Numerous mobile applications are available that aim at supporting sustainable physical activity and fitness training in sedentary or low-trained healthy people. However, the evaluation of the quality of these applications often suffers from severe shortcomings such as reduction to selective aspects, lack of theory or suboptimal methods. What is still missing, is a framework that integrates the insights of the relevant scientific disciplines.
In this paper, we propose an integrative framework comprising four modules: training, behavior change techniques, sensors and technology, and evaluation of effects. This framework allows to integrate insights from training science, exercise physiology, social psychology, computer science, and civil engineering as well as methodology. Furthermore, the framework can be flexibly adapted to the specific features of the mobile applications, e.g., regarding training goals and training methods or the relevant behavior change techniques as well as formative or summative evaluation.
Self-Organizing Maps (SOMs) are steadily more integrated as data-analysis tools in human movement and sport science. One of the issues limiting researchers’ confidence in their applications and conclusions concerns the (arbitrary) selection of training parameters, their effect on the quality of the SOM and the sensitivity of any subsequent analyses. In this paper, we demonstrate how quality and sensitivity may be examined to increase the validity of SOM-based data-analysis. For this purpose, we use two related data sets where the research question concerns coordination variability in a volleyball spike. SOMs are an attractive tool for analysing this problem because of their ability to reduce the highdimensional time series to a two-dimensional problem while preserving the topological, non-linear relations in the original data. In a first step, we systematically search the SOM parameter space for a set of options that produces significantly lower continuity, accuracy and combined map errors and we discuss the sensitivity of SOM-based analyses of coordination variability to changes in training parameters. In a second step, we further investigate the effect of using different numbers of trials and variables on the SOM quality and sensitivity. These sensitivity analyses are able to validate the conclusions from statistical tests. Using this type of analysis can guide researchers to select SOM parameters that optimally represent their data and to examine how they affect the subsequent analyses. This may also enforce confidence in any conclusions that are drawn from studies using SOMs and enhance their integration in human movement and sport science.
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