Exploratory analysis of the use of a secondary prevention application for unhealthy alcohol use
Notice bibliographique
Résumé
Background and aims: Unhealthy alcohol use is a leading risk factor for mortality and morbidity worldwide. Smartphone applications are a recently developed approach to secondary prevention of unhealthy alcohol use. As smartphone apps targeting unhealthy alcohol use is an emerging field, a better understanding of its use will provide information that may help future research. We used data from two recent studies on the efficacy of a smartphone app to study how the app is used, by whom and what characteristics are associated with its use. Methods: The data analyzed in this work come from the two randomized controlled trials assessing the effect of the same smartphone intervention. The first study, held in Switzerland, provided access to the smartphone app to university students with unhealthy alcohol use. The second study, held in Canada, assessed the efficacy of the smartphone app in a sample recruited from the general population. First, we studied the app different modules’ frequency of use. Then, we investigated whether there were any specific uses associated with selected participants’ characteristics. Analyses were conducted separately in the Swiss and Canadian samples. Results: The Swiss sample, recruited in a student population, had a mean age (SD) of 22 (2.8) years. The Canadian sample, recruited from the general population, was older, with a mean age of 41.7 (12.5) years. In terms of alcohol consumption, the Canadian sample consumed more alcohol than the Swiss sample, with a mean of 30.5 (19.5) drinks per week against 8.9 (8.6) drinks a week. Regarding the use of the app, the median number of openings per module ranged from 0 to 1 for both samples. In both samples, the “game-type” and “assessment-type” modules were the most often open modules. The “follow-up” modules, which were little used by the Swiss sample, were used more by the Canadian sample. The following participants characteristics were significantly associated with the use of the various modules: gender, AUDIT score (“Alcohol Use Disorder Identification Test”, a score indicative of the severity of alcohol use and its consequences), level of education and age at baseline (Swiss sample); AUDIT score, formal treatment for alcohol use, highest level of education achieved and age at baseline (Canadian sample). Conclusion: Overall, participants made very little use of the app. Nevertheless, while usage was limited it seemed that this level of use was enough to have an effect on drinking, as the two studies showed a significant effect on drinking. The Swiss sample showed a greater interest in the more entertaining, interactive “game-type” and “assessment-type” modules. The Canadian sample showed interest in the interactive “game-type” and “assessment-type” modules. However, unlike the Swiss sample, the Canadian population, also demonstrated an interest in the “follow-up” modules. This study highlighted the fact that preferences for using the various modules differed according to the characteristics of the participants, and that the modules used matched the age and the severity of alcohol use. Thus, to optimize the effectiveness of the app, it seems essential to adjust its content according to the target audience.
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 | 0,013 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 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) | 0,005 | 0,001 |
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 ».