Effect of Lifestyle Health Coaching on the Prevalence of Metabolic Syndrome and its Component Risk Factors
Bibliographic record
Abstract
Metabolic syndrome (MS) is a constellation of interrelated coronary heart disease and type 2 diabetes risk factors of metabolic origin (MS risk factors) that are associated with increased cardiovascular event rates. Although MS risk factors are strongly influenced by lifestyle, few data are available on the effect of lifestyle health coaching (LHC) on MS risk factors. PURPOSE: In this study, we determined the prevalence of MS and its component MS risk factors on entry into and after ∼ 1 year of participation in LHC. METHODS: Subjects were 7,929 consecutive adults (mean age = 52 ± 12 years) who completed both a baseline evaluation on entry into a LHC program and a follow-up evaluation after ∼1 year of participation in the LHC program. LHC included individualized coaching, predominantly via the telephone and Internet, on exercise training, nutrition counseling, weight management, stress management and tobacco cessation. MS and its 5 individual component MS risk factors were defined in accordance with the National Cholesterol Education Program Adult Treatment Panel III Guidelines; however, a fasting glucose >100 mg/dl (rather than > 110 mg/dl) was used. In individuals with MS at baseline and data on all 5 individual MS risk factors both at baseline and follow-up (n=1,887), results were analyzed to determine the number of individuals with >3, 3, 4, or 5 MS risk factors and the number of individuals with each of the individual MS risk factors. RESULTS: Results were as follows:TABLECONCLUSIONS: LHC was associated with a 30.9% reduction in the prevalence of MS and had a favorable impact on all 5 MS risk factors.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".