The relationship of high-intensity cross-training with arterial stiffness
Bibliographic record
Abstract
Background Central arterial stiffness is a cardiovascular risk factor that can be readily affected through engagement in physical exercise training, with resistance and aerobic exercise having disparate affects. Despite the growing popularity of high-intensity cross-training (HICT), little is currently known about the effects of this mixed modality exercise stimulus on arterial stiffness. Therefore, the purpose of this study was to characterize the arterial stiffness of habitual HICT participants vs. aerobically active and sedentary controls using a cross-sectional design. Methods A total of 30 participants were recruited: 10 middle-aged long-term participants of HICT (CrossFit) and 20 age, sex, and height matched controls (10 recreationally active, 10 sedentary). Central and peripheral pulse wave velocities were measured for the carotid-femoral and femoral-dorsalis pedis arterial segments. Aerobic fitness (maximal oxygen uptake, VO 2max ) was measured and typical exercise participation rates were self-reported for each group. Results HICT participants manifested central pulse wave velocity (PWV) (5.3 ± 1.0 m/s, mean ± SD) and VO 2max (43 ± 6 mL/kg/min) values nearly identical to active controls. Both active groups had significantly better values than sedentary controls (7.1 ± 1.0 m/s, p ≤ 0.001; and 32 ± 7 mL/kg/min, p = 0.01). No differences were observed in peripheral PWV between groups. Conclusion Habitual participation in HICT exercise was not associated with increased central nor peripheral arterial stiffness. Long-term HICT participants presented with similar fitness and arterial stiffness as compared with participants who practiced traditional aerobic exercise. Compared to sedentary living, HICT may offer musculoskeletal and cardiovascular health benefits without negatively impacting arterial stiffness.
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) | 0.003 | 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".