3,142 research outputs found

    A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

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    Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used

    Childhood Obesity: The Role of Health Policy

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    This first policy report from the CPRC and the NICHQ Childhood Obesity Action Network was released on March 18, 2008 in Miami Florida as part of the Second Childhood Obesity Congress

    Shaping a Healthier Generation: Successful State Strategies to Prevent Childhood Obesity

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    Provides an overview of the socioeconomic and environmental risk factors and costs of childhood obesity. Presents examples of state policies to prevent the epidemic by promoting healthy behaviors in child care, school, community, and healthcare settings

    Estimation of obesity levels based on computational intelligence

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    Obesity is a worldwide disease that affects people of all ages and gender; in consequence, researchers have made great efforts to identify factors that cause it early. In this study, an intelligent method is created, based on supervised and unsupervised techniques of data mining such as Simple K-Means, Decision Trees (DT), and Support Vector Machines (SVM) to detect obesity levels and help people and health professionals to have a healthier lifestyle against this global epidemic. In this research the primary source of collection was from students 18 and 25 years old at institutions in the countries of Colombia, Mexico, and Peru. The study takes a dataset relating to the main causes of obesity, based on the aim to reference high caloric intake, a decrease of energy expenditure due to the lack of physical activity, alimentary disorders, genetics, socioeconomic factors, and/or anxiety and depression. In the selected dataset, 178 students participated in the study, 81 male and 97 female. Using algorithms including Decision Tree, Support Vector Machine (SVM), and Simple K-Means, the results show a relevant tool to perform a comparative analysis among the mentioned algorithms

    F as in Fat: How Obesity Policies Are Failing in America, 2004

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    Examines national and state obesity rates and government policies. Focuses on setting a baseline of current policies and programs, and offers a comprehensive look at their range and quality

    Reducing Obesity: Policy Strategies From the Tobacco War

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    Outlines the impact of obesity on health, healthcare costs, and productivity. Reviews successful policy interventions to reduce tobacco use and considers whether excise or sales tax, labeling requirements, and advertising bans could lower obesity rates

    F as in Fat: How Obesity Threatens America's Future 2011

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    Outlines 2008-10 national and state obesity rates, health indicators, and policies to address the epidemic; regional, economic, and social barriers to healthy choices; impact of the 2010 healthcare reform and Let's Move initiative; and recommendations

    F as in Fat: How Obesity Policies are Failing in America, 2007

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    Presents findings from an annual study of obesity rates in the United States and provides an update of trends in obesity rates and policies at the federal and state level. Explores potential strategies for dealing with obesity

    Persuasive 2006 : first international conference on persuasive technology for human well-being : Eindhoven, The Netherlands, May 18-19, 2006 : adjunct proceedings

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    The adjunct proceedings contain the extended abstracts of the posters that were presented during the poster sessio

    F as in Fat: How Obesity Policies Are Failing in America, 2008

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    Examines trends in national and state obesity rates, related health indicators, and policies. Outlines a strategy involving government, community groups, schools, employers, insurers, food and agricultural industries, and others to address the problem
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