J04 An exploration of the deceptive effects of caffeine on morning performance

Authors

  • Liv Blow Liverpool John Moores University
  • Hayden Snowe Liverpool John Moores University
  • Michael Chadwick Liverpool John Moores University
  • Ellie Brownrigg Liverpool John Moores University
  • Jack Fernley Liverpool John Moores University
  • Harvey Middleton Liverpool John Moores University
  • George Porritt Liverpool John Moores University
  • Dani Hajdukiewicz Liverpool John Moores University
  • Kyle Durkin Liverpool John Moores University
  • Ben J. Edwards Liverpool John Moores University

DOI:

https://doi.org/10.19164/gjsscmr.v1i3.1537

Abstract

To compete in evening finals, athletes typically compete in competition heats or quarterfinals in the morning when their bodies are biologically weaker. Performance related qualities that are greater in the evening include force production, power output (~3-14% variation), time-trial performance, and repeated sprints which is ~3 and 5% greater (Drust et al., 2005, Acta Physiol Scand, 183, 181-190). The body clock, motivation and higher core and muscle temperatures in the evening are all related to this daily fluctuation in performance. The most effective nutritional ergogenic for performance is caffeine. The aim of this study was to investigate the effects of deception, a placebo pill vs. No Pill on repeated sprint performance (RSP) and grip strength (GP). The experimental protocol was approved by the institution ethics board. Nine participants ingested 1 pill (maltodextrin) at 06:30 h, entered the laboratory at 07:00 h and had their ear temperature recorded. They then completed a Perceived Onset of Mood questionnaire followed by measures of right- and left-hand grip strength followed by a repeated sprints protocol (10 × 20m, 30 s rest periods). Blood lactate, glucose levels and ear temperature were recorded three times during the protocol – with heart rate, rating of perceived exertion and finishing times (Witty GATE, Microgate Srl, Bolzano, Italy) measured at the end of each sprint. The data was analysed by General Linear modelling with repeated measures.

Published

2024-06-07