Multi-Level, Multi-Component Intervention Improves Dietary Intake in Native American Adults
Bibliographic record
Abstract
To assess impact of a multi-level, multi-component (MLMC) intervention on dietary intake in Native American adults. A MLMC obesity intervention ([blinded for review]) was implemented among Native American adults in six rural tribal communities in the US Southwest and Midwest. [Blinded for review] included institutional level components, demonstrations, and distribution of educational media/materials in food stores, worksites, and schools. Communities were randomized to intervention (Group 1; n = 3) or control (Group 2; n = 3). Data collection surveys included a semi-quantitative Block food frequency questionnaire and an Adult Impact Questionnaire for demographics and anthropometrics. This analysis assessed change in daily energy and macronutrient intake from baseline to follow-up, daily intake of types of dietary fat, and daily servings of food groups as defined by the 2015–2020 Dietary Guidelines for Americans using multilevel mixed-effects linear regression with clustering at the community level. All models were adjusted for age, gender, education, Material Style of Life score (proxy for socio-economic status), smoking, and baseline dependent variable of interest. 492 participants completed both baseline and post-intervention surveys: 31 were excluded due to reports of daily caloric intake outside of established cut-offs (<500 kcal or >7000 kcal per day), and 15 were excluded due to incomplete data, for a total n = 446 for this analysis. The between group effect was significant for change in energy intake (−188.21 kcal; P = 0.050), total grams of carbohydrate (−22.48 g; P = 0.047), total grams of fat (−8.82 g; P = 0.044), and total grams of saturated fat (−3.19 g; P = 0.031), with greater decreases observed in Group 1. Change in total grams of sugar trended towards significance (−12.06 g; P = 0.057); similarly, greater decreases were observed in Group 1. Change in daily servings of fats and sweets was also significantly different between groups (−0.39 servings; P = 0.036), with greater decreases observed in Group 1. MLMC interventions can significantly impact dietary intake among rural Native American adults. This is especially important given the high risk of obesity and other diet-related chronic diseases among Native American adults. National Heart, Lung, and Blood Institute
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".