High Intensity Interval Training Rapidly Increases Skeletal Muscle Oxidative Capacity In Previously Sedentary Middle-aged Adults
Bibliographic record
Abstract
There is growing appreciation of the potential for high intensity interval training (HIT) to rapidly stimulate metabolic adaptations that resemble traditional endurance training, despite a low total exercise volume (Exerc Sports Sci Rev 36:58-63, 2008). However, much of this work has been conducted on young active individuals (≤30 yr) and the results may not be generalized to older, less active populations. In addition, many studies have employed "all out“, variable-load exercise interventions (e.g., repeated Wingate Tests) that may not be safe, practical or well tolerated by certain individuals. PURPOSE: To determine the effect of a short program of low-volume, submaximal, constant-load HIT on skeletal muscle metabolic adaptations in sedentary middle-aged individuals who may be at higher risk for inactivity-related disorders. METHODS: Inactive but otherwise healthy men (n = 3) and women (n = 4) with a mean (±SE) age, body mass index and peak oxygen uptake (VO2peak) of 45±2 yr, 27±2 kg·m2 and 30±1 ml·kg-1·min-1 took part in the study. Subjects performed 6 training sessions over 2 wk. Each session consisted of 10 × 1 min cycling at 60% of peak power elicited during a ramp VO2peak test (∼90% of maximal heart rate) with 1 min recovery between intervals. Needle muscle biopsy samples (v. lateralis) were obtained before training and ∼72 h after the final training session. RESULTS: Muscle oxidative capacity, as reflected by the maximal activity of citrate synthase, increased by 22% after training (12.9 ± 0.7 vs. 10.6 ± 0.6 mmol·kg protein-1·h-1), which is comparable to changes previously reported after 2 wk of Wingate-based HIT in young active subjects. CONCLUSIONS: A short program of low-volume, constant-load HIT is a time-efficient strategy to rapidly increase skeletal muscle oxidative capacity in previously sedentary middle-aged men and women. Supported by CIHR.
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 | 0.000 |
| 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) | 0.001 | 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".