Evaluation of Two Mobile Health Apps in the Context of Smoking Cessation: Qualitative Study of Cognitive Behavioral Therapy (CBT) Versus Non-CBT-Based Digital Solutions
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth) apps can offer users numerous benefits, representing a feasible and acceptable means of administering health interventions such as cognitive behavioral therapy (CBT). CBT is commonly used in the treatment of mental health conditions, where it has a strong evidence base, suggesting that it represents an effective method to elicit health behavior change. More importantly, CBT has proved to be effective in smoking cessation, in the context of smoking-related costs to the National Health Service (NHS) having been estimated to be as high as £2.6bn in 2015. Although the evidence base for computerized CBT in mental health is strong, there is limited literature on its use in smoking cessation. This, combined with the cost-effectiveness of mHealth interventions, advocates a need for research into the effectiveness of CBT-based smoking cessation apps. OBJECTIVE: The objective of this study was, first, to explore participants' perceptions of 2 mHealth apps, a CBT-based app, Quit Genius, and a non-CBT-based app, NHS Smokefree, over a variety of themes. Second, the study aimed to investigate the perceptions and health behavior of users of each app with respect to smoking cessation. METHODS: A qualitative short-term longitudinal study was conducted, using a sample of 29 smokers allocated to one of the 2 apps, Quit Genius or Smokefree. Each user underwent 2 one-to-one semistructured interviews, 1 week apart. Thematic analysis was carried out, and important themes were identified. Descriptive statistics regarding participants' perceptions and health behavior in relation to smoking cessation are also provided. RESULTS: The thematic analysis resulted in five higher themes and several subthemes. Participants were generally more positive about Quit Genius's features, as well as about its design and information engagement and quality. Quit Genius users reported increased motivation to quit smoking, as well as greater willingness to continue using their allocated app after 1 week. Moreover, these participants demonstrated preliminary changes in their smoking behavior, although this was in the context of our limited sample, not yet allowing for the finding to be generalizable. CONCLUSIONS: Our findings underscore the use of CBT in the context of mHealth apps as a feasible and potentially effective smoking cessation tool. mHealth apps must be well developed, preferably with an underlying behavioral change mechanism, to promote positive health behavior change. Digital CBT has the potential to become a powerful tool in overcoming current health care challenges. The present results should be replicated in a wider sample using the apps for a longer period so as to allow for generalizability. Further research is also needed to focus on the effect of greater personalization on behavioral change and on understanding the psychological barriers to the adoption of new mHealth solutions.
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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 | 0,004 | 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 ».