Effect of a Snack Bar Optimized to Reduce Alcohol Bioavailability: A Randomized Controlled Clinical Trial in Healthy Individuals
Bibliographic record
Abstract
Alcohol intoxication impairs judgment and reaction times and the level of blood alcohol concentration (BAC) is highly correlated with accidents and injury. We hypothesized that a food optimized to delay gastric emptying, a reduced alcohol bioavailability bar (RABB), would decrease postprandial BAC and alcohol bioavailability with greater caloric-efficiency than control foods. Therefore, we evaluated the RABB in a randomized, crossover trial in 21 overnight fasted healthy adults (10 male, 11 female). Just before consuming a moderate dose of alcohol (0.3–0.35 g/kg body weight), participants ate either (1) no food (NF, 0 kcal), (2) the RABB (210 kcal), (3) a savory snack mix (SSM, 210 kcal), or (4) a multicomponent meal (MCM, 635 kcal) and their BAC was measured over 90 minutes using a breathalyzer, the primary endpoint being peak BAC (pBAC). pBACs were analyzed by repeated measures analysis of variance (ANOVA) ( F = 107.5, P < .0001) with the differences between means assessed using Tukey's honestly significant difference test. The pBAC of each group was different ( P < .001) from all other groups (NF = 0.064 ± 0.003, SSM = 0.047 ± 0.002, RABB = 0.031 ± 0.002, MCM = 0.020 ± 0.002%; mean ± standard error of the mean). Furthermore, the bioavailability of alcohol over 90 minutes (BA90) was reduced compared to the NF group by similar margins (SSM = 22.0 ± 2.2, RABB = 45.0 ± 3.8, MCM = 67.9 ± 3.1%) with the mean BA90 of each group different from all other groups ( P < .001). Compared to the NF condition, the average reduction of pBAC per 100 calories of food consumed was higher for the RABB (24.0%) than either the SSM (11.8%) or the MCM (10.7%). This study demonstrates that the RABB can reduce both pBAC and alcohol bioavailability with high caloric-efficiency.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".