Comparing the Keyto App and Device with Weight Watchers’ WW App for Weight Loss: Protocol for a Randomized Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Obesity and being overweight are major contributing factors for many diseases. Calorie restricted diets often fail to result in sustained long-term weight loss. Very low–carbohydrate, high-fat ketogenic diets have been suggested to have superior metabolic and weight loss effects. Keyto is a low-cost, highly scalable mobile health (mHealth) app paired with a noninvasive biofeedback tool aimed at facilitating weight loss through a personalized healthy and predominantly plant- and fish-based ketogenic diet. OBJECTIVE This protocol describes a randomized trial comparing the efficacy of the Keyto mHealth app and device intervention to that of Weight Watchers’ WW app in individuals who are overweight or obese. The primary outcome is weight loss after 12 weeks. Secondary and exploratory outcomes, including metabolic and cardiovascular risk factors, will be assessed at 12, 24, and 48 weeks. METHODS A total of 144 participants will be recruited and randomized to either the Keyto program or Weight Watchers program. Study participants will be guided through the study via video conference or phone calls and will undergo a fasting blood analysis performed by a third-party diagnostic lab at weeks 0 and 12 to assess metabolic and cardiovascular risk markers. All participants will be asked to weigh themselves daily on a study-provided Bluetooth-enabled scale. Participants randomized to the Keyto arm will also be asked to measure their breath acetone levels, a measure of ketosis, with the Keyto device 3 times per day. RESULTS Recruitment started in December 2019. Rolling recruitment is expected to be completed by July 2020. Data collection and analysis of the primary intervention phase is expected to be completed in October 2020. The 24- and 48-week follow-ups are expected to be completed in January 2021 and July 2021, respectively. CONCLUSIONS This trial will provide high-quality evidence regarding the efficacy of the Keyto weight loss program in individuals who are overweight and obese in a free-living condition. This study also fills a gap by examining the impact of a ketogenic diet emphasizing plant- and fish-based fats on blood lipid profile and cardiovascular disease risk. CLINICALTRIAL ClinicalTrials.gov NCT04165707; https://clinicaltrials.gov/ct2/show/NCT04165707. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/19053
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.138 | 0.023 |
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".