Accuracy of Body Mass Index and Obesity Status in Police Trainees

The first aim of this study was to compare body mass index (BMI) (indirect method) classification with the body fat percent (PBF) (direct method) and to determine how BMI classifies subjects with different levels of skeletal muscle mass percent (PSMM). The second aim was to determine the prevalence of overweight and obesity status among police trainees (PTs). A total of 103 male PTs participated in this research: age = 21.46 ± 0.64 years, body mass (BM) = 75.97 ± 8.10 kg, body height (BH) = 174.07 ± 6.31 cm, BMI = 25.05 ± 2.12 kg/m2. The InBody 370 multichannel bioelectrical impedance analysis (BIA) measured body composition. Study results indicated that muscular PTs could be misclassified as overweight and that PBF identified more subjects as obese. Namely, three PTs were obese according to BMI, while 13 were obese according to PBF. The information provided by this research could be used to help professionals understand the importance of measuring body composition, and the inaccuracies in BMI classification. In conclusion, whenever possible PSMM and PBF should replace the utilization of BMI to screen overweight and obesity in PTs. Agencies may think of using BIA as non-invasive, quick and inexpensive measurement tool.

Breastfeeding, rapid growth in the first year of life and excess weight at age 2 years: the 3D Cohort Study

Objective: To assess relationships between breastfeeding, rapid growth in the first year of life, and overweight/obesity status at age 2. Design: As part of an observational, longitudinal study beginning in early pregnancy, multivariable logistic regressions were used to assess associations between breastfeeding duration (total and exclusive) and rapid weight gain (RWG) between birth and age 1, and to determine predictors of overweight/obesity status at age 2. Setting: 9 hospitals located in the province of Quebec, Canada. Participants: 1599 term infants participating in the 3D Cohort Study Results: Children having RWG in the first year and those having excess weight at age 2 accounted for 28% and <10%, respectively. In multivariable models, children breastfed <6 months and from 6 months to <1 year were, respectively, 2.5 times (OR: 2.45; 95% CI: 1.76-3.41) and 1.8 times (OR: 1.78; 1.29-2.45) more likely to show RWG up to age 1 compared to children breastfed ≥ 1 year. Children exclusively breastfed < 3 months had significantly greater odds of RWG in the first year (OR: 1.94; 1.25-3.04) compared to children exclusively breastfed for ≥6 months. Associations between breastfeeding duration (total or exclusive) and excess weight at age 2 were not detected. RWG in the first year was found to be the main predictor of excess weight at age 2 (OR: 6.98; 4.35-11.47). Conclusions: The potential beneficial effects of breastfeeding on rate of growth in the first year of life suggest that interventions promoting breastfeeding are relevant for obesity prevention early in life.

Post-COVID-19 Fatigue: Demographic Distribution and Relation to Chronic Illnesses

Objective: This retrospective study examined the relationship between post-COVID (PC) fatigue and the presence of chronic illnesses. Materials and Methods: Electronic health records of 122 Parkview PC Clinic patients were reviewed for demographics (age, gender, race), chronic illnesses (diabetes, hypothyroidism, hypertension, congestive heart failure, anemia, cancer, orthostatic hypotension), obesity (weight, BMI), complaint of fatigue, and activities affected by fatigue. Data was analyzed using Chi-square tests for categorical variables (or Fisher’s exact test for small cell sizes) and t-tests for continuous variables. Open thematic coding of activities most affected by PC fatigue was performed. Results: 95 out of 122 patients (77.9%) were found to have PC fatigue. Except for measures related to obesity, none of the chronic illnesses assessed were correlated with the presence of fatigue. The weight of PC fatigue patients was significantly higher than those without fatigue (p=0.04). Examination of BMI and obesity status also indicated significantly higher BMI (p=0.01) and levels of obesity (p= 0.004) in PC fatigue groups compared to the PC non-fatigue groups. No significant differences were observed between the groups in either PT test. Examination of patient described activities effected by PC fatigue found work (30.4%), daily stamina (28.6%) and ability to exercise (16.7%) most impacted by PC fatigue. Other activities affected by PC fatigue included housework (5.6%), hobbies (3.7%), shopping (1.9%) and driving (1.9%). Conclusion: 77.9% of our PC patients showed symptoms of fatigue. This is comparable with existing literature. None of the demographic variables and PT tests, or most of the chronic health conditions investigated, were correlated with the presence of fatigue in PC patients. In contrast, measures associated with obesity, including high BMI and weight, were significantly associated with increased PC fatigue presentation. Open thematic coding of qualitative variables indicated work, daily stamina, and exercise were most affected in PC patients.

The Association Between Veteran Status and Obesity Differs Across Race/Ethnicity

Purpose This study aims to evaluate the interaction between veteran status and race/ethnicity on obesity status. Design Cross-sectional survey Setting The 2013–2017 National Health Interview Survey Sample A total of 151,765 adults (8.62% veterans and 91.38 nonveterans) with 69.30% identifying as White, 13.05% identifying as Hispanic, 12.57% identifying as Black, and 5.08% identifying as Asian Measures Obesity status (measured using self-reported body mass index), race/ethnicity, survey year, age, marital status, educational attainment, federal poverty level, health insurance, type of insurance, self-reported health status, and whether participant had a usual care source. Analysis Weighted logistic regression analysis Results In a fully adjusted model, there was no evidence that veterans overall had higher odds of obesity compared to nonveterans (adjusted odd ratio (aOR): 1.05, 95% CI: .99, 1.11). White veterans had lower odds of obesity compared to White nonveterans (OR: .93, 95% CI: .87, .98). Hispanic veterans had higher odds of obesity compared to Hispanic nonveterans (aOR: 1.53, 95% CI: 1.23, 1.90). There was no evidence of an association between veteran status and obesity status for Black and Asian adults. Conclusions Effectual prevention strategies are needed to decrease obesity risks among active and retired Hispanic veterans.

Assessing the Use of Prescription Drugs in Obese Respondents in the National Health and Nutrition Examination Survey

Introduction: Obesity is a common disease and a known risk factor for many other conditions such as hypertension, type 2 diabetes, and cancer. Treatment options for obesity include lifestyle changes, pharmacotherapy, and surgical interventions such as bariatric surgery. In this study, we examine the use of prescription drugs and dietary supplements by the individuals with obesity. Methods: We conducted a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) data 2003-2014. We used multivariate logistic regression to analyze the correlations of demographics and obesity status with the use of prescription drugs and dietary supplement use. We also built machine learning models to predict prescription drug and dietary supplement use using demographic data and obesity status. Results: Individuals with obesity are more likely to take cardiovascular agents (OR=1.265, 95% CI 1.222-1.311) and metabolic agents (OR=1.398, 95% CI 1.343-1.456) than individuals without obesity. The best performing prediction model for predicting prescription drugs had the accuracy of 74.5% and the AUROC of 0.817. Conclusions: This study can inform clinical practice and patient education of the use of prescription drugs and dietary supplements and their correlation with obesity.

Body Roundness Index Is a Superior Obesity Index in Predicting Diabetes Risk Among Hypertensive Patients: A Prospective Cohort Study in China

Objective: Individuals with both hypertension and diabetes have been confirmed to significantly increase the risk of cardiovascular disease morbidity and mortality compared with those with only hypertension or diabetes. This study aimed to evaluate the potential of different anthropometric indices for predicting diabetes risk among hypertensive patients.Methods: The study group consisted of 6,990 hypertensive adults without diabetes who were recruited in China. Demographic and clinical assessment, physical examinations, laboratory tests, and anthropometric measurements, including body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and novel indices (ABSI, AVI, BAI, BRI, CI, WWI, and WHHR), were performed at baseline and during the (median) 3-year follow-up. Cox regression analyses were conducted to estimate effects from these indices for the onset of diabetes. Receiver operator characteristic (ROC) analyses were conducted to assess the predictive capacities of the anthropometric indices and determine the optimal cut-points.Results: A total of 816 (11.7%) developed diabetes during our prospective study. Multivariate Cox regression analyses revealed weight, WC, WHR, WHtR, BAI, BRI, and WWI as the independent risk factor for diabetes among hypertensive patients, regardless of whether it was treated as a continuous or categorical variable (P &lt; 0.05). Further Cox analyses combining BMI and different central obesity indices showed that elevated WC, WHR, WHtR, AVI, BRI, CI, regardless of the general obesity status, were found to be each independently associated with increased diabetes risk (P &lt; 0.05). Dynamic increases of BRI &lt; 5.24 to BRI ≥ 5.24 were associated with increased risk (HR = 1.29; 95% CI, 1.02, 1.64), and its reversal was associated with reduced risk (HR = 1.56; 95% CI, 1.23, 1.98) compared with the others (HR = 1.95; 95% CI, 1.63, 2.32). ROC analysis indicated that the areas under the ROC curves (AUC) of the anthropometric indices ranged from 0.531 to 0.63, with BRI (cut-off value = 4.62) and WHtR having the largest area.Conclusions: Based on this novel study, BRI was the most superior predictor and independent determinant for diabetes onset among the hypertensive population. Hypertensive patients with BRI &gt; 4.62, regardless of general obesity status, were at high risk of diabetes. Thus, the prompt screening and diagnosis of diabetes should be carried out among these patients for timely integrated intervention.

The association between obesity and problematic smartphone use among school-age children and adolescents: a cross-sectional study in Shanghai

Background The study aimed to investigate the association between content-based problematic smartphone use and obesity in school-age children and adolescents, including variations in the association by educational stage and sex. Methods Two-stage non-probability sampling was used to recruit 8419 participants from nineteen primary schools, five middle schools, and thirteen high schools in Shanghai in December 2017. Obesity was identified by body mass index (BMI), which was obtained from the school physical examination record, while problematic smartphone use was measured by the Revised Problematic Smartphone Use Classification Scale as the independent variable. Results The rates of obesity varied with educational stages, while problematic smartphone use increased with educational stages. Male students reported higher obesity rates (37.1%vs19.4%, P < 0.001) and greater problematic smartphone use scores (25.65 ± 10.37 vs 22.88 ± 8.94, P < 0.001) than female students. Problematic smartphone use for entertainment (smartphone users addicted to entertainment games, music, videos, novels and other applications) was positively associated to obesity status for primary school [odds ratio (OR), 1.030; 95% confidence interval (95% CI), 1.005–1.057] and high school students (OR, 1.031; 95% CI, 1.004–1.059). For female students, problematic smartphone use for entertainment was positively associated with obesity status (OR, 1.046; 95% CI, 1.018–1.075). Conclusions Problematic smartphone use may be associated with obesity in children and adolescents. The association differed based on the educational stage and sex, and the difference possessed dimensional specificity.

Combination of ADASYN-N and Random Forest in Predicting of Obesity Status in Indonesia: A Case Study of Indonesian Basic Health Research 2013

Obesity is a pathological condition due to the accumulation of excessive fat needed for body functions. The risk factors for obesity are related to their obesity status. Various machine learning approaches are an alternative in predicting obesity status. However, in most cases, the available datasets are not sufficiently balanced in their data classes. The existence of data imbalances can cause the prediction results to be inaccurate. The purpose of this paper is to overcome the problem of data class imbalance and predict obesity status using the 2013 Indonesian Basic Health Research (RISKESDAS) data. Adaptive Synthetic Nominal (ADASYN-N) can be used to balance obesity status data. The balanced obesity status data is then predicted using one of the machine learning approaches, namely Random Forest. The results obtained show that through ADASYN-N with a balance level parameter of 1 (β = 100%) after synthetic data generation and Random Forest with a tree number of 200 and involving 7 variables as risk factors, giving the results of the classification of obesity status which is good. This can be seen from the AUC value of 84.41%.