Obesity, open access最新文献

{"title":"Validation of a White-light 3D Body Volume Scanner to Assess Body Composition.","authors":"Jose Medina-Inojosa,&nbsp;Virend Somers,&nbsp;Sarah Jenkins,&nbsp;Jennifer Zundel,&nbsp;Lynne Johnson,&nbsp;Chassidy Grimes,&nbsp;Francisco Lopez-Jimenez","doi":"10.16966/2380-5528.127","DOIUrl":"https://doi.org/10.16966/2380-5528.127","url":null,"abstract":"<p><strong>Introduction: </strong>Estimating body fat content has shown to be a better predictor of adiposity-related cardiovascular risk than the commonly used body mass index (BMI). The white-light 3D body volume index (BVI) scanner is a non-invasive device normally used in the clothing industry to assess body shapes and sizes. We assessed the hypothesis that volume obtained by BVI is comparable to the volume obtained by air displacement plethysmography (Bod-Pod) and thus capable of assessing body fat mass using the bi-compartmental principles of body composition.</p><p><strong>Methods: </strong>We compared BVI to Bod-pod, a validated bicompartmental method to assess body fat percent that uses pressure/volume relationships in isothermal conditions to estimate body volume. Volume is then used to calculate body density (BD) applying the formula density=Body Mass/Volume. Body fat mass percentage is then calculated using the Siri formula (4.95/BD - 4.50) × 100. Subjects were undergoing a wellness evaluation. Measurements from both devices were obtained the same day. A prediction model for total Bod-pod volume was developed using linear regression based on 80% of the observations (N=971), as follows: Predicted Bod-pod Volume (L)=9.498+0.805*(BVI volume, L)-0.0411*(Age, years)-3.295*(Male=0, Female=1)+0.0554*(BVI volume, L)*(Male=0, Female=1)+0.0282*(Age, years)*(Male=0, Female=1). Predictions for Bod-pod volume based on the estimated model were then calculated for the remaining 20% (N=243) and compared to the volume measured by the Bod-pod.</p><p><strong>Results: </strong>Mean age among the 971 individuals was 41.5 ± 12.9 years, 39.4% were men, weight 81.6 ± 20.9 kg, BMI was 27.8 ± 6.3kg/m². Average difference between volume measured by Bod-pod- predicted volume by BVI was 0.0 L, median: -0.4 L, IQR: -1.8 L to 1.5 L, R2=0.9845. Average difference between body fat measured-predicted was-1%, median: -2.7%, IQR: -13.2 to 9.9, R2=0.9236.</p><p><strong>Conclusion: </strong>Volume and BFM can be estimated by using volume measurements obtained by a white- light 3D body scanner and the prediction model developed in this study.</p>","PeriodicalId":91587,"journal":{"name":"Obesity, open access","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645052/pdf/nihms871798.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35532878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability of a 3D Body Scanner for Anthropometric Measurements of Central Obesity.","authors":"J. Medina-Inojosa, V. Somers, Taiwo N. Ngwa, Ling Hinshaw, F. Lopez‐Jimenez","doi":"10.16966/2380-5528.122","DOIUrl":"https://doi.org/10.16966/2380-5528.122","url":null,"abstract":"BACKGROUND\u0000Central obesity poses a significant risk for cardiovascular diseases, but the reproducibility of manual measurements of waist and hip circumferences has been questioned. An automated 3D body scanner that uses white light rays could potentially increase the reliability of these anthropometric measurements.\u0000\u0000\u0000METHODS\u0000We assessed the reproducibility of anthropometric measurements performed manually and using a 3D-scanner in 83 adult volunteers. Manual measures of WC and HC were obtained using unmarked, non-elastic ribbons in order to avoid observer and confirmation bias. The 3D-scanner was used to create body images and to obtain WC and HC measurements in an automated fashion.\u0000\u0000\u0000RESULTS\u0000The inter-observer mean differences were 3.9 ± 2.4 cm for WC; 2.7 ± 2.4 cm, for HC, and 0.006 ± 0.02 cm for WHR. Intra-observer mean differences for manual measurements were 3.1 ± 1.9 cm for WC, 1.8 ± 2.2 cm for HC and 0.11 ± 0.1 cm for WHR. The 3D-scanner variability for WC was 1.3 ± 0.9 cm, for HC was 0.8 ± 0.1 and 0.005 ± 0.01 cm for WHR. All means were significantly different (p<0.05) between manual and automated methods.\u0000\u0000\u0000CONCLUSION\u0000The 3D-scanner is a more reliable and reproducible method for measuring WC, HC and WHR to detect central obesity.","PeriodicalId":91587,"journal":{"name":"Obesity, open access","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91294652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Health Equity Problem for Low Income Children: Diet Flexibility Requires Physician Authorization.","authors":"Jodi D Stookey","doi":"10.16966/2380-5528.105","DOIUrl":"https://doi.org/10.16966/2380-5528.105","url":null,"abstract":"<p><p>USDA programs, such as the Child and Adult Care Food Program (CACFP), School Breakfast Program (SBP), and/or National School Lunch Program (NSLP), enable child care centers and schools to provide free and reduced price meals, daily, to millions of low income children. Despite intention to equalize opportunity for every child to have a healthy diet, USDA program rules may be contributing to child obesity disparities and health inequity. USDA program rules require child care centers and schools to provide meals that include a specified number of servings of particular types of foods and beverages. The rules are designed for the average, healthy weight child to maintain weight and growth. They are not designed for the underweight child to gain weight, obese child to normalize weight, or pre-diabetic child to avoid incident diabetes. The rules allow for only one meal pattern and volume, as opposed to a flexible spectrum of meal patterns and portion sizes. Parents of children who participate in the CACFP, SBP, and/or NSLP do not have control over the amount or composition of the subsidized meals. Parents of overweight, obese, or diabetic children who participate in the subsidized meal programs can request dietary change, special meals or accommodations to address their child's health status, but child care providers and schools are not required to comply with the request unless a licensed physician signs a \"Medical statement to request special meals and/or accommodations\". Although physicians are the only group authorized to change the foods, beverages, and portion sizes served daily to low income children, they are not doing so. Over the past three years, despite an overweight and obesity prevalence of 30% in San Francisco child care centers serving low income children, zero medical statements were filed to request special meals or accommodations to alter daily meals in order to prevent obesity, treat obesity, or prevent postprandial hyperglycemia. Low income children have systematically less dietary flexibility than higher income children, because of reliance on free or reduced-price meals, federal food program policy, and lack of awareness that only physicians have authority to alter the composition of subsidized meals in child care centers and schools. Compared with higher income children, low income children do not have equal opportunity to change their daily dietary intake to balance energy requirements.</p>","PeriodicalId":91587,"journal":{"name":"Obesity, open access","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4844226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34438338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychological Health and Overweight and Obesity Among High Stressed Work Environments.","authors":"Pouran D Faghri,&nbsp;Christina Mignano,&nbsp;Tania B Huedo-Medina,&nbsp;Martin Cherniack","doi":"10.16966/2380-5528.101","DOIUrl":"https://doi.org/10.16966/2380-5528.101","url":null,"abstract":"<p><p>Correctional employees are recognized to underreport stress and stress symptoms and are known to have a culture that discourages appearing \"weak\" and seeking psychiatric help. This study assesses underreporting of stress and emotions. Additionally, it evaluates the relationships between stress and emotions on health behaviors. Correctional employees (n=317) completed physical assessments to measure body mass index (BMI), and surveys to assess perceived stress, emotions, and health behavior (diet, exercise, and sleep quality). Stress and emotion survey items were evaluated for under-reporting via skewness, kurtosis, and visual assessment of histograms. Structural equation modeling evaluated relationships between stress/emotion and health behaviors. Responses to stress and negatively worded emotions were non-normally distributed whereas responses to positively-worded emotions were normally distributed. Emotion predicted diet, exercise, and sleep quality whereas stress predicted only sleep quality. As stress was a poor predictor of health behaviors and responses to stress and negatively worded emotions were non-normally distributed it may suggests correctional employees are under-reporting stress and negative emotions.</p>","PeriodicalId":91587,"journal":{"name":"Obesity, open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990460/pdf/nihms-740666.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34326169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}