Dietary Outcomes of a Multilevel, Multicomponent, Cluster Randomized Obesity Intervention in 6 Native American Communities in the Upper Midwest and Southwest United States
Bibliographic record
Abstract
Impacts of colonization on dietary intake have led to high rates of obesity and noncommunicable diseases among Native American adults. Multilevel, multicomponent (MLMC) interventions may improve dietary intake. To assess the impact of a MLMC obesity intervention, OPREVENT2 (Obesity Prevention and Evaluation of InterVention Effectiveness in NaTive North Americans 2; clinicaltrials.gov NCT02803853), on dietary intake in Native American adults in Intervention versus Comparison communities. A cluster-randomized controlled trial was performed among participants in 6 communities randomized to Intervention (n = 3 and Comparison (n = 3). Adults aged 18 to 75 were recruited from tribal communities in the Southwest and upper Midwest United States from September 2016 to May 2017 (n = 601). This analysis included participants who completed baseline and follow-up surveys (82% retention), reported dietary intake between 500 and 7000 kcal/d, and had no missing data for outcomes of interest (n = 446). The intervention was implemented from May 2017, to November 2018. OPREVENT2 integrated individual, environmental, social, and structural factors and was implemented in food stores, worksites, schools, and community media outlets in Intervention communities. Activities included taste tests, cooking demonstrations, and stocking healthier items in food stores and were reinforced by a social m)edia campaign, posters, brochures, and booklets focused on nutrition. Individual-level dietary intake among participating Native American adults was assessed via modified Block food-frequency questionnaire at preintervention and postintervention. Multilevel mixed-effects linear regression, with clustering at the community level, was performed. Between-group effects were significant (P < 0.05) for intake of carbohydrates (-23 g/d), total fat (-9 g/d), saturated fats (-3 g/d), and monounsaturated fats (-4 g/d), with greater decreases in Intervention communities. Between-group effect for total sugar (-12 g/d in Intervention communities) was not statistically significant. This MLMC intervention was associated with significantly improved carbohydrate, total fat, and saturated fat intake among Native American adults. These changes are important for improving health within this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".