Effects of Recovery Method After Exercise on Performance, Immune Changes, and Psychological Outcomes
Bibliographic record
Abstract
STUDY DESIGN: Randomized controlled trial using a repeated-measures design. OBJECTIVES: To examine the effects of commonly used recovery interventions on time trial performance, immune changes, and psychological outcomes. BACKGROUND: The use of cryotherapy is popular among athletes, but few studies have simultaneously examined physiological and psychological responses to different recovery strategies. METHODS: Nine active men performed 3 trials, consisting of three 50-kJ "all out" cycling bouts, with 20 minutes of recovery after each bout. In a randomized order, different recovery interventions were applied after each ride for a given visit: rest, active recovery (cycling at 50 W), or cryotherapy (cold tub with water at 10°C). Blood samples obtained during each session were analyzed for lactate, IL-6, total leukocyte, neutrophil, and lymphocyte cell counts. Self-assessments of pain, perceived exertion, and lower extremity sensations were also completed. RESULTS: Time trial performance averaged 118 ± 10 seconds (mean ± SEM) for bout 1 and was 8% and 14% slower during bouts 2 (128 ± 11 seconds) and 3 (134 ± 11 seconds), respectively, with no difference between interventions (time effect, P≤.05). Recovery intervention did not influence lactate or IL-6, although greater mobilization of total leukocytes and neutrophils was observed with cryotherapy. Lymphopenia during recovery was greater with cryotherapy. Participants reported that their lower extremities felt better after cryotherapy (mean ± SEM, 6.0 ± 0.7 out of 10) versus active recovery (4.8 ± 0.9) or rest (2.8 ± 0.6) (trial effect, P≤.05). CONCLUSION: Common recovery interventions did not influence performance, although cryotherapy created greater immune cell perturbation and the perception that the participants' lower extremities felt better.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".