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Energy Cost of Running Under Hypogravity in Well-Trained Runners and Triathletes: A Biomechanical Perspective

athletes. Sports Med, 37 (7), 575-586. doi:10.2165/00007256-200737070-00002 Borg, G. (1970). Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med, 2 (2), 92-98. Cohen, J. (1973). Eta-Squared and Partial Eta-Squared in Fixed Factor Anova Designs. Educational and Psychological Measurement, 33 (1), 107-112. doi:10.1177/001316447303300111 di Prampero, P. E. (1981). Energetics of muscular exercise. Rev Physiol Biochem Pharmacol, 89 , 143-222. Donelan, J. M., & Kram, R. (2000). Exploring dynamic similarity in human running using

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Evaluation of mobile applications for fitness training and physical activity in healthy low-trained people - A modular interdisciplinary framework

International Conference on Human-Computer Interaction (pp. 268-278). Cham: Springer. Bondaronek, P., Alkhaldi, G., Slee, A., Hamilton, F. L., & Murray, E. (2018). Quality of publicly available physical activity apps: Review and content analysis. JMIR mHealth and uHealth , 6 (3), e53. Borg, G. (1998). Borg´s perceived exertion and pain scales . Champaign, II.: Human Kinetics. Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015). Accuracy of smartphone applications and wearable devices for tracking physical activity data. Journal of the

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Decision Support System for Mitigating Athletic Injuries

, Diers, D.M., & Flor, H. (2015). Recovery-stress balance and injury risk in professional football players: A prospective study. Journal of Sports Sciences, 33, 2140-2148. Lucas, P., van der Gaag, L., & Abu-Hanna, A. (2004). Bayesian networks in biomedicine and healthcare. Artificial Intelligence in Medicine, 30, 201-214. Neapolitan, R. (2004). Learning bayesian networks . Englewood Cliffs NJ: Prentice Hall. Nicholson, J., Holmes, E., Lindon, J., & Wilson, I. (2004). The challenge of modelling mammalian biocomplexity. Nature Biotechnology, 22

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