Feasibility and Usability of an Artificial Intelligence—Powered Gamification Intervention for Enhancing Physical Activity Among College Students: Quasi-Experimental Study
Notice bibliographique
Résumé
Background: Physical activity (PA) is vital for physical and mental health, but many college students fail to meet recommended levels. Artificial intelligence (AI)-powered gamification interventions through mobile app have the potential to improve PA levels among Chinese college students. Objective: This study aimed to assess the feasibility and usability of an AI-powered gamification intervention. Methods: A quasi-experimental study spanning 2 months was conducted on a sample of college students aged 18 to 25 years old from 18 universities in Beijing. PA data were recorded using the ShouTi Fitness app, and participant engagement was evaluated through surveys. User satisfaction was gauged through the System Usability Scale, while the intervention's feasibility was assessed through Spearman rank correlation analysis, Mann-Whitney tests, and additional descriptive analyses. Results: As of July 2023, we enrolled 456 college students. In total, 18,073 PA sessions were recorded, with men completing 8068 sessions and women completing 10,055 sessions. The average PA intensity was 7 metabolic equivalent of energy (MET)s per session. Most participants preferred afternoon sessions and favored short-duration sessions, with men averaging 66 seconds per session and women 42 seconds. The System Usability Scale score for the intervention based on app is 65.2. Users responded positively to the integration of AI and gamification elements, including personalized recommendations, action recognition, smart grouping, dynamic management, collaborative, and competition. Specifically, 341 users (75%) found the AI features very interesting, 365 (80%) were motivated by the gamification elements, 364 (80%) reported that the intervention supported their fitness goals, and 365 (80%) considered the intervention reliable. A significant positive correlation was observed between the duration of individual PA and intervention duration for men (ρ=0.510, P<.001), although the correlation was weaker for women (ρ=0.258, P=.046). However, the frequency of PA declined after 35 days. Conclusions: This study provides pioneering evidence of the feasibility and usability of the AI-powered gamification intervention. While adherence was successfully demonstrated, further studies or interventions are needed to directly assess the impact on PA levels and focus on optimizing long-term adherence strategies and evaluating health outcomes.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».