Gender Differences in Effectiveness of the Complete Health Improvement Program (CHIP)
Bibliographic record
Abstract
OBJECTIVE: To determine the differential effect of gender on outcomes of the Complete Health Improvement Program, a chronic disease lifestyle intervention program. DESIGN: Thirty-day cohort study. SETTING: One hundred thirty-six venues around North America, 2006 to 2009. PARTICIPANTS: A total of 5,046 participants (33.5% men, aged 57.9 ± 13.0 years; 66.5% women, aged 57.0 ± 12.9 years). INTERVENTION: Diet, exercise, and stress management. MAIN OUTCOME MEASURES: Body mass index, diastolic blood pressure, systolic blood pressure, lipids, and fasting plasma glucose (FPG). ANALYSIS: The researchers used t test and McNemar chi-square test of proportions, at P < .05. RESULTS: Reductions were significantly greater for women for high-density lipoprotein (9.1% vs 7.6%) but greater for men for low-density lipoprotein cholesterol (16.3% vs 11.5%), total cholesterol (TC) (13.2% vs 10.1%), triglycerides (11.4% vs 5.6%), FPG (8.2% vs 5.3%), body mass index (3.5% vs 3%), diastolic blood pressure (5.5% vs 5.1%), and TC/high-density lipoprotein (6.3% vs 1.4%) but not different for systolic blood pressure (6% vs 5%). The greatest reductions were in participants with the highest baseline TC, low-density lipoprotein, triglycerides, and FPG classifications. CONCLUSIONS AND IMPLICATIONS: The Complete Health Improvement Program effectively reduced chronic disease risk factors among both genders, but particularly men, with the largest reductions occurring in individuals at greatest risk. Physiological or behavioral factor explanations, including differences in adiposity and hormones, dietary intake, commitment and social support, are explored. Researchers should consider addressing gender differences in food preferences and eliciting commitment and differential support modes in the development of lifestyle interventions such as the Complete Health Improvement Program.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".