Background: Data on nutritional information and digital gameplay are limited among young adults in Germany.
Objective: This survey aimed to gather data on nutritional information sources and digital games for nutritional education (preferences, motives, and behaviors) among young adults at both Munich universities in Germany.
Methods: An online survey was developed by an multidisciplinary research group using EvaSys, an in-house survey software.
"Serious games" are a novel and entertaining approach for nutritional education. The aim of this pilot study was to evaluate the short-term effectiveness of "Fit, Food, Fun" (FFF), a serious game to impart nutritional knowledge among children and adolescents. Data collection was conducted at two secondary schools in Bavaria, Germany.
View Article and Find Full Text PDFIEEE Trans Vis Comput Graph
January 2020
We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models. Our framework combines an iterative XAI pipeline with eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building. To operationalize the framework, we present explAIner, a visual analytics system for interactive and explainable machine learning that instantiates all phases of the suggested pipeline within the commonly used TensorBoard environment.
View Article and Find Full Text PDFBackground: Use of novel information and communication technologies are frequently discussed as promising tools to prevent and treat overweight and obesity in children and adolescents.
Objective: This survey aims to describe the preferences, motives, and needs of children and adolescents regarding nutrition and digital games.
Methods: We conducted a survey in 6 secondary schools in the southern region of Germany using a 43-item questionnaire.