Abstract MP005: Do Worksite Wellness Programs Improve Dietary Behaviors and Adiposity? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Worksites are promising venues for promoting health, given considerable time spent at work and opportunities for environmental change. Yet, the impact of worksite wellness programs (WWPs) on diet and adiposity, as well as the most relevant WWP components, are not established. Methods: Following MOOSE and PRISMA guidelines, we conducted a systematic review and meta-analysis of the impact of multi-component WWP trials (RCT or quasi-experimental) on diet and adiposity. Data were extracted in duplicate and pooled using inverse variance random effects meta-analysis. Pre-specified sources of heterogeneity (study design, world region, worksite type, duration, WWP components) were analyzed by meta-regression and subgroup analysis. Funnel plots, Begg’s, and Egger’s tests evaluated potential publication bias. Results: From 6612 abstracts, we identified 48 studies assessing WWPs and diet or adiposity. Most were in the US (54%) or Europe (23%), with diet (64%) and exercise/weight loss (20%) as main targets. Intervention components were variable (Figure). Most common outcomes were intakes of fruits and vegetables (F&V) (19 studies), total fat or fat subtypes (18), and dietary fiber (4); and BMI (35) and waist circumference (WC) (10). Median duration was 12 months (range: 1-48 mo). In pooled analyses, WWP increased intake of F&V, especially fruits (Figure). Significant effects were not identified for dietary fiber, total fat, or fat subtypes. WWP also reduced BMI (Figure) and WC (-2.03 cm, 95% CI:-3.88,-0.20). Trial duration significantly modified effects on BMI (<12 mo duration: -0.64 kg/m 2 ; 12+ mo: -0.16 kg/m 2 ; P-interaction=0.046); but not WC or F&V intake. Additional findings for heterogeneity, including WWP components, and publication bias will be presented. Conclusions: These novel findings support effectiveness of WWP for increasing F&V and reducing BMI and WC.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".