Engagement With Gamification Elements in a Smoking Cessation App and Short-term Smoking Abstinence: Quantitative Assessment
Notice bibliographique
Résumé
BACKGROUND: Gamification in smoking cessation apps has been found to improve cognitive outcomes associated with higher odds of quitting. Although some research has shown that gamification can also positively impact behavioral outcomes such as smoking cessation, studies have largely focused on physical activity and mental health. Only a few studies have explored the effects of gamification on smoking cessation outcomes, of which the majority have adopted qualitative methodologies and/or assessed engagement with apps using self-report. OBJECTIVE: This study aimed to explore levels of user engagement with gamification features in a smoking cessation app via in-app metrics. Specifically, the objective of this paper was to investigate whether higher engagement with gamification features is associated with the likelihood of quitting in the short term. METHODS: Data from a larger online study that recruited smokers seeking to quit were analyzed to address the objectives presented in this paper. The study took place between June 2019 and July 2020, and participants were primarily recruited via social media posts. Participants who met the eligibility criteria used 1 of 2 mobile apps for smoking cessation. In-app metrics shared by the developer of one of the smoking cessation apps, called Kwit, were used to assess engagement with gamification features. Out of 58 participants who used the Kwit app, 14 were excluded due to missing data or low engagement with the app (ie, not opening the app once a week). For the remaining 44 participants, mean (SD) values were calculated for engagement with the app using in-app metrics. A logistic regression model was used to investigate the association between engagement with gamification and 7-day smoking abstinence. RESULTS: In total, data from 44 participants who used the Kwit app were analyzed. The majority of participants were male, married, and employed. Almost 30% (n=13) of participants self-reported successful 7-day abstinence at the end of the study. On average, the Kwit app was opened almost 31 (SD 39) times during the 4-week study period, with the diary feature used the most often (mean 22.8, SD 49.3). Moreover, it was found that each additional level unlocked was associated with approximately 22% higher odds of achieving 7-day abstinence after controlling for other factors such as age and gender (odds ratio 1.22, 95% CI 1.01-1.47). CONCLUSIONS: This study highlights the likely positive effects of certain gamification elements such as levels and achievements on short-term smoking abstinence. Although more robust research with a larger sample size is needed, this research highlights the important role that gamification features integrated into mobile apps can play in facilitating and supporting health behavior change.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».