Keyto app and device versus WW app on weight loss and metabolic risk in adults with overweight or obesity: A randomized trial
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether a Mediterranean-style, ketogenic diet mobile health application (app) with breath acetone biofeedback is superior to a calorie-restricted, low-fat diet app in promoting weight loss. METHODS: , 71% female) were randomized to one of the interventions delivered entirely via app. Participants received a wireless scale and were instructed to take daily weight measurements. A third-party laboratory collected blood samples at baseline and 12 weeks. RESULTS: Weight loss at 12 weeks was greater in the ketogenic (-5.6 kg; 95% CI: -6.7 kg to -4.5 kg) compared with the low-fat group (-2.5 kg; 95% CI: -3.6 kg to -1.4 kg) (between-group difference: -3.1 kg; 95% CI: -4.6 kg to -1.5 kg; p < 0.001). Weight loss at 24 weeks indicated durability of the effect (between-group difference: -5.5 kg; 95% CI: -8.3 kg to -2.8 kg; p < 0.001). Secondary/exploratory outcomes of hemoglobin A1c and liver enzymes were improved to a greater extent in the ketogenic diet group (p < 0.01). CONCLUSIONS: Among adults with overweight/obesity, a ketogenic diet app with breath acetone biofeedback was superior to a calorie-restricted diet app at promoting weight loss in a real-world setting.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".