The Sustainability of a Workplace Wellness Program That Incorporates Gamification Principles: Participant Engagement and Health Benefits After 2 Years
Bibliographic record
Abstract
Purpose: To evaluate the results of a workplace wellness program that incorporates gamification principles. Design: In this prospective cohort study, the participation rate and observed health outcomes were evaluated after approximately 2 years. Setting and participants: All permanent employees (n = 775) of a national company located in Canada were eligible to participate. Intervention: The wellness program included web-based challenges (team or individual) incorporating gamification strategies to improve exercise, nutrition, weight reduction, and mental health management behaviors. Measures and analysis: The primary outcomes were employee participation rates. The secondary pre-specified outcomes were the sustained benefits of the program on physical and mental health measures. Results: Participation rates in the health screenings were 78% (baseline), 54% (year 1), and 56% (year 2). Participation in the 4 team web-based challenges ranged from 33% to 68% with 76% to 86% of participants tracking their activity on at least half of the days. After 2 years, there were significant clinical improvements in systolic blood pressure (−1.3mm Hg), total cholesterol/high-density lipoprotein (HDL) ratio (−0.14), glycated haemoglobin (HbA 1c ; −0.1%), weekly physical activity (+264 Metabolic Equivalents [METs]), perceived stress score (−17%), insomnia severity index (−16%), general fatigue (−10%), and reductions in the cardiovascular age gap (−0.3 years). Greater benefits occurred among employees at higher risk. Conclusions: Workplace wellness programs that evolve over time and focus primarily on fun and competitive challenges may support long-term participation, behavior change, and sustained improvements in clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".