The efficacy of computerized interventions to reduce cannabis use: A systematic review and meta-analysis

Accepted Manuscript The efficacy of computerized interventions to reduce cannabis use: A systematic review and meta-analysis

Alexandre Olmos, Judit Tirado-Muñoz, Magí Farré, Marta Torrens PII: DOI: Reference:

S0306-4603(17)30453-7 doi:10.1016/j.addbeh.2017.11.045 AB 5385

To appear in:

Addictive Behaviors

Received date: Revised date: Accepted date:

2 February 2017 30 November 2017 30 November 2017

Please cite this article as: Alexandre Olmos, Judit Tirado-Muñoz, Magí Farré, Marta Torrens , The efficacy of computerized interventions to reduce cannabis use: A systematic review and meta-analysis. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Ab(2017), doi:10.1016/ j.addbeh.2017.11.045

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT The Efficacy of Computerized Interventions to Reduce Cannabis Use: A Systematic Review and Meta-Analysis Alexandre Olmos1,*, Judit Tirado-Muñoz2,*, Magí Farré3,4,*, Marta Torrens4,5,* 1

Universitat Pompeu Fabra-Universitat Autònoma de Barcelona, Barcelona, 08003

2

Addiction Research Group, IMIM-Institut Hospital del Mar d’ Investigacions Mèdiques, Barcelona,

T

08003 3

08916 4

Universitat Autònoma de Barcelona, Bellaterra, 08193

5

SC RI P

Clinical Pharmacology Department, Hospital Universitari Germans Trias I Pujol (IGTP), Badalona,

Institut de Neuropsiquiatria i Addiccions, Hospital del Mar, Barcelona and IMIM (Institut Hospital del

NU

Mar d’Investigacions Mèdiques), Barcelona, 08003

*

Corresponding Author Marta Torrens MD, PhD

ED

MA

All authors contributed equally to this work.

PT

Unidad Adicciones, INAD-Parc de Salut Mar Passeig Marítim 25-29, 08003 Barcelona, Spain

AC

CE

E-mail: [email protected]

ACCEPTED MANUSCRIPT Abstract

Background and Aims. Cannabis is the most widely consumed illicit drug. Although it is too early to confirm the impact of legalization, the use of cannabis appears to be on the rise in some countries due to its authorization for medical/recreational purposes. Among different types of therapeutic approaches to reduce cannabis use, computerized interventions are becoming a new treatment option. To assess their

SC RI P

T

efficacy, a systematic review and meta-analysis was conducted.

Methods. A systematic review and meta -analysis was performed employing randomized controlled clinical trials indexed in MEDLINE and PsycINFO. The principal outcome measure was cannabis use, and the secondary one was the use of other substances during interventions. A subgroup analysis was conducted by

control condition. Results. The

meta-analysis

included

NU

length of follow-up, number of sessions, age group, type of analysis, and type of

nine

studies

with 2963 participants.

MA

Computerized interventions resulted in significant reductions in the use of cannabis (standardized mean difference [SMD]: -0.19; 95% CI: -0.26, -0.11) and other

ED

substances (SMD: -0.27; 95% CI: -0.46, -0.08). Conclusions. Computerized interventions examined in the present study reduced the frequency of cannabis and other substance use. Limitations included the

PT

recalculation of dichotomous and continuous data as SMD and the lower number of studies included in the secondary outcome. Computerized interventions could be a

AC

CE

viable option to reduce cannabis use.

Keywords: cannabis, computerized interventions, meta-analysis, randomized controlled clinical trial, systematic review, treatment

ACCEPTED MANUSCRIPT 1. Introduction Worldwide, cannabis is the most widely cultivated, trafficked, and consumed illicit drug, with around 183 million (3.8%) users (UNODC, 2016). Its consumption has been related to short- and long-term health effects as reported in the literature (Volkow, Baler, Compton, & Weiss, 2014; WHO, 2016). Although it is too early to confirm the impact of legalization on drug use, the consumption of cannabis appears to be on the rise in some countries due to its authorization for medical/recreational

SC RI P

T

purposes (Hall & Lynskey, 2016; Hasin et al., 2016; Spitho ff, Emerson, & Spithoff, 2015). Such a finding confirms the need to provide a broader range of treatment options for users (Sobesky & Gorgens, 2016). Indeed, the development and evaluation of accessible treatment options for cannabis users presents a challenge of crucial importance.

intervention

programs,

NU

There are specific treatment programs for cannabis users. They include early comprehensive

family-based

treatment,

counseling,

prevention information, brief intervention, web-based programs, motivational

MA

interviewing, and cognitive behavioral therapy (WHO, 2016). While the demand for the treatment of cannabis use disorder is increasing worldwide (EMCDDA, 2013)

ED

only a few problematic consumers seek professional medical assistance (Teesson, 2012), and a limited number of studies have assessed the effectiveness of interventions (WHO, 2016).

PT

It should be noted that therapy-based interventions for cannabis have several drawbacks that can be partially solved through computerized interventions. A

CE

number of such programs for substance use have been developed and evaluated; nevertheless, their deployment continues to be infrequent. The programs include

AC

cognitive-behavioral therapy (CBT), chat features, tailored messaging, and motivational interviewing (Rooke, Copeland, Norberg, Hine, & McCambridge, 2013). The possibilities offered through digital media and their potential applications for the treatment of substance use disorders are promising (Moore, Fazzino, Garnet, Cutter, & Barry, 2011). They are particularly advantageous, since they facilitate access to geographical areas where resources are limited and no healthcare personnel are required for the interventions (Titov, 2007). Motivational interviewing and CBT are jointly more effective than other therapies such as

contingency management, social support, and

mindfulness-based

meditation. The combination is more effective when treatment is comprised of more

ACCEPTED MANUSCRIPT than four sessions and is delivered for a period greater than one month (Gates, Sabioni, Copelan, Le Foll, & Gowing, 2016). Computerized interventions with proven efficacy have not only been developed for substance abuse such as alcohol and tobacco, but also for anxiety disorders (Andersson et al., 2006), obesity (Tate, Wing, & Winett, 2001), eating disorders (Fernández-Aranda et al., 2009), and chronic pain and fatigue problems (Brattberg, 2006). Several meta-analytic reviews have sought to determine the effectiveness of

SC RI P

T

computerized interventions. Bailey’s meta-analysis evaluated their efficacy for the promotion of sexual health and demonstrated that they had positive effects on selfefficacy, intention, and sexual behavior (Bailey et al., 2010). Furthermore, a meta analysis including studies of chronically ill patients found improvements in assessed outcomes (greater knowledge of asthma treatment and nutritional status, augmented

NU

exercise time, and increased participation in healthcare, among other) in individuals using computerized interventions compared to those employing non-computerized ones (Wantland, Portillo, Holzemer, Slaughter, & McGhee, 2004). Similar positive

MA

evidence supporting computerized interventions was recently found for problematic alcohol use in a systematic review (Sundström, Blankers, & Khadjesari, 2016).

ED

Previous systematic reviews have supported the efficacy of computeri zed treatments for alcohol consumption (Carey, Scott-Sheldon, Elliot, Bolles, & Carey, 2009) and tobacco use (Rooke, Thorsteinsson, Karpin, Copeland, & Allsop, 2010). Only one,

PT

however, focused on cannabis and, while reporting initially positive short-term results for reducing consumption, it was based on limited data and across diverse samples

CE

(Tait et al., 2013). Upgrading and evaluating the efficacy of computerized interventions is warranted as such interventions are viable supplements to therapy-

AC

based interventions (Cochran et al., 2015; Paruk, Jhazbhay, Singh, Sartorius , & Burns, 2016).

The aim of the present systematic review and meta-analysis was to determine the efficacy of computerized interve ntions in decreasing cannabis use and /or other substances and to compare them to a control condition.

2. Methods 2.1 Data search and quality criteria

ACCEPTED MANUSCRIPT This review was registered with the international prospective register of systematic reviews (PROSPERO 2015: 32604). It was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta -Analyses (PRISMA) recommendations (Moher, Liberati, Tezlaff, & Altman, 2009). The MEDLINE (1970 to 2015) and PsycINFO (1970 to November 30, 2015) databases were searched using a combination of terms; the search strategy with keywords is described in Table 1. In addition, to ensure that all relevant trials have been included in the review, the

SC RI P

T

reference lists of identified reviews were also hand-searched. Eligibility criteria for inclusion in the systematic review were (1) randomized controlled trials, (2) frequency of cannabis use as the outcome, and (3) analysis of the efficacy of Internet-delivered treatments vs the control condition. We excluded studies that tried to improve the therapist’s skills and those including dyads of mother-son for

NU

performing results.

The characteristics of the included studies are described in Table 2. Data extraction of the studies included the following information: authors, recruitment, participants

MA

analyzed, study interventions (brief description), participant characteristics, length of follow-up, outcomes assessed, results, type of analysis (intention to treat vs

ED

completers only), dichotomous or continuous data, and information on whether the

PT

article was included or not in the meta-analysis.

MEDLINE

Time frame of

AC

Database

CE

Table 1. Description of search terms Keywords

Limitations applied

Mesh (marijuana OR

Human, randomized

cannabis) OR mesh

clinical trial, English

search

1970 to 2015

(cannabis use OR marihuana) OR mesh (hashish) AND mesh (online OR computerized) OR mesh (ehealth OR online) OR mesh (web-

ACCEPTED MANUSCRIPT based therapy) AND mesh (treatment OR intervention)

1970 to

(substance-related

Human, randomized

November 30,

disorders OR drug use OR

clinical trial

2015

drug users OR addiction)

SC RI P

AND (cannabis OR

T

PsycINFO

cannab* OR marihuana OR cannabis use OR

hashish) AND (online OR computerized OR ehealth

NU

OR Internet OR webbased therapy) AND (treatment OR

AC

CE

PT

ED

MA

intervention)

ACCEPTED MANUSCRIPT 2.2 Quality assessment and data extraction The authors A.O.T and J.T.M independently assessed all articles against the eligibility criteria. In cases of disagreement, the decision to include or exclude each trial was taken by consensus among the authors A.O.T, J.T.M, M.T, and M.F. The methodological quality was assessed independently by A.O.T and J.T.M using the Risk of Bias Tool for reporting randomized controlled trials. Differences between authors were resolved through discussion by the authors A.O.T, J.T.M, M.T, and

T

M.F. The Risk of Bias Tool has quality interpretation with ratings of “yes” (low risk),

SC RI P

“no” (high risk), and “unclear” (unclear risk) for seven key domains: sequence generation, allocation concealment, blinding of outcome assessment, blinding of participants, incomplete outcome data, selective outcome reporting, and other

NU

sources of bias.

2.3 Statistical analysis

MA

The principal outcome measure was cannabis use and the secondary outcome measure was the use of other substances during interventions. Use of other

ED

substances outcomes included the following: cocaine, methamphetamines, opiates, ecstasy (Ondersma et al., 2007; Schwinn et al., 2010), and illicit and non-illicit drugs (Walton et al., 2013). A subgroup analysis was conducted by the length of follow-up,

PT

number of sessions, age group, type of analysis, and type of control condition. The measure used to assess efficacy was the standardized mean difference (SMD),

CE

and mean difference (MD) using 95% confidence intervals (CIs). Review Manager Software (version 5.0) was employed for data entry and statistical analysis. The

AC

meta-analysis was performed using a fixed effects model. Seven studies presented the outcome data as continuous data (Becker, Haus, Sullivan, & Schaub, 2014; Elliott, Carey, & Vanable, 2014; Newton, Teesson, Andre ws, & Vogl, 2010; Rooke, Copeland, Norberg, Hine, & McCambridge, 2013; Schwinn, Schinke, & Di Noia, 2010; Tossmann, Jonas, Tensil, Lang, & Strüber, 2011; Walton et al., 2013) and two as dichotomous (Kay-Lambkin, Baker, Kelly, & Lewin, 2011; Ondersma, Svikis, & Schuster, 2007), therefore, the odds ratios were recalculated as SMDs, allowing both types of data to be pooled. The standard errors of the log odd ratios were converted to standard errors of the SMD by multiplying by the same constant (3/π =0 .5513). This allowed the standard error for the log odds ratio and hence a confidence interval

ACCEPTED MANUSCRIPT to be calculated (Tirado-Muñoz, Gilchrist, Hegarty, & Torrens, 2014).The degree of heterogeneity was quantified to determine the consistency among the trials included in the meta-analysis (I2 of 25% was considered low, 50% moderate, and 75% high

AC

CE

PT

ED

MA

NU

SC RI P

T

heterogeneity).

ACCEPTED MANUSCRIPT Table 2. Description of trials included in the systematic review

Website

IC1: 114 IC2: 102 CC: 109

IC1: CPF IC2: MI CC: PE

1

IC: eTOKE CC: Assessm ent IC1:TBI IC2:CBI CC: Assessm ent

Elliot et al. (2014) USA

Priv ate univ ersity

IC: 76 CC: 85

Kay Lambkin (2009) Australia

Communi ty based

Total: 43

Kay Lambkin, (2011) Australia

Rural and urban areas

IC1: 42 IC2: 33 CC: 34

Lee (2010) USA

Univ ersity

Total: 320

Newton (2010) Australia

High schools

IC: 331 CC:275

Ondersm a (2007) USA

Obstetric hospital

IC: 39 CC: 37

IC: CBI CC: Assessm ent

Website

IC: 84 CC: 58

IC: CBI CC: Educatio n

Website

IC: 118 CC: 118

Tossman n et al. (2011) Germany

Website

IC: 863 CC: 429

Walton (2013) USA

Primary medical care

AC

IC1: 77 IC2: 104 CC: 94

1

10

10

3

6

1

Participant characteris tics

FU (mont hs)

Outco mes measur ed

Results

Dichotom ous/ Continuou s

ITT/ CO

Metaanaly sis

29-30 y ears old 80% male 20% f emale

2

Cannab is use

Not signif ica nt

Continuou s

ITT

Y es

18-23 y ears old 48% male 52% f emale

1

Cannab is use

Not signif ica nt

Continuou s

CO

Y es

35 y ears old 46% male 54% f emale

3, 6, and 12

Cannab is use

Continuou s

CO

No

40 y ears old 57% male 43% f emale

3

Cannab is use

Dichotomo us

ITT

Y es

17-19 y ears old 47% male 54% f emale

3 and 6

Cannab is use

Continuou s

CO

No

13 y ears old 60% male 40% f emale

6 and 12

Cannab is use

No signif ica nt

Continuou s

CO

Y es

4

Cannab is use Other substan ce use

Not signif ica nt

Dichotomo us

ITT

Y es

6 weeks and 3 month s

Cannab is use

Not signif ica nt

Continuou s

CO

Y es

13-14 y ears old 100% f emale

6

Cannab is use Other substan ce use

Signif ica nt

Continuou s

CO

Y es

25 y ears old 70% male 30%f emale

3

Cannab is use

Signif ica nt

Continuou s

ITT

Y es

16 y ears old 33% male 67% f emale

3, 6, and 12

Cannab is use Other substan ce use

Not signif ica nt

Continuou s

ITT

Y es

25 y ears old 100% f emale

ED

IC: Program Real Teen CC: Assessm ent IC: QTS program CC: Waiting list IC1: TBI IC2: CBI CC: Inf ormati on

CE

Schwinn et al. (2010) USA

IC1: CBT/MI + CAC IC2: CBT/MI CC: PCT IC: C-PF CC: Assessm ent IC: Climate course CC: Educatio n

PT

Rooke et al. (2013) Australia

Numb er of sessio ns

6

12

50

1

T

Study intervent ion

SC RI P

Participa nts analyzed

NU

Becker (2014) Switzerla nd

Recruitm ent

MA

Author/ RCT

18 y ears old 60% male 40% f emale

---------------

Not signif ica nt

---------------

IC = intervention condition; CC = control condition; C-PF = computer-delivered personalized feedback; MI = motivational interviewing; PE = psychoeducation; TBI = therapist-based intervention; CBI = computer-based intervention; C-CBT = computer-delivered cognitive behavioral therapy; CAC = clinician-assisted computer; PCT = person-centered therapy; ITT = intention to treat; CO = completers only; FU = follow -up.

ACCEPTED MANUSCRIPT 3. Results 3.1 Study selection The search resulted in 608 titles and/or abstracts. A total of 574 abstracts were excluded because they did not mention Internet interventions for cannabis use. Thirty-four studies were selected for assessment and to read in full-text. The reasons for exclusion are reported in Figure 1. A total of 11 trials were included in the systematic review (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin, Baker,

SC RI P

T

Lewin, & Carr, 2009; Kay-Lambkin et al., 2011; Lee, Neighbors, Kilmer, & Larimer, 2010; Newton et al., 2010; Ondersma et al., 2007; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2011; Walton et al., 2013) and 9 in the meta-analysis (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Ondersma et al., 2007; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al.,

NU

2011; Walton et al., 2013) (see Figure 1). Two were not included in the metaanalysis because the number of participants analyzed by groups was not reported (Kay-Lambkin et al., 2009; Lee et al., 2010). Thus, data could not be entered into the

MA

RevMan software to be compared with the other included studies by control and intervention conditions. The authors were asked about the numbers per group, but

ED

there was no response during this period.

In general, telephone interventions and text messaging interventions were removed as the platform they employ is much less prominent than other technologies and they

PT

had markedly different approaches compared to the other interventions in the metaanalysis. Accordingly, keywords such as “mobile” or “mHealth” related to these

AC

CE

interventions were not included in the search strategy.

3.2 Study characteristics While cannabis use was the main outcome of the included studies, other outcomes were also analyzed. For instance, alcohol consumption was measured in five studies (Kay-Lambkin et al., 2011; Newton et al., 2010; Ondersma et al., 2007; Schwinn et al., 2010; Walton et al., 2013). Newton et al. (2010) evaluated cannabis knowledge and cannabis expectancies, depression outcome was assessed in two studies (KayLambkin et al., 2011; Tossmann et al., 2011), perceived risk of cannabis use was measured by Walton et al. (2013), and two manuscripts evaluated readiness to change (Lee et al., 2010; Tossmann et al., 2011).

ACCEPTED MANUSCRIPT Of the 11 randomized controlled trials (RCTs), five were from the United States (Elliott et al., 2014; Lee et al., 2010; Ondersma et al., 2007; Schwinn et al., 2010; Walton et al., 2013), four were from Australia (Kay-Lambkin et al., 2009; KayLambkin et al., 2011; Newton et al., 2010; Rooke et al., 2013), one was from Switzerland (Becker et al., 2014), and one was from Germany (Tossmann et al., 2011). The final 9 trials comprising the meta-analysis had a total of 1724 participants receiving computerized intervention and 1239 as controls (N = 2963). The mean age

SC RI P

T

of the sample was 20.1 years, with 45.54% males and 54.46% females. Two studies

AC

CE

PT

ED

MA

NU

included only women (Ondersma et al., 2007; Schwinn et al., 2010).

ACCEPTED MANUSCRIPT

Records identified through database searching (n = 608)

Additional records identified through other sources (n = 0)

SC RI P

T

Identification

Figure 1. Flow chart

Screening

Records after duplicates removed (n = 596)

MA

NU

Records screened (n = 596)

ED PT CE AC

Included

Eligibility

Full-text articles assessed for eligibility (n = 22)

Studies included in qualitative synthesis (n = 11)

Records excluded (n = 574)

Full-text articles excluded, with reasons (n = 11) Telephone intervention (n = 2) Dyads (n = 2) Protocol (n = 2) Therapist intervention (n = 1) Opiods (n = 1) Not RCT (n = 1) Prevention study (n = 1) Same study as other included (n = 1)

Studies included in quantitative synthesis (meta-analysis) (n = 9)

RCT = randomized controlled trial.

3.3 Quality and publication bias assessment The risk of bias summary is described in Table 3. Seven key domains were assessed. Blinding of the participants presented “high risk” in all manuscripts as

ACCEPTED MANUSCRIPT each of the participants knew they were either receiving a computerized intervention (intervention condition) or not. Therefore, this domain is unreliable. See Table 3 for other domains. The last domain was other biases. It consisted of early cessation for benefit, inappropriate influence funders, severe baseline imbalances , or design-specific risk of bias. For this domain, three studies were rated as “high risk” (Kay-Lambkin et al., 2009; Ondersma et al., 2007; Schwinn et al., 2010), two as “unclear” (Elliott et al.,

SC RI P

T

2014; Lee et al., 2010), and six as “low risk” (Becker et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Rooke et al., 2013; Tossmann et al., 2011; Walton et al., 2013). Kay-Lambkin et al. (2011) assumed no change in the participants who did not complete follow-up at 3 months (and this could improve the primary outcome). Eight trials presented lack of objective confirmation (Becker et al., 2014; Elliott et al., 2014;

NU

Kay-Lambkin et al., 2011; Newton et al., 2010; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2011 Walton et al., 2013). Four trials had problems with the generalizability of the sample (Newton et al., 2010; Schwinn et al., 2010; Ondersma

MA

et al., 2007; Walton et al., 2013) and therefore required replication to other samples. Tossmann et al. (2011) conducted intention to treat (ITT) and per-protocol (PP)

ED

analyses and obtained varying results because of differential attrition in both groups . Three studies had low levels of attrition (Becker et al., 2014; Ondersma et al., 2007; Rooke et al., 2013). One month follow-up was completed by Elliot et al. (2014),

PT

which is considered a short follow-up assessment. The characteristics of the participants at baseline were similar in most of the studies comprising the present

CE

meta-analysis (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2009; KayLambkin et al., 2011; Lee et al., 2010; Ondersma et al., 2007; Rooke et al., 2013;

AC

Schwinn et al., 2010; Tossmann et al., 2011; Walton et al., 2013). Baseline characteristics of included studies are reported in Table 2. Three trials randomized participants into three groups (Becker et al., 2014; Kay-Lambkin et al., 2011; Walton et al., 2013): two intervention groups (computerized and therapist interventions), and one control group. Recruitment biases were not identified in any of the included studies (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2009; KayLambkin et al., 2011; Lee et al., 2010; Newton et al., 2010; Ondersma et al., 2007; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2011; Walton et al., 2013).

ACCEPTED MANUSCRIPT

PT

ED

MA

NU

SC RI P

T

Table 3. Risk of bias summary of cannabis use interventions: review of authors’ appraisals about each risk of bias item for each included study.

CE

Note: Green = yes (low risk of bias); yellow = unclear, and red = no (high risk of bias).

3.4 Main and subgroup analysis

AC

The RCTs included in the meta-analysis compared the computerized interventions to the control conditions. All intervention conditions were based on a computerized program, but not all were identical (see Table 2). Moreover, control conditions varied for each study, for instance: waiting list (Tossmann et al., 2011), psychoeducation (Becker et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Rooke et al., 2013; Walton et al., 2013), no intervention (Schwinn et al., 2010), and assessment alone (Elliott et al., 2014; Ondersma et al., 2007). When trials in the meta-analysis had more than one intervention group, data from the most relevant one to address the aim of the systematic review were included in the meta-analysis.

ACCEPTED MANUSCRIPT The frequency of cannabis consumption was used to evaluate the efficacy of interventions (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Ondersma et al., 2007; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2010; Walton et al., 2013). Consumption was measured using questionnaires (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2010; Walton et al., 2013) and urine drug screening (Ondersma et al., 2007). Cannabis use

SC RI P

T

follow-up measures were based on self-reporting (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2010; Walton et al., 2013 ) and urine drug screening (Ondersma et al., 2007)

A secondary outcome was other substance use (Schwinn et al., 2010; Ondersma et

NU

al., 2007; Walton et al., 2013) in order to assess whether reductions in cannabis consumption could also diminish other substance use. The latest follow-up data were included in the meta-analysis for all studies comparing the intervention and control

MA

conditions.

Subgroup analysis was conducted according to the length of follow-up, the intensity

ED

of the intervention (number of sessions), age group, type of analysis, and type of control intervention. The follow-up length was divided into six months or more (Newton et al., 2010; Schwinn et al., 2010; Walton et al., 2013) and less than six

PT

months (Becker et al., 2014; Elliott et al., 2014; Kay-Lambkin et al., 2011; Ondersma et al., 2007; Rooke et al., 2013; Tossmann et al., 2011). The number of sessions

CE

was divided into five or more sessions (Kay-Lambkin et al., 2010; Newton et al., 2010; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2011) and fewer

AC

than five (Becker et al., 2014; Elliott et al., 2014; Ondersma et al., 2007; Walton et al., 2013). Age was split into participants 20 years or older (Becker et al., 2014; KayLambkin et al., 2011; Ondersma et al., 2007; Tossmann et al., 2011) and those under 20 (Elliott et al., 2014; Newton et al., 2010; Rooke et al., 2013; Schwinn et al., 2010; Walton et al., 2013). The type of analysis was divided into ITT (Becker et al., 2014; Kay-Lambkin et al., 2011; Ondersma et al., 2007; Tossmann et al., 2011; Walton et al., 2013) and completers only (Elliott et al., 2014; Newton et al., 2010; Rooke et al., 2014; Schwinn et al., 2010). The type of control intervention was divided into psychoeducation (Becker et al., 2014; Newton et al., 2010; Rooke et al.,

ACCEPTED MANUSCRIPT 2013; Kay-Lambkin et al., 2011; Walton et al., 2013) and waiting list (Elliott et al., 2014; Ondersma et al., 2007; Schwinn et al., 2010; Tossmann et al., 2010). The subgroup analyses were conducted because outcomes were assessed based on combining multiple time periods and interventions with different intensity in terms of the number of sessions. These were considered to be factors that could affect the evaluation of the efficacy of the interventions. The average of the follow-up months and the average number of sessions of the studies were used to create both groups

SC RI P

T

for the subanalysis.

We also assessed whether the type of control condition and analysis were issues that could influence the evaluation of the efficacy of the computerized interventions. Gender was also regarded as a potential factor; however, not all of the studies

NU

provided this information. As a result, this subgroup analysis could not be performed.

MA

3.5 Cannabis use results

Nine trials assessed the frequency of cannabis consumption (Becker et al., 2014;

ED

Elliott et al., 2014; Kay-Lambkin et al., 2011; Newton et al., 2010; Ondersma et al., 2007; Rooke et al., 2013; Schwinn et al., 2010; Tossmann et al., 2010; Walton et al., 2013). A total of 1724 cannabis users were included in the intervention group and

PT

1239 in the control group. Participants allocated to the computerized intervention showed a significant reduction in use compared to the control conditions (SMD: -

CE

0.19; 95% CI: -0.26, -0.11) (see Figure 2), with no significant heterogeneity among trials (chi = 5.55, df = 8, P = 0.7, I2 = 0%). Testing for the overall effect was Z = 4.94

AC

(P < 0.00001). When the study with the highest effect size (Schwinn et al., 2010) was excluded, the overall effect size showed a small reduction, with Z = 4.36 (SMD: -0.17; 95% CI: -0.25, -0.10) and no significant heterogeneity (chi2 = 3.92, df = 7, P = 0.79, I2 = 0%). When the study with the lowest effect size (Kay-Lambkin et al., 2011) was excluded, the overall effect size was similar, with Z = 5.08 (SMD: -0.20; 95% CI: -0.27, -0.12) and no significant heterogeneity (chi2 = 3.84, df = 7, P = 0.8, I2 = 0%).

ACCEPTED MANUSCRIPT 3.6 Other substance use results Three trials analyzed other substance use (Ondersma et al., 2007; Schwinn et al., 2010; Walton et al., 2013). A total of 234 cannabis consumers were included in the intervention group and 249 in the control group. The results demonstrated that computerized interventions reduced other substance use significantly compared to the control condition (SMD: -0.27; 95% CI: -0.46, -0.08). The heterogeneity (I2 ) was

SC RI P

T

26% (see Figure 3). The test for the overall effect was Z = 2.79 (P = 0.005)

ED

MA

NU

Figure 2. Frequency of cannabis use results.

AC

CE

PT

CI = confidence interval; IV = inverse variance; SE = standard error.

ACCEPTED MANUSCRIPT

SC RI P

CI = confidence interval; IV = inverse variance; SE = standard error.

T

Figure 3. Other substance use results.

3.7 Subgroup analysis

We performed a subgroup analysis to examine the overall mean effect of the interventions divided into different groups by the length of the follow-up (≥ 6 months vs < 6 months), number of sessions (≥ 5 sessions vs < 5 sessions), age group (> 20

NU

years old vs < 20 years old), type of analysis (ITT vs completers only), and type of control intervention (psychoeducation vs waiting list, computer vs therapist).

MA

Comparing outcomes assessed at different follow-up periods, age group, type of analysis, and type of control condition did not appear to have an impact on the efficacy of interventions (see Table 4). The intensity of the interventions (number of

ED

sessions) subgroup analysis showed significant results between the intervention condition and the control condition in > 5 sessions (SMD: -0.21; 95% CI: -0.29, -

AC

CE

PT

0.12).

ACCEPTED MANUSCRIPT Table 4. Subgroup analysis for cannabis use Length of follow-up,

SMD

95% CI

Studies

Heterogeneity

Participants (n)

2

number of sessions,

(I )

age group, type of analysis, and type of

Exp

Control

control intervention

-0.18

-0.30, -0,05

3

22

526

487

< 6 months

-0.19

-0.29, -0.10

6

0

1198

752

> 5 sessions

-0.21

-0.29, -0.12

5

0

1418

914

< 5 sessions

-0.11

-0.27, 0.05

4

0

306

325

-0.31, -0.10

4

0

1058

609

-0.28, -0.06

5

0

666

610

-0.19

-0.28, -0.09

5

0

1135

703

-0.19

-0.31, -0.11

4

0

589

536

PE

-0.13

-0.24, -0.01

5

0

628

570

WL

-0.23

-0.33, -0.14

4

0

1096

669

< 20 years

-0.17

MA

PT

-0.20

CE

Type of analysis

CO

AC

ITT

ED

Age group

> 20 years

NU

Number of sessions

SC RI P

> 6 months

T

Length of follow-up

Type of control condition

ITT = intention to treat; CO = completers only; PE = psychoeducation; WL = waiting list;

ACCEPTED MANUSCRIPT CBI = computer-based intervention; TBI = therapist-based intervention; SMD = Standard mean difference; CI = confidence interval; Exp = experimental group

4. Discussion In the present meta-analysis, computerized interventions demonstrated efficacy in reducing cannabis consumption with statistically significant results (SMD: -0.19; 95% CI: -0.26, -0.11) that concurred with Tait et al. (2013). Most of the analyzed

T

subgroups did not reveal a significant impact on the efficacy of interventions.

SC RI P

Regarding the intensity of interventions, > 5 sessions influenced efficacy as described by Porath-Waller et al. (2010), who reported that intervention efficacy increased with the number of sessions. The present meta-analysis includes the five studies from Tait et al. (2013) (Kay-Lambkin et al., 2011; Newton et al., 2010; Ondersma et al., 2007; Schwinn et al., 2010; Tossmann et al., 2011) and four new

NU

studies (Becker et al., 2014; Elliot et al., 2014; Rooke et al., 2013; Walton et al., 2014), thus comprising 9 studies in total. Five manuscripts from Tait el al. (2013)

MA

were not included in the present meta-analysis because they did not meet the eligibility criteria (Fang, Schinke, & Cole, 2010; Jonas, Tossmann, Tensil, Leuschner, & Struber, 2012; Lee et al., 2010; Schinke et al., 2009a; Schinke et al., 2009b). The

ED

reasons for exclusion are as follows: three were dyads of mother-son (Fang et al., 2010; Schinke et al., 2009a; Schinke et al., 2009b), one was a German manuscript

PT

(Jonas et al., 2012); and one was included in the systematic review but not in the meta-analysis due to statistic limitations reported in the results section (Lee et al.,

CE

2010) .

Although our meta-analysis has a similar approach, it has updated the information

AC

and, moreover, focused only on treatment interventions and not prevention in order to provide a more focused evaluation. Computerized interventions have demonstrated efficacy in reducing tobacco and alcohol (Rooke et al., 2010) although, in a similar manner to Tait et al. (2013), the average effect size of the computerized interventions was small (d = 0.14). The implementation of computerized interventions for cannabis treatment is growing due to their high cost-effectiveness, easy dissemination, and capacity to diminish potential barriers (such as training and staff time) (Elliott et al., 2014; Ondersma et al., 2007). Furthermore, the present meta-analysis has demonstrated that their efficacy is maintained over time (at least 6 months). Such a finding is relevant due to

ACCEPTED MANUSCRIPT the fact that DSM-5 guidelines consider abstinence between 3-12 months to be significant. The aim is to achieve a continued remission; thus, it would be appropriate to evaluate the effectiveness of computerized interventions beyond 12 months. Nevertheless, it should be noted that environmental factors such as school failure, smoking,

family

disruptions,

cannabis

use

among

family

members,

low

socioeconomic level, and living in rural areas can contribute negatively to the period of abstinence (American Psychiatric Association, 2014; Paruk et al., 2016). Some of

SC RI P

T

these environmental risk factors, such as living in rural areas, may be reduced by using computerized interventions facilitating accessibility to treatment and the subsequent maintenance of abstinence.

Some of the included studies had multiple intervention groups . Our meta-analysis, however, did not assess differences between therapist-based and computerized

NU

interventions, as it is not recommended to compare two active interventions without a control group. Future research should address this knowledge gap. Therapist-based interventions may present some problems/barriers for patients.

MA

They are expensive (Walton et al., 2014) and have been shown to be less costeffective than computerized interventions (Blankers, Nabitz, Smit, Koeter, &

ED

Schippers, 2012). In addition, some patients prefer to avoid counselors due to stigmatization (Walker et al., 2011), and there is the possibility of individual variability among therapists, even with manual-based treatment. This factor could be avoided

PT

with computerized interventions, which have demonstrated improved accessibility for some cannabis users (Rooke et al., 2013).

CE

It is, therefore, necessary to develop and implement computerized interventions in order to improve cannabis consumption outcomes and other health issues (such as

AC

early psychiatric disorders). Excessive cannabis use during adolescence is related to worse and more persistent negative outcomes than in adulthood (Moore et al., 2007). Intervention programs could improve accessibility to therapy in specific cases, such as users who recognize their cannabis problems but are not prepared to attend certain addiction treatment settings. Furthermore, these innovative treatment options may be particularly relevant due to the recent legalization of medicinal/recreational cannabis use, which might encourage long-term marijuana consumption among adolescents (Hall & Lynskey, 2016). This is an area that requires further research, and developing countries could benefit from these interventions as a n inexpensive treatment option.

ACCEPTED MANUSCRIPT Our meta-analysis presents some possible limitations. The first is the recalculation of the SMD to jointly analyze dichotomous and continuous trials. The second is the estimation in some follow-ups of the number of participants in each group as this information was not provided by the authors (Kay-Lambkin et al., 2011; Ondersma et al., 2007) and had to be obtained through initial flow chart figures. Another limitation was the use of self-reported instruments in the majority of the studies. Only one study employed biochemical verification to measure cannabis use during the follow-

SC RI P

T

up measures. Text messaging and interactive voice response (IVR) interventions were not included in the present meta-analysis. Finally, the low number of studies included in the secondary outcome assessed in this meta-analysis was because not

NU

all of the studies evaluated the secondary outcome (other substance use).

5. Conclusions

We conclude that the computerized interventions examined in the present study

MA

reduced the frequency of cannabis and other substance use. Computerized interventions, in addition to other approaches, could be a useful treatment option to

PT

Conflicts of Interest

ED

reduce cannabis consumption.

CE

All of the other authors declare that they have no conflicts of interest.

AC

Acknowledgements

We thank Warren Meredith for reading the manuscript and for his suggestions.

Funding Sources This study was supported in part by grants from the Instituto de Salud Carlos III (ISCIII-FEDER

PI14/00715;

Red

de

Trastornos

Adictivos-RTA-FEDER

RD12/0028/0009, RD16/0017/0010, and RD16/0017/0003), the Ministerio de Sanidad-Plan Nacional sobre Drogas (PNSD 2015I054), and the European Commission-Directorate General for Research and Innovation (Horizon 2020)

ACCEPTED MANUSCRIPT (creating medically driven integrative bioinformatics applications focused on oncology, CNS disorders, and their comorbidities: MedBioinformatics contract number: 634143). References

American Psychiatric Association, (2014). Diagnostic and statistical manual of mental

SC RI P

T

disorders: DSM-5 (Washington, DC Author).

Andersson, G., Carlbring, P., Holmström, A., Sparthan, E., Furmark, T., NilssonIhrfelt, E., … Ekselius, L. (2006). Internet-based self-help with therapist feedback and in vivo group exposure for social phobia: a randomized controlled trial. Journal

NU

of Consulting and Clinical Psychology, 74, 677-686.

Bailey, J. V., Murray, E., Rait, G., Mercer, C. H., Morris, R. W., Peacock, R., …

MA

Nazareth, I. (2005). Interactive computer-based interventions for sexual health promotion. Cochrane database of Systematic Review, 8(9).

ED

Becker, J., Haug, S., Sullivan, R., & Schaub, M. (2014). Effectiveness of different Web-based interventions to prepare co-smokers of cigarettes and cannabis for

PT

double cessation: a three-arm randomized controlled trial. Journal of Medical Internet Research,16(2), 273. http://dx.doi.org/ 10.2196/jmir.3246.

CE

Blankers, M., Nabitz, U., Smit, F., Koeter, M., & Schippers, G. (2012). Economic evaluation of internet-based interventions for harmful alcohol use alongside a

AC

pragmatic randomized controlled trial. Journal of Medical internet Research, 14,134.

Brattberg, G. (2006). Internet-based rehabilitation for individuals with chronic pain and burnout: a randomized trial. International Journal of Rehabilitation Research, 29, 221-227.

Carey, K. B., Scott-Sheldon, L. A., Elliott, J. C., Bolles, J. R., & Carey, M. P. (2009). Computer-delivered interventions to reduce college student drinking: A metaanalysis. Addiction, 104, 1807–1819.

ACCEPTED MANUSCRIPT

Cochran, G., Stitzer, M., Campbell, A. N., Hu, M. C., Vandrey, R., & Nunes, E. V. (2015). Web-based treatment for substance use disorders : Differential effects by primary substance. Addictive Behaviors, 45, 191–194. http://dx.doi.org/10.1016/j.addbeh.2015.02.002.

Cooper, Z. D., & Haney, M. (2014). Investigation of sex-dependent effects of

SC RI P

T

cannabis in daily cannabis smokers. Drug and Alcohol Dependence, (212), 85–91.

Davis, M. L., Powers, M. B., Handelsman, P., Medina, J. L., Zvolensky, M., & Smits J. A. (2015). Behavioral therapies for treatment-seeking cannabis users: a metaanalysis of randomized controlled trials. Evaluation & the Health Professions, 38(1),

NU

94–114. http://dx.doi.org/10.1177/0163278714529970.

Elliott, J. C., Carey, K. B., & Vanable, P. A. (2014). A preliminary evaluation of a

MA

web-based intervention for college marijuana use. Psychology of Addictive

ED

Behaviors, 28(1), 288–293. http://dx.doi.org/10.1037/a0034995.

European Monitoring Centre for Drugs and Drug Addiction (2013). Perspective on

PT

drugs. Characteristics of frequent and high-risk cannabis users. Statistical bulletin.

Fang, L., Schinke, S. P., & Cole, K. C. (2010). Preventing substance use among

CE

early Asian-American adolescent girls: initial evaluation of a web-based, motherdaughter program. Journal of Adolescent Health, 47, 529-532.

AC

http://dx.doi.org/10.1016/j.jadohealth.2010.03.011

Fernandes, S., Ferigolo, M., Benchaya, M. C., Moreira, C., Pierozan, P. S., Mazoni, C. G., & Barros, H. M. (2010). Brief Motivational Intervention and telemedicine : A new perspective of treatment to marijuana users. Addictive Behaviors, 35(8), 750– 755. http://dx.doi.org/10.1016/j.addbeh.2010.03.001.

Fernández-Aranda, F., Nuñez, A., Martínez, C., Krug, I., Cappozzo, M., Carrard, I., … Lam, T. (2009). Internet-based cognitive-behavioral therapy for bulimia nervosa: a controlled study. Cyberpsychology and Behavior, 12, 37-41.

ACCEPTED MANUSCRIPT

Flórez-Salamanca, L., Secades-Villa, R., Budney, A. J., Garcia-Rodriguez, O., Wang S., & Blanco, C. (2013). Probability and predictors of cannabis use disorders relapse: Results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug and Alcohol Dependence, 132(0), 127–133.

Gates, P. J., Norberg, M. M., Copeland, J., & Digiusto, E. (2012). Randomized

SC RI P

T

controlled trial of a novel cannabis use intervention delivered by telephone. Addiction, 107(12), 2149–2158. http://dx.doi.org/10.1111/j.1360-0443.2012.03953.

Gates, P. J., Sabioni, P., Copeland, J., Le Foll, B., & Gowing L. (2016). Psychosocial interventions for cannabis use disorders. Cochrane database of Systematic Reviews,

NU

5(5).

Gilchrist, G., Swan, D., Widyaratna, K., Márquez-Arrico, J.E., Hughes, E., Mdege,

MA

N.D., … Tirado-Muñoz, J. (2017). A systematic review and meta-analysis of psychosocial interventions to reduce drug and sexual blood borne virus risk

ED

behaviours among people who inject drugs. AIDS and Behavior. http://dx.doi.org/ 10.1007/s10461-017-1755-0.

PT

Hall, W. D., & Lynskey, M. (2016). Evaluating the public health impacts of legalizing

CE

recreational cannabis use in the United States. Addiction, 111(10), 1764-1773.

Hall, W. D., & Pacula, R. L. (2003) Cannabis Use and Dependence: Public Health

AC

and Public Policy.Cambridge University Press.

Harris, S. K., Csémy, L., Sherritt, L., Starostova, O., Van Hook, S., Johnson, J., & Knight, J. R. (2012). Computer-Facilitated Substance Use Screening and Brief Advice for Teens in Primary Care : An International trial. Pediatrics, 129(6), 10721082. Hasin, D. S., Wall, M., Keyes, K. M., Cerdá, M., Schulenberg, J., O’Malley, P. M. & Feng, T. (2015). Medical marijuana laws and adolescent marijuana use in the USA

ACCEPTED MANUSCRIPT from 1991 to 2014: results from annual, repeated cross-sectional surveys. Lancet Psychiatry, 2(7), 601-608. Jonas, B., Tossmann, P., Tensil, M., Leuschner, F., & Struber, E. (2012). Efficacy of a single-session online-intervention on problematic substance use [German]. Sucht, 58, 173-182. http://dx.doi.org/10.1024/0939-5911.a000182.

Kay-Lambkin, F. J., Baker, A. L., Lewin, T. J., & Carr, V. J. (2009). Computer-based

T

psychological treatment for comorbid depression and problematic alcohol and / or

SC RI P

cannabis use : a randomized controlled trial of clinical efficacy. Addiction, 378–388. http://dx.doi.org/10.1111/j.1360-0443.2008.02444.

Kay-Lambkin, F. J, Baker, A. L., Kelly, B., & Lewin, T. J. (2011). Clinician-assisted

NU

computerized versus therapist delivered treatment for depressive and addictive disorders: a randomized controlled trial. The Medical Journal of Australia, 195, 44–

MA

50.

King, V. L., Brooner, R. K., Peirce, J. M., Kolodner, K., & Kidorf, M. S. (2014). A

ED

randomized trial of Web-based video conferencing for substance abuse counseling. Journal of Substance Abuse Treatment, 46(1), 36–42.

PT

http://dx.doi.org/10.1016/j.jsat.2013.08.009.

Lee, C. M., Neighbors, C., Kilmer, J. R., & Larimer, M. E. (2010). A brief web-based

CE

personalized feedback selective intervention for college student marijuana use: a

AC

randomized clinical trial. Psychology of Addictive Behaviors, 24, 265–273.

Moher, D., Liberati, A., Tezlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic review and meta-analyses: the PRISMA statement. Journal of Clinical Epidemiology, 62, 1006-1012.

Moore, B. A., Fazzino, T., Garnet, B., Cutter, C. J., & Barry, D. T. (2011). Computedbased interventions for drug user disorders: A systematic review. Journal of substance abuse treatment, 40: 215-223.

ACCEPTED MANUSCRIPT Moore, T. H., Zammit, S., Lingford-hughes, A., Barnes, T. R., Jones, P. B., Burke, M., & Lewis, G. (2007). Cannabis use and risk of psychotic or affective mental health outcomes : a systematic review. Lancet, 319–328. Murray, E., Burns, J., See T. S., Lai, R., & Nazareth, I. (2005). Interactive Health Communication Applications for people with chronic disease. Cochrane Database Systematic Review, CD004274.

SC RI P

T

Newton, N. C., Andrews, G., Teesson, M., & Vogl, L. E. (2009). Delivering prevention for alcohol and cannabis using the internet: A cluster randomized controlled trial. Preventive Medicine, 48(6), 579–584. http://dx.doi.org/10.1016/j.ypmed.2009.04.009.

Newton, N. C., Teesson, M., Vogl, L. E., & Andrews, G. (2010). Internet-based

NU

prevention for alcohol and cannabis use: Final results of the Climate Schools course. Addiction, 105(4), 749–759. http://dx.doi.org/10.1111/j.1360-0443.2009.02853.

MA

Ondersma, S. J., Svikis, D. S., & Schuster, C. R. (2007). Computer-based brief intervention a randomized trial with postpartum women. American Journal of

ED

Preventive Medicine, 32, 231–238.

Paruk, S., Jhazbhay, K., Singh, K., Sartorius, B., & Burns, J. K. (2016). A

PT

comparative study of socio-demographic and substance use correlates in early-onset

CE

psychosis. Early Intervention in Psychiatry. http://dx.doi.org/10.1111/eip.12330.

Porath-Waller, A. J., Beasley, E., & Beirness, D. J. (2010). A meta-analytic review of

AC

school based prevention for cannabis use. Health Education & Behavior, 37, 709– 723. http://dx.doi.org/10.1177/1090198110361315. Rigter, H., Henderson, C. E., Pelc, I., Tossmann, P., Phan, O., Hendriks, V., & Rowe, C. L. (2013). Multidimensional family therapy lowers the rate of cannabis dependence in adolescents : A randomized controlled trial in Western European outpatient settings. Drug and Alcohol Dependence, 130(1-3), 85–93. http://dx.doi.org/10.1016/j.drugalcdep.2012.10.013.

ACCEPTED MANUSCRIPT Rooke, S., Copeland, J., Norberg, M., Hine, D., & McCambridge, J. (2013). Effectiveness of a self-guided web-based cannabis treatment program: Randomized controlled trial. Journal of Medical Internet Research, 15(2), 1–14. http://dx.doi.org/10.2196/jmir.2256. Rooke, S., Thorsteinsson, E., Karpin, A., Copeland, J., & Allsop, D. (2010). Computer-delivered interventions for alcohol and tobacco use : a meta-analysis.

SC RI P

T

Addiction, 1381–1390.

Schaub, M. P., Haug, S., Wenger, A., Berg, O., Sullivan, R., Beck, T., & Stark, L. (2013). Can reduce the effects of chat-counseling and web-based self-help, webbased self-help alone and a waiting list control program on cannabis use in problematic cannabis users : A randomized controlled trial. BMC Psychiatry, 13(1), 1.

NU

http://dx.doi.org/10.1186/1471-244X-13-305.

Schaub, M. P., Wenger, A., Berg, O., Beck, T., & Stark, L. (2015). A web-based self-

MA

help intervention with and without chat counseling to reduce cannabis use in problematic cannabis users : Three-arm randomized controlled trial. BMC psychiatry,

ED

17(10), 232. http://dx.doi.org /10.1186/1471-244X-13-305.

Schinke, S. P., Fang, L., & Cole, K. C. (2009a). Computer-delivered, parent-

PT

involvement Intervention to prevent substance use among adolescent girls.

CE

Preventive Medicine, 49, 429–435.

Schinke, S. P., Fang, L., & Cole, K. C. (2009b). Preventing substance use among

AC

adolescent girls: 1-year outcomes of a computerized, mother-daughter program. Addictive Behaviors, 34, 1060–1064.

Schwinn, T. M., Schinke, S., & Di Noia, J. (2010). Preventing drug abuse among adolescent girls: outcome data from an internet-based intervention. Prevention Science, 11, 24–32.

Sobesky, M., & Gorgens, K. (2016). Cannabis and adolescents: Exploring the substance misuse treatment provider experience in a climate of legalization. International Journal of Drug Policy, 33, 66-74.

ACCEPTED MANUSCRIPT

Spithoff, S., Emerson, B., & Spithoff, A. (2015). Cannabis legalization: adhering to public health best practice. Canadian Medical Association Journal, 187(16), 12111216. Sundström, C., Blankers, M., & Khadjesari, Z. (2016). Computer-Based Interventions for Problematic Alcohol Use: a Review of Systematic Reviews. International Journal

SC RI P

T

of Behavioral Medicine.

Tait, R. J., Spijkerman, R., & Riper, H. (2013). Internet and computer based interventions for cannabis use. Drug and Alcohol Dependence, 133(2), 295–304. http://dx.doi.org/10.1016/j.drugalcdep.2013.05.012.

NU

Tate, D., Wing, R., & Winett, R. (2001). Using internet technology to deliver a behavioral weight loss program. JAMA, 285, 1172-1177.

MA

Teesson, M., Slade, T., Swift, W., Mills, K., Memedovic, S., Mewton, L., … Hall, W. (2012). Prevalence, correlates and comorbidity of DMS-IV cannabis use and

ED

cannabis use disorders in Australia. Australian and New Zealand Journal of Psychiatry, 46(12): 1182-1192.

PT

Tirado-Muñoz, J., Gilchrist, G., Hegarty, K., & Torrens, M. (2014). The efficacy of cognitive behavioural therapy and advocacy interventions for women who have

CE

experienced intimate partner violence : A systematic review and meta-analysis.

AC

Annals of Medicine, 1–20. http://dx.doi.org/10.3109/07853890.2014.941918.

Titov, N. (2007). Status of computerized cognitive behavioural therapy for adults. Australian and New Zealand Journal of Psychiatry, 41: 95-114

Tossmann, H. P., Jonas, B., Tensil, M. D., Lang, P., & Strüber, E. (2011). A controlled trial of an internet-based intervention program for cannabis users. Cyberpsychology, Behavior and Social Networking, 14(11), 673–679. http://dx.doi.org/10.1089/cyber.2010.0506.

ACCEPTED MANUSCRIPT United Nations Office on Drugs and Crime. (2016). World Drug Report. United Nations, New York.

Volkow, N. D., Baler, R. D., Compton, W. M. & Weiss, R. B. (2014). Adverse health effects of marijuana use. New England Journal of Medicine, 370(23), 2219-2227. http://dx.doi.org/10.1056/NEJMra1402309.

SC RI P

T

Walker, D. D., Stephens, R., Roffman, R., DeMarce, J., Lozano, B., Towe, S., & Berg, B. (2011). Randomized Controlled Trial of Motivational Enhacement Therapy with Non-treatment Seeking Adolescent Cannabis Users: A Further Test of the Teen Marijuana Check-up. Psychology of Addictive Behaviors, 25(3), 474-484.

NU

http://dx.doi.org/10.1037/a0024076.

Walton, M. A., Bohnert, K., Resko, S., Barry, K. L., Chermack, S. T., Zucker, R. A., & Blow, F. C. (2013). Computer and therapist based brief interventions among

MA

cannabis-using adolescents presenting to primary care: One year outcomes. Drug and Alcohol Dependence, 132(3), 646–653.

ED

http://dx.doi.org/10.1016/j.drugalcdep.2013.04.020.

Walton, M. A., Resko, S., Barry, K. L., Chermack, S. T., Zucker, R. A., Zimmerman,

PT

M. A., & Blow, F. C. (2014). A randomized controlled trial testing the efficacy of a brief cannabis universal prevention program among adolescents in primary care.

CE

Addiction, 109(5), 786–797. http://dx.doi.org/10.1111/add.12469.

AC

Wantland, D. J., Portillo C. J., Holzemer W. L., Slaughter R., & McGhee E. M. (2004). The effectiveness of Web-bases vs non-Web-based interventions: a metaanalysis of behavioral change outcomes. Journal of Medical Internet Research, 6, 40.

World Health Organization (2016).The health and social effects of no-medical cannabis use. Geneva: World Health Organization.

ACCEPTED MANUSCRIPT Highlights

T SC RI P NU MA ED PT CE

 

Computerized intervention resulted in significant reductions in cannabis use and in other substances use. Computerized interventions could be a treatment option to reduce cannabis use. Comparing outcomes assessed at different follow-up periods, age group, type of analysis and type of control condition did not appear to impact the efficacy of interventions

AC

