A personalized, multi-platform nutrition, exercise, and lifestyle coaching program: A pilot in women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it