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Digital interventions targeting excessive substance use and substance use disorders: a comprehensive and systematic
Magnus Johansson1,2, Danilo Romero1,2, Miriam Jakobson1,2
1Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
Abstract:
Addictive substances are prevalent world-wide, and their use presents a substantial and persistent public health problem. A wide range of digital interventions to decrease use and negative consequences thereof have been explored, differing in approach, theoretical grounding, use of specific technologies, and more. The current study was designed to comprehensively map the recent (2015-2022) extant literature in a systematic manner, and to identify neglected and emerging knowledge gaps. Four major databases (Medline, Web of Science Core Collection, and PsychInfo) were searched using database-specific search strategies, combining terms related to clinical presentation (alcohol, tobacco or other drug use), technology and aim. After deduplication, the remaining n=13,917 unique studies published were manually screened in two stages, leaving a final n=3,056 studies, the abstracts of which were subjected to a tailored coding scheme. Findings revealed an accelerating rate of publications in this field, with randomized trials being the most common study type. Several meta-analyses on the topic have now been published, revealing promising and robust effects. Digital interventions are being offered on numerous levels, from targeted prevention to specialized clinics. Detailed coding was at times made difficult by inconsistent use of specific terms, which has important implications for future meta-analyses. Moreover, we identify several gaps in the extant literature - few health economic assessments, unclear descriptions of interventions, weak meta-analytic support for some type of interventions, and limited research on many target groups, settings and new interventions like video calls, chatbots and artificial intelligence - that we argue are important to address in future research.
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