Hematological Adaptations to Training With and Without Heat
Sebastien Racinais, David Nichols, Nathan Townsend, Gavin Travers, Scott Cocking, Harry A. Brown, Jonathan Rubio, Julien D. Périard
Hematological Adaptations to Training With and Without Heat
Whilst modifications in thermoregulatory responses and plasma volume during heat acclimation (HA) are well researched, much less is known regarding hemoglobin mass. The aim of this study was to investigate the hematological adaptations associated with a long-term, progressive, work-matched controlled heart rate HA protocol.
Ten males (VO2peak: 4.50 ± 0.50 L/min) completed two three-week training interventions consisting of HA (36 °C and 59% RH) and exercise in temperate conditions (TEMP: 18 °C and 60% RH) in a counter-balanced crossover design. Weekly training included 5 consecutive laboratory-based sessions (i.e. 4 controlled heart rate training and 1 repeated sprint training) and 2 days off.
Hemoglobin mass decreased from day 4 of training in HA (−22 [−37, −8] g, P < 0.001) but not TEMP (+2 [−12, +17] g, P = 0.743), returning to baseline at the end of HA (−7 [−22, +7] g, P = 0.333). As compared to day 1, several other adaptations were present from day 5 onward in HA including a decrease in heart rate at rest (−4 [−8, −0] beats/min, P = 0.040) and at a given work rate (−6 [−10, −1] beats/min, P = 0.012), an increase in whole-body sweat rate (+0.3 [+0.1, +0.5] L/h, P = 0.015), and an increase in power output (+18 [+8, +28] W, P < 0.001); while there was no changes in TEMP (P ≥ 0.143). Plasma volume increased in both HA (+168 [+23, +314] mL) and TEMP (+166 [+20, +311] mL) by the 11th day of training (P ≤ 0.027).
While training in both hot or temperate conditions led to plasma volume increases, training in the heat lead to specific physiological adaptations, including a transient decrease in hemoglobin mass that was rapidly reversed within a few days of HA.
Training / Cycling / Heat acclimatization / Exercise
[1.] |
|
[2.] |
|
[3.] |
Bates D, Maechler M, Bolker B, Walker S, Christensen RHB, Singmann H, Dai B, Scheipl F, Grothendieck G, Green P. Package ‘lme4’. 2009. https://lme4.r-forge.r-project.org/. Accessed 21 Feb 2024.
|
[4.] |
|
[5.] |
|
[6.] |
|
[7.] |
|
[8.] |
|
[9.] |
|
[10.] |
|
[11.] |
|
[12.] |
|
[13.] |
|
[14.] |
|
[15.] |
|
[16.] |
|
[17.] |
|
[18.] |
|
[19.] |
|
[20.] |
|
[21.] |
|
[22.] |
|
[23.] |
|
[24.] |
|
[25.] |
|
[26.] |
Lenth R. Emmeans: Estimated marginal means, aka least-squares means. R package version 1.9.0. 2023. https://CRAN.R-project.org/package=emmeans. Accessed 21 Feb 2024.
|
[27.] |
|
[28.] |
|
[29.] |
|
[30.] |
|
[31.] |
|
[32.] |
|
[33.] |
|
[34.] |
|
[35.] |
|
[36.] |
|
[37.] |
|
[38.] |
|
[39.] |
|
[40.] |
R Core Team. . R: a language and environment for statistical computing, 2022 Vienna, Austria R Foundation for Statistical Computing
|
[41.] |
|
[42.] |
|
[43.] |
|
[44.] |
|
[45.] |
|
[46.] |
|
[47.] |
|
[48.] |
|
[49.] |
|
[50.] |
|
[51.] |
|
[52.] |
|
[53.] |
|
[54.] |
|
[55.] |
|
[56.] |
|
[57.] |
|
[58.] |
|
[59.] |
|
[60.] |
|
[61.] |
|
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