Prior arm-crank exercise has no effect on postprandial lipaemia in nondisabled adults
Bibliographic record
Abstract
A single bout of cycling or running performed in the evening can reduce postprandial lipaemia (PPL) the following morning, although this is currently unknown for upper-body exercise. The aim of this study was to determine if a bout of arm-crank exercise (high-intensity interval [HIIE] or moderate-intensity continuous [MICE]), can attenuate PPL in noninjured individuals. Eleven healthy and recreationally active participants (eight males, three females; age: 27 ± 7 years; body mass index: 23.5 ± 2.5 kg·m–2) volunteered to participate in three trials: HIIE (10 × 60 s at 80% peak power output), MICE (50% peak power output of isocaloric duration), and a no-exercise control condition. Each exercise bout was performed at 18:00, and participants consumed a standardised evening meal at 20:00. Following an overnight fast, a 5-h mixed-macronutrient tolerance test was performed at 08:00. There were no significant differences in triglyceride incremental area under the curve between HIIE (192 ± 94 mmol·L–1 per 300 min), MICE (184 ± 111 mmol·L–1 per 300 min), and the no-exercise condition (175 ± 90 mmol·L–1 per 300 min) ( P = 0.46). There were no significant differences in incremental area under the curve for glucose ( P = 0.91) or insulin ( P = 0.59) between conditions. Upper-body MICE and HIIE performed in the evening do not influence PPL the following morning, in normotriglyceridemic individuals. Clinical Trials Registration: NCT04277091. Novelty: Arm-crank exercise has no effect on PPL when performed the evening prior to a mixed-macronutrient meal test. Upper-body sprint interval exercise should be investigated as a potential solution to reduce PPL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".