necessary to implement intersectoral policies to promote changes in eating habits and encourage the practice of physical activity, taking into account economic, social, cultural, and environmental aspects.

ACCURACY AND PREDICTIVE CAPABILITY OF BODY MASS INDEX IN EVALUATION OF OBESITY AND BODY FATNESS LEVEL IN POLICE OFFICERS

This study evaluated the accuracy and predictive value of body mass index (BMI) in evaluation of obesity and body fatness. Data on BMI and percent body fat (PBF) were collected on 953 male police officers who were allocated into age groups: 20-29 years, 30-39 years, and 40-49 years. BMI &gt; 30.0 kg/m2 and PBF &gt; 25% were classified as obese, and those with lower values were classified as non-obese. Chi-square was used to evaluate the accuracy in classification in obese and non-obese when officers’ BMI was matched to PBF. Pearson’s correlation and linear regression analyses determined the prediction value of BMI. Chi-square revealed significant difference in obesity prevalence when evaluated by BMI and PBF, with classification accuracy of 44.5%-71.8%, depending on age. BMI had moderate prediction value of body fatness. If the assessment of PBF is not attainable, BMI needs to be used carefully as it is likely to underestimate obesity among police officers.