Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Predicting repeat power ability through common field assessments: is repeat power ability a unique physical quality?

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • بيانات النشر:
      PeerJ Inc.
    • الموضوع:
      2024
    • Collection:
      Directory of Open Access Journals: DOAJ Articles
    • نبذة مختصرة :
      Background The repeat power ability (RPA) assessment is used to test the ability to repeatedly produce maximal ballistic efforts with an external load. The underpinning physical qualities influencing RPA are undetermined. This study aimed to gain further insight into the physical qualities that determine RPA by analysing the association between physical qualities and an assessment of RPA. Materials and methods Ten well-trained male field hockey players performed an RPA assessment consisting of 20 repetitions of loaded countermovement jumps (LCMJ20), with a percent decrement score of peak power output calculated. Over a two-week period, each participant performed the YoYo Intermittent Recovery Test 2 (IRT2), a repeated speed ability assessment incorporating a 180° change of direction (RSA180), a 40-meter linear speed test (40 mST), an isometric mid-thigh pull (IMTP), a countermovement jump (CMJ), and a 3-repetition maximum half squat (HS) assessment. Pearson’s correlation analysis was used to determine the strength of relationships between each assessment variable and the LCMJ20. The assessment variables with the strongest relationships within each assessment were used in a stepwise multiple linear regression analysis to determine the best predictor model of LCMJ20. Results RSA180percent decrement score (RSA180% had a very strong, significant relationship with LCMJ20 (r = 0.736: p < 0.05). HS relative strength (HSrel) was found to have a significant and very strong, negative relationship with LCMJ20 (r = − 0.728: p < 0.05). Stepwise multiple linear regression analysis showed RSA180 to explain 48.4% of LCMJ20 variance (Adjusted R2 = 0.484) as the only covariate included in the model. Conclusion The findings indicate that RSA180 as a repeated high intensity effort (RHIE) task is strongly related to LCMJ20 and is also the best predictor of LCMJ20. This may suggest that RPA can provide practitioners with information on RHIE performance. The variance between assessment methods indicates that RPA may be a ...
    • ISSN:
      2167-8359
    • Relation:
      https://peerj.com/articles/16788.pdf; https://peerj.com/articles/16788/; https://doaj.org/toc/2167-8359; https://doaj.org/article/054316a0b88b459c86227de4709503ea
    • الرقم المعرف:
      10.7717/peerj.16788
    • الدخول الالكتروني :
      https://doi.org/10.7717/peerj.16788
      https://doaj.org/article/054316a0b88b459c86227de4709503ea
    • الرقم المعرف:
      edsbas.D2DC4F8A