The validity and reliability of the my jump 2 app for measuring the reactive strength index and drop jump performance', ABSTRACT BACKGROUNDː This is the first study to independently assess the concurrent validity and reliability of the My Jump 2 app for measuring drop jump performance. It is also the first to evaluate the app's ability to measure the reactive strength index (RSI).METHODSː Fourteen male sport science students (age: 29.5 ± 9.9 years) performed three drop jumps from 20 cm and 40 cm (totalling 84 jumps), assessed via a force platform and the My Jump 2 app. Reported metrics included reactive strength index, jump height, ground contact time, and mean power. Measurements from both devices were compared using the intraclass correlation coefficient (ICC), Pearson product moment correlation coefficient (r), Cronbach's alpha (α), coefficient of variation (CV) and Bland-Altman plots.RESULTSː Near perfect agreement was seen between devices at 20 cm for RSI (ICC = 0.95) and contact time (ICC = 0.99) and at 40 cm for RSI (ICC = 0.98), jump height (ICC = 0.96) and contact time (ICC = 0.92); with very strong agreement seen at 20 cm for jump height (ICC = 0.80). In comparison with the force plate the app showed good validity for RSI (20 cm: r = 0.94; 40 cm; r = 0.97), jump height (20 cm: r = 0.80; 40 cm; r = 0.96) and contact time (20 cm = 0.96; 40 cm; r = 0.98).CONCLUSIONSː The results of the present study show that the My Jump 2 app is a valid and reliable tool for assessing drop jump performance.
Groups of agents following fixed behavioral rules can be limited in performance and efficiency. Adaptability and flexibility are key components of intelligent behavior which allow agent groups to improve performance in a given domain using prior problemsolving experience. We motivate the utility of individual learning by group members in the context of overall group behavior. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use a testbed problem from the Distributed Artificial Intelligence literature to show that simultaneous learning by group members can lead to significant improvement in group performance and efficiency over agent groups following static behavioral rules.
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