| Literature DB >> 31500618 |
Samantha Dick1, Eadaoin Whelan2, Martin P Davoren3,4, Samantha Dockray2, Ciara Heavin5, Conor Linehan2, Michael Byrne6.
Abstract
BACKGROUND: Illicit substance misuse is a growing public health problem, with misuse peaking among 18-25 year-olds, and attendance at third-level education identified as a risk factor. Illicit substance misuse has the potential to harm mental and physical health, social relationships, and impact on academic achievements and future career prospects. Digital interventions have been identified as a vehicle for reaching large student populations and circumventing the limited capacity of student health services for delivering face-to-face interventions. Digital interventions have been developed in the area of alcohol and tobacco harm reduction, reporting some effectiveness, but the evidence for the effectiveness of digital interventions targeting illicit substance misuse is lacking. This review aims to systematically identify and critically appraise studies examining the effectiveness of digital interventions for illicit substance misuse harm reduction in third-level students.Entities:
Keywords: Harm reduction; Mhealth; Student; Substance misuse
Mesh:
Year: 2019 PMID: 31500618 PMCID: PMC6734361 DOI: 10.1186/s12889-019-7583-6
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Inclusion and Exclusion Criteria
| Inclusion Criteria | • Any study deploying a web-based or mobile digital intervention with the aim of reducing harm from substance misuse. • Studies reporting substance misuse as a primary or secondary measure. • Studies reporting a measure of the effectiveness of the intervention. • Studies whose study population consists of students enrolled in third-level institutions (e.g. college or university). • Studies whose definition of “substance misuse” includes any illicit drug, psychoactive drug, or misuse of prescription medication. |
| Exclusion criteria | • Any study deploying a non-digital intervention only. • Studies reporting a non-third-level population (e.g. young adults, adolescents, secondary school students). • Studies reporting interventions targeting only alcohol and/or tobacco. • Non-English language studies |
Fig. 1PRISMA flowchart of study selection process
Summary of Included Studies
| Author | Study Type | Sample Size | Outcome Measures | Intervention Type | Delivery | Population | Critical Appraisal Score | Overall Result |
|---|---|---|---|---|---|---|---|---|
| Lee et al. 2010 [ | RCT | 341 | Marijuana use and consequences of use. Also evaluated contemplation to change and family history of drug problems as potential mediators of efficacy | Personalised feedback and descriptive norm correction | Online | Students reporting any use of Marijuana in past 3-months | Moderate | No overall effect on use or consequences. Significant reduction in use in students higher in contemplation. |
| Elliott & Carey 2012 [ | CCT | 245 | Descriptive norms, injunctive norms, marijuana use/initiation | “eTOKE” personalised feedback and descriptive norm correction | Online | Students reporting no marijuana use in past 30 days | Weak | No significant effect on marijuana initiation, but less exaggerated norms. |
| Elliott et al. 2014 [ | CCT | 317 | Marijuana use, problems, user disorder symptoms, and descriptive norms | “eTOKE” personalised feedback and descriptive norm correction | Online | Students reporting past-month marijuana use | Weak | Minimal to small effect for user frequency, problems, disorder symptoms and medium changes for all descriptive norms. |
| Epton et al. 2014 [ | RCT | 1445 | Fruit/veg intake, physical activity, alcohol consumption, smoking, health status, recreational drug use, BMI, health service usage, academic performance, social cognitive variables. | “U@Uni” Self-affirmation manipulation, theory based messages, implementation intention tasks | Online | All incoming undergraduate students | Weak | Increase in recreational drug use in intervention arm |
| Palfai et al. 2014 [ | CCT | 123 | Frequency of marijuana use, marijuana related consequences, readiness to change, perceived norms. | “Marijuana eCHECK UP TO GO” personalised feedback and descriptive norm correction | Online in Student Health Service and off-site | Students attending Student Health Service reporting at least monthly Marijuana use | Moderate | No effect on marijuana use. Medium effect on negative consequences. Significantly reduced student estimates of peer marijuana use. |
| Cameron et al. 2015 [ | RCT | 2621 | Fruit/veg intake, physical activity, alcohol consumption, smoking, health status, recreational drug use, BMI, health service usage, social cognitive variables. | “U@Uni:LifeGuide” Self-affirmation manipulation, theory based messages, implementation intention tasks | Online | All incoming undergraduate students | Weak | Non-significant effect on recreational drug use |
| Christoff & Boerngen-Lacerda 2015 [ | CCT | 458 | ASSIST score (alcohol, tobacco, marijuana, cocaine, amphetamine-stimulants, inhalants, sedatives, hallucinogens, opioids) | “ASSIST/MBI” ASSIST screening and Motivational Brief Intervention | Online on-site | Students with moderate/high ASSIST scores | Weak | Small positive effect, reduction in ASSIST score for marijuana. |
| Haug et al. 2017 [ | Pre-Post | 1067 | Perceived stress, self-management and coping behaviours, interpersonal skills, at risk alcohol use, tobacco smoking, and cannabis use. | “Ready4life” – personalised feedback and weekly messages based on social cognitive theory | Online on-site plus off-site text messages | All students with a mobile phone | Weak | No significant pre-post differences in the percentage of persons using cannabis. |