Changes in Muscle and Cerebral Deoxygenation and Perfusion during Repeated Sprints in Hypoxia to Exhaustion

Abstract

During supramaximal exercise, exacerbated at exhaustion and in hypoxia, the circulatory system is challenged to facilitate oxygen delivery to working tissues through cerebral autoregulation which influences fatigue development and muscle performance. The aim of the study was to evaluate the effects of different levels of normobaric hypoxia on the changes in peripheral and cerebral oxygenation and performance during repeated sprints to exhaustion. Eleven recreationally active participants (six men and five women; 26.7 ± 4.2 years, 68.0 ± 14.0 kg, 172 ± 12 cm, 14.1 ± 4.7% body fat) completed three randomized testing visits in conditions of simulated altitude near sea-level (~380 m, FIO2 20.9%), ~2000 m (FIO2 16.5 ± 0.4%), and ~3800 m (FIO2 13.3 ± 0.4%). Each session began with a 12-min warm-up followed by two 10-s sprints and the repeated cycling sprint (10-s sprint: 20-s recovery) test to exhaustion. Measurements included power output, vastus lateralis, and prefrontal deoxygenation [near-infrared spectroscopy, delta (Δ) corresponds to the difference between maximal and minimal values], oxygen uptake, femoral artery blood flow (Doppler ultrasound), hemodynamic variables (transthoracic impedance), blood lactate concentration, and rating of perceived exertion. Performance (total work, kJ; −27.1 ± 25.8% at 2000 m, p < 0.01 and −49.4 ± 19.3% at 3800 m, p < 0.001) and pulse oxygen saturation (−7.5 ± 6.0%, p < 0.05 and −18.4 ± 5.3%, p < 0.001, respectively) decreased with hypoxia, when compared to 400 m. Muscle Δ hemoglobin difference ([Hbdiff]) and Δ tissue saturation index (TSI) were lower (p < 0.01) at 3800 m than at 2000 and 400 m, and lower Δ deoxyhemoglobin resulted at 3800 m compared with 2000 m. There were reduced changes in peripheral [Δ[Hbdiff], ΔTSI, Δ total hemoglobin ([tHb])] and greater changes in cerebral (Δ[Hbdiff], Δ[tHb]) oxygenation throughout the test to exhaustion (p < 0.05). Changes in cerebral deoxygenation were greater at 3800 m than at 2000 and 400 m (p < 0.01). This study confirms that performance in hypoxia is limited by continually decreasing oxygen saturation, even though exercise can be sustained despite maximal peripheral deoxygenation. There may be a cerebral autoregulation of increased perfusion accounting for the decreased arterial oxygen content and allowing for task continuation, as shown by the continued cerebral deoxygenation.

Influence of averaging method on muscle deoxygenation interpretation during repeated‐sprint exercise

“…Therefore, studies that only employed predetermined averaging windows may have underestimated the true magnitude of Δ%[HHb] change induced by repeated‐sprint exercise. To more accurately represent muscle oxygenation changes in response to repeated‐sprint exercise, values need to be determined from a moving average approach . However, a moving average may not be appropriate when exercise protocols incorporate other muscular activity around repeated‐sprint bouts.…”

“…However, using a Butterworth filter, a single data point from the resulting output can be used with assurance that it reflects the characteristics of the surrounding data. Although our choice to use a Butterworth filter was based on previous research, other smoothing/filtering techniques which eliminate outliers may also yield similar results. Readers should also be aware that these data and analytical methods were collected during isokinetic sprints where cadence was constrained to 120 rpm, and, although muscle oxygenation patterns appear similar, one may exert caution when analyzing NIRS signal in nonisokinetic conditions where cadence is influenced by gear ratio and neuromuscular fatigue.…”

“…Six methods were used to obtain a single peak and nadir %Δ[HHb] value for every sprint and recovery period based on the methods outlined in previous research …”

“…Hence, this method is less prone to underestimation of the maximum rate of de‐ and reoxygenation. However, as these calculations are susceptible to signal bias from outliers, especially when a shorter averaging window is used, we recommend using a digital filter or other smoothing/filtering techniques prior to analysis. We also suggest that future studies should avoid predetermined analysis windows and focus on the determination of a single value for peaks and nadirs from a rolling approach.…”

“…For example, when a low‐pass filter is used, a cutoff frequency is chosen so that lower signal frequencies remain and higher frequencies are attenuated . Such filters have been employed to smooth the NIRS signal during repeated‐sprint exercise, but again, the relevance of such technique has yet to be confirmed compared to more widely used averaging methods.…”