The Effect of Recovery Strategies on Lactate Clearance and High-Intensity Exercise Performance
Bibliographic record
Abstract
High-intensity exercise results in an accumulation of lactate (La-) in the blood and muscle. Previously it has been suggested that the accumulation of La- can result in decrements in performance and is a factor in the development of fatigue as it affects acid-base status. It has been shown that active recovery (AR) can result in faster La- clearance when compared to passive recovery (PR), and can be beneficial to performance. PURPOSE: To compare the effects of two recovery intensities, relative to individual thresholds, and PR on performance of a bicycle sprint task and La- clearance. METHODS: On three separate days nine male subjects (27.5±3.6 yrs) performed three supramaximal exercise bouts at 120% of maximum aerobic power (MAP) for 60% of the time to exhaustion (TTE). These bouts were separated by 5 min of PR, AR or combined active recovery (CAR). The third bout was followed by 14 min of the same recovery intensity. Recovery intensities were as follows: PR (rest), AR at 50% of the workload difference between the individual anaerobic threshold (IAT) and the individual ventilatory threshold (IVT) below the IAT (IVT-50%δT), or CAR at the IAT workload for 5 min and at the IVT-50%δT workload thereafter. Five 10 s sprints were performed approximately 2 min post-recovery. Blood La- concentration and power parameters (Peak Power (PP), Mean Power (MP), Total Work (TW) and Fatigue Index FI)) were compared using targeted dependent t-tests. RESULTS: No consistent differences were observed between recover strategies with respect to PP and MP. PP was greater in the 4th sprint for both CAR and PR than AR (99.3±3.9% and 95.8±5.4% vs 90.4±8.6%, respectively, p<0.05). La- values were significantly lower in the AR trial vs PR from the 6th min of recovery onward (p<0.05). La- was lower at the 6th and 14th min of recovery in CAR compared to PR(p<0.05). La- differed only at the 9th min between AR and CAR (61.8±14.4% vs 72.2±21.4%, respectively, p<0.05). CONCLUSION: AR and CAR both demonstrated improved La- clearance when compared to PR. Differences in La- clearance did not affect performance on a repeat sprint task. Clearance of lactate does not appear to be beneficial to performance when a moderate duration recovery period is employed.
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 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 | 0.001 |
| 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".