Gamification as a Tool for Promoting Physical Exercise and Healthy Eating Habits in Healthcare Worker Women: Effects on Cardiometabolic Health and Physical Fitness at Workplace
Bibliographic record
Abstract
Purpose To evaluate the effectiveness of gamified versus nongamified health promotion interventions on cardiometabolic health and fitness parameters in healthcare worker women. Design Randomized parallel group trial. Setting A public outpatient health center in Brazil. Subjects Women employees (included: n = 29; lost to follow-up: n = 1; analyzed: n = 28). Interventions 8 weeks of gamified (n = 15) or nongamified (n = 13) interventions, consisting of health lectures, nutritional counseling, and supervised exercise training. The gamified group was divided into teams that received points based on completion of health goals/tasks. Measures Anthropometric, cardiometabolic and physical fitness parameters. Analysis Two-way ANOVA with repeated measures (group vs. time), and Bonferroni post hoc tests. Results Body mass (-1.5 ± 1.5 kg), waist circumference (-1.6 ± 3.0 cm), HbA1C (-.2 ± .3%), triglycerides (-21.5 ± 48.2 mg/dl), systolic (-11.1 ± 7.9 mmHg) and diastolic (-7.1 ± 5.8 mmHg) blood pressure, as well as sit and reach (3.9 ± 3.0 cm) and six-minute walking (56 ± 37 m) performance improved ( P < .05) only after the gamified intervention. Sit-to-stand performance improved after both the gamified (-1.18 ± 1.24 s) and nongamified (-1.49 ± 1.87 s) interventions. Conclusion The gamified intervention was more effective than the nongamified intervention for improving cardiometabolic and physical fitness parameters, suggesting that gamification may be an effective tool for promoting health in healthcare worker women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".