The impact of sprint interval training with or without weight loss on substrate oxidation in adults: A secondary analysis of the i‐<scp>FLEX</scp> study
Bibliographic record
Abstract
Endurance exercise training and weight loss (WL) have been associated with changes in fat oxidation. However, there is limited evidence investigating the impact of sprint interval training (SIT)-induced WL on fat oxidation in adults. To investigate the impact of SIT with or without WL on fat oxidation, 34 adults aged 19-60 years (males, n = 15) took part in 4-week SIT. SIT consisted of 30-s Wingates starting with two intervals and working up to four interspersed with 4 min of active recovery. Fat oxidation was estimated via indirect calorimetry using a metabolic cart during submaximal cycling. Following the intervention, participants were classified into a WL group (weight change >0 kg) or a non-WL group (weight change ≤0 kg). No difference in resting fat oxidation (p = 0.642) and respiratory exchange ratio (RER) (p = 0.646) were observed between the groups. There was a significant interaction for the WL group with increased submaximal fat oxidation usage (p = 0.005) and decreased submaximal RER over the duration of the study (p = 0.017). When adjusted for baseline weight and sex, submaximal fat oxidation usage remained significant (p < 0.05), while RER did not (p = 0.081). The WL group had higher work volume, relative peak power, and mean power than the non-WL group (p < 0.05). Short-term SIT elicited significant improvements in submaximal RER and fat oxidation (FOx) in adults that lost weight, which may be explained by an increase in work volume throughout SIT training.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".