Pacing, the Missing Piece of the Puzzle to High-intensity Interval Training

Abstract

This study examined physiological and perceptual responses to matched work high-intensity interval training using all-out and 2 even-paced methodologies. 15 trained male cyclists performed 3 interval sessions of three 3-min efforts with 3 min of active recovery between efforts. The initial interval session was completed using all-out pacing, with the following 2 sessions being completed with computer- and athlete-controlled pacing in a randomised and semi-counterbalanced manner. Computer- and athlete-controlled intervals were completed at the mean power from the corresponding interval during the all-out trial. Oxygen consumption and ratings of perceived exertion were recorded during each effort. 20?min following each session, participants completed a 4-km time trial and provided sessional rating of perceived exertion. Oxygen consumption was greater during all-out (54.1?6.6 ml.kg?1.min?1; p<0.01) and athlete-controlled (53.0?5.8 ml.kg?1.min?1; p<0.01) compared with computer-controlled (51.5?5.7 ml.kg?1.min?1). Total time ?85% maximal oxygen consumption was greater during all-out compared to both even-paced efforts. Sessional ratings of perceived exertion were greater after all-out compared to both even-paced sessions. Mean 4-km power output was lower after all-out compared with both even paced intervals. Distribution of pace throughout high-intensity interval training can influence perceptual and metabolic stress along with subsequent performance and should be considered during the prescription of such training.

Similar Time Near VO2max Regardless of Work Rate Manipulation in Cycling Interval Training

“…85%VȮ 2 max was accompanied by higher mean HR during an all-out strategy in comparison with constant intensity approaches [4]. Although these aspects indicate that HR could be an important ally for training monitoring, for interval training near P max and efforts of ~1-min the HR lag at exercise onset, with a much slower response of HR compared with VȮ , and the HR inertia at exercise cessation could overestimate the actual physiological strain of the training session [21].…”

“…These results agree with those observed herein, indicating that work rate manipulation during work-matched training and not performed until exhaustion [9] may not impact tVȮ 2 max. However, this might not be the only aspect explaining the differences as Zadow et al [4]…”

“…The study has been performed in accordance with the ethical standards of the journal [10] and the Declaration of Helsinki. The sample size was selected by convenience and based on previous studies investigating work rate manipulation on tVȮ 2 max, in which the number of subjects ranged from 7 to 15 participants [4][5][6][7]9]. All participants were healthy with no known musculoskeletal or cardiorespiratory disease, and none were taking medications known to affect the cardiorespiratory system.…”

The Manipulation of Pace within Endurance Sport

“…For instance, Thomas et al ( 2013 ) found that several participants were unable to complete the same amount of work during an externally controlled, even paced cycling trial, when compared with prior self-paced 20 km cycling trials. Conversely, several studies have also observed lower physiological stress during externally controlled trials (Zadow et al, 2015 ; Brick et al, 2016 ), when compared with matched work self-paced trials, possibly as a result of reduced cognitive load (Garcin et al, 2008 ; Lander et al, 2009 ). However, despite of lower physiological strain perceptual responses (i.e., RPE) were similar (Thomas et al, 2013 ) or even higher (Lander et al, 2009 ) in externally controlled trials.…”

“…However, it should also be noted that within the current pacing literature various methods have been used in order to enforce specific pacing strategies. Such methods have included ergometers set to automatically regulate intensity regardless of stroke rate or cycling cadence (Abbiss et al, 2009 ), or requirements of athletes to maintain a given intensity based on visual (Zadow et al, 2015 ) or audio feedback (Thompson et al, 2003 ; Skorski et al, 2014 ). Furthermore, some studies used simulated competitors (i.e., avatars) to examine the effects of their presence on self-selected pacing strategies (Shei et al, 2016 ).…”