Effective Behavioral Changes through a Digital mHealth App: Exploring the Impact of Hedonic Well-Being, Psychological Empowerment and Inspiration
Notice bibliographique
Résumé
BACKGROUND: New mobile health (mHealth) software apps are emerging and are providing the foundation to radically transform the practice and reach of medical research and care. For this study we collaborated with Quit Genius, a cognitive behavioral therapy (CBT) based mHealth app that helps users quit smoking, to explore the effective design of a digital mHealth app; one that delivers important benefits to its users and helps them change their behaviors for a healthier lifestyle. OBJECTIVE: The specific aims of this study were to (1) explore the key role of CBT program progress, (2) examine the gamification design app elements that deliver significant benefits (ie, empowerment, well-being, inspiration) to users, (3) explore the effectiveness of these app elements to help users quit smoking or reduce the number of cigarettes smoked, and (4) identify and describe any potential drivers and hindrances arising from the app design elements. METHODS: We developed an online survey and sent an email invitation to 4144 individuals, who had previously or were at the time using the Quit Genius mHealth app, to encourage participation in the study. We matched the online survey data with objective app usage data of the study participants. RESULTS: A dataset of 190 completed responses was used. At the time of the survey, respondents had completed an average of 60% of the CBT program in the Quit Genius mHealth app. Of the respondents, 36.3% (69/190) noted to have quit smoking successfully after using the Quit Genius app. As for those who remained smokers after using the app (N=121), the number of cigarettes smoked per day was significantly reduced by 59.6%. The ability of the app to enhance users' hedonic well-being and psychologically empower them in their daily lives was identified as being key in helping users quit smoking. Specifically, the results show that users whose well-being was enhanced through the app were 1.72 times more likely to quit smoking successfully. Moreover, a one-unit increase on a 7-point Likert scale in the app's ability to empower smokers in their daily lives led to a reduction of cigarettes smoked per day of 53%. The app's inspiration to users, however, was negatively associated with quitting success and the reduction in cigarette smoked per day. CONCLUSIONS: The findings offer important insights for the effective design of digital mHealth apps. Specifically, we find that perceived psychological empowerment and enhanced hedonic well-being from the mobile solution may be a more impactful way to support the effectiveness of mobile cognitive behavioral therapy for smoking cessation than eliciting strong inspiration.
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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 | 0,001 | 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 | 0,001 | 0,001 |
| 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 ».