The effectiveness of polarized versus time-matched high-intensity interval training in untrained individuals
Bibliographic record
Abstract
Training intensity distribution (TID) refers to the structured allocation of training volume across the moderate, heavy, and severe intensity zones (Rosenblat et al., 2025). Various TIDs have been shown to differentially alter mitochondrial adaptation (Granata et al., 2018), possibly even influenced by training status (Rosenblat et al., 2025). We sought to test the differential adaptive effects of polarized (POL) and high-intensity interval (HIIT) training in sedentary and recreationally active individuals attempting to meet the WHO physical activity minimum guidelines of approximately 150 minutes/week (WHO, 2010). To date, 12 sedentary and recreationally active individuals were randomly allocated to six weeks of either HIIT (four HIIT sessions per week) or POL (two HIIT sessions and two zone two sessions per week) cycling. Participants were stratified by age (18-35 or 36-60), sex (male or female), and training status (tier 0 or 1; McKay et al, 2021). Each HIIT session consisted of four intervals of four minutes at 80% work rate peak (WRpeak), spaced with three minutes of unloaded cycling between each interval. Zone two sessions consisted of 60 minutes of continuous cycling at 95% of the first lactate threshold work rate (LT). Muscle biopsies were taken pre and post training, while performance testing (WRpeak, LT test) took place pre, mid (week 3), and post training. At week 3 in the HIIT group change in both WRpeak (HIIT: 22.883 ∓ 6.309; POL: 22.880 ∓ 7.891 W) and LT (HIIT: 18.67 ∓ 27.558 W; POL: 6.40 ∓ 8.764 W) was higher, but these differences were not significant (p = 0.999, p = 0.368). Findings from the complete dataset and muscle biopsy analysis (primary outcome, ongoing) will inform how individuals seeking to meet WHO physical activity guidelines should structure their weekly training to optimize aerobic and mitochondrial adaptations.
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) | 0.002 | 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".