Dataset Information

An online tool for obesity intervention and public health.

ABSTRACT: Though the United States of America (U.S.A.) obesity rate shows signs of leveling off, rates remain high. Poor nutrition contributes to the development of obesity, and physical inactivity is an important cause of numerous diseases and directly linked to obesity. Efforts to improve diet, increase physical activity and pursue other behavioral changes seem imperative. However, the effective management of intervention strategies for large number of participants are challenging because services in primary, secondary, and tertiary cares are often under-resourced, relatively uncoordinated with other parts of the health system. It is thus necessary to have accompanying intervention strategies that can be carried out at population level. In this paper, we describe an online intervention tool designed for the Obesity Prevention Tailored for Health II project to help achieve such goals.The first part of the online tool locates healthy food stores and recreational programs within a specified distance of a participant's home or a place of interest. The food environments include fruit & vegetable stores, farmers' markets and grocery stores, and the companying popup window shows the street address and contact information of each store. The parks and recreational programs are displayed on names of park or recreational program, types of program available, and city each amenity belongs to. The tool also provides spatial coverage of vegetation greenness, air pollution and of historical traffic accidents involving active travel. The second part of the tool provides optimized travel options for reaching various amenities. By incorporating bicycling, walking and public transit into the trip planner, this online tool helps increase active transport and reduce dependence on automobiles. It promotes transportation that encourages safety awareness, physical activity, health, recreation, and resource conservation.We developed the first Google-based online intervention tool that assists obese and overweight participants in finding food and recreational amenities around locations of interest and identifying optimized routes that fit their personal preferences. This tool can also serve general public and policy makers for education, disease prevention and health promotion.

SUBMITTER: Su JG

PROVIDER: S-EPMC4748626 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundCOVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis.MethodsWe performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated through metrics like Area Under the Receiver Operating Characteristics Curve (AUC), accuracy, sensitivity, specificity, and precision. We selected the top 10 predictive variables based on their significance in disease prediction. The data were then split into a training set (70% of patients) and a validation set (30% of patients) for model validation.ResultsWe identified 31 indicators with significant disparities. The XGBoost model outperformed others, with an AUC of 0.920 and high precision, sensitivity, specificity, and accuracy. The top 10 features (Age, Monocyte%, Mean Platelet Volume, Lymphocyte%, SIRI, Eosinophil count, Platelet count, Hemoglobin, Platelet Distribution Width, and Neutrophil count.) were crucial in constructing a more precise predictive model. The model demonstrated strong performance on both training (AUC = 0.977) and validation (AUC = 0.912) datasets, validated by decision curve analysis and calibration curve.ConclusionML models that incorporate CBC parameters offer an innovative and effective tool for data analysis in COVID-19. They potentially enhance diagnostic accuracy and the efficacy of therapeutic interventions, ultimately contributing to a reduction in the mortality rate of this infectious disease.

Project description:ObjectiveThe objective of this study was to develop a measurement tool to capture local public health department (LHD) organizational characteristics that align with implementation of equity-oriented practice, which may be used to gauge progress in building public health structures and functions that address the needs of vulnerable populations and reduce health inequities.MethodsWe developed and tested a measurement tool, with practitioner input, based on an implementation science framework and informed by previous work defining public health essential services and practice recommendations for health equity. Measures assessed types of vulnerable populations served by the LHD, organizational climate, and four equity-oriented practice areas, including: assessment and planning, monitoring and analysis, leadership support, and obesity prevention. We also assessed opportunities for capacity building by identifying training needs of practitioners. Primary data were collected from Missouri local health department practitioners (n = 92, 80% response rate) via an online questionnaire, with a subset of the sample providing data for test-retest reliability.ResultsMeasures of equity-oriented implementation climate indicated areas of variability with respect to strengths and gaps across LHDs. While implementation climate was strong with respect to perceived importance (86%), a substantial proportion of LHDs cited concern over other priorities conflicting with equity-oriented implementation (32%). Likewise, a strong internal push (67%) was often accompanied by limited external political (25%) and community support (40%). Implementation climate measures generally had good to excellent reliability and were significantly associated with areas of equity-oriented practice. Frequently identified (>70%) training needs included improving skills in applying frameworks, assessment methods, and evaluating collaborations around equity.ConclusionWe developed a theory-based, practitioner-informed questionnaire to assess capacity for equity-oriented practice and identify opportunities for capacity building in local public health departments to engage in effective change toward health equity.

Dataset Information

An online tool for obesity intervention and public health.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets