The Impact of Telemonitoring and Telehealth Coaching on General Nutrition Knowledge in Overweight and Obese Individuals: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
(1) Background: General nutrition knowledge is a fundamental pillar of well-being and healthy lifestyles. This study aimed to measure the general nutrition knowledge questionnaire (GNKQ) scores of overweight and obese participants who joined a pilot randomized controlled trial (RCT) and the association between changes in GNKQ scores and changes in anthropometric measures. (2) Methods: A total of 30 and 25 participants had completed the trial at the 3- and 6-month visits, respectively. All participants enrolled in a randomized controlled trial (RCT) and received a hypocaloric-tailored diet and three online nutrition education sessions over 6 months. The participants were randomly divided into two groups: an intervention group supported with weekly telemonitoring and monthly telehealth coaching vs. a control group. The Arabic-validated GNKQ was used, covering four sections: dietary recommendations; food groups and nutrient sources; healthy food choices; and associations between the diet-disease relationship and weight. (3) Results: The findings show that both the intervention and control groups showed improvements in GNKQ scores over time, with the intervention group demonstrating significant increases in overall nutrition knowledge and specific areas, such as the diet-disease relationship and weight management, at 3 months. In addition, changes in GNKQ scores had a significant negative association with BMI and visceral fat percentage. The findings underline the benefits of supporting dietary weight loss interventions with telemonitoring and telehealth coaching, suggesting that an increase in nutrition knowledge may relate to lower body fat metrics. Nevertheless, the small sample size and high attrition rate of participants were the main limitations of this study, such that large populations are required to confirm the reliability of the obtained findings.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".