Tailored Food is Medicine Programs as an effective approach to address dietary intake and blood pressure among rural and urban adults
Bibliographic record
Abstract
Abstract Background Food is Medicine (FIM) programs have shown potential at improving health outcomes, reducing food insecurity, and increasing dietary intake. However, few interventions have sought to individually tailor these programs based on user preferences and constraints. This program utilized a screening decision tool to allocate adults to a tailored FIM program to examine process and clinical outcomes. Methods Adults ages 18-64 with hypertension were screened for food insecurity at two large hospital systems (one rural, one urban) in Kentucky. Participants who screened positively and wanted assistance with food were referred to the Food as Health Alliance hub to receive either medically tailored meals or grocery prescription (Rx). Medically tailored meals (MTM) provided 5 meals per week for 12 weeks. The grocery Rx program provided $100 each month for 3 months to purchase food consistent with guidelines for people with hypertension. Baseline and post-intervention outcomes were obtained from electronic medical records, and process measures included engagement, dose, and program acceptability. Semi-structured interviews were completed with a subset of 20 participants to obtain qualitative feedback on the program. Results A total of 159 participants referred were enrolled, and 144 participants completed all measures (complete case rate of 91%). There were no significant changes within grocery Rx or MTM for the primary outcome of systolic or diastolic blood pressure from baseline to post intervention. However, there were significant effects on dietary intake, financial strain, and self-reported general health among grocery Rx and MTM recipients. Among those receiving Supplemental Nutrition Assistance Program (SNAP) benefits who received grocery Rx, there was a significant change in systolic (-8.75 95% CI-16.83,-.67) and diastolic blood pressure (-5.42 95% CI-10.72,-.13) relative to those not receiving SNAP. Qualitative findings aligned with the quantitative findings in that participants reported high levels of satisfaction and that the program allowed them to eat healthier, helped ease the burdensome cost of food, and improved various aspects of their health. Conclusion A tailored FIM program can improve dietary intake and reduce blood pressure among key subpopulations participating in the program in the short-term. Clinical trial registration NCT07011251
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".