Effects of a Mobile App Called Quittr, Which Utilizes Premium Currency and Games Features, on Improving Engagement With Smoking Cessation Intervention: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Numerous mobile health (mHealth) apps have been developed to support smokers attempting to quit smoking. Although these apps have been reported to be successful, only modest improvements in the quit rate have been measured. It has been proposed that efforts to improve user engagement and retention may improve the quit rate further. Owing to the high cost of smoking-related disease, it is considered worthwhile to pursue even small improvements. OBJECTIVE: The aim of this study was to test a novel smartphone app that leverages premium currency strategies developed by the mobile games industry in an attempt to improve engagement and retention with a smoking cessation intervention. METHODS: We designed and developed a smoking cessation app called "Quittr" in line with previously developed smoking cessation mHealth apps. In addition to this established framework, we added a stand-alone fully featured city-building clicker-style game called "Tappy Town," and a premium virtual currency called "QuitCoins." The user earns QuitCoins for using the app in a way that contributes positively toward their quit attempt, and they can redeem these coins in Tappy Town for bonuses. To establish whether these features improved engagement and retention, we ran a 5-month randomized controlled trial where the intervention group had the full app with the extra games features, while the control group had the standard app only. Recruitment was performed via web-based advertising. Participants (N=175) had no direct contact with the researchers or other support staff. RESULTS: No significant differences in terms of engagement, retention, or smoking outcomes were found between the control and intervention groups. However, survey data indicated that the majority of the participants valued Tappy Town (10/17, 59%) and the QuitCoins rewards system (13/17, 77%). Usage data also suggested that Tappy Town was widely played and was generally appealing to users (mean total time spent in app, control group: 797 seconds vs intervention group: 3502 seconds, P<.001). Analysis of the results suggests that users in the intervention group may have been negatively affected by the aspects of the chosen design, and some theories were explored to explain this unexpected outcome. CONCLUSIONS: Although the novel features of the Quittr app failed to improve the key outcomes measured in this study, there were enough positive indications to warrant further exploration of the concept. Additional research will be required to identify and correct any design flaws that may have adversely affected our participants before a follow-up study can be completed. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Register ACTRN12617000491369; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372661&isReview=true.
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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 ».