Functional Threshold Power as an Alternative to Lactate Thresholds in Road Cycling
Bibliographic record
Abstract
ABSTRACT: Sitko, S, Cirer-Sastre, R, Corbi, F, and López-Laval, I. Functional threshold power as an alternative to lactate thresholds in road cycling. J Strength Cond Res 36(11): 3179-3183, 2022-This study assessed the relationship between functional threshold power (FTP) and 7 lactate landmarks (Dmax, modified Dmax, fixed blood lactate concentrations of 2 and 4 mmol·L -1 , lactate increases of 1 and 2 mmol·L -1 above baseline, and lactate increases of 1.5 mmol·L-1 above the point of minimum ratio between lactate and work rate) in a sample of 46 road cyclists with a wide range of fitness levels (age 38 ± 9 years, height 177 ± 9 cm, body mass 71.4 ± 8.6 kg, body mass index 22.7 ± 2.2 kg·m -1 , fat mass 7.8 ± 4%, and V̇ o2 max = 61.1 ± 9.1 ml·min -1 ·kg -1 ). The cyclists performed a graded exercise test in which power outputs (POs) at the lactate landmarks were identified. Functional threshold power was established as 95% of the PO during a 20-minute test. Significance was set as p < 0.05. Statistical analyses revealed large to very large correlations between PO, relative PO (RPO), and cadence at FTP and lactate thresholds (LTs) established through Dmax, modified Dmax, and fixed lactate concentrations of 4 mmol·L -1 ( r = 0.68-0.93). Significant differences ( p < 0.001) were also observed for PO and RPO at FTP, fixed blood lactate concentrations of 2 mmol·L -1 , and lactate increases of 1 mmol·L -1 above baseline. Therefore, although FTP estimated from a 20-minute test is strongly related to several lactate landmarks, caution is required when substituting this concept for LTs. This information will allow coaches, cyclists, and scientists to better choose assessments when attempting to estimate LT through power-based field testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".