Assessing the Feasibility and Acceptability of the Daybreak Drink Tracker: Prospective Observational Study
Notice bibliographique
Résumé
Background: Excessive alcohol use is associated with significant harms, with wide-ranging social and economic impacts. Efforts to prevent and reduce the harmful use of alcohol are a public health priority. Smartphone apps have the potential to provide accessible and cost-effective support to those seeking to reduce alcohol consumption; however, the evidence base regarding which components are effective is lacking. Self-monitoring is considered as one of the most effective components for behavior change across multiple health domains, yet there is mixed evidence for its role in the alcohol use space. An improved understanding of the use, acceptability, and outcomes of smartphone apps and their inherent components is required to determine their potential role in alcohol behavior change. Objective: We investigated the feasibility and acceptability of the Drink Tracker, a novel feature of the commercially available Daybreak (Hello Sunday Morning) app. Methods: The Daybreak app is accessible worldwide via major app stores and is offered free of charge to Australian residents. Individuals (aged over 18 years) registering for Daybreak were invited to access the Drink Tracker to monitor their alcohol consumption as part of an uncontrolled observational prospective study. Feasibility was assessed via uptake and frequency of use of the Drink Tracker. Acceptability was measured via participant feedback to determine overall satisfaction, perceived helpfulness, and likelihood of recommending the Drink Tracker to others. Self-reported changes in alcohol consumption (Alcohol Use Disorders Identification Test score) and psychological distress (Kessler Psychological Distress Scale score) at 3-month follow-up were also measured. Preliminary data collected for the first 4 months (October 2023 to February 2024) of the study were reported, including 3-month follow-up outcomes. Results: Feasibility was demonstrated, with almost 70% (2847/4119) of those registering for Daybreak going on to access the Drink Tracker. Of those accessing the Drink Tracker, 71.1% (n=2024) consented to research, comprising the final participant sample. Frequency of use was high, with over half of participants (1112/2024, 54.9%) using the Drink Tracker more than once, and more than one-third (757/2024, 37.4%) using the Drink Tracker more than 5 times. Of the 30 participants completing a 3-month follow-up, acceptability was high, with 73% (n=22) reporting high satisfaction levels with the Drink Tracker overall, 87% (n=26) indicating it was easy to use and rating a mean score of 7.7 (SD 2.8) out of 10 in terms of likelihood of recommending to others. Significant reductions in alcohol consumption (P<.001) and psychological distress scores (P<.001) were observed at the 3-month follow-up. Conclusions: Our results suggest that the Daybreak Drink Tracker is highly feasible and acceptable in supporting individuals accessing commercially available smartphone apps to change their relationship with alcohol. While positive clinical outcomes were observed, the absence of a control group disallows any conclusions with regard to the efficacy of the Drink Tracker. Further testing via a randomized controlled trial is required.
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 | 0,007 | 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».