Does the Rate of Heat Storage Define Exercise Intensity Level During Self-paced Exercise at a Fixed Rating of Perceived Exertion?
Bibliographic record
Abstract
Studies evaluating whether exercise intensity during self-paced exercise at a fixed rating of perceived exertion (RPE) is primarily modulated by the rate of body heat storage have yielded mixed results due to the different methods used to calculate the rate of body heat storage (i.e., thermometry and partitional calorimetry). PURPOSE: To evaluate via direct calorimetry whether changes in the rate of whole-body heat storage mediates exercise intensity during self-paced exercise at a fixed RPE. METHODS: Ten trained male cyclists participated in three experimental trials conducted on separate days. Following a 20-min baseline period, participants cycled in a direct air calorimeter during HOT (35°C), NORMAL (25°C) and COOL (15°C) conditions at a fixed RPE of 16, self-regulating their workload in order to maintain this RPE until they could no longer maintain 70% of their starting workload. Whole-body heat loss (evaporative and dry) and metabolic heat production were measured by direct and indirect calorimetry respectively. Body heat storage was measured as the temporal summation of heat production and heat loss. RESULTS: The starting self-selected workload was lower in HOT compared to NORMAL and COOL (151 ± 30 vs. 165 ± 26 vs. 165 ± 35 W, respectively). Power output declined over time in all conditions (P<0.05), however a faster decrease was observed in HOT (P<0.05). This led to a shorter exercise time in HOT relative to NORMAL and COOL (57 ± 19 vs. 73 ± 22 vs. 68 ± 26 min, respectively). The rate of heat storage decreased over time in all conditions (P<0.05), however it was greater in COOL and lowest in HOT throughout exercise. In general, the rate of heat storage was significantly greater in COOL relative to HOT and greater in NORMAL compared to HOT after the 5th min of exercise. Further, the rate of heat storage was generally greater in COOL relative to NORMAL after the 15th min of exercise (P<0.05). Taken together, the change in body heat storage during exercise was ~2-fold greater during COOL and ~1.5-fold greater during NORMAL compared to HOT (961 ± 438 vs. 740 ± 258 vs. 478 ± 186 kJ, respectively). CONCLUSIONS: We show that self-paced exercise intensity at a fixed RPE is not primarily mediated by differences in the rate of body heat storage. SUPPORT: Natural Sciences and Engineering Research Council of Canada (held by Glen P. Kenny).
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".