Preliminary Efficacy of a Gamified Mobile App for Promoting Self-Health Management Among Nurses in the Post-COVID Era: Single-Group Pre-Post Study
Bibliographic record
Abstract
Background The COVID-19 pandemic has significantly affected health care professionals, especially nurses, who have experienced elevated levels of stress, burnout, and physical health challenges. In the postpandemic era, supporting their well-being is crucial. Gamification, which is the application of game design elements in nongame contexts, has emerged as a promising strategy to promote engagement in health behaviors. This study explores the use of a gamified mobile app to support self-health management among nurses recovering from the COVID-19 experience. Objective This study aimed to evaluate the preliminary efficacy of a gamified mobile app for promoting self-health management among nurses who experienced the COVID-19 pandemic. The study examined whether gamification could enhance engagement, improve physical health outcomes, and encourage sustainable behavior change. Methods A single-arm pre-post intervention study was conducted using a user-centered design. The app was developed based on the Octalysis framework and goal-setting theory, incorporating personalized exercise prescriptions and health monitoring features. Nurses from a regional hospital in Hsinchu, Taiwan, participated in the 8-week intervention. Data were collected through interviews, pre- and postintervention surveys, and app usage analytics. Key outcomes included changes in step counts, BMI, and user engagement. Results After the intervention, BMI classification improved significantly. The proportion of participants classified as obese decreased from 38.5% (90/234) to 13.7% (32/234), and the proportion of those classified as overweight increased from 24.8% (58/234) to 34.6% (81/234). Overall, the combined proportion of overweight or obese participants declined from 63.2% (148/234) to 48.3% (113/234), and that of participants with normal BMI increased from 18.4% (43/234) to 33.8% (79/234) (χ24=29.98; P<.001). Octalysis tool results showed strong motivational engagement, with the highest scores in development and accomplishment (mean 7.29), epic meaning and calling (mean 7.05), and empowerment of creativity and feedback (mean 6.55). Conclusions The gamified mobile app demonstrated promising efficacy in enhancing self-health management among nurses in the post-COVID era by increasing physical activity and improving BMI. Gamification elements, such as achievement, purpose, and feedback, were effective in sustaining engagement. Further studies are recommended to assess long-term outcomes and broader applicability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".