Effect of Lifestyle Health Coaching on Multiple Cardiovascular Disease Risk Factors: Comparison with Cardiac Rehabilitation
Bibliographic record
Abstract
During the past decade, lifestyle health coaching (LHC) has gained increased popularity as a strategy to facilitate behavior change. Few data are available on the effect of LHC on multiple cardiovascular disease (CVD) risk factors. PURPOSE: In this multi-center study, we evaluated the effect of LHC on multiple CVD risk factors in 9,134 consecutive adults and compared the effect of a phase 2 cardiac rehabilitation (CR) program in 11,875 consecutive patients. METHODS: Outcome measures were assessed at baseline and after approximately 12 weeks of LHC and CR. LHC included individualized coaching, predominantly via the telephone and Internet, on exercise training, nutrition, weight management, stress management and tobacco cessation interventions. CR was conducted at multiple centers in the United States. RESULTS: At baseline, CR patients were significantly (p<0.05) older (69 ± 11 years vs. 51 ± 12 years), and had a higher prevalence of atherosclerotic CVD (95.3% vs. 10.6%) and diabetes (23.7% vs. 9.1%) than those participating in LHC. For participants with abnormal baseline risk factors, statistically significant improvements (p <0.05) were observed for multiple variables, as follows: blood pressure (LHC, -9/7 mmHg; CR, -10/10 mmHg; p <0.05 for CR versus LHC); LDL cholesterol (LHC, -22 mg/dl; CR, -50 mg/dl; p <0.05 for CR versus LHC); HDL cholesterol (LHC, 3 mg/dl; CR, 5 mg/dl; p <0.05 for CR versus LHC); triglycerides (LHC, -46 mg/dl; CR, -52 mg/dl; p=NS for CR versus LHC); fasting glucose (LHC, -13 mg/dl; CR, -15 mg/dl; p=NS for CR versus LHC); and body weight (LHC, -5.8 lbs; CR, -2.5 lbs; p <0.05 for LHC versus CR). In participants in the LHC program with a baseline Framingham 10-year coronary heart disease risk score >10%, the score decreased by 20.5% (relative risk reduction, p <0.05). CONCLUSIONS: These data serve to document the magnitude of improvement in multiple CVD risk factors in response to participation in approximately 12 weeks of LHC. The data demonstrate that LHC results in clinically relevant improvements in multiple CVD risk factors and that the precise magnitude of improvement may be similar to, less than or greater than that observed with CR depending on the specific risk factor in question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".