- Open Access
Digital interventions for substance use disorders in young people: rapid review
Substance Abuse Treatment, Prevention, and Policy volume 18, Article number: 13 (2023)
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.
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.
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.
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.
Adolescence and young adulthood are critical periods for first-time substance use, with peak levels occurring between ages 18–25 in most countries and for most types of drugs . Alcohol use is most prevalent among young people worldwide, with 26.5% of 155 million adolescents ages 15–19 identified as users : in Europe (43.8%), the Americas (38.2%) and the Western Pacific Region (37.9%) . Concerning tobacco, 155 million people who smoke were identified in the 15–24 year age group for 2019, with an estimated global prevalence of 20.1% for males and 4.95% for females . Smoking rates exceeded 33% for youth in the Pacific Islands, Europe (Bulgaria, Croatia, Latvia, France), Chile, Turkey, and Greenland . Current use of the Electronic Nicotine Delivery Systems (ENDS) by youth (ages 8–20) was estimated at 7.8% , while past-month e-cigarette use among US teens increased 78% by 2017 . Cannabis, considered relatively benign by youth when legalized , is the third most widely used substance . Around 14 million or 5.7% of students 15–16 years old used cannabis in 2019 , with especially high use reported for Oceania (18%), the Americas (12.5%), and Europe (12%) . Illicit drug use (heroin) among US high school students reached 7.0% in some urban centers by 2017 but was masked by lower national averages , while rates of cocaine, methamphetamine, and heroin use among young adults reached 11.4% in 2018 . A US college study reported illicit substance use ranging from 6% for nonmedical use of prescription opioids to 21% for stimulants in 2020 .
Substance use in adolescence and young adulthood is associated with multiple adverse health outcomes, including high mortality (15–27%) among young people 15–29 years old from accidents and injuries due to alcohol consumption . Alcohol and tobacco use were associated with increased long-term risks for cancers, cardiovascular and chronic respiratory diseases . Health risks for young people who smoke included poor diet, inactivity, stress, and poor sleep hygiene, but also increased heavy episodic drinking, cannabis, and other drug use . Studies observed the same progression to cigarette, marijuana, cannabis, and illicit drug use in vaping, as well as poisoning and severe withdrawal symptoms . Research has identified marijuana use as a potential gateway to illicit drug use and the onset of psychiatric disorders in adolescents and young adults .
Self-isolation, social distancing and other public health measures enacted during the Covid-19 pandemic have exacerbated drug use and disrupted service delivery , creating barriers as unmet support needs increased among young people [14,15,16]. The global impact of the pandemic in terms of substance use and overall mental health has yet to be fully understood . While increased tobacco and cannabis use was the single change noted in the early months of the pandemic across Europe , North American evidence showed overall increased substance use among young people [19, 20]. Trends also show associations between alcohol, tobacco and/or marijuana use and the initiation of illicit substances over time [9, 21,22,23,24,25]. In particular, the pandemic has worsened the ongoing opioid crisis , including misuse of prescription drugs by US youth . The US and Canada reported more drug overdose-related deaths , with opioid toxicity deaths increasing roughly 66%, from 1,038 in 2019 to 1,792 by March 2021 .
The evidence on substance use interventions related to prevention, early intervention and treatment suggests that prevention interventions, typically delivered in educational settings, show greater effectiveness when targeting generic substance use than substance-specific programs  and may lower the odds of lifetime substance use . Providing information on harms was ineffective, whereas skills development was a more effective approach . Unfortunately, youth seeking substance use treatment have long faced multiple barriers related to treatment access, waitlists, costs, and stigma [33,34,35]. Moreover, many young people tend to delay or avoid help-seeking due to a preference for self-management , negative perceptions of services and professionals , and concerns about the stigma of mental illness [35, 37]. In fact, studies of young people in Western countries found that approximately 25% used services at all for mental health or substance-related problems [38,39,40,41], with many preferring the anonymity of online resources for accessing health information, education, and treatment [42,43,44]. As nearly all youth use the internet, and given the recent service environment, research on digital interventions has flourished, showing effectiveness for technologies based on internet, virtual reality, smartphones, video games, and telehealth for mental health problems [45,46,47,48], including substance use problems . Digital technologies provide readily available, self-help alternatives and support for in-person treatment [15, 49, 50].
Few reviews concerned with digital interventions for youth substance use have been published [50, 51], with most focusing on a single substance (e.g., cannabis) or digital intervention (e.g. web-based intervention) without providing an overview of which digital interventions and technologies have been developed to support youth with substance use problems. Given recent trends in substance use and the shift to virtual treatment, this review provides an overview of recently published digital interventions with attention to how they meet user and research needs. This is the first rapid review to map the types of digital technologies in terms of substances targeted, level of treatment (prevention, early intervention, treatment) and expected outcomes, providing tangible information for researchers and front-line providers and with eventual relevance for young people using substances.
The review addressed the following research question: What is the nature of digital technologies used in substance use interventions for young people, focusing on a single or multiple substances? The description and assessment of the various digital technologies included: (a) study and sample characteristics, (b) level on the spectrum of treatment interventions (prevention intervention, early intervention, treatment intervention) , c) targeted outcomes of the digital technologies for people using substances (e.g., behaviours, knowledge, perceptions of beliefs, attitudes, motivation or intentions); and (d) the underlying theoretical or therapeutic approaches used.
Given the evolving shift to virtual care with the onset of the pandemic, rapid review methodology was used. While there is no consensus definition, the literature describes rapid review as a form of knowledge synthesis that streamlines and accelerates systematic review methods [53, 54]. The rapid review takes a more descriptive than critical approach and generally presents results as a narrative summary . Easing certain requirements of full systematic reviews, rapid reviews may use a single research question, limit database sources and search years, and reduce research timeframes, allowing for timely completion and the delivery of recommendations to decision makers, healthcare professionals, policy makers or consumers, while saving resources [54, 55, 57]. The methods adopted in this study followed Khangura et al. . The AMSTAR systematic review checklist is included in Supplementary Materials .
The search strategy involved several databases: MEDLINE (via PubMed), the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects (DARE), Health Technology Assessment Database (HTA) and PROSPERO. The initial search was conducted in February 2021 for articles published between January 2015 and December 2020 and updated in January 2022 for studies published between January and December 2021. The search was limited to the previous 6 years, as research on digital interventions is a recent field. The terms used in the MeSH search strategy included: "Substance-Related Disorders" OR ‘’Smoking’’) AND ("Video Games" OR "Internet-Based Intervention" OR "Mobile Applications" OR "Virtual Reality" OR "Therapy, Computer-Assisted") AND ("Adolescent" OR "Young Adult"). Articles identified in the search were downloaded into the Endnote reference manager and duplicates removed.
The inclusion criteria were: (1) studies evaluating digital health interventions for substance-related disorders in youth; (2) primary, empirical studies using quantitative, qualitative and mixed-methodologies and review articles related to the topic; (3) populations including youth and young adults from 12 to 29 years old . If a study age range was + or—1 year from our eligibility criteria, the study was included to ensure that important information was not omitted for the target population. For studies that expressed age as a mean rather than an age range, those with a mean age between 16–24 and a standard deviation below 4 were included; and (4) English or French language studies. Articles with interventions pertaining to substance-related disorders in combination with non-mental health issues (e.g., HIV or reproductive health) were excluded, as were dissertations and studies where participants were not exposed to the interventions (e.g., protocols, editorials, descriptive studies).
Study selection and data extraction
Study selection involved a two-phase study identification process that included title/abstract and full text screening. First, two reviewers (MM and MF) independently screened titles and abstracts and assessed studies for inclusion using the Rayyan screening tool (Qatar Computing Research Institute), a web-based tool designed to facilitate study identification in knowledge synthesis projects . Disagreements about study inclusion were resolved by consensus. Abstracts with insufficient information to screen for all the eligibility criteria passed directly to full-text screening. Second, for studies that met the inclusion criteria in the title-abstract selection, a single rater (MM) read the full texts to confirm that they met the inclusion criteria, and a second rater (MF) reviewed this work. Reviews and meta-analyses identified in the MeSH search were set aside for a secondary search of reference lists.
A data extraction grid was developed based on the following categories: (1) study characteristics: aims/hypotheses, setting, methods, targeted outcomes and main findings; (2) sample characteristics: age, population, sample size; (3) intervention characteristics: type of intervention (technology), substances targeted, description of intervention, theoretical or therapeutic model, and level or spectrum of the intervention (prevention intervention, early intervention, treatment intervention) . As recommended by the Cochrane rapid review methods group, a single reviewer extracted the data (MM) and the others (MF and JS) verified the data for correctness and completeness .
The extracted data were synthesized, and interventions organized using an adaptation of a framework by Stockings et al. , which describes a three-level treatment spectrum for youth substance use interventions. 1) Prevention interventions aim to reduce interest in using substances, limit availability by making substances more difficult to obtain or consume, or discourage their use with criminal or other sanctions. 2) Early interventions identify youth at risk or showing signs of problematic substance use, aiming to reduce use before it escalates. They include harm reduction approaches focused on restricting or minimizing the negative effects of substance use. 3) Treatment aims at addressing problematic, heavy or dependent patterns of drug use and may focus on family, peers or the broader community as well as affected individuals ( p.282.). While Stockings also describes a number of important population-based interventions for reducing youth substance use, like restrictions on alcohol sales outlets, legal age limits on alcohol and tobacco use, and prohibitions against the use of controlled substances in many countries, these strategies were beyond the scope of this study which concerned only individual-level interventions. Targeted outcomes identified for each study were categorized according to behaviours, knowledge, perceptions or beliefs, attitudes, motivation or intentions, cravings, cognition, mood, skills, and functioning. Risk of bias assessments and critical appraisal of studies were not conducted due to the heterogeneity of study methodologies.
The searches yielded 192 records, in total. Screening of the titles and abstracts for eligibility criteria produced 90 articles for full-text screening, with 102 records excluded. Of the 90 articles retained for full-text screening, 48 did not meet eligibility criteria and were excluded. A secondary search of reference lists was conducted on the three identified review articles [50, 51, 60], leaving 39 primary studies for review, to which 4 handpicked studies identified in the secondary searches were added. In all, the final review included 43 studies (See Fig. 1: Study Flow Chart).
Study and sample characteristics
The 43 studies represent research conducted in 10 countries: the USA (25 studies), Australia (5), the UK and the Netherlands (3 each), Italy (2), and 1 each from China, France, Spain, Switzerland, and Taiwan. The studies described 39 different digital interventions that were distinguished in terms of which technology they used (See Table 1: Study characteristics and findings for 43 studies; See also Appendix 1: Definitions of digital intervention technologies). Just over one third were web-based (without a game component) (14/39: 35.9%), followed by game-based (10/39: 25.6%), mobile-based (7/39: 17.9%), computer or tablet-based (5/39: 12.8%), and virtual reality-based interventions (3/39: 7.7%). Web-based interventions were used in studies with youth and young adults ages 11–30, game-based interventions for ages 11–27, mobile-based (computer or tablet) for ages 12–35, and virtual reality interventions for ages 11–22.
Just over half of the digital interventions targeted alcohol use (20/39: 51.3%), followed by tobacco or nicotine interventions (5/39: 12.8%), of which slightly more than half (3/5) addressed e-cigarette use. Digital interventions for cannabis use (2/39: 5.1%), opioids (2/39: 5.1%), and ketamine (1/39: 2.6%) were less common. Nine of the 39 interventions targeted multiple or any substances (9/39: 23.1%), of which three studies targeted alcohol and cannabis use, alcohol and tobacco use (n = 1), cannabis and tobacco (n = 1), alcohol, nicotine, and caffeine (n = 1), and any substance (n = 3).
Regarding the therapeutic or theoretical approaches used, most were feedback interventions (18/39: 46.1%), nearly all of which (13 of 18) provided comparisons with normative substance use among peers. As well, all of the 18 feedback interventions were for alcohol use and all were early interventions. Nine of the 39 interventions reported using a skills training approach, and 4 used cognitive bias modification. Importantly, only one of the 39 interventions employed a harm minimization approach. Definitions of these approaches can be found in Appendix 2. The names of the digital interventions, if provided by authors, were reported in Table 1; otherwise, a descriptive identifier was given.
Spectrum of substance use treatment interventions and intended outcomes
Regarding the three levels on the spectrum of substance use treatment , the vast majority in this review were early interventions (28/39: 71.8%), with nearly equal occurrences of prevention (6/39: 15.4%) and treatment (5/39: 12.8%) interventions. Figure 2 illustrates this distribution, including the types of digital technologies associated with each level (see Table 2). In terms of substances, most early interventions were geared towards alcohol use (20/28: 71%). No prevention interventions targeted alcohol or cannabis use only (Table 2).
Figure 3 organizes the 39 interventions according to the three-level treatment spectrum (prevention, early intervention, and treatment) and maps the designated outcomes for each intervention. The most common designated outcome was a change in behaviour (33/39). Most early interventions (24/28) and nearly all treatment interventions (4/5) targeted behaviour change, compared with only half of studies using prevention interventions (3/6). Prevention interventions more often designated knowledge, perceptions or beliefs, attitudes, and intention to use substances as the intended outcomes.
The nature of digital technologies in substance use interventions for youth
To address the main research question on the nature of digital technologies used in substance use interventions for young people, this section brings together the data on the five types of digital interventions identified in the review, describing which substances they targeted, the theoretical or therapeutic approaches used and level on the spectrum of treatment interventions.
Web-based interventions were the most commonly used technology (14/39), with most (8/14) targeting alcohol use: Alcooquiz , One Too Many , the DEAL project , the Geisner et al. intervention., Miller et al. intervention , Schuckit et al. intervention , Tuliao et al. intervention  and ALERTA ALCOHOL . All 8 interventions for alcohol use were early interventions, mainly geared to young adults, and all, except the DEAL project, provided users with feedback on their alcohol use (e.g., risks, consequences) [61, 66, 68, 70, 71, 74, 75]. Two web-based interventions targeted tobacco use (2/14): Let’s talk about smoking , and the Put it Out Project . Let’s talk about smoking was a treatment intervention consisting of a brief intervention and motivational decision support , while the Put it out Project was an early intervention, based on US clinical guidelines and trans-theoretical models of behaviour change . The Walukevich-Dienst  intervention, an early intervention with screening and personalized feedback, was the only web-based intervention for cannabis. Another, POP4Teens, was a web-based intervention for opioid use prevention based on psychoeducation, social influence, and skills training . Other web-based interventions (2/14) targeted multiple, or any, substances: the Climate School courses [63,64,65] and RealTeen, tested in two studies by Schwinn et al. [72, 73]. The Climate Schools courses were a prevention intervention with an in-person component for alcohol and cannabis that used a social influence and harm-minimization approach [63,64,65]. RealTeen targeted prevention of any substance use and was based on psychoeducation, skills training, goal setting, social learning theory, and a resiliency framework [72, 73].
Game-based interventions, the second most common type of digital technology (10/39), mainly targeted alcohol use (5/10) and were all early interventions. They included the Boendermaker et al. intervention , Campus GANDR v2 , Ray’s Night Out , Alcohol Alert , and the Labrie et al. intervention . These interventions encompassed a range of theoretical approaches: cognitive bias modification , I-Change model and personalized feedback , motivational interviewing, from the Information-Motivation-Behavioural 2 skills health behaviour model and social learning theory , personalized normative feedback [81, 84] and self-determination theory . HitnRun was the only game-based intervention for tobacco use, an early intervention based on peer contagion, that integrated principles of Go/No-Go training . Recovery Warrior , a treatment intervention based on Social Cognitive Theory, Repetitive priming, and Reinforcement Theory of Motivation was the single game-based intervention targeting opioid use. Finally, Smoke-SCREEN , Arise  and Tetris  were the three (of 10) game-based interventions targeting multiple or any substances. Smoke-SCREEN targeted cannabis and tobacco prevention with behavioural skills development ; Arise for any substance and based on coping skills training ; while Tetris was an early intervention for alcohol, nicotine, and caffeine using elaborated intrusion theory and visual interference .
Seven of the 39 identified interventions were mobile interventions, most (4/7) targeting alcohol use. They included the Boendermaker et al. intervention , D-ARIANNA, tested in two studies by Carra et al. [88, 89], Drinks Meter , and SmarTrek  and all were early interventions for alcohol use. Boendermaker et al. used approach-avoidance training (cognitive bias modification) , while other interventions for alcohol included a feedback component. SmarTrek used motivational interviewing with ecological momentary interventions and personalized feedback . D-ARIANNA did not specify a theoretical model but resembled a brief intervention with personalized feedback [88, 89]. Drinks Meter used personalized normative feedback based on psychoeducation . The remaining mobile interventions (3/7) targeted multiple or any substances: ACHESS , MobileCoach Tobacco + , and MiSARA . ACHESS was a treatment intervention for general substance use among adolescents at discharge from residential treatment, using ecological momentary intervention for support . MobileCoach Tobacco + and MiSARA were early interventions. MobileCoach Tobacco + was for alcohol and tobacco, based on the Health Action Process Approach (HAPA), that took in a social norms approach, normative feedback, and social cognitive theory . MiSARA was a support-based intervention for alcohol and cannabis use, based on personalized feedback, motivational interviewing, mindfulness, and behavioral activation .
Computer or Tablet-based interventions
Five of the 39 interventions were computer- or tablet-based. Most (3/5) targeted alcohol use: the Ellis et al. , Tello et al. , and Walton et al.  interventions. All were early interventions. Ellis et al. and Walton et al. included a brief feedback component and motivational interviewing [94, 99], while the Walton et al. intervention drew upon cognitive behavioural treatment and self-determination theory . The Tello et al. alcohol intervention used cognitive bias modification . The remaining two computer-based interventions included the unnamed intervention using Cannabis Approach Avoidance Training (CAAT), tested in two studies by Jacobus et al. and Karoly et al. [95, 96], which was the only intervention for cannabis, and an early intervention. The Knight et al. intervention  was an early intervention for multiple substances (alcohol or cannabis). Knight et al. employed motivational interviewing, psychoeducation, and a brief feedback intervention.
Virtual reality interventions
Virtual reality was the technology least employed among interventions in the review (3/39). Two interventions targeted tobacco/nicotine use and were both prevention-oriented: Invite only VR, tested in two studies by Weser, et al. [102, 103] and the Guo et al. intervention . Invite Only VR targeted e-cigarette use and derived from behaviour change theories, the theory of planned behaviour and social cognitive theory [102, 103], while the Guo et al. tobacco prevention intervention was based on Keller’s ARCS (attention, relevance, confidence, and satisfaction) motivation model . The virtual reality-based intervention by Man et al. targeted ketamine use . This was a treatment intervention focused on cognitive problems in young adults using ketamine from a substance use clinic as well as residential and rehabilitation programs. This intervention involved training in cognitive and vocational skills.
As barriers to mental health and addiction services intensified during the Covid-19 pandemic, this rapid review was undertaken to provide an overview of studies on digital interventions for substance use among young people. The final review included 43 studies in total published between 2015 and 2021, describing 39 different interventions. We extracted and compiled data from these studies according to digital technologies used, substances targeted, the underlying theoretical or therapeutic models informing the interventions, and intended outcomes. The interventions were then organized according to treatment level (prevention, early intervention, treatment) following the spectrum of interventions framework developed by Stockings et al. Overall, this mapping of interventions reveals recent efforts in the addictions field to meet the needs of young people, particularly through digital education and interventions targeting some substances more than others. As well, early intervention programs reflected increasing personalization and interactivity, yet remained short on skills training. While many interventions recognized the harms of substance use, interventions based on an overall harm reduction approach were conspicuously absent in this review.
Level on the spectrum of interventions and intended outcomes
For all types of technologies except virtual reality, the great majority of interventions (72%) were early interventions, outnumbering prevention, and treatment interventions at 14% each. The relative lack of treatment level interventions was interesting, as most of these interventions targeted youth in residential or outpatient treatment for serious addiction (e.g., opioids, ketamine). Yet, the paucity of prevention interventions was even more surprising, as substance use prevention is known to lower the odds of lifetime substance use . As well, prevention interventions aimed to reduce interest in, or discourage substance use, as opposed to early interventions and treatment interventions dealing with youth at risk or already using drugs. Prevention programs tend to measure attitudes and knowledge rather than the incidence of substance use or harms . Failing to report on behaviour outcomes diverts from evaluating the effects of prevention interventions on substance use, for example the cost-effectiveness of the intervention .
Types of digital technologies
Virtual reality-based interventions have also emerged as an effective way to provide substance use interventions, yet, according to findings in this review, virtual reality continues to receive little research attention. Virtual reality-based interventions have been used for their potential to simulate interactivity and motivate learning, and for their immersive properties [103, 110]. A recent review identified significant advantages related to virtual reality-based technologies for delivering educational content , while another review found that virtual reality may be effective for reducing substance use among adults . However, the authors noted that more randomized controlled trials were still needed to establish efficacy.
Overall, the findings in this review related to substances targeted revealed a serious disjunction between the substances preferred by youth and those targeted by digital interventions. Alcohol use emerged as the substance most consistently targeted by all digital technologies, except virtual reality. The focus on alcohol use in half of the interventions was a hopeful sign, given that alcohol is the substance of choice for most youth . However, much less research attention has been directed to other substances. For instance, tobacco and nicotine, the focus of only 12.8% of interventions in this review, is the second most prevalent substance used by youth . Our review included only three interventions for e-cigarettes, despite the recent surge in e-cigarette use among young people internationally . Moreover, our review identified only 2 interventions (6.6%) for cannabis, which was surprising given the increasing incidence of cannabis use, particularly in countries where cannabis has been legalized [6, 113], and research suggesting that people using marijuana are at high risk of graduating to illicit substances [9, 114].
The low number of digital interventions in the review for illicit drug use was unsurprising given the generally limited research on this type of intervention [51, 52, 115]. Only two treatment interventions and one prevention intervention targeted illicit drugs, among the interventions for mixed substances. As well, only one prevention digital intervention in the review targeted opioid use, despite expert opinion that prevention approaches are an underutilized strategy for mitigating the youth opioid crisis, given the low access to treatment for opioid use . The limited attention to illicit drugs is concerning, since use of hard drugs in the early years, even when halted, is associated with premature decline in general health . The increased risks of non-prescribed fentanyl and heroin use at 50% and 44%, respectively during the pandemic , underscore the urgency of developing digital and other treatment interventions for opioid use.
Interventions targeting any substance, or multiple substances, accounted for roughly one fourth of interventions in the review, another positive result given the prevalent use of combined substances and the reported increase in poly-substance use . However, this review identified very few prevention interventions for either alcohol and cannabis or tobacco and cannabis. Given the high co-occurring use of alcohol, tobacco, and cannabis , associated in turn with the later initiation of illicit substances in young adulthood [21, 22, 119], the lack of prevention interventions for these substances is especially problematic. Given the mixed evidence on how substance use shifted during the pandemic, in relation to alcohol, tobacco or nicotine, and cannabis, firm conclusions have yet to be reached on current research needs regarding digital interventions for these substances.
Theoretical and therapeutic approaches
For all types of technologies, except virtual reality, interventions in this review most often included a feedback component designed as an early intervention to halt escalation into problematic use. Feedback interventions provide information on personal substance use and associated risks. They may include a normative feedback component, based on a social norms approach, aiming to correct the tendency to overestimate substance use in others . Social norms interventions are widely researched but, as Dempsey et al. observe , interventions using this approach may, or may not aim to change misperceptions. They further question the approach for lack of a robust theoretical model and the need for evaluation research that includes process evaluations and qualitative studies on patient experience with social norms interventions. The small effect of feedback interventions was consistent with a growing body of evidence suggesting that information provision for substance use tends to be ineffective in young people [26, 121]. Skills training seems more effective than information provision in prevention interventions, although there is still insufficient evidence for early interventions . As such, approaches such as skills training, underutilized in the digital interventions identified in this review, may merit some further exploration. Virtual reality-based interventions, used for skills training in two of the three interventions cited in this review, may be particularly well suited for practicing skills .
Concerning harm reduction, only one intervention, the Climate Schools course [63,64,65], a web-based intervention for alcohol and cannabis prevention, used an explicit harm reduction approach that showed promising results for alcohol and cannabis knowledge, and for alcohol consumption and intended use. Harm reduction is an alternative to traditional abstinence-based treatment approaches that create barriers to treatment for young people who continue to use drugs [123, 124]. Harm reduction principles also promote more responsive and non-stigmatizing services by recognizing the realities of poverty, racism, social isolation, past trauma, sex-based discrimination, and other social inequities affecting individual vulnerability and the capacity to effectively deal with drug-related harms [125, 126]. Harm reduction approaches have been used successfully in early intervention programs to change attitudes through education [127, 128], and are also endorsed by professionals as an essential treatment approach for young people using opioids and other illicit drugs [123, 124, 129], who are known to prefer harm reduction to abstinence and sometimes devise their own harm reduction strategies [130, 131].
With stakeholders calling for a paradigm shift in the response to youth substance use , new avenues for the future evaluation of digital interventions may be considered. One possibility could involve the development of interventions using a harm reduction framework adapted to the socio-cultural realities and needs of young people using substances and delivered sequentially to support them along their substance use trajectories. Digital technologies with more interactive components may also be considered, game-based interventions for instance that have shown effectiveness in studies of mental health conditions [132, 133], and virtual reality interventions, given some evidence of its effectiveness in adult studies. Moreover, the effectiveness of digital interventions may be enhanced by the participation of young people using substances as full partners in the design, testing and evaluation of digital interventions that concern them, as shown in video game implementation studies [134, 135]. Future research also needs to delve more deeply into various types of digital interventions in terms of their objectives and appropriateness for specific substances, while taking into account the characteristics of youth populations like age and substance use trajectories over time.
The findings of this rapid review included several limitations that should be addressed. As the review used a limited number of databases, the findings may underestimate the actual number of published interventions and give an imprecise account of research attention regarding the types of technologies, substances targeted, or therapeutic and theoretical approaches used. As only English articles were identified and analyzed, this review may have missed important interventions published in languages other than English. A critical appraisal of the included studies was not conducted, due to the heterogeneity of study methodologies and focus of the research team on the nature of interventions rather than their outcomes, and the desire to provide a comprehensive picture of digital interventions for substance use. Future reviews should assess the efficacy of digital interventions and technologies using critical appraisal and focusing on specific substance or digital interventions.
Web-based interventions were the most common type of technology identified in this review, suggesting that digital interventions with more immersive components, such as game-based and virtual reality-based interventions were underutilized and may merit further exploration. The great majority of interventions also focused on alcohol use, revealing the need for more research attention to tobacco, cannabis, and the co-use of alcohol, tobacco, and cannabis, as well as illicit drugs. Given the predominance of early interventions in the review, the need for more prevention interventions for substance use becomes clear, especially interventions for generic substance use. Digital prevention interventions should also target substance use behaviours, as intended outcomes, to establish the efficacy of prevention-oriented interventions and improve available evidence. A prevention approach in interventions for opioid use may help mitigate the ongoing opioid crisis in North America. While most digital interventions included a feedback component, skills training approaches, underutilized in this review, may also prove effective.
Availability of data and materials
Data screened and analysed in this rapid review are publicly available through the PubMed database (https://pubmed.ncbi.nlm.nih.gov/). Articles screened in this review may be requested from the corresponding author.
United Nations Office on Drugs and Crime. World Drug Report. 2021.
Poznyak V, Rekve D. Global status report on alcohol and health 2018. World Health Organization; 2018.
Reitsma MB, Flor LS, Mullany EC, Gupta V, Hay SI, Gakidou E. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and initiation among young people in 204 countries and territories, 1990–2019. The Lancet Public Health. 2021;6(7):e472–81.
Yoong SL, Hall A, Leonard A, McCrabb S, Wiggers J, Tursan d’Espaignet E, et al. Prevalence of electronic nicotine delivery systems and electronic non-nicotine delivery systems in children and adolescents: a systematic review and meta-analysis. The Lancet Public Health. 2021;6(9):e661–73.
Chadi N, Hadland SE, Harris SK. Understanding the implications of the “vaping epidemic” among adolescents and young adults: A call for action. Subst Abus. 2019;40(1):7–10.
Wadsworth E, Hammond D. International differences in patterns of cannabis use among youth: Prevalence, perceptions of harm, and driving under the influence in Canada. England & United States Addict Behav. 2019;90:171–5.
Brighthaupt SC, Schneider KE, Johnson JK, Jones AA, Johnson RM. Trends in Adolescent Heroin and Injection Drug Use in Nine Urban Centers in the U.S., 1999–2017. J Adolesc Health. 2019;65(2):210–5.
Volkow ND, Han B, Einstein EB, Compton WM. Prevalence of substance use disorders by time since first substance use among young people in the US. JAMA Pediatr. 2021;175(6):640–3.
Kollath-Cattano C, Hatteberg SJ, Kooper A. Illicit drug use among college students: The role of social norms and risk perceptions. Addict Behav. 2020;105: 106289.
Hall WD, Patton G, Stockings E, Weier M, Lynskey M, Morley KI, et al. Why young people’s substance use matters for global health. The lancet Psychiatry. 2016;3(3):265–79.
Ramo DE, Thrul J, Vogel EA, Delucchi K, Prochaska JJ. Multiple Health Risk Behaviors in Young Adult Smokers: Stages of Change and Stability over Time. Ann Behav Med. 2020;54(2):75–86.
Scheier LM, Griffin KW. Youth marijuana use: a review of causes and consequences. Curr Opin Psychol. 2021;38:11–8.
Lundahl LH, Cannoy C. COVID-19 and Substance Use in Adolescents. Pediatr Clin North Am. 2021;68(5):977–90.
Hawke LD, Barbic SP, Voineskos A, Szatmari P, Cleverley K, Hayes E, et al. Impacts of COVID-19 on Youth Mental Health, Substance Use, and Well-being: A Rapid Survey of Clinical and Community Samples: Répercussions de la COVID-19 sur la santé mentale, l’utilisation de substances et le bien-être des adolescents : un sondage rapide d’échantillons cliniques et communautaires. Can J Psychiatry. 2020;65(10):701–9.
Hawke LD, Sheikhan NY, MacCon K, Henderson J. Going virtual: youth attitudes toward and experiences of virtual mental health and substance use services during the COVID-19 pandemic. BMC Health Serv Res. 2021;21(1):340.
Vahratian A, Blumberg SJ, Terlizzi EP, Schiller JS. Symptoms of anxiety or depressive disorder and use of mental health care among adults during the COVID-19 Pandemic - United States, August 2020-February 2021. MMWR Morb Mortal Wkly Rep. 2021;70(13):490–4.
Zolopa C, Burack JA, O’Connor RM, Corran C, Lai J, Bomfim E, et al. Changes in Youth Mental Health, Psychological Wellbeing, and Substance Use During the COVID-19 Pandemic: A Rapid Review. Adolesc Res Rev. 2022;7(2):161–77.
Manthey J, Kilian C, Carr S, Bartak M, Bloomfield K, Braddick F, et al. Use of alcohol, tobacco, cannabis, and other substances during the first wave of the SARS-CoV-2 pandemic in Europe: a survey on 36,000 European substance users. Subst Abuse Treat Prev Policy. 2021;16(1):36.
Dumas TM, Ellis W, Litt DM. What Does Adolescent Substance Use Look Like During the COVID-19 Pandemic? Examining Changes in Frequency, Social Contexts, and Pandemic-Related Predictors. J Adolesc Health. 2020;67(3):354–61.
Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental health, substance use, and suicidal ideation during the COVID-19 Pandemic _ United States, June 24–30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(32):1049–57.
Griffin KW, Lowe SR, Botvin C, Acevedo BP. Patterns of adolescent tobacco and alcohol use as predictors of illicit and prescription drug abuse in minority young adults. J Prev Interv Community. 2019;47(3):228–42.
Thompson K, Holley M, Sturgess C, Leadbeater B. Co-Use of Alcohol and Cannabis: Longitudinal Associations with Mental Health Outcomes in Young Adulthood. Int J Environ Res Public Health. 2021;18(7):3652.
McCabe SE, Arterberry BJ, Dickinson K, Evans-Polce RJ, Ford JA, Ryan JE, et al. Assessment of Changes in Alcohol and Marijuana Abstinence, Co-Use, and Use Disorders Among US Young Adults From 2002 to 2018. JAMA Pediatr. 2021;175(1):64–72.
Cook C, Phelan M, Sander G, Stone K, Murphy F. The Case for a Harm Reduction Decade: Progress, potential and paradigm shifts. London, UK 2016.
Chang L-H, Couvy-Duchesne B, Liu M, Medland SE, Verhulst B, Benotsch EG, et al. Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug Alcohol Depend. 2019;197:271–9.
Niles JK, Gudin J, Radcliff J, Kaufman HW. The Opioid Epidemic Within the COVID-19 Pandemic: Drug Testing in 2020. Popul Health Manag. 2021;24(S1):S43–51.
Pelham WE, Tapert SF, Gonzalez MR, McCabe CI, Lisdahl KM, Alzueta E, et al. Early adolescent substance use before and during the COVID-19 pandemic: A longitudinal survey in the ABCD study cohort. J Adolesc Health. 2021;69(3):390–7.
Imtiaz S, Nafeh F, Russell C, Ali F, Elton-Marshall T, Rehm J. The impact of the novel coronavirus disease (COVID-19) pandemic on drug overdose-related deaths in the United States and Canada: a systematic review of observational studies and analysis of public health surveillance data. Subst Abuse Treat Prev Policy. 2021;16(1):87.
Special Advisory Committee on the Epidemic of Opioid Overdoses. Opioid- and Stimulant-related Harms in Canada. Ottawa: Public Health Agency of Canada; 2022.
Steele RG, Elkin T, Roberts M. Handbook of evidence-based therapies for children and adolescents: Springer; 2020.
Franzese AT, Blalock DV, Blalock KM, Wilson SM, Medenblik A, Costanzo PR, et al. Regulatory Focus and Substance Use in Adolescents: Protective Effects of Prevention Orientation. Subst Use Misuse. 2021;56(1):33–8.
Stockings E, Hall WD, Lynskey M, Morley KL, Reavley N, Strang J, et al. Prevention, early intervention, harm reduction, and treatment of substance use in young people. The lancet Psychiatry. 2016;3(3):280–96.
Ronis ST, Slaunwhite AK, Malcom KE. Comparing Strategies for Providing Child and Youth Mental Health Care Services in Canada, the United States, and The Netherlands. Adm Policy Ment Health. 2017;44(6):955–66.
Hawke LD, Mehra K, Settipani C, Relihan J, Darnay K, Chaim G, et al. What makes mental health and substance use services youth friendly? A scoping review of literature. BMC Health Serv Res. 2019;19(1):257.
Wisdom JP, Cavaleri M, Gogel L, Nacht M. Barriers and facilitators to adolescent drug treatment: Youth, family, and staff reports. Addiction Research & Theory. 2011;19(2):179–88.
Heflinger CA, Hinshaw SP. Stigma in child and adolescent mental health services research: understanding professional and institutional stigmatization of youth with mental health problems and their families. Adm Policy Ment Health. 2010;37(1–2):61–70.
Earnshaw VA, Bogart LM, Menino D, Kelly JF, Chaudoir SR, Brousseau N, et al. Disclosure, Stigma, and Social Support among Young People Receiving Treatment for Substance Use Disorders and their Caregivers: A Qualitative Analysis. Int J Ment Health Addict. 2019;17(6):1535–49.
Auerbach RP, Alonso J, Axinn WG, Cuijpers P, Ebert DD, Green JG, et al. Mental disorders among college students in the World Health Organization World Mental Health Surveys. Psychol Med. 2016;46(14):2955–70.
Werlen L, Puhan MA, Landolt MA, Mohler-Kuo M. Mind the treatment gap: the prevalence of common mental disorder symptoms, risky substance use and service utilization among young Swiss adults. BMC Public Health. 2020;20(1):1470.
Gulliver A, Griffiths KM, Christensen H. Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review. BMC Psychiatry. 2010;10(1):113.
Woodgate RL, Sigurdson C, Demczuk L, Tennent P, Wallis B, Werner P. The meanings young people assign to living with mental illness and their experiences in managing their health and lives: systematic review of qualitative evidence. JBI Database System Rev Implement Rep. 2017;15(2):276–401.
Kaess M, Moessner M, Koenig J, Lustig S, Bonnet S, Becker K, et al. A plea for the sustained implementation of digital interventions for young people with mental health problems in the light of the COVID-19 pandemic. J psychol psychiatry and allied disciplines. 2020;62(7):916–8.
Leech T, Dorstyn DS, Li W. eMental health service use among Australian youth: a cross-sectional survey framed by Andersen’s model. Aust Health Rev. 2019;44(6):891–7.
Pretorius C, Chambers D, Coyle D. Young People’s Online Help-Seeking and Mental Health Difficulties: Systematic Narrative Review. J Med Internet Res. 2019;21(11): e13873.
Hollis C, Falconer CJ, Martin JL, Whittington C, Stockton S, Glazebrook C, et al. Annual Research Review: Digital health interventions for children and young people with mental health problems - a systematic and meta-review. J Child Psychol Psychiatry. 2017;58(4):474–503.
Lal S, Adair CE. E-mental health: a rapid review of the literature. Psychiatr Serv. 2014;65(1):24–32.
Lattie EG, Adkins EC, Winquist N, Stiles-Shields C, Wafford QE, Graham AK. Digital Mental Health Interventions for Depression, Anxiety, and Enhancement of Psychological Well-Being Among College Students: Systematic Review. J Med Internet Res. 2019;21(7): e12869.
Wozney L, McGrath PJ, Newton AS, Hartling L, Curran J, Huguet A. RE-AIMing e-mental health: A rapid review of current research. Ottawa: Mental Health Commission of Canada; 2017.
Aboujaoude E, Salame W. Technology at the Service of Pediatric Mental Health: Review and Assessment. J Pediatr. 2015;171:20–4.
Boumparis N, Loheide-Niesmann L, Blankers M, Ebert DD, Korf D, Schaub MP, et al. Short- and long-term effects of digital prevention and treatment interventions for cannabis use reduction: A systematic review and meta-analysis. Drug Alcohol Depend. 2019;200:82–94.
Tomazic T, Jerkovic OS. Online Interventions for the Selective Prevention of Illicit Drug Use in Young Drug Users: Exploratory Study. J Med Internet Res. 2020;22(4): e17688.
Stockings E, Hall WD, Lynskey M, Morley KI, Reavley N, Strang J, et al. Prevention, early intervention, harm reduction, and treatment of substance use in young people. Lancet Psychiatry. 2016;3(3):280–96.
Garritty C, Gartlehner G, Nussbaumer-Streit B, King VJ, Hamel C, Kamel C, et al. Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews. J Clin Epidemiol. 2021;130:13–22.
Watt A, Cameron A, Sturm L, Lathlean T, Babidge W, Blamey S, et al. Rapid reviews versus full systematic reviews: An inventory of current methods and practice in health technology assessment. Int J Technol Assess Health Care. 2008;24(2):133–9.
Tricco AC, Antony J, Zarin W, Strifler L, Ghassemi M, Ivory J, et al. A scoping review of rapid review methods. BMC Med. 2015;13:224.
Khangura S, Konnyu K, Cushman R, Grimshaw J, Moher D. Evidence summaries: the evolution of a rapid review approach. Syst Rev. 2012;1(1):10.
Shea BJ, Grimshaw JM, Wells GA, Boers M, Andersson N, Hamel C, et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol. 2007;7(1):10.
Butler-Jones D. The Chief Public Health Officer’s Report on the state of public health in Canada, 2011: Youth and young adults - Life in transition. Ottawa: The Public Health Agency of Canada; 2011.
Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.
Kazemi DM, Li S, Levine MJ, Auten B, Granson M. Systematic Review of Smartphone Apps as a mHealth Intervention to Address Substance Abuse in Adolescents and Adults. J Addict Nurs. 2021;32(3):180–7.
Bertholet N, Cunningham JA, Faouzi M, Gaume J, Gmel G, Burnand B, et al. Internet-based brief intervention for young men with unhealthy alcohol use: a randomized controlled trial in a general population sample. Addiction. 2015;110(11):1735–43.
Brunette MF, Ferron JC, Robinson D, Coletti D, Geiger P, Devitt T, et al. Brief Web-Based Interventions for Young Adult Smokers With Severe Mental Illnesses: A Randomized. Controlled Pilot Study Nicotine Tob Res. 2018;20(10):1206–14.
Champion KE, Newton NC, Stapinski L, Slade T, Barrett EL, Teesson M. A cross-validation trial of an Internet-based prevention program for alcohol and cannabis: Preliminary results from a cluster randomised controlled trial. Aust N Z J Psychiatry. 2016;50(1):64–73.
Newton NC, Teesson M, Mather M, Champion KE, Barrett EL, Stapinski L, et al. Universal cannabis outcomes from the Climate and Preventure (CAP) study: a cluster randomised controlled trial. Substance abuse treatment, prevention, and policy. 2018;13(1):34.
Teesson M, Newton NC, Slade T, Chapman C, Birrell L, Mewton L, et al. Combined prevention for substance use, depression, and anxiety in adolescence: a cluster-randomised controlled trial of a digital online intervention. The Lancet Digital health. 2020;2(2):e74–84.
Davies EL, Lonsdale AJ, Hennelly SE, Winstock AR, Foxcroft DR. Personalized digital interventions showed no impact on risky drinking in young adults: a pilot randomized controlled trial. Alcohol Alcohol. 2017;52(6):671–6.
Deady M, Mills KL, Teesson M, Kay-Lambkin F. An Online Intervention for Co-Occurring Depression and Problematic Alcohol Use in Young People: Primary Outcomes From a Randomized Controlled Trial. J Med Internet Res. 2016;18(3): e71.
Geisner IM, Varvil-Weld L, Mittmann AJ, Mallett K, Turrisi R. Brief web-based intervention for college students with comorbid risky alcohol use and depressed mood: does it work and for whom? Addict Behav. 2015;42:36–43.
Marsch LA, Moore SK, Grabinski M, Bessen SY, Borodovsky J, Scherer E. Evaluating the Effectiveness of a Web-Based Program (POP4Teens) to Prevent Prescription Opioid Misuse Among Adolescents: Randomized Controlled Trial. JMIR Public Health Surveill. 2021;7(2): e18487.
Miller MB, Leavens EL, Meier E, Lombardi N, Leffingwell TR. Enhancing the efficacy of computerized feedback interventions for college alcohol misuse: An exploratory randomized trial. J Consult Clin Psychol. 2016;84(2):122–33.
Schuckit MA, Smith TL, Kalmijn J, Skidmore J, Clausen P, Shafir A, et al. The impact of focusing a program to prevent heavier drinking on a pre-existing phenotype, the low level of response to alcohol. Alcohol Clin Exp Res. 2015;39(2):308–16.
Schwinn TM, Schinke SP, Hopkins J, Keller B, Liu X. An Online Drug Abuse Prevention Program for Adolescent Girls: Posttest and 1-Year Outcomes. J Youth Adolesc. 2018;47(3):490–500.
Schwinn TM, Schinke SP, Keller B, Hopkins J. Two- and three-year follow-up from a gender-specific, web-based drug abuse prevention program for adolescent girls. Addict Behav. 2019;93:86–92.
Tuliao AP, Mullet ND, Hawkins LG, Holyoak D, Weerts M, Gudenrath T. Examining the role of a brief online alcohol use risk feedback on accessing information about available treatment resources for alcohol issues. Addict Behav. 2019;96:164–70.
Vargas-Martinez AM, Trapero-Bertran M, Lima-Serrano M, Anokye N, Pokhrel S, Mora T. Measuring the effects on quality of life and alcohol consumption of a program to reduce binge drinking in Spanish adolescents. Drug Alcohol Depend. 2019;205: 107597.
Vogel EA, Ramo DE, Meacham MC, Prochaska JJ, Delucchi KL, Humfleet GL. The Put It Out Project (POP) Facebook Intervention for Young Sexual and Gender Minority Smokers: Outcomes of a Pilot, Randomized. Controlled Trial Nicotine Tob Res. 2020;22(9):1614–21.
Walukevich-Dienst K, Neighbors C, Buckner JD. Online personalized feedback intervention for cannabis-using college students reduces cannabis-related problems among women. Addict Behav. 2019;98: 106040.
Abroms LC, Leavitt LE, Van Alstyne JM, Schindler-Ruwisch JM, Fishman MJ, Greenberg D. A Motion Videogame for Opioid Relapse Prevention. Games Health J. 2015;4(6):494–501.
Boendermaker WJ, Boffo M, Wiers RW. Exploring Elements of Fun to Motivate Youth to Do Cognitive Bias Modification. Games Health J. 2015;4(6):434–43.
Duncan LR, Hieftje KD, Pendergrass TM, Sawyer BG, Fiellin LE. Preliminary investigation of a videogame prototype for cigarette and marijuana prevention in adolescents. Subst Abus. 2018;39(3):275–9.
Earle AM, LaBrie JW, Boyle SC, Smith D. In pursuit of a self-sustaining college alcohol intervention: Deploying gamified PNF in the real world. Addict Behav. 2018;80:71–81.
Hides L, Quinn C, Cockshaw W, Stoyanov S, Zelenko O, Johnson D, et al. Efficacy and outcomes of a mobile app targeting alcohol use in young people. Addict Behav. 2018;77:89–95.
Jander A, Crutzen R, Mercken L, Candel M, de Vries H. Effects of a Web-Based Computer-Tailored Game to Reduce Binge Drinking Among Dutch Adolescents: A Cluster Randomized Controlled Trial. J Med Internet Res. 2016;18(2): e29.
LaBrie JW, de Rutte JL, Boyle SC, Tan CN, Earle AM. Leveraging copresence to increase the effectiveness of gamified personalized normative feedback. Addict Behav. 2019;99: 106085.
Sanchez RP, Bartel CM. The Feasibility and Acceptability of “Arise”: An Online Substance Abuse Relapse Prevention Program. Games Health J. 2015;4(2):136–44.
Scholten H, Luijten M, Granic I. A randomized controlled trial to test the effectiveness of a peer-based social mobile game intervention to reduce smoking in youth. Dev Psychopathol. 2019;31(5):1923–43.
Skorka-Brown J, Andrade J, Whalley B, May J. Playing Tetris decreases drug and other cravings in real world settings. Addict Behav. 2015;51:165–70.
Carra G, Crocamo C, Schivalocchi A, Bartoli F, Carretta D, Brambilla G, et al. Risk Estimation Modeling and Feasibility Testing for a Mobile eHealth Intervention for Binge Drinking Among Young People: The D-ARIANNA (Digital-Alcohol RIsk Alertness Notifying Network for Adolescents and young adults) Project. Subst Abus. 2015;36(4):445–52.
Carra G, Crocamo C, Bartoli F, Carretta D, Schivalocchi A, Bebbington PE, et al. Impact of a Mobile E-Health Intervention on Binge Drinking in Young People: The Digital-Alcohol Risk Alertness Notifying Network for Adolescents and Young Adults Project. J Adolesc Health. 2016;58(5):520–6.
Coughlin LN, Nahum-Shani I, Philyaw-Kotov ML, Bonar EE, Rabbi M, Klasnja P, et al. Developing an Adaptive Mobile Intervention to Address Risky Substance Use Among Adolescents and Emerging Adults: Usability Study. JMIR Mhealth Uhealth. 2021;9(1): e24424.
Dennis ML, Scott CK, Funk RR, Nicholson L. A Pilot Study to Examine the Feasibility and Potential Effectiveness of Using Smartphones to Provide Recovery Support for Adolescents. Subst Abus. 2015;36(4):486–92.
Haug S, Castro RP, Kowatsch T, Filler A, Schaub MP. Efficacy of a technology-based, integrated smoking cessation and alcohol intervention for smoking cessation in adolescents: results of a cluster-randomised controlled trial. J Subst Abuse Treat. 2017;82:55–66.
Kazemi DM, Borsari B, Levine MJ, Shehab M, Nelson M, Dooley B, et al. Real-time demonstration of a mHealth app designed to reduce college students hazardous drinking. Psychol Serv. 2019;16(2):255–9.
Ellis JD, Grekin ER, Beatty JR, McGoron L, LaLiberte BV, Pop DE, et al. Effects of narrator empathy in a computer delivered brief intervention for alcohol use. Contemp Clin Trials. 2017;61:29–32.
Jacobus J, Taylor CT, Gray KM, Meredith LR, Porter AM, Li I, et al. A multi-site proof-of-concept investigation of computerized approach-avoidance training in adolescent cannabis users. Drug Alcohol Depend. 2018;187:195–204.
Karoly HC, Schacht JP, Jacobus J, Meredith LR, Taylor CT, Tapert SF, et al. Preliminary evidence that computerized approach avoidance training is not associated with changes in fMRI cannabis cue reactivity in non-treatment-seeking adolescent cannabis users. Drug Alcohol Depend. 2019;200:145–52.
Knight JR, Sherritt L, Gibson EB, Levinson JA, Grubb LK, Samuels RC, et al. Effect of Computer-Based Substance Use Screening and Brief Behavioral Counseling vs Usual Care for Youths in Pediatric Primary Care: A Pilot Randomized Clinical Trial. JAMA Netw Open. 2019;2(6): e196258.
Tello N, Bocage-Barthelemy Y, Dandaba M, Jaafari N, Chatard A. Evaluative conditioning: A brief computer-delivered intervention to reduce college student drinking. Addict Behav. 2018;82:14–8.
Walton MA, Chermack ST, Blow FC, Ehrlich PF, Barry KL, Booth BM, et al. Components of Brief Alcohol Interventions for Youth in the Emergency Department. Subst Abus. 2015;36(3):339–49.
Guo JL, Hsu HP, Lai TM, Lin ML, Chung CM, Huang CM. Acceptability Evaluation of the Use of Virtual Reality Games in Smoking-Prevention Education for High School Students: Prospective Observational Study. J Med Internet Res. 2021;23(9): e28037.
Man DWK. Virtual reality-based cognitive training for drug abusers: A randomised controlled trial. Neuropsychol Rehabil. 2020;30(2):315–32.
Weser VU, Duncan LR, Pendergrass TM, Fernandes CS, Fiellin LE, Hieftje KD. A quasi-experimental test of a virtual reality game prototype for adolescent E-Cigarette prevention. Addict Behav. 2021;112: 106639.
Weser VU, Duncan LR, Sands BE, Schartmann A, Jacobo S, François B, et al. Evaluation of a virtual reality E-cigarette prevention game for adolescents. Addict Behav. 2021;122: 107027.
Sanchez R, Bartel C. The Feasibility and Acceptability of “Arise”: An Online Substance Abuse Relapse Prevention Program. Games for health journal. 2015;4:136–44.
Brown M, O’Neill N, van Woerden H, Eslambolchilar P, Jones M, John A. Gamification and Adherence to Web-Based Mental Health Interventions: A Systematic Review. JMIR Ment Health. 2016;3(3): e39.
Papastergiou M. Digital Game-Based Learning in high school Computer Science education: Impact on educational effectiveness and student motivation. Comput Educ. 2009;52(1):1–12.
Tüzün H, Yılmaz-Soylu M, Karakuş T, İnal Y, Kızılkaya G. The effects of computer games on primary school students’ achievement and motivation in geography learning. Comput Educ. 2009;52(1):68–77.
Baranowski T, Buday R, Thompson DI, Baranowski J. Playing for real: video games and stories for health-related behavior change. Am J Prev Med. 2008;34(1):74–82.
Ferrari M, McIlwaine SV, Reynolds JA, Archie S, Boydell K, Lal S, et al. Digital Game Interventions for Youth Mental Health Services (Gaming My Way to Recovery): Protocol for a Scoping Review. JMIR Res Protoc. 2020;9(6): e13834.
Checa D, Miguel-Alonso I, Bustillo A. Immersive virtual-reality computer-assembly serious game to enhance autonomous learning. Virtual Real. 2021:1-18. https://0-doi-org.brum.beds.ac.uk/10.1007/s10055-021-00607-1. Epub ahead of print.
Hamilton D, McKechnie J, Edgerton E, Wilson C. Immersive virtual reality as a pedagogical tool in education: a systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education. 2021;8(1):1–32.
Tsamitros N, Sebold M, Gutwinski S, Beck A. Virtual Reality-Based Treatment Approaches in the Field of Substance Use Disorders. Curr Addict Rep. 2021;8(3):399–407.
Zuckermann AME, Battista KV, Belanger RE, Haddad S, Butler A, Costello MJ, et al. Trends in youth cannabis use across cannabis legalization: Data from the COMPASS prospective cohort study. Prev Med Rep. 2021;22: 101351.
Ford JA, Arrastia MC. Pill-poppers and dopers: A comparison of non-medical prescription drug use and illicit/street drug use among college students. Addict Behav. 2008;33(7):934–41.
Dick S, Whelan E, Davoren MP, Dockray S, Heavin C, Linehan C, et al. A systematic review of the effectiveness of digital interventions for illicit substance misuse harm reduction in third-level students. BMC public health. 2019;19(1):1244.
Compton WM, Jones CM, Baldwin GT, Harding FM, Blanco C, Wargo EM. Targeting Youth to Prevent Later Substance Use Disorder: An Underutilized Response to the US Opioid Crisis. Am J Public Health. 2019;109(S3):S185–9.
Kertesz SG, Pletcher MJ, Safford M, Halanych J, Kirk K, Schumacher J, et al. Illicit drug use in young adults and subsequent decline in general health: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Drug Alcohol Depend. 2007;88(2–3):224–33.
Zuckermann AME, Williams G, Battista K, de Groh M, Jiang Y, Leatherdale ST. Trends of poly-substance use among Canadian youth. Addictive Behaviors Reports. 2019;10: 100189.
McCabe SE, Engstrom CW, Kcomt L, Evans-Polce R, West BT. Trends in binge drinking, marijuana use, illicit drug use, and polysubstance use by sexual identity in the United States (2006–2017). Substance abuse. 2022;43(1):194–203.
Dempsey RC, McAlaney J, Bewick BM. A Critical Appraisal of the Social Norms Approach as an Interventional Strategy for Health-Related Behavior and Attitude Change. Front Psychol. 2018;9:2180.
Foxcroft DR. "Form ever follows function. This is the law. " A prevention taxonomy based on a functional typology. Adicciones. 2014;26(1):10–4.
Foxcroft DR, Tsertsvadze A. Universal alcohol misuse prevention programmes for children and adolescents: Cochrane systematic reviews. Perspect Public Health. 2012;132(3):128–34.
Taylor JL, Johnson S, Cruz R, Gray JR, Schiff D, Bagley SM. Integrating Harm Reduction into Outpatient Opioid Use Disorder Treatment Settings : Harm Reduction in Outpatient Addiction Treatment. J Gen Intern Med. 2021;36(12):3810–9.
Jenkins EK, Slemon A, Haines-Saah RJ. Developing harm reduction in the context of youth substance use: insights from a multi-site qualitative analysis of young people’s harm minimization strategies. Harm Reduct J. 2017;14(1):53.
Elliott R, Malkin I, Gold J. Establishing safe injection facilities in Canada: legal and ethical issues. Can HIV AIDS Policy Law Rev. 2002;6(3):7–10.
Curry K. In Pursuit of Higher Pleasures: The Moral Value of Criminalizing Drug Users and the Utilitarian Case for Decriminalization [dissertation]. Ottawa (CA): Saint Paul University; 2019.
Vogl LE, Newton NC, Champion KE, Teesson M. A universal harm-minimisation approach to preventing psychostimulant and cannabis use in adolescents: a cluster randomised controlled trial. Subst Abuse Treat Prev Policy. 2014;9:24.
McKay M, Sumnall H, McBride N, Harvey S. The differential impact of a classroom-based, alcohol harm reduction intervention, on adolescents with different alcohol use experiences: a multi-level growth modelling analysis. J Adolesc. 2014;37(7):1057–67.
Kimmel SD, Gaeta JM, Hadland SE, Hallett E, Marshall BDL. Principles of Harm Reduction for Young People Who Use Drugs. Pediatrics. 2021;147(Suppl 2):S240–8.
Paul B, Thulien M, Knight R, Milloy MJ, Howard B, Nelson S, et al. “Something that actually works”: Cannabis use among young people in the context of street entrenchment. PLoS ONE. 2020;15(7): e0236243.
Goldman JE, Waye KM, Periera KA, Krieger MS, Yedinak JL, Marshall BDL. Perspectives on rapid fentanyl test strips as a harm reduction practice among young adults who use drugs: a qualitative study. Harm Reduct J. 2019;16(1):3.
Cheng VWS, Davenport T, Johnson D, Vella K, Hickie IB. Gamification in Apps and Technologies for Improving Mental Health and Well-Being: Systematic Review. JMIR Ment Health. 2019;6(6): e13717.
Fleming TM, Bavin L, Stasiak K, Hermansson-Webb E, Merry SN, Cheek C, et al. Serious Games and Gamification for Mental Health: Current Status and Promising Directions. Front Psych. 2017;7:215.
Fitzgerald M, Ratcliffe G. Serious Games, Gamification, and Serious Mental Illness: A Scoping Review. Psychiatric services (Washington, DC). 2020;71(2):170–83.
Oliver S, Clarke-Jones L, Rees R, Milne R, Buchanan P, Gabbay J, et al. Involving consumers in research and development agenda setting for the NHS: developing an evidence-based approach. Health Technol Assess. 2004;8(15):1–148 III-IV.
This study was supported by a combination of grants from the Healthy Brains, Healthy Lives (HBHL) New Recruit Start-Up Supplements Award (MF), and the Fonds de Recherche du Québec–chercheurs-boursiers Junior 1 Award (grant no. 283375) (MF).
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Additional file 1.
AMSTAR – a measurement tool to assess the methodological quality of systematic reviews.
Additional file 2: Appendix 1.
Definitions of digital intervention technologies (1-3).
Additional file 3: Appendix 2.
Definitions of the most common theoretical or therapeutic approaches used indigital interventions for substance use among young people (4-11).
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Monarque, M., Sabetti, J. & Ferrari, M. Digital interventions for substance use disorders in young people: rapid review. Subst Abuse Treat Prev Policy 18, 13 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s13011-023-00518-1
- Digital interventions
- Substance use
- Harm reduction
- Young people
- Mental health
- Rapid review