Examining Motivations to Play Pokémon GO and Their Influence on Perceived Outcomes and Physical Activity
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Notice bibliographique
Résumé
BACKGROUND: Pokémon GO is the most played augmented reality game in history. With more than 44 million players at the peak of its popularity, the game has sparked interest on its effects on the young population's health. OBJECTIVE: This pilot study examined motivations to start playing Pokémon GO among a sample of US college students, and how motivations were associated with perceived outcomes of the playing experience and physical activity derived while playing. METHODS: In November 2016, we asked a sample of 47 US college students (all Pokémon GO players) to complete online surveys and install an ecological momentary assessment (EMA) tool and step counter on their smartphones. The EMA tool prompted a set of questions on playing behavior and physical activity, 3 times per day (12:00 PM, 7:00 PM, and 10:00 PM), for 7 days. We used a factorial analysis to identify 3 distinctive groups of players based on their motivations to start playing Pokémon GO. We tested differences across motivation groups related to 5 unique outcomes using 1-way analysis of variance. RESULTS: We extracted 3 interpretable factors from the clustering of motivations to start playing Pokémon GO: Pokémon and video game fans (n=26, 55% of the sample), physical activity seekers (n=8, 17%), and curious & social (n=13, 28%). The clusters differed significantly on the enjoyment of different aspects of the game, particularly battling, discovering new places, and meeting new people, as well as differences in agreement that playing improved mood and made them more social. Days when playing Pokémon GO were associated with higher number of steps reported at the end of the day, especially among physical activity seekers, but also for Pokémon and video game fans. All groups perceived traffic as a major threat to playing. CONCLUSIONS: Days during which Pokémon GO was played were positively associated with a set of beneficial health behaviors, including higher physical activity levels, more socialization, and better mood. Results, however, depended on personal motivations and expectations when joining the game. These results highlight the importance of taking motivation into account when attempting to extract conclusions from the Pokémon GO phenomenon to enhance future exergames' designs or health interventions.
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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 | 0,000 | 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 | 0,001 | 0,001 |
| 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 découle