Power Output Manipulation from Below to Above the Gas Exchange Threshold Results in Exacerbated Performance Fatigability

Abstract

Introduction: Performance fatigability is substantially greater when exercising in the severe-versus heavy-intensity domain. However, the relevance of the boundary between moderate-and heavy-intensity exercise, the gas exchange threshold (GET), to performance fatigability is unclear. This study compared alterations in neuromuscular function during work-matched exercise above and below the GET. Methods: Seventeen male participants completed work-matched cycling for 90, 110, and 140 min at 110%, 90%, and 70% of the GET, respectively. Knee extensor isometric maximal voluntary contraction (MVC), high-frequency doublets (Db100), low-to high-frequency doublet ratio (Db10:100), and voluntary activation were measured at baseline, 25%, 50%, 75%, and 100% of task completion. During the initial baseline visit and after each constant work rate bout, ramp-incremental exercise was performed, and peak power output and oxygen uptake ( VO 2peak ) were determined. Results: After the 70% and 90% GET trials, similar reductions in MVC (−14% ± 6% and −14% ± 8%, respectively, P = 0.175) and Db100 (−7% ± 9% and −6% ± 9%, respectively, P = 0.431) were observed. However, for a given amount of work completed, reductions in MVC (−25% ± 15%, P = 0.008) and Db100 (−12% ± 8%, P = 0.029) were up to 2.6-fold greater during the 110% than the 90% GET trial. Peak power output and VO 2peak during ramp-incremental exercise were reduced by 7.0% ± 11.3% and 6.5% ± 9.3%, respectively, after the 110% GET trial relative to the baseline ramp (P ≤ 0.015), with no changes after the moderate-intensity trials (P ≥ 0.078). Conclusions: The lack of difference in fatigability between the trials at 70% and 90% GET, coupled with the greater fatigability at 110% relative to 90% GET, shows that exceeding the moderate-to heavy-intensity boundary has implications for performance fatigability, while also impairing maximal exercise performance capacity.

Changes in running economy and running technique following 6 months of running with and without wearable‐based real‐time feedback

“…Interestingly, for one of these individuals, the comfortable running speed assessed in the lab did not substantially differ from the average running speed in‐field as assessed with the wearable (3.23 vs. 3.16 ms −1 in the lab vs. in‐field, respectively). This suggests that some individuals may be training structurally too fast, which could contribute to a higher injury risk by increasing fatigue, and depleting carbohydrate stores, 50 which in turn may increase the risk of e.g., bone injuries 51 . This therefore may suggest the need for real‐time feedback on an appropriate training intensity to optimize performance and reduce injury risk for some individuals.…”

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

“…Third, Morris et al did not provide feedback on the relative intensity of the training sessions, which could have led to some participants' training too hard. 2,64 This may, in turn, lead to exacerbated fatigue 7 and further contribute to the ineffectiveness of the intervention. In contrast, the application in our study incorporated periodic ''stable speed run'' sessions (Appendix Table A5) that aimed to maintain the running speed within 5% of the comfortable running speed as determined with the talk test during the baseline run.…”

“…While training load reflects the combination of volume, intensity, and frequency of training, intensity reflects a modifiable and potentially relevant factor for real-time feedback. In support of the potential relevance of intensity-related feedback, large-scale wearable data suggest that recreational runners often perform training sessions at a relatively higher intensity compared with well-trained runners, 2 which may, in turn, partly contribute to their higher injury risk by exacerbating fatigue 7 and increasing tissue damage and the corresponding probability of failure (ie, injury). 19,24 Wearables offer a promising method to quantify running technique and training intensity outside of the laboratory, and they can provide real-time feedback on these aspects.…”

Per‐step and cumulative load at three common running injury locations: The effect of speed, surface gradient, and cadence

“…A final consideration is that injuries are multifactorial, with both biological and mechanical factors contributing to injury development. For example, while our modeling approach would predict the same injury risk for two individuals running at the same absolute speed, running above the first ventilatory threshold results in more glycogen depletion, 71 prolonged cardiac parasympathetic recovery, 72 and higher overall central and muscular fatigue 73 . This suggests that injury risk may differ between individuals at a given speed, slope, or step frequency depending on biological factors such as the relative exercise intensity.…”