A personalized, multi-platform nutrition, exercise, and lifestyle coaching program: A pilot in women
Bibliographic record
Abstract
The aim of this pilot study was to examine if a personalized web-based multi-platform nutrition, exercise, and lifestyle coaching program, supported weight loss and the reduction of chronic disease risk factors in overweight or obese women. Twenty-eight women completed the program, which represented 50% of those who provided baseline data. The program consisted of a one-year curriculum with daily exercise, nutritional habits, and health behaviour lessons along with access to a one-on-one coach. The workouts, habits, and lessons were available via computer, tablet, and mobile device which, along with coaching, facilitated self-monitoring and accountability. At baseline and 12-months, weight, waist circumference, fat mass, muscle mass, blood pressure, total cholesterol, low density lipoproteins, high density lipoproteins, triglycerides, C reactive protein, and fasting glucose were collected. Over the 12 months, women who completed the program, (average age 49.64 (SD 10.99) years), lost 16.52 (SD 13.63) lbs (P < 0.001), and reduced waist circumference by 3.56 (SD 2.31) in (P < 0.0001). Diastolic blood pressure decreased by 3.77 (SD 7.25) mm Hg (P = 0.02) and high density lipoproteins increased by 0.16 (SD 0.28) mmol/L (P = 0.01). No other risk factors changed significantly. Compliance was a significant predictor of weight loss (P < 0.01). In conclusion, women who completed the web-based program experienced significant weight loss (8.62% of initial body weight) coming predominantly from body fat. Chronic disease risk factors also improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".