Assessing and targeting key lifestyle cardiovascular risk factors at the workplace: Effect on hemoglobin A1c levels
Bibliographic record
Abstract
Purpose Despite the key role played by lifestyle habits in the epidemic of type 2 diabetes (T2D), nutritional quality and physical activity are not systematically considered in clinical practice. The project was conducted to verify whether assessing/targeting lifestyle habits could reduce hemoglobin A1c (HbA1c) levels of employees. Methods The intervention consisted of a 3-month competition among teams of five employees to favor peer-based support in the adoption of healthier lifestyle habits (Eat better, Move more, and Quit smoking) (n = 900). A comprehensive cardiometabolic/cardiorespiratory health assessment was conducted before and after the contest (nutrition/physical activity questionnaires, blood pressure, anthropometric measurements, lipid profile, HbA1c, fitness). HbA1c levels were used to identify individuals with prediabetes (5.7%–6.4%) or T2D (≥6.5%). Results At baseline, 51% of the employees had increased HbA1c levels (≥5.7%). The HbA1c levels were associated with waist circumference, independently of body mass index. Subjects with prediabetes showed a higher waist circumference as well as a more deteriorated cardiometabolic profile compared to workers with normal HbA1c levels. After the intervention, employees with elevated HbA1c significantly reduced their HbA1c levels. Conclusion Results suggest that assessing/targeting key lifestyle correlates of the cardiometabolic profile represents a relevant approach to target abdominal obesity and fitness with a significant impact on HbA1c levels. Key MessagesThe prevalence of employees with prediabetes or undiagnosed type 2 diabetes (T2D) was rather high in our cohort, suggesting that, from a public health standpoint, identification of those individuals is not optimal.Employees with prediabetes or T2D showed a higher waist circumference and a more deteriorated cardiometabolic risk profile compared to those with normal HbA1c levels.The significant reduction in HbA1c levels observed in response to the 3-month intervention supports the notion that a program which assesses and manages cardiometabolic risk at the workplace by also focusing on key lifestyle factors (nutritional quality and physical activity levels) represents an interesting option to reduce the risk of developing diabetes among high-risk individuals or to improve glycemic control and related cardiometabolic risk in patients with T2D.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".