Digital Catalysts for Noncommunicable Disease Prevention Serious Games and Gamified Applications: Framework Design Study
Notice bibliographique
Résumé
BACKGROUND: Unhealthy behaviors can cause so-called noncommunicable diseases (NCDs), which are on the rise. Notable examples include chronic respiratory diseases, diabetes, cardiovascular diseases, and various types of cancer. They are responsible for approximately 41 million deaths annually, which accounts for a staggering 74% of all global deaths. Major risk factors include physical inactivity, the use of tobacco, unhealthy diets, the harmful use of alcohol, and poor mental health, which can be classified as modifiable behavioral risk factors. Other factors include metabolic and environmental risk factors, such as air pollution. Many individuals struggle to make informed decisions about their health, which contributes to the risk factors mentioned earlier and, ultimately, can lead to the development of one or more NCD. OBJECTIVE: This research presents design and standardization considerations to enable the exchange of medical and game data to maximize their impact and usefulness. Serious games and gamified applications that strategically use behavior change techniques and educational content can help users change their behavior on a lasting basis, thereby reducing the aforementioned NCD risk factors. Still, each of them is currently independently designed and cannot interact with other applications. METHODS: We previously developed serious games and gamified applications to prevent NCDs. These served as the foundation of an interoperable framework for NCD prevention games and applications. On the basis of a comprehensive analysis, 6 key areas were identified, ultimately leading to a framework definition that was then evaluated against the already-developed games and applications. RESULTS: This paper presented a novel interoperable framework to support the design and development of serious games and gamified applications that enable individuals to achieve sustainable behavior change and improve their overall health and well-being by defining 6 key areas, emphasizing interoperability, and exchanging meaningful medical and game data. CONCLUSIONS: The framework presented in this study covers the major design and implementation aspects of NCD prevention games and applications in 6 key areas. Therefore, researchers should consider these guidelines when creating novel serious games and applications in those areas. The framework also intensively encourages the use of standards in the domain of medical informatics to ensure the semantic interoperability of patients' data produced. Thus, it promotes the exchange of meaningful data to improve patient care and anonymous data use for research.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».