Impact of High-Intensity Interval Training, Moderate-Intensity Continuous Training, and Resistance Training on Endothelial Function in Older Adults
Bibliographic record
Abstract
PURPOSE: It is unclear if high-intensity interval training (HIIT) elicits superior improvements in brachial artery (BA) flow-mediated dilation (FMD) responses (i.e., endothelial-dependent vasodilation) than moderate-intensity continuous training (MICT) or resistance training (RT) in otherwise healthy older adults. Whether HIIT enhances lower-limb FMD responses and/or augments low flow-mediated constriction (L-FMC) (endothelial-dependent vasoconstriction) responses more than MICT or RT is also unknown. We tested the hypothesis that HIIT would improve BA and popliteal artery (POP) FMD and L-FMC responses more than MICT or RT in healthy older adults. METHODS: Thirty-eight older adults (age, 67 ± 6 yr) performed 6 wk of either HIIT (2 × 20 min bouts alternating between 15-s intervals at 100% of peak power output [PPO] and passive recovery [0% PPO]; n = 12), MICT (34 min at 60% PPO; n = 12), or whole-body RT (8 exercises, 2 × 10 repetitions; n = 14). The L-FMC and FMD were measured before and after training using high-resolution ultrasound and quantified as the percent change in baseline diameter during distal cuff occlusion and after cuff release, respectively. RESULTS: Resting BA blood flow and vascular conductance (both, P < 0.003) were greater after HIIT only. The HIIT and MICT similarly increased BA-FMD (pre-post: both, P < 0.001), but only HIIT improved BA L-FMC (P < 0.001). Both HIIT and MICT similarly enhanced POP FMD and L-FMC responses (both, P < 0.045). Resistance training did not impact FMD or L-FMC responses in either artery (all, P > 0.20). CONCLUSIONS: HIIT and MICT, but not RT, similarly improved lower-limb vasodilator and vasoconstrictor endothelial function in older adults. Although HIIT and MICT groups enhanced BA vasodilator function, only HIIT improved resting conductance and endothelial sensitivity to low-flow in the BA. In the short-term, HIIT may be most effective at improving peripheral vascular endothelial function in older adults.
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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 | 0.001 | 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".