Fasting diets: what are the impacts on eating behaviors, sleep, mood, and well-being?
Bibliographic record
Abstract
Fasting diets (FDs) have drawn great attention concerning their contribution to health and disease over the last decade. Despite considerable interest in FDs, the effect of fasting diets on eating behaviors, sleep, and mood-essential components of diet satisfaction and mental health- has not been addressed comprehensively. Understanding the critical role that fasting plays in these elements will open up potential treatment avenues that have not yet been explored. The aim of the present paper was to conduct a comprehensive critical review exploring the effects of fasting on eating behaviors, sleep, and mood. There is currently a lack of clarity regarding which fasting option yields the most advantageous effects, and there is also a scarcity of consistent trials that assess the effects of FDs in a comparable manner. Similarly, the effects and/or treatment options for utilizing FDs to modify eating and sleep behaviors and enhance mood are still poorly understood. Further researches aiming at understanding the impacts of various fasting regimes, providing new insights into the gut-brain axis and offering new treatment avenues for those with resistant anxiety and depression, are warranted. Alteration of eating behaviors can have lasting effects on various physiological parameters. The use of fasting cures can underpin ancient knowledge with scientific evidence to form a new approach to the prevention and treatment of problems associated with co-morbidities or challenges pertaining to eating behaviors. Therefore, a thorough examination of the various fasting regimens and how they impact disease patterns is also warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".