Effect of cycling specialization on effort and physiological responses to uphill and flat cycling at similar intensity
Bibliographic record
Abstract
Abstract Power output is considered one of the best tools to control external loads in cycling, but the relationship between a target power output and the physiological responses may suffer from the effects of road gradient, which is also affected by cyclist specialization. The objective was to determine the effects of cyclist specialization on effort perception and physiological response (heart rate and lactate concentration) while sustaining efforts at similar power output but riding on two different road gradients. Nineteen male competitive road cyclists performed two randomized trials of 10 min at 0% (velodrome) and 10 min at 6% road gradient (field uphill), at an intensity of 10% ± 3% below the individual's functional threshold power. Cadence was kept between 75 and 80 rpm in both trials and posture remained unchanged during the tests. Heart rate, speed, cadence, power output, blood lactate, and rate of perceived effort were measured for each trial. K ‐means cluster analyses differentiate uphill (n = 10) and flat specialists (n = 9) according to lactate responses. Flat specialists presented lower heart rate ( p < 0.001 and ES = 0.2), perceived exertion ( p < 0.01 and ES = 0.7), and blood lactate concentration ( p < 0.001 and ES = 0.7) riding on the flat than uphill. Uphill specialists presented lower perceived exertion ( p < 0.01 and ES = 0.8) and blood lactate concentration ( p < 0.01 and ES = 0.5) riding uphill than on the flat. In conclusion, the combination of cyclist specialization and road gradient affects physiological and effort perception parameters in response to a similar power output demand. These factors deserve attention in training schedules and monitoring performance using power output data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".