Real-Time Mobile Monitoring of Drinking Episodes in Young Adult Heavy Drinkers: Development and Comparative Survey Study
Notice bibliographique
Résumé
BACKGROUND: Binge drinking, defined as consuming five or more standard alcoholic drinks for men (four for women) within a 2-hour period, is common among young adults and is associated with significant alcohol-related morbidity and mortality. To date, most research on this problem in young adults has relied upon retrospective questionnaires or costly laboratory-based procedures. Smartphone-based ecological momentary assessment (EMA) may address these limitations by allowing researchers to measure alcohol use and related consequences in real time and in drinkers' natural environments. To date, however, relatively less research has systematically examined the utility of this approach in a sample of young adults targeting real-world heavy drinking episodes specifically. OBJECTIVE: This study aimed to evaluate the feasibility, acceptability, and safety of a smartphone-based EMA method targeting binge drinking and related outcomes in heavy drinking young adults during real-world drinking occasions. METHODS: Young adult binge drinkers in the smartphone group (N=83; mean 25.4 (SD 2.6) years; 58% (48/83) male; bingeing on 23.2% (6.5/28) days in the past month) completed baseline measures of alcohol use and drinking-related consequences, followed by up to two smartphone-based EMA sessions of typical drinking behavior and related outcomes in their natural environments. They also completed next-day and two-week follow-up surveys further assessing alcohol use and related consequences during the EMA sessions and two weeks after study participation, respectively. A separate demographic- and drinking-matched safety comparison group (N=25) completed the baseline and two-week follow-up surveys but did not complete EMA of real-world drinking behavior. RESULTS: Most participants (71%, 59/83) in the smartphone group engaged in binge drinking during at least one 3-hour EMA session, consuming 7.3 (SD 3.0) standard alcoholic drinks. They completed 87.2% (507/581) system-initiated EMA prompts during the real-world drinking episode, supporting the feasibility of this approach. The procedure was acceptable, as evidenced by high participant ratings for overall satisfaction with the EMA software and study procedures and low ratings for intrusiveness of the mobile surveys. Regarding safety, participants endorsed few drinking-related consequences during or after the real-world drinking episode, with no adverse or serious adverse events reported. There were no differences between the groups in terms of changes in drinking behavior or consequences from baseline to two-week follow-up. CONCLUSIONS: This study provided preliminary support for the feasibility, acceptability, and safety of a smartphone-based EMA of real-time alcohol use and related outcomes in young adult heavy drinkers. The results suggest that young adults can use smartphones to safely monitor drinking even during very heavy drinking episodes. Smartphone-based EMA has strong potential to inform future research on the epidemiology of and intervention for alcohol use disorder by providing researchers with an efficient and inexpensive way to capture large amounts of data on real-world drinking behavior and consequences.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».