Investigating the Role of Exercise Pattern in Acute Cardiovagal Recovery
Bibliographic record
Abstract
PURPOSE: Research on intermittent training has mainly focused on the effects of exercise intensity while overlooking the specific effect of the modulations associated with alternating exercise and recovery. This study investigated how the frequency of modulations during moderate-intensity exercise affects postexercise vagal reactivation. METHODS: Healthy, active females and males 18-39 yr old were recruited for the study. Participants completed three treadmill running sessions on separate days. Each moderate-intensity session accumulated 30 min at 90% of the intensity associated with the second ventilatory threshold and was performed as either high-frequency intermittent (HiFi; 15 × [2 min + 2 min recovery]), low-frequency intermittent (LoFi; 5 × [6 min + 2 min recovery]), or moderate-intensity continuous training (MICT; 1 × 30 min). Heart rate recovery (HR rec ) at 1 min and heart rate variability recovery (HRV rec ; lnRMSSD) were assessed in response to submaximal constant-speed tests performed before (CST1) and after (CST2) each of the exercise sessions. HR rec , HRV rec , blood lactate (BLa), and blood pressure were also collected during the exercise sessions. RESULTS: Twenty-one individuals (8 females, 13 males) participated in the study. HR rec from CST2 was faster in HiFi versus MICT ( P < 0.001), whereas HRV rec post-CST2 was higher after HiFi versus both LoFi ( P = 0.024) and MICT ( P < 0.001). BLa increased in all conditions ( P = 0.007) but remained lower during HiFi compared with LoFi and MICT (both P < 0.001). Diastolic blood pressure did not change during exercise with HiFi ( P = 0.939) but decreased during LoFi ( P = 0.006) and MICT ( P = 0.008). CONCLUSIONS: Exercise pattern influences the physiologic response to exercise. Higher frequencies of modulations can preserve vagal activity and expedite postexercise recovery, suggesting moderate-intensity intermittent exercise as a potential strategy to mitigate autonomic impact and acute physiological stress while maintaining total work performed.
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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 | 0.001 |
| 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".