Digital interventions for substance use disorders in young people: rapid review
Notice bibliographique
Résumé
BACKGROUND: Young people are disproportionately more likely than other age groups to use substances. The rise in substance use and related harms, including overdose, during the Covid-19 pandemic has created a critical need for more innovative and accessible substance use interventions. Digital interventions have shown effectiveness and can provide more engaging, less stigmatizing, and accessible interventions that meet the needs of young people. This review provides an overview of recent literature on the nature of recently published digital interventions for young people in terms of technologies used, substances targeted, intended outcomes and theoretical or therapeutic models employed. METHODS: Rapid review methodology was used to identify and assess the literature on digital interventions for young people. An initial keyword search was conducted using MEDLINE the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects (DARE), Health Technology Assessment Database (HTA) and PROSPERO for the years 2015-2020, and later updated to December 2021. Following a title/abstract and full-text screening of articles, and consensus decision on study inclusion, the data extraction process proceeded using an extraction grid developed for the study. Data synthesis relied on an adapted conceptual framework by Stockings, et al. that involved a three-level treatment spectrum for youth substance use (prevention, early intervention, and treatment) for any type of substance. RESULTS: In total, the review identified 43 articles describing 39 different digital interventions. Most were early interventions (n = 28), followed by prevention interventions (n = 6) and treatment interventions (n = 5). The identified digital technologies included web-based (n = 14), game-based (n = 10), mobile-based (n = 7), and computer-based (n = 5) technologies, and virtual reality (n = 3). Most interventions targeted alcohol use (n = 20) followed by tobacco/nicotine (n = 5), cannabis (n = 2), opioids (n = 2), ketamine (1) and multiple, or any substances (n = 9). Most interventions used a personalized or normative feedback approach and aimed to effect behaviour change as the intended outcome. Interestingly, a harm reduction approach guided only one of the 39 interventions. CONCLUSIONS: While web-based interventions represented the most common type of technology, more recently developed immersive and interactive technologies such as virtual reality and game-based interventions call for further exploration. Digital interventions focused mainly on alcohol use, reflecting less concern for tobacco, cannabis, co-occurring substance use, and illicit drug use. Specifically, the recent exacerbation in the opioid crisis throughout North American underlines the urgent need for more prevention-oriented digital interventions for opioid use. The uptake of digital interventions among youth also depends on the incorporation of harm reduction approaches.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».