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Assessment of the Speed and Power of Push-Ups Performed on Surfaces with Different Degrees of Instability.

Moisés MarquinaJesús Rivilla-GarcíaAlfonso de la RubiaJorge Lorenzo Calvo
Published in: International journal of environmental research and public health (2022)
(I) Training in unstable conditions, with different elements, platforms, or situations, has been used because there is a significant increase in muscle activation, balance, proprioception, and even sports performance. However, it is not known how the devices used are classified according to performance variables, nor the differences according to instability experience. (II) This study aims to analyze the differences in power and speed in push-ups with different situations of instability in trained and untrained male subjects. Power and speed in push-up exercise were analyzed in 26 untrained and 25 trained participants in 6 different situations (one stable and five unstable) (1) stable (PS), (2) monopodal (PM), (3) rings (PR), (4) TRX ® (PT), (5) hands-on Bosu ® (PH) (6) feet on Bosu ® (PF). The variables were analyzed using a linear position transducer. (III) The best data were evidenced with PS, followed by PR, PM, PT, PH and PF. The trained subjects obtained better results in all the conditions analyzed in mean and maximum power and speed values ( p < 0.001). The decrease in these variables was significantly greater in the untrained subjects than in the trained subjects in the PR situation (8% and 18% respectively). In PF there were differences between groups ( p < 0.001), reaching between 32-46% in all variables. The difference between the two groups was notable, varying between 12-58%. (IV) The results showed a negative and progressive influence of instability on power and speed in push-ups. This suggests that instability should be adapted to the subject's experience and is not advisable in untrained subjects who wish to improve power.
Keyphrases
  • resistance training
  • body composition
  • high intensity
  • air pollution
  • skeletal muscle
  • escherichia coli
  • machine learning
  • staphylococcus aureus
  • risk assessment