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Computer-based programmes for the prevention and management of illicitrecreational drug use: A systematic review

Sara K. Wood, Lindsay Eckley, Karen Hughes, Katherine A. Hardcas-tle, Mark A. Bellis, Jochen Schrooten, Zsolt Demotrovics, Lotte Voorham

PII: S0306-4603(13)00269-4DOI: doi: 10.1016/j.addbeh.2013.09.010Reference: AB 4035

To appear in: Addictive Behaviors

Please cite this article as: Wood, S.K., Eckley, L., Hughes, K., Hardcastle, K.A., Bellis,M.A., Schrooten, J., Demotrovics, Z. & Voorham, L., Computer-based programmes forthe prevention and management of illicit recreational drug use: A systematic review,Addictive Behaviors (2013), doi: 10.1016/j.addbeh.2013.09.010

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Title: Computer-based programmes for the prevention and management of illicit

recreational drug use: a systematic review.

Authors: Sara K. Wood1*

, Lindsay Eckley1, Karen Hughes

1, Katherine A. Hardcastle

1, Mark

A. Bellis1, Jochen Schrooten

2, Zsolt Demotrovics

3 and Lotte Voorham

4.

1 Centre for Public Health, Liverpool John Moores University

15-21 Webster Street, Liverpool L3 2ET, UK

Sara K. Wood (Senior Researcher) MSc; Lindsay Eckley (Senior Researcher) PhD;

Karen Hughes (Reader in Behavioural Epidemiology) PhD; Katherine A. Hardcastle

(Researcher) MSc; Mark A. Bellis (Director, Centre for Public Health and North West

Public Health Observatory) DSc.

2

VAD (Vereniging voor Alcohol en andere Drugproblemen)

Vanderlindenstraat 15, 1030 Brussels, Belgium

Jochen Schrooten (Staff Officer), MSc

3 Institute of Psychology, Eötvös Loránd University

Budapest H-1064, Hungary

Zsolt Demotrovics (Director, Institute of Psychology), PhD

4 Trimbos Institute.

Da Costakade 45, 3521 VS Utrecht, The Netherlands

Lotte Voorham (Researcher), MSc

* Corresponding author

Sara K Wood,

Centre for Public Health, Liverpool John Moores University, 15-21 Webster Street, Liverpool

L3 2ET, UK.

E-mail: s.k.wood@ljmu.ac.uk

Tel: 0151 231 4511; Fax: 0151 231 4552

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Abstract

The last few decades have seen increasing use of computer-based programmes to address

illicit recreational drug use but knowledge about their effectiveness is limited. We conducted

a systematic review to examine evidence on these programmes. Eight electronic databases

were searched to identify primary research studies evaluating computer-based programmes to

prevent or reduce use of illicit recreational drugs. From an initial 3,413 extracted studies, 10

were identified for inclusion, covering a range of intervention types, target groups and

settings. Universal drug prevention programmes were effective in reducing the frequency of

recreational drug use in the mid-term (<12 months), but not immediately post intervention.

Programmes targeting recreational drug users showed more inconsistent results but were

generally effective in reducing use of drugs both immediately and in the mid-term.

Computer-based programmes have the potential for use in addressing recreational drug use

when targeted both universally and at illicit drug users, at least in the mid-term. However,

longer term evaluations are needed to better understand the duration of effects. Given the

benefits that computer-based programmes can have over traditional delivery methods,

research is needed to better understand the value of human contact in health interventions and

help inform whether, and how much, professional contact should be involved in computer-

based programmes.

Key words: drug, substance, intervention studies, computer-based, prevention.

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1. Introduction

The use of illicit drugs for recreational purposes is a significant public health concern,

particularly among younger age groups. Within Europe, past year prevalence of cannabis use

among those aged 16-34 years ranges from 0.9% (Romania) to 21.6% (Czech Republic),

whilst past year cocaine use ranges from 0.1% (Romania) to 7.7% (UK), and ecstasy use

from 0.2% (Romania and Sweden) to 5.3% (UK) (EMCDDA, 2011). Similar levels are

reported elsewhere. For instance, in 2010, the rate of current illicit drug use among US youths

aged 18-25 years was 21.5% (US Department of Health and Human Services, 2011), while in

Canada, past year illicit drug use among the same age group was 26.3% (Health Canada,

2010). Use of drugs among young people is particularly concerning since initiation of drug

use early in life (e.g. before age 18) can be a risk factor for problematic use in adulthood

(Chen et al, 2009). The health, social and economic costs that illicit drug use imposes on

individuals, communities and wider societies can be substantial and have been well-

documented (e.g. Kaushik et al, 2011; Large et al, 2011; Mǿrland et al, 2011; Hall &

Degenhardt, 2009; Kuhns & Clodfelter, 2009; Rogers et al, 2009; Anderson & Mueller, 2008;

Cartwright, 2008; Coughlin & Mavor, 2006; Andlin-Sobocki, 2004; Macleod et al 2004;

Godfrey et al, 2002). Consequently, preventing and reducing drug use among both adolescent

and adult populations is a priority in many countries.

Internationally, a range of interventions have been implemented to help individuals address

illicit recreational drug use. These have included: education and awareness-raising campaigns

(Pan & Bai, 2009; Wakefield et al, 2010; Werb et al, 2011); skill-building programmes

(Botvin & Griffin, 2005; Coggans et al, 2003; Faggiano et al, 2005); psychosocial

programmes such as motivational interviewing, counselling or behavioural therapy (Denis et

al, 2006; Magill & Ray, 2009; Lundahl et al, 2010; Smedslund et al, 2011) and programmes

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challenging social norms and attitudes towards drug use (Perkins, 2003; Zhao et al, 2006).

Programmes have traditionally been delivered via health or other professionals (e.g.

teachers/youth workers), often in group-based settings. However, over the last few decades

there has been increasing use of computer-based programmes (either internet-based or stand

alone programmes) designed for self-completion.

The use of a computer to deliver drug prevention/reduction programmes may present a

number of challenges. For example, potential users may not have access to a home computer,

particularly in more deprived geographical areas (Hughes et al, 2002). Additionally, internet

content may be restricted by firewalls that ban specific drug terms. However, they can also

hold a number of important advantages over more traditional delivery methods. Since they do

not require a professional to deliver the programme, computer-based interventions are less

restrictive in their availability, overcoming physical, socio-economical and geographical

constraints whilst potentially engaging large numbers of individuals. Even when combined

with some degree of direct contact, the capacity of professionals is increased by considerably

reducing the time they must dedicate to individual users (Titov, 2007). This has two

important implications: a potential reduction in overall implementation costs, and increased

feasibility for their use in busy settings where professional time is often limited (e.g. health

services). In some cases, programmes are available “around the clock”, allowing individuals

to access materials where and when they choose (Bock et al, 2008). In addition, computer-

based delivery allows individuals to engage programmes at their own pace and as often as

desired (Spek et al, 2007). This flexibility may help increase initiation with an intervention

and reduce subsequent drop-out rates. Thus, computer-based programmes offer a means of

providing standardised yet individualised interventions with a high degree of fidelity (Botvin,

2004).

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Users of computer-based interventions may also benefit from perceived privacy and

anonymity, offering a solution to any concerns around stigmatisation or embarrassment about

drug use (Gega et al, 2004). Although full anonymity may not necessarily be achieved (for

example, users may require a login and password to access the programme), many users find

it easier to disclose information about themselves via a computer (Rhodes et al, 2003), with

lack of face-to-face interaction reducing social desirability and pressure for a user to respond

in a particular way (Skårderud, 2003).

The benefits offered by computer-based programmes suggest good potential for their use in

reducing/preventing illicit recreational drug use and an understanding of their effectiveness is

therefore essential. Reviews of computer-based programmes suggest that they can be both

effective and cost effective in addressing related risky behaviours such as alcohol and

tobacco use (e.g. Rooke et al, 2010; Myung et al, 2009; Bock et al, 2008). However, whilst

there have been a range of evaluations of computer-based programmes for addressing illicit

drug use, there have only been a few attempts to bring this information together in a

systematic way. One study reviewed the use of computer-based interventions for addressing

drug use disorders. Compared to treatment-as-usual, computer-based interventions were

associated with less substance use and greater motivation for change (Moore et al, 2010). A

further study reviewed computer-based alcohol and drug (tobacco and cannabis) prevention

programmes set in school environments. Findings suggested that these programmes had good

potential for reducing alcohol and drug use among adolescents, although only one study in

the review included cannabis use outcomes (Champion et al, 2013). To our knowledge, there

have been no reviews focusing solely on recreational drug use (excluding problematic use).

This review aims to fill this gap by conducting a systematic review of the evidence. Our

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objective was to establish whether computer-based interventions are effective in reducing

illicit recreational drug use. Our outcome measure was drug use behaviour (e.g. recent drug

use or frequency of use).

2. Material and methods

We searched eight electronic databases (Medline, ERIC, PsychINFO, Cochrane, ASSIA,

Social Sciences Citation Index, CINHAL and IBSS) to identify primary research studies

published up to May 2011 that evaluated computer-based interventions to prevent, reduce or

manage illicit recreational drug use. We defined illicit recreational drug use as use of any

illegal drug such as cannabis, ecstasy, cocaine and gammahydroxybutyrate (GHB) (with the

exception of more problematic drugs such as heroin) and non-medical use of prescription

drugs. We did not include legal substances such as alcohol or tobacco since these have been

reported in detail elsewhere (e.g. Rooke et al, 2010; Myung et al, 2009; Bock et al, 2008).

The search strategy can be found in Figure 1. A total of 5,272 studies were identified (3,413

unique articles). Two reviewers independently screened the titles and abstracts of studies to

assess their potential in answering the research question. A total of 175 relevant articles were

identified for potential inclusion and a further 17 identified through hand searching reference

lists. All relevant articles (n=192) were reviewed independently by two reviewers for

inclusion using the following criteria: 1) the study evaluates a computer-based intervention

for preventing, reducing or managing illicit recreational drug use; 2) the study outcomes

include at least one quantitative measure relating to drug behaviours; and 3) the study reports

changes in outcome measures from baseline to post intervention. Papers were excluded if: 1)

they targeted pregnant/postpartum women or dependent substance users; 2) they included

parental involvement since this may have shifted the onus for change away from individuals;

and 3) there was no control/comparison group. References were also excluded if results were

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duplicated within another study contained in the review (n=2). This resulted in a total of 10

included papers. A flow chart of the search process is provided in Figure 2.

Included studies were quality assessed independently by two reviewers using the Quality

Assessment Tool for Quantitative Studies (Effective Public Health Practice Project [2011];

see Table 1). For each study, data on the target group and setting, inclusion criteria, design,

participants, intervention, retention and outcome measures were extracted by one reviewer

and checked for accuracy by a second (see Tables 2 and 3). It was not possible to combine

study results in a meta-analysis due to the wide variation in study outcome measures,

reporting time frames, comparison groups, and follow up periods used across studies. For this

reason, we adopted a narrative approach to analysis. However, to aid understanding and

cohesion of results, we converted outcome measures to standardised mean differences

(Cohen’s d effect sizes) where information was available (authors were contacted for extra

information where needed), presenting these and the direction of effect for summary

presentation (Table 3). Approximate d values were calculated for studies reporting odds

ratios or risk ratios using the formula proposed by Chinn (2000). Results were analysed in

two groups: studies that evaluated universal drug prevention programmes, and those that

evaluated programmes targeting recreational drug users. This is because effects are likely to

be greater for targeted populations, where baseline level of drug use will likely be higher and

where there may be more desire to change drug-related behaviours.

3. Results

3.1 Study characteristics

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Study characteristics are presented in Table 2. Studies covered three broad intervention types,

often combining one type with another: skills training (n=6), education (n=4), and therapy

(n=4). Whilst all interventions were mostly computer based, five studies included some

degree of additional input from a professional (e.g. therapist or teacher). Studies covered a

wide range of settings including school/college (n=4), community (n=2), medical settings

(n=1), internet (n=2) and workplace (n=1). The mean age of participants ranged from 13 to

44 years. Most studies used mixed gender samples, whilst two targeted only females. Target

groups varied, including school pupils (n=3), adolescents (n=1), individuals seeking help for

drug use (n=2), college students using marijuana (n=1), employees (n=1), HIV affected

outpatients (n=1), and individuals with co-morbid depression and substance misuse (n=1).

Five studies compared intervention participants to a control group (no intervention), two

compared intervention participants to individuals undergoing usual care (which may have

included aspects of drug prevention/management), and three compared intervention

participants to those completing alternative drug prevention/management programmes. Five

studies evaluated universal drug prevention programmes and five evaluated programmes

targeted specifically at recreational drug users.

3.2 Study quality

Whilst all studies were rated strongly for design, most rated weaker on: selection bias (e.g.

using non-random selections of participants or having a low participation rate), confounders

(e.g. not controlling for important group differences within either the design or analyses),

data collection measures (e.g. not reporting the measurement tools to be valid or reliable),

and withdrawals (e.g. having a low follow up rate or not reporting follow up rates). However,

the assessment tool used was primarily developed for clinical quantitative studies. In general,

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non-clinical studies will rate weaker because there is less reporting of this type of information

in published articles.

3.3 Universal drug prevention programmes

Five studies explored the effects of universal, computer-based interventions to prevent the

use of recreational drugs (Table 3). Three of these compared the intervention group to a

control group receiving no intervention. Although no significant effects were reported for any

of the three studies post intervention (immediately following the intervention), one study

(Schwinn et al, 2010) found significant reductions in the frequency of marijuana and

polydrug use six months after the end of the intervention, although effect sizes were fairly

small (~-0.3). One study (Newton et al, 2010) compared an intervention group with usual

health education. Again, while no significant effects were reported post intervention, there

were significant reductions in the use of cannabis at a six month follow up (small effect size).

However, by 12 months, this effect had subsided. The last study compared an intervention

group to a group receiving an alternative drug prevention programme delivered by

professionals (Marsch et al, 2006). There were no significant differences in the frequency of

marijuana use reported between groups post intervention, with little apparent change over

time for either group (although statistical analyses of changes were not reported for separate

groups).

3.4 Programmes targeted at recreational drug users

Five studies explored the effects of computer-based interventions among recreational drug

users (Table 3). Two of these compared an intervention group to a control group receiving no

intervention. Whilst one of the studies reported a significant reduction in past month use of

cannabis 40 days later (small effect size; Tossman et al [2011]), the other found no

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intervention effects on marijuana use at a three and six month follow up (Lee et al, 2010).

One study compared an intervention group to a group receiving usual care (Gilbert et al,

2008). This study used two measures of drug use (current use of drugs and number of days

used in the past month), reporting mixed findings. Whilst intervention participants were

significantly less likely to report current use of drugs post intervention and three months later

(moderate and large effect sizes respectively), no significant changes were reported in the

number of days used between intervention and comparison groups for either time period.

Two studies compared an intervention group with alternative interventions. One found a

significant positive effect on the frequency of drug use (effect size not measurable; Kay-

Lambkin et al [2009]). Here, although the frequency of cannabis use reduced for all three

interventions explored at a 9 month follow up (brief intervention, computer-based, therapist-

based), reductions were significantly greater for the computer-delivered and therapist-

delivered groups than the brief intervention group. The second study reported a significant

reduction in the number of days of cannabis use post intervention for both the intervention

and alternative programme groups (Budney et al, 2011).

4. Discussion

This systematic review aimed to establish the effectiveness of computer-based interventions

in reducing illicit recreational drug use. Results from the five studies that explored the use of

universal drug prevention programmes suggest that although such programmes show no

immediate effects, they can be useful in reducing illicit recreational drug use in the mid term

(up to six months later, but effect sizes are fairly small). Results from the five studies

targeting recreational drug users were more inconsistent. However, the generally positive

results suggest potential for use in reducing drug use in both the immediate and mid-term.

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Three studies included in the review compared computer-based programmes to alternative,

professional led interventions. In general, results from these studies suggest that computer-

based programmes can be just as effective as professional led interventions. This is an

encouraging finding, since computer based interventions can offer a number of important

advantages over professional led programmes. They may be particularly advantageous in

settings where conventional forms of intervention would be beneficial but not feasible. This

includes health settings, which may be utilised frequently by illicit recreational drug users,

but where professional time per patient is severely limited. No papers in our review explored

the use of computer-based programmes within health settings. This gap in evidence should be

filled through investigating the feasibility and effectiveness of use in a variety of health

environments (e.g. emergency departments, GP practices and outpatient clinics).

From the ten included studies in this review, only one examined the longer term effects of

intervention (Newton et al, 2010). Here, the positive effects seen at a six month follow up had

diminished by 12 months. It appears likely therefore that booster sessions will be required

after one year to maintain the effects seen in the mid-term. However, more long term research

is clearly needed in this area to better evaluate effectiveness. There is also a need for more

rigorous evaluation methodologies and reporting to increase the quality of studies in this area.

This should include controlling for group differences within study design or analysis,

reporting the validity and reliability of measurement tools, and reporting withdrawal rates.

Additionally, with the vast majority of studies included in our review conducted in English

speaking countries (n=9), there is much need for research elsewhere to determine whether

computer based programmes are transferable to other cultures.

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Although this paper is the first to focus specifically on recreational drug use, the findings

reported in this review are generally in line with a review of computer-based interventions for

drug use disorders that reported better drug behavioural outcomes compared with treatment-

as-usual control groups (Moore et al, 2011). Other reviews of computer-based programmes

have found improvements over a range of health outcomes, including alcohol and tobacco use

(Rooke et al, 2010; Myung et al, 2009; Bock et al, 2008), nutrition, safer sexual behaviour

and binge/purge behaviours (Portnoy et al, 2008).

Importantly, any consideration of computer-delivered programmes needs to be weighed

carefully against any possible barriers that this mode of delivery entails, as well as lost

opportunities for alternative interventions involving human contact. For instance, it would be

important to consider any potential exclusion of certain population groups (users with

minimal access to computers, less opportunity for private use of a computer, or fewer

computer skills) and how these should best be addressed. Additionally, there is not currently

much understanding of the value of human contact in health interventions, nor how much

human contact is optimal. Five of the studies included in our review utilised at least some

degree of professional input in addition to the computer-based programme. Although it was

not possible to compare the effectiveness of these interventions with others that contained no

professional input, previous research assessing programmes for drug, alcohol and tobacco use

suggests that programmes containing some degree of therapist contact may see larger

reductions in substance use (Newman et al, 2011). Greater understanding of the value of

human contact within health interventions is essential and will help inform whether, and how

much, professional contact should be involved in computer-based programmes.

4.1 Limitations

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There are a number of limitations presented by our review. Firstly, due to the variation in

comparison groups, outcome measures and reporting time periods, it was not possible to

combine results in a meta-analysis. This would have strengthened results by providing an

indication of overall effect and thus assistance in interpreting the mixed findings reported.

Secondly, due to the variation in target groups across studies, it was not possible to categorise

results further to explore any differences emerging between, for example, school children and

adult groups. It is possible that school children and adults may respond differently to

computer-based delivery methods (due to differences in familiarity with computers and

computer-based learning, for example). Thirdly, findings were limited by the lack of detailed

data reported in a small number of papers that restricted our ability to calculate effect sizes.

For instance some papers only reported raw data where a significant effect was found. Effect

sizes can provide useful supplementary information to levels of significance, providing some

indication of the level of change observed as a result of the intervention. This hampered the

ability to compare results across studies. Lastly, this review explores delivery type rather than

intervention content. Although it was not possible to analyse the varying types of content

separately, it is likely that the effectiveness of programmes will vary by the type of material

provided. As the evidence base for computer-based programmes expands, it will be possible

to begin teasing out the effects of different programme types, establishing whether certain

styles are more applicable to computer based programmes than others.

5. Conclusions

This review suggests that computer-based programmes have the potential for use in

addressing illicit recreational drug use when targeted at both universal populations and illicit

drug users. However, more research is needed to establish long term effectiveness (>12

months) and explore the use of programmes outside of English speaking countries. While it

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seems likely that computer-based programmes can be just as effective as professional led

interventions, more research is needed to better understand the value of human contact in

health interventions and to determine optimal levels of professional input.

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Role of funding sources

The research leading to these results has received funding from the European Community's

Drug Prevention and Information Programme under grant agreement no.

JUST/2009/DPIP/AG/0930 - eSBIRTes (Electronic Screening, Brief Intervention and

Referral to Treatment for (poly) drug users in Emergency Services. The funders had no role

in the study design, collection, analysis or interpretation of data, writing the manuscript, or

the decision to submit the paper for publication.

Contributors

JS, ZD, LV, KH, SKW, MAB designed the study. LE, SKW, KH conducted literature

searches, study selection, data extraction and quality assessment with input from KAH. SKW,

KAH, KH, MAB wrote the manuscript with input from JS, ZD and LV. All authors reviewed

the study findings and read and approved the final version before submission.

Conflict of Interest

All authors declare that they have no conflicts of interest.

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Figure 1: sample search strategy (Medline)

1. (MH "Substance-Related Disorders+")

2. AB/TI (substance N2 abuse*) or (substance N2 use*) or (substance N2 misuse) or (substance N2

dependen*) or (substance N2 disorder*) or (substance N2 addict*) or (substance N2 volatile) or

(substance N2 poly)

3. AB/TI (Drug N2 abuse*) or (drug N2 use*) or (drug N2 misuse) or (drug N2 dependen*) or (drug N2

disorder*) or (drug N2 addict*) or (drug N2 volatile) or (drug N2 poly)

4. cannabis or hashish or marijuana

5. N-Methyl-3,4-methylenedioxyamphetamine or ecstasy or MDMA

6. crack cocaine or cocaine

7. GHB or gamma-Hydroxybutyric acid or gammahydroxybutyrate or gamma hydroxybutyrate or gamma

hydroxyl butyrate or sodium oxybate

8. Or/1-7

9. AB/TI (screening N2 tool*) or (screening N2 instrument*) or (screening N2 test) or (identify* N2

tool*) or (identify* N2 instrument*) or (identify* N2 test)

10. AB/TI (brief N2 advice) or (brief N2 intervention*) or (brief N2 interview*)

11. AB/TI (motivational N2 advice) or (motivational N2 intervention*) or (motivational N2 interview*)

12. AB/TI (referral N2 guide*) or (referral N2 guidance) or (referral N2 tool*) or (referral N2 protocol*) or

(referral N2 instrument) or (referral N2 pathway) 13. AB/TI (referral N2 treatment)

14. AB/TI (self-help or self-edu* or edu* or guid* or program* or module*)

15. AB/TI (goal AND setting)

16. Or/9-15

17. 8 and 16

18. AB/TI (online or internet or web or world wide web or electronic or web site or web page or

technology or computer*) 19. 17 and 18

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Figure 2: flow chart of search process

5,272 references retrieved for

title/abstract review

175 identified as potentially

relevant and full-text reviewed

1 unpublished study

identified from research

group

1,860 duplicates excluded

3,238 excluded; not relevant in

answering the research questions

17 additional relevant studies

identified through checking

reference lists

182 excluded: 129 not a computer based intervention

22 do not include drug related outcomes

15 targeted pregnant women or dependent substance users

5 included parental involvement

7 full paper not available 2 no control group for comparison

2 duplicated results from included paper

10 included in review

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Table 1: Study quality

Study and date Selection

bias

Study

design Confounders Blinding

Data

collection Withdrawals

OVERALL

RATING

Budney et al (2011) 1 3 3 2 1 2 12

Deitz et al (2011) 1 3 1 2 1 3 11

Gilbert et al (2008) 1 3 3 2 1 3 13

Kay-Lambkin et al (2009) 1 3 1 2 1 3 11

Lee et al (2010) 1 3 3 2 2 3 14

Marsch et al (2006) 2 3 1 2 1 1 10

Newton et al (2010) 2 3 1 2 2 2 12

Schwinn et al (2010) 1 3 3 2 1 3 13

Tossman et al (2011) 1 3 1 2 1 1 9

Williams et al (2005) 2 3 3 2 1 1 12

Quality assessments were made using the Quality Assessment Tool for Quantitative Studies (Effective Public Health Practice Project). Ratings were assessed as follows: 1 =

weak; 2 = moderate; 3 = strong. A higher score indicates a better quality.

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Table 2: Study characteristics

Author

year and

country

Setting and

target sample Eligibility criteria

Study

design Intervention

Control or

comparison N

%

male

Mean

age

(range)

Retenti

on

Budney

et al

(2011)

USA

Community;

Members of the

local community

seeking treatment

for cannabis use

disorder.

Age 18+; DSM-IV

diagnosis of cannabis

abuse, use cannabis

on >=50D in past 90D;

no dependence on

alcohol/other drugs; no

treatment for drug use;

no emotional distress.

CT Computer delivered nine session individual therapy

including MET, CBT and CM. This included feedback

report, goal setting exercises, and skills training (e.g.

problem solving, coping, managing thoughts and drug

refusal). Included three 15-30 min sessions with

therapist.

Duration: 12W.

Therapist delivered

nine session

individual therapy

involving the same

components as the

computer

intervention.

Duration: 12W.

38 47% I: 32.9

C: 32.7

61%

Deitz et

al (2011)

USA

Workplace;

female employees

of a hospital.

Access to a computer

with internet.

CT Web-based interactive program consisting of:

medication facts; safe administration of prescription

medicines; avoidance of drug abuse and alternatives to

medications. Contained self assessments on current or

anticipated drug use. Duration: 4W.

Wait-list control

group.

362 0% 44

(21-75)

95%

Gilbert et

al (2008)

USA

Outpatient

clinics; HIV

affected

outpatients

Aged 18+; HIV+ for

3M or longer;

RCT Computer programme showing Video Doctor clips that

delivered interactive risk reduction messages and

educational worksheets. The programme produced a

cue sheet for medical providers suggesting counselling

statements. Booster video clip session at 3M.

Duration: Brief session lasting 24 minutes, plus

additional booster session at 3M.

Usual care 476 79% I: 43.9

C: 44.3

82-83%

Kay-

Lambkin

et al

(2009)

Australia

Community;

members of the

community or

those referred

from alcohol

treatment, mental

health or primary

health care

settings with co-

morbid depression

and substance

Score of 17+ Beck

Depression Inventory

II; lifetime diagnosis of

major depressive

disorder, current

problematic alcohol

disorder/weekly use of

cannabis; absence of

brain injury or

cognitive impairment;

aged 16+; ability to

RCT Brief intervention for depression and substance misuse,

nine sessions of MI and CBT delivered by computer,

and brief 10-15 minute weekly psychologist input.

Duration: 3M

1) Brief

intervention plus no

further treatment.

2) Brief

intervention plus

nine sessions of MI

and CBT delivered

by psychologist.

Duration: 3M

97 46% 35.4

(18-61)

85%

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Author

year and

country

Setting and

target sample Eligibility criteria

Study

design Intervention

Control or

comparison N

%

male

Mean

age

(range)

Retenti

on

misuse. understand English.

Lee et al

(2010)

USA

College; first year

marijuana using

students at a US

university.

Aged between 17 and

19; use of any

marijuana in the last

3M.

RCT Personalised computerised feedback intervention based

on MI. Included feedback on marijuana use, perceived

and actual norms, perceived pros/cons of using, and

training for avoiding/changing use of marijuana.

Duration: not reported

No intervention

341 45% 18.0 92%

6M

follow

up

Marsch

et al

(2006)

USA

School; 7th grade

students in four

public schools

across the state of

Vermont.

NR

CT Interactive computer based drug prevention programme

(15 sessions) promoting protective factors, training in

drug refusal skills, social competency and attitudes

against drug use.

Duration: academic year

Drug abuse

prevention (15

sessions) given by

teacher. Duration:

academic year.

272 55% I:12.5

C:12.2

NR

Newton

et al

(2010)

Australia

School; Year 8

students from 10

independent

schools across

Sydney.

NR RCT Alcohol and cannabis prevention programme; 12

lessons including reasons for using cannabis and its

consequences, and drug refusal skills. Lessons

comprised a 15-20 minute internet component followed

by a teacher-delivered activity. Duration: 6M

Usual health

classes, most

including syllabus-

based drug

education.

Duration: academic

year

764 60% 13.1 79%

12M

follow

up.

Schwinn

et al

USA&

Canada

Internet; 7th

, 8th

and 9th

grade girls

accessing the

website

kiwibox.com

NR RCT 12 internet based sessions covering personal and social

skills and skills specific to dealing with drug use

opportunities, e.g. goal setting, decision making,

coping, self esteem, peer pressure and drug facts.

Duration: 6 weeks

No intervention 236 0% 14 91%

6M

follow

up

Tossman

et al

(2011)

Germany

Internet; “Quit

the Shit” website

users wishing to

reduce/ cease

cannabis use

NR RCT Online counselling programme including 50 minute

online chat with psychotherapist, online cannabis use

diary and detailed personal feedback by counsellor

each week Duration: 50 days

Wait list control

group

129

2

71% 24.7 48%

Williams

et al

(2005)

USA

School; 6th

and 7th

grade students

from public

schools in New

York

NR RCT 10 session computer based substance abuse prevention

programme using interactive audio and video content.

Included knowledge and skill based components for

resisting social influences and reducing motivation to

use substances. Duration: 6W

No intervention

(wait list control

group)

230 50% NR

(12-13)

53%

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MET = motivational enhancement therapy; CBT = cognitive behavioural therapy; CM = contingency management; MI = motivational interviewing; NR = not reported; CT = controlled trial;

RCT = randomised controlled trial; I = intervention; C=control; M = months; W = weeks; D = days

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Table 3: Observed effects of interventions on drug behaviours, effect and effect size (where calculable)

Study Outcome Time frame Comparison group Follow up period, effect and effect size

PI 40D 3 M 6 M 9 M 12 M

UNIVERSAL PROGRAMMES

Deitz et al (2011)

Nonmedical analgesic use (Yes/No) NR No intervention (wait-list control) ≈

-0.23

Nonmedical sedative use (Yes/No) NR No intervention (wait-list control) ≈

0.19

Nonmedical tranquiliser use

(Yes/No)

NR No intervention (wait-list control) ≈

0.41

Nonmedical stimulants use

(Yes/No)

NR No intervention (wait-list control) ≈

1.03

Schwinn et al (2010)

Marijuana use (frequency scale) Last 30 days No intervention ≈

-0.32

Polydrug use (frequency scale) Last 30 days No intervention ≈

-0.34

Williams et al (2005) Drug use (frequency scale) Current No intervention (wait-list control) ≈

Newton et al (2010) Cannabis use (frequency scale) Past 3 months Usual health classes ≈

0.17

-0.17 ≈

-0.22

Marsch et al (2006) Marijuana use (frequency scale) Current Alternative drug prevention training ≈

PROGRAMMES TARGETING RECREATIONAL DRUG USERS

Lee et al (2010) Marijuana use (days) Past 3 months No intervention ≈

0.01 ≈

-0.05

Tossman et al (2011) Cannabis use (days) Last 30 days No intervention (wait-list control)

-0.23

Gilbert et al (2008)

Drug use (yes/no) Current Usual care

-0.46

-0.88

Drug use (days) Past month Usual care ≈ ≈

Budney et al (2011) Cannabis use (% days used) Last 90 days Therapist delivered therapy ≈

-0.10

Kay-Lambkin et al

(2009)

Cannabis use (occasions per day) Past month Brief intervention only

Kay-Lambkin et al Cannabis use (occasions per day) Past month Therapist delivered intervention ≈

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Study Outcome Time frame Comparison group Follow up period, effect and effect size

PI 40D 3 M 6 M 9 M 12 M

(2009)

PI=post intervention; D=days; M=months; NR=not reported; ≈=no significant difference between groups;

=significant improvement for intervention group vs. comparison.

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Highlights

We conducted a systematic review of computer-based programmes to address recreational

drug use.

Universal programmes reduced the frequency of use in the mid-term only (<12 months).

Those targeting drug users varied but were generally effective post intervention and mid-term.

Computer-based programmes have potential for use in addressing recreational drug use.

More long term research is needed to better understand the duration of effects.