<p>Exercise Training and Fasting: Current Insights</p>
Bibliographic record
Abstract
Fasting is defined as the abstinence from consuming food and/or beverages for different periods of time. Both traditional and modern healthcare systems recommend fasting as a therapeutic intervention for the management of several chronic, non-infectious diseases. Exercising during a fasting state increases lipolysis in adipose tissue while also stimulating peripheral fat oxidation, resulting in increased fat utilization and weight loss. A key focus of this review is to assess whether endurance training performed while fasting induces specific training adaptations, where increased fat oxidation improves long-term endurance levels. Fasting decreases body weight, lean body and fat content in both trained and untrained individuals. Several studies indicate a broader impact of fasting on metabolism, with effects on protein and glucose metabolism in sedentary and untrained subjects. However, there are conflicting data regarding the effects of fasting on glucose metabolism in highly trained athletes. The effects of fasting on physical performance indicators also remain unclear, with some reporting a decreased performance, while others found no significant effects. Differences in experimental design, severity of calorie restriction, duration, and participant characteristics could, at least in part, explain such discordant findings. Our review of the literature suggests that there is little evidence to support the notion of endurance training and fasting-mediated increases in fat oxidation, and we recommend that endurance athletes should avoid high intensity training while fasting.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".