213,822 research outputs found

    BMI-for-age graphs with severe obesity percentile curves: Tools for plotting cross-sectional and longitudinal youth BMI data

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    Abstract Background Severe obesity is an important and distinct weight status classification that is associated with disease risk and is increasing in prevalence among youth. The ability to graphically present population weight status data, ranging from underweight through severe obesity class 3, is novel and applicable to epidemiologic research, intervention studies, case reports, and clinical care. Methods The aim was to create body mass index (BMI) graphing tools to generate sex-specific BMI-for-age graphs that include severe obesity percentile curves. We used the Centers for Disease Control and Prevention youth reference data sets and weight status criteria to generate the percentile curves. The statistical software environments SAS and R were used to create two different graphing options. Results This article provides graphing tools for creating sex-specific BMI-for-age graphs for males and females ages 2 to <20 years. The novel aspects of these graphing tools are an expanded BMI range to accommodate BMI values ˃35 kg/m2, inclusion of percentile curves for severe obesity classes 2 and 3, the ability to plot individual data for thousands of children and adolescents on a single graph, and the ability to generate cross-sectional and longitudinal graphs. Conclusions These new BMI graphing tools will enable investigators, public health professionals, and clinicians to view and present youth weight status data in novel and meaningful ways

    Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media

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    A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming" and other forms of "sizeism" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.Comment: This is a preprint of a short paper accepted at ICWSM'17. Please cite that version instea

    Social Vulnerability, Diabetes, and Obesity in Georgia

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    This project examines factors of health geography and population geography by examining the spatial correlation between diabetes and obesity prevalence among the population of Georgia. Diabetes and obesity are closely linked together, however, they are still far apart in various aspects which can be from personal to environmental impacts like geographical locations. This research attempts to investigate diabetes and obesity prevalence by examining the SVI of given counties of a state. This project uses data from the Atlanta-based Centers for Disease Control and Prevention (CDC) and Opendata (which can be found on ESRI). Future studies can examine these health factors through spatial analysis in ArcPro using geoprocessing tools. The research hopes to aid in future applied research using geospatial means (hardware/software) that help us better understand aspects of health geography in Georgia

    Understanding the Relationship between Parental Income and Multiple Child Outcomes: a decomposition analysis

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    In this paper we explore the association between family income and children’s cognitive ability (IQ and school performance), socio-emotional outcomes (self esteem, locus of control and behavioural problems) and physical health (risk of obesity). We develop a decomposition technique that allows us to compare the relative importance of the adverse family characteristics and home environments of low income children in accounting for different outcomes. Using rich cohort data from the UK we find that poor children are disadvantaged at age 7 to 9 across the full spectrum of outcomes, the gradient being strongest for cognitive outcomes and weakest for physical health. We find that some aspects of environment appear to be associated with the full range of outcomes - for example, maternal smoking and breastfeeding, child nutrition, parental psychological functioning. We also find some some aspects of the environment of higher income households hinder child development. We conclude that many aspects of growing up in poverty are harmful to children’s development, and that narrowly-targeted interventions are unlikely to have a significant impact on intergenerational mobility.Child outcomes, income, pathways, mediating factors

    Understanding the relationship between parental income and multiple child outcomes: A decomposition analysis

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    In this paper we explore the association between family income and children's cognitive ability (IQ and school performance), socio-emotional outcomes (self esteem, locus of control and behavioural problems) and physical health (risk of obesity). We develop a decomposition technique that allows us to compare the relative importance of the adverse family characteristics and home environments of low income children in accounting for different outcomes. Using rich cohort data from the UK we find that poor children are disadvantaged at age 7 to 9 across the full spectrum of outcomes, the gradient being strongest for cognitive outcomes and weakest for physical health. We find that some aspects of environment appear to be associated with the full range of outcomes - for example, maternal smoking and breastfeeding, child nutrition, parental psychological functioning. We also find some some aspects of the environment of higher income households hinder child development. We conclude that many aspects of growing up in poverty are harmful to children's development, and that narrowly-targeted interventions are unlikely to have a significant impact on intergenerational mobility.child outcomes, income, pathways, mediating factors

    Residential racial composition and black-white obesity risks: differential effects of neighborhood social and built environment

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    pre-printThis study investigates the association between neighborhood racial composition and adult obesity risks by race and gender, and explores whether neighborhood social and built environment mediates the observed protective or detrimental effects of racial composition on obesity risks. Cross-sectional data from the 2006 and 2008 Southeastern Pennsylvania Household Health Survey are merged with census-tract profiles from 2005-2009 American Community Survey and Geographic Information System-based built-environment data. The analytical sample includes 12,730 whites and 4,290 blacks residing in 953 census tracts. Results from multilevel analysis suggest that black concentration is associated with higher obesity risks only for white women, and this association is mediated by lower neighborhood social cohesion and socioeconomic status (SES) in black-concentrated neighborhoods. After controlling for neighborhood SES, black concentration and street connectivity are associated with lower obesity risks for white men. No association between black concentration and obesity is found for blacks. The findings point to the intersections of race and gender in neighborhood effects on obesity risks, and highlight the importance of various aspects of neighborhood social and built environment and their complex roles in obesity prevention by socio-demographic groups

    A Preliminary Investigation of Child, Parent and Programme Leader Reflections on Participation in and Delivery of a Family- Based Weight Intervention Programme.

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    Childhood obesity is considered to be the greatest public health risk to children today, placing young people at considerable risk for adult obesity and consequent CVD, diabetes, liver dysfunction, and other morbidities (Doro-Altan et al., 2008; Singh et al., 2008). As a result numerous interventions with the potential to reduce obesity levels or associated risk of chronic diseases have been devised (Steinberger et al., 2003; Flynn et al., 2006). Not withstanding the need for further quantitative evaluation of the effect of such interventions, key publications have now called for qualitative evaluations to be undertaken in order to create an evidence base from the views of participants that may highlight why certain interventions may be more, or less successful (National Institute for Health and Clinical Excellence, 2006; Luttikhuis et al., 2009). In response to these very recent calls, this abstract intends to present, from qualitative methods of enquiry, preliminary findings of parent, child and programme leader experiences of, reflections on and future intentions following participation in and delivery of a nationally implemented family-based weight intervention programme in the UK. Data from semi-structured interviews with 6 families who completed the programme in December 2008 and 1 programme leader will be presented. Informal thematic analysis will be utilised to identify emergent themes with data presentation accentuating the qualitative, ‘lived’ experience of the programme and the impact of the various aspects of the intervention on intentions for future behaviours. It is anticipated that the outcomes of this study will help to inform the organisation, content, implementation and nature of future intervention programmes in order to enhance their effectiveness

    Deciphering the link and direction between attention-deficit/hyperactivity disorder symptoms and obesity: Common behavioural or prenatal pathways?

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    Growing evidence suggests an association between attention-deficit/hyperactivity disorder (ADHD) and obesity, although very little is understood about the nature of this link. The aims of this thesis were to examine the following aspects of the ADHD-obesity association: (1) the directionality of the link from childhood to adolescence, (2) behavioural mediators during childhood and adolescence, and (3) prenatal risk factors common for both disorders. Participants were from the Northern Finland Birth Cohort (NFBC) 1986 (N=9479). Data were obtained on pregnancy and birth factors, and child/adolescent mental health, obesity, and lifestyle factors. Regression analyses showed that ADHD symptoms significantly predicted obesity, rather than in the opposite direction, from childhood to adolescence. Mediation analyses examined potential underlying behavioural factors – physical activity and binge-eating, and showed that physical inactivity mediated the longitudinal ADHD symptom-obesity association. Further, there was a bidirectional, longitudinal association between physical inactivity and ADHD symptoms. ADHD and obesity may share common prenatal risk factors, including prenatal exposure to cortisol. This was studied using a quasi-experimental approach by examining the impact of prenatal exposure to synthetic glucocorticoids (sGC). Results from propensity-score and mixed-effects methods showed that prenatal sGC increased the risk for general psychiatric disturbance and inattention symptoms, but not obesity, in childhood. Placental size may represent another common prenatal contributing factor; placental size was positively associated with behaviour problems, including ADHD symptoms, in child and adolescent boys, but was not associated with obesity. This thesis addresses important unexplored aspects of the association between ADHD and obesity, and provides insight into risk factors for both disorders. The direction of the association was driven from ADHD symptoms to obesity, and physical inactivity was a behavioural mediator underlying the link. Although there was no evidence that both disorders share common prenatal risk, prenatal sGC and placental size were positively associated with ADHD symptoms.Open Acces

    You are what you eat, or are you? The challenges of translating high-fat-fed rodents to human obesity and diabetes

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    Obesity and type 2 diabetes mellitus (T2DM) are rapidly growing worldwide epidemics with major health consequences. Various human-based studies have confirmed that both genetic and environmental factors (particularly high-caloric diets and sedentary lifestyle) greatly contribute to human T2DM. Interactions between obesity, insulin resistance and β-cell dysfunction result in human T2DM, but the mechanisms regulating the interplay among these impairments remain unclear. Rodent models of high-fat diet (HFD)-induced obesity have been used widely to study human obesity and T2DM. With \u3e9000 publications on PubMed over the past decade alone, many aspects of rodent T2DM have been elucidated; however, correlation to human obesity/diabetes remains poor. This review investigates the reasons for this translational discrepancy by critically evaluating rodent HFD models. Dietary modification in rodents appears to have limited translatable benefit for understanding and treating human obesity and diabetes due—at least in part—to divergent dietary compositions, species/strain and gender variability, inconsistent disease penetrance, severity and duration and lack of resemblance to human obesogenic pathophysiology. Therefore future research efforts dedicated to acquiring translationally relevant data—specifically human data, rather than findings based on rodent studies—would accelerate our understanding of disease mechanisms and development of therapeutics for human obesity/T2DM

    Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

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    Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page
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