Insufficient Sleep as a Contributor to Weight Gain: An Update
Bibliographic record
Abstract
Behavioral sleep restriction is becoming endemic in modern times. The evidence taken as a whole suggests that insufficient sleep plays a role in the risk of obesity. At present it appears very likely that insufficient sleep results in increased food intake, while there is little support that it results in reduced energy expenditure. New studies provide evidence that insufficient sleep enhances hedonic stimulus processing in the brain underlying the drive to consume food and are consistent with the notion that reduced sleep may lead to greater propensity to overeat. Recent studies also suggest that short sleep duration preferentially increases abdominal adiposity, possibly through a hyperactivation of the hypothalamo-pituitary-adrenal axis. Individuals attempting to lose weight should also consider getting adequate amounts of sleep in addition to limiting calorie intake and increasing physical activity to improve the success of their weight loss intervention. Finally, preliminary results by our research group lend support to the effect that increasing sleeping time in short-duration sleepers has the potential to limit adiposity gain over time. A proof of principle study on a randomized sample is currently under way to assess whether sleep extension is feasible and whether it influences body weight. In summary, the preponderance of the evidence supports taking a pragmatic approach and encouraging a good night’s sleep as an adjunct to other health promotion measures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".