1,720,979 research outputs found

    STOP: A gamified approach to support obese patients in changing their health habits

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    Obesity is a challenge for the whole world and it is necessary to find new ways to reduce it and to help people in this situation by facilitating the acquisition of healthy habits to replace unhealthy ones. STOP is a project aimed at facing the challenge of obesity through a digital methodology and through a synergy between experts from the industry and academia. By tracing patients' habits, integrating these data with other data already present in databases and providing personalized paths and feedback the «STop Obesity Platform» can be a valuable help for both patients and Healthcare Professionals. All this is inserted in a gamification frame by the creation of an app that establishes an analogy to the wellknown Dorian Gray mirror with the aim of encouraging the performance of the user or the acquisition of healthy behavior through a stimulating and engaging experience. This paper shows the main objectives of the project, defines the general structure of the app and provides some examples of prototype application

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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