Preliminary Efficacy of a Gamified Mobile App for Promoting Self-Health Management Among Nurses in the Post-COVID Era: Single-Group Pre-Post Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».