Lifestyle Characteristics Among People With Diabetes and Prediabetes
Bibliographic record
Abstract
This chapter describes lifestyle characteristics of the diabetes population in the United States, including information on dietary habits, physical activity, smoking, and health-seeking behaviors. In general, data indicate that many people with diabetes, like the rest of the U.S. population, are not meeting dietary recommendations, especially for fruits and vegetables, where only about one-quarter are consuming the suggested amount. Fruits, vegetables, and percent calories from macronutrients are measured most frequently, while micronutrients are rarely examined in national studies.Overall, only one-third of people with diabetes meet physical activity recommendations, and people with diabetes engage in less physical activity than those without diabetes.According to nationally representative data, people who smoke comprise about one-fifth of the diabetes population compared to a slightly higher proportion of smokers among those without diabetes. There has been a decreasing trend in the proportion of smokers among people with and without diabetes, ranging from a high of 35.6% in the 1970s to a low of 19.9% in the 1990s.The available published national data on health-seeking behaviors among people with diabetes suggest a greater percentage of people with diabetes have reduced their intake of high fat foods, received advice to quit smoking, visited a physician regularly, and changed their physical activity than those without diabetes, although most people reported engaging in these behaviors regardless of diabetes status. Across diabetes status groups, most people report that they are practicing weight control.This comprehensive review and compilation of data on lifestyle characteristics among people with diabetes demonstrates that healthy lifestyle behaviors are not at optimal rates in America’s diabetes population. Although a number of lifestyle education programs have been designed and implemented in the country since Diabetes in America’s last publication, more creative ideas are still necessary for sustainable, efficient, and cost-effective national programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".