Perceptions Toward an Attentional Bias Modification Mobile Game Among Individuals With Low Socioeconomic Status Who Smoke: Qualitative Study
Notice bibliographique
Résumé
Background: Attentional bias modification (ABM) can help address implicit attention from individuals who smoke toward smoking cues, which trigger cravings and lapses that impede smoking cessation. We developed an ABM mobile game, Fruit Squish, to support individuals who smoke and are quitting as part of a multicomponent smoking cessation mobile app, Quit Journey. Users advance in the game by tapping on neutral (ie, fruit) rather than smoking-related (eg, cigarette pack) imagery that they are presented with, essentially training them to avoid focusing on smoking cues. Objective: This study aimed to gauge acceptance of an ABM smoking cues mobile game among young adults who smoked and were socioeconomically disadvantaged. Methods: We recruited 38 individuals who smoked cigarettes, aged 18-29 years, who were neither 4-year college graduates nor enrollees in 4-year colleges to participate in 12 semistructured digital focus groups. Sessions were audio recorded and transcribed verbatim. We used ATLAS.ti software to code the transcripts for salient themes based on the Second Unified Theory of Acceptance and Use of Technology constructs (ie, effort expectancy, facilitating conditions, hedonic motivation, performance expectancy, and social influence) and sentiment (ie, negative, neutral, and positive). Results: Performance expectancy of the mobile game was the dominant technology acceptance construct discussed (34/110, 30.90%). Perceived usefulness of the game was mixed in sentiment owing to perceptions that the game aimed to distract individuals who smoke during cravings and concerns that cue imagery in the game could trigger cravings. Hedonic motivation was the second most discussed technology acceptance construct (17/110, 15.45%), with participants describing the game as neither fun nor engaging. Participants referenced their past experiences with mobile games and mobile device characteristics as facilitating conditions for using the game (10/110, 9.09%). Although effort expectancy was minimally discussed (6/110, 5.45%), the game was characterized as easy to use. To improve the game, participants suggested adding new levels with increasing difficulty (eg, increase stimuli speed and limit session time) and new game elements (eg, leaderboard). Other suggestions included improving game graphics and renaming the game to capture its relation to smoking cessation. Conclusions: Young adults with low socioeconomic status who smoke had mixed reactions to a mobile smoking cues ABM game. Results suggest the need to communicate the rationale underlying ABM games to users and their potential positive effects on smoking cessation. To promote the uptake and sustained use of ABM mobile games, they need to be on par with commercially available entertainment mobile apps. Research is needed to explore the efficacy of gamified ABM on cognitive biases in real-life settings.
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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 | 0,000 | 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 | 0,000 | 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 ».