Feasibility and Engagement of a Mobile App Preparation Program (Kwit) for Smoking Cessation in an Ecological Context: Quantitative Study
Notice bibliographique
Résumé
Background Mobile health apps can facilitate access to effective treatment and therapeutic information services. However, the real-world effectiveness of mobile apps for smoking cessation and their potential impact in everyday settings remain unclear. Objective In an ecological context, this study aimed to estimate the engagement rate of a mobile app–based smoking cessation preparation program and its potential impact on users’ willingness, ability, and readiness to quit smoking. Methods A total of 2331 “organic users” (ie, users who discover and install a mobile app on their own, without any prompts) chose 1 of 2 program versions of the mobile app (Kwit): the basic version or the premium version. Both versions were identical in design, with 4 more evidence-based content items and strategies in the premium version. Outcomes were analyzed based on automated data registered in the app (engagement rate, motivation to quit, motivation type, motivation levels, and satisfaction level). Mann-Whitney and χ2 tests were used to compare the results of both groups. Results As expected, in the ecological context, a high dropout rate was observed at different moments. A significant difference was observed between the 2 versions (n=2331; χ21=5.4; P=.02), with a proportionally higher engagement rate in the premium version (premium=4.7% vs basic=2%). Likewise, differences were also observed between the 2 groups in terms of reasons to quit (n=2331; χ24=19; P≤.001; V=0.08), motivation type (n=2331; χ27=14.7; P=.04), and motivation level. Users of the app’s premium version more frequently reported “well-being” (23.3% vs 17.9%) and “planning a pregnancy” (7.4% vs 4.4%) as their primary reasons for quitting smoking compared to those with the basic version. Moreover, they reported being more likely to be driven in the smoking cessation process by intrinsic motivation (premium=28% vs basic=20.4%), as well as feeling significantly more willing (z score=156,055; P≤.001; Cohen d=0.15), able (z score=172,905; P=.04; Cohen d=0.09), and ready (z score=166,390; P=.005; Cohen d=0.12) to stop smoking than users who had the basic version before completion of the preparation program. Among participants who finished each version of the program (premium: 9/189, 4.8%; basic: 47/2142, 2.19%), significant improvements in motivation levels were observed in both groups, although in different areas for each group (willingness levels for the premium group and ability for the basic group). Conclusions These results suggest that even in ecological contexts where engagement rates are meager, the Kwit preparation program can address ambivalence by increasing willingness to change, self-confidence, and readiness to quit among its users, especially those who feel less able to do so. Further development and evaluations are needed to better understand determinants for regular mobile health apps.
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 | 0,008 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».