Bibliographic record
Abstract
Background Adherence to nutritional guidelines for chronic disease prevention and management remains a challenge in clinical practice. Innovative strategies are needed to help optimise dietary behaviour change. Objective The objective of this study was to determine if a nutrigenomics-guided lifestyle intervention programme could be used to motivate greater dietary adherence and change in dietary intake short-term, moderate-term and long-term compared to the gold-standard population-based weight management intervention (Group Lifestyle Balance (GLB)/Diabetes Prevention Programme (DPP)). Design The Nutrigenomics, Overweight/Obesity, and Weight Management (NOW) randomised controlled trial is a pragmatic, parallel-group, superiority clinical trial (n=140), which was conducted at the East Elgin Family Health Team (EEFHT). GLB weight management groups were prerandomised 1:1 to receive either the standard GLB programme or a modified GLB+nutrigenomics (GLB+NGx) programme. Three 24-hour recalls were collected at baseline, 3, 6 and 12 months using the validated multiple pass method. Research assistants collecting the three 24-hour recalls were blinded to the participants’ group assignments. Statistical analyses included split plot analyses of variance (ANOVAs), two-way ANOVAs, binary logistic regression, χ2 and Fisher’s exact tests. Using the Theory of Planned Behaviour as guidance, key confounding factors of behaviour change were considered in the analyses. This study was registered with clinicaltrials.gov (NCT03015012). Results Only the GLB+NGx group significantly reduced their total fat intake from baseline to 12-month follow-up (from 36.0%±4.8% kcal to 30.2%±8.7% kcal, p=0.02). Long-term dietary adherence to total fat and saturated fat guidelines was also significantly (p<0.05) greater in the GLB+NGx group compared to the standard GLB group. Conclusions Weight management interventions guided by nutrigenomics can motivate long-term improvements in dietary fat intake above and beyond gold-standard population-based interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".