Five weeks of heat training increases haemoglobin mass in elite cyclists
Bibliographic record
Abstract
New Findings What is the central question of this study? Do haemoglobin mass and red blood cell volume increase in elite cyclists training in a hot environment compared to a control group training at normal temperature? What is the main finding and its importance? Five weeks of heat training increases haemoglobin mass in elite cyclists. There are small to intermediate effect sizes for exercise parameters favouring heat training. Abstract In this study we tested the hypothesis that performing 1 h of regular light exercise in a heat chamber (HEAT; 37.8 ± 0.5°C; 65.4 ± 1.8% humidity) 5 times week −1 for a total of 5 weeks increases haemoglobin mass (Hb mass ) and exercise performance in elite cyclists ( = 76.2 ± 7.6 ml min −1 kg −1 ). Twenty‐three male volunteers were assigned to HEAT ( n = 11) or CON ( n = 12; 15.5 ± 0.1°C; 25.1 ± 0.0% humidity) training groups. Hb mass was determined before and after the intervention period in conjunction with an extensive exercise test protocol (conducted at 16–19°C). HEAT increased ( P < 0.05) Hb mass by 42 g from 893 ± 78 to 935 ± 108 g whereas Hb mass remained unchanged (+6 g) in CON. Furthermore, statistical analysis revealed a time–group interaction ( P < 0.05). The greater increase in Hb mass in HEAT, however, did not manifest in a greater increase in (225 ± 274 ml min −1 in HEAT and 161 ± 202 ml min −1 in CON). While HEAT reduced ( P < 0.05) lactate levels during some of the submaximal exercise tests, there was no statistical difference between other performance parameters. There were, however, small to intermediate effect sizes favouring HEAT for lactate threshold power output (2.8 ± 3.9 vs . −0.4 ± 5.1% change, effect size (ES) = 0.34), gross economy in the fatigued state (0.19 ± 0.42 vs . −0.12 ± 0.49%‐point change, ES = 0.52) and 15 min mean power (6.9 ± 8.4 vs . 3.4 ± 5.1% increase, ES = 0.22). This study demonstrates an increase in Hb mass and small to intermediate effect sizes on exercise variables in elite cyclists following a 5‐week heat training intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".