A Mediterranean-Based Ketogenic Diet Application Compared to a Calorie-Restricted Low-Fat Diet Application on Cardiometabolic Risk in Adults With Overweight or Obesity
Bibliographic record
Abstract
To determine the effects of a Mediterranean-based ketogenic weight loss diet app paired with a breath acetone biofeedback device compared to a relevant comparator arm on blood biomarkers of cardiometabolic risk in a hands-off, real-world setting. Participants (N = 155) with overweight/obesity (41 ± 11 years, BMI = 32 ± 9 kg/m2, 71% female) were randomized to one of two diet groups, with interventions delivered entirely via mobile app without in-person interaction with study staff: i) a Mediterranean-based ketogenic diet paired with a breath acetone biofeedback device (Keyto); or ii) a calorie-restricted low-fat diet (WW). Participants took daily weight measurements on an at-home wireless scale and a third-party laboratory obtained fasted blood samples at baseline and at 12 weeks. Based on intention-to-treat analysis, participants in the Keyto group lost more weight than those in the WW group (−3.1 kg; 95% CI, −4.6 kg to −1.5 kg; P < 0.001). Likewise, those randomized to the Keyto app experienced greater improvements in markers of glycemic control (HbA1c; −0.2%; 95% CI, −0.3% to −0.1%; P < 0.001) and hepatic function (alanine aminotransferase, alkaline phosphatase, globulin; P < 0.01) compared to the WW group. In follow-up analyses accounting for baseline weight and change in body mass, the effects of group assignment on these cardiometabolic risk markers were found to remain significant independent of differences in weight loss between groups. No other differences in blood markers, including lipids and lipoproteins, were found. Among adults with overweight or obesity, assignment to the Keyto app, as compared to the WW app, resulted in greater weight loss and cardiometabolic improvements that appeared to be independent of weight loss. These findings suggest that a Mediterranean-based ketogenic diet app paired with a breath acetone biofeedback device is effective at improving cardiometabolic health beyond weight loss in a real-world setting. Canadian Institutes of Health Research (CIHR), Michael Smith Foundation for Health Research (MSFHR), Mitacs Accelerate International with Keyto Inc. as the industry partner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".