Perceptions of the Use of Mobile Technologies for Smoking Cessation: Focus Group Study With Individuals of Low Socioeconomic Status Who Smoke
Notice bibliographique
Résumé
BACKGROUND: The use of mobile technologies to deliver behavioral health interventions, including smoking cessation support, has grown. Users' perceptions are important determinants of the adoption and use of new technologies. However, little is known about users' perceptions of mobile technologies as smoking cessation aids, particularly among disadvantaged individuals who smoke. OBJECTIVE: This study aimed to examine the acceptance of mobile technologies for smoking cessation among young adults with low socioeconomic status who smoke. METHODS: In total, 38 current cigarette smokers, 18 to 29 years old, who wanted to quit and did not have a 4-year college degree nor were enrolled in a 4-year college, participated in 12 semistructured digital focus groups. The moderation guide was guided by the Unified Theory of Acceptance and Use of Technology. Discussions were audio recorded, transcribed verbatim, and coded for the Unified Theory of Acceptance and Use of Technology constructs (ie, effort expectancy, facilitating conditions, performance expectancy, and social influence), sentiment (ie, negative, neutral, and positive), and purpose of using mobile technologies (ie, lifestyle and health management and smoking cessation) following a deductive thematic analysis approach. RESULTS: Participants had positive experiences using mobile technologies for lifestyle and health management, primarily for fitness and dietary purposes. Salient themes were facilitating conditions of use (44/80, 55%), with prior experiences and costs subthemes, followed by perceived usefulness of mobile technologies in helping users attain health goals (22/80, 27.50%), which were generally positive. Ease of use (11/80, 13.75%) and social influences (3/80, 3.75%) were minimally discussed. Conversely, participants had limited awareness of smoking cessation uses of mobile technologies, which was the primary barrier under facilitating conditions discussed (33/51, 64.70%). Participants expressed skepticism about the usefulness of mobile technologies in helping them quit smoking (14/51, 27.45%). Effort expectancy was not discussed, given participants' limited prior use. Social influences on mobile technology use for smoking cessation were minimally discussed (4/51, 7.84%). CONCLUSIONS: The use of mobile technologies for smoking cessation was unknown to young adults with low socioeconomic status who smoke. To reduce cigarette smoking and associated health disparities, increasing awareness and use of evidence-based mobile-based smoking cessation interventions are needed. Smoking cessation interventions should incorporate features perceived as useful and easy to use to capitalize on positive user experiences and the acceptability of mobile technologies for lifestyle and health management.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».