Time-course effects of functional fitness sessions performed at different intensities on the metabolic, hormonal, and BDNF responses in trained men
Bibliographic record
Abstract
BACKGROUND: To investigate the time-course effects of a self-regulated training session (performed at an rating perceived exertion of 6/10), all-out session, and a control session on the metabolic, hormonal, and brain derived neurotrophic factor (BDNF) responses in Functional-Fitness (FFT) participants. METHODS: ; 2000 m rowing test 7.35 ± 0.18 min; 1RM back squat 135.6 ± 21.9 kg) performed a FFT session under two different conditions: all-out, or with the intensity controlled to elicit an rating perceived exertion (RPE) of 6 in the Borg 10-point scale (RPE6). A control session (no exercise) was also completed. Metabolic (lactate and creatine kinase), hormonal (testosterone and cortisol), and BDNF responses were assessed pre, post-0 h, 1 h, 2 h and 24 h after the sessions. RESULTS: Creatine kinase concentrations were significantly higher (p ≤ 0.05) after 24 h for both training sessions. Total and free testosterone concentrations were lower post-2 h for all-out when compared to the RPE6 session (p ≤ 0.05). Serum cortisol concentration increased post-0 h (p = 0.011) for RPE6 and post-0 h (p = 0.003) and post-1 h (p = 0.030) for all-out session when comparing to baseline concentrations. BDNF was significantly higher (p = 0.002) post-0 h only for the all-out session when compared to baseline. A positive correlation between blood lactate concentrations and BDNF (r = 0.51; p = 0.01) was found for both effort interventions. CONCLUSIONS: A single FFT session when performed in all-out format acutely increases the concentrations of serum BDNF. However, physiological stress markers show that the all-out session requires a longer recovery period when compared to the RPE6 protocol. These findings can be helpful to coaches and practitioners design FFT session.
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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 | 0.000 | 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) | 0.003 | 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".