Process and Information Needs When Searching for and Selecting Apps for Smoking Cessation: Qualitative Study Using Contextual Inquiry
Notice bibliographique
Résumé
BACKGROUND: Hundreds of apps are available to support people in their quest to quit smoking. It has been hypothesized that selecting an app from a sizable volume without any aid can be overwhelming and difficult. However, little is known about how people choose apps for smoking cessation and what exactly people want to know about an app before choosing to install it. Understanding the decision-making process may ultimately be helpful in creating tools to help people meaningfully select apps. OBJECTIVE: The aim of this study is to obtain insights into the process of searching and selecting mobile apps for smoking cessation and map the range of actions and the accompanying reasons during the search, focusing on the information needs and experiences of those who aim to find an app. METHODS: Contextual inquiries were conducted with 10 Dutch adults wanting to quit smoking by using an app. During the inquiries, we observed people as they chose an app. In addition, 2 weeks later, there was a short semistructured follow-up interview over the phone. Through convenience and purposive sampling, we included participants differing in gender, age, and educational level. We used thematic analysis to analyze the transcribed interviews and leveraged a combination of video and audio recordings to understand what is involved in searching and selecting apps for smoking cessation. RESULTS: The process of finding smoking cessation apps is comprehensive: participants explored, evaluated, and searched for information; imagined using functions; compared apps; assessed the trustworthiness of apps and information; and made several decisions while navigating the internet and app stores. During the search, the participants gained knowledge of apps and developed clearer ideas about their wishes and requirements. Confidence and trust in these apps to help quitting remained quite low or even decreased. Although the process was predominantly a positive experience, the whole process took time and energy and caused negative emotions such as frustration and disappointment for some participants. In addition, without the participants realizing it, errors in information processing occurred, which affected the choices they made. All participants chose an app with the explicit intention of using it. After 2 weeks, of the 10 participants, 6 had used the app, of whom only 1 extensively. CONCLUSIONS: Finding an app in the current app stores that contains functions and features expected to help in quitting smoking takes considerable time and energy, can be a negative experience, and is prone to errors in information processing that affect decision-making. Therefore, we advise the further development of decision aids, such as advanced filters, recommender systems and curated health app portals, and make a number of concrete recommendations for the design of such systems.
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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 | 0,021 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,010 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».