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A Web-Based Self-Help Intervention With and Without Chat Counseling to Reduce Cannabis Use in Problematic Cannabis Users: Three-Arm Randomized Controlled Trial

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Background: After alcohol and tobacco, cannabis is the most widely used psychoactive substance in many countries worldwide. Although approximately one in ten users develops serious problems of dependency, only a minority attend outpatient addiction counseling centers. A Web-based intervention could potentially reach those users who hesitate to approach such treatment centers. Aims: To test the efficacy of a Web-based self-help intervention with and without chat counseling - Can Reduce - in reducing the cannabis use of problematic cannabis users as an alternative to outpatient treatment services. Methods: Altogether, 436 participants were recruited by various online and offline media for the Web-based trial. A total of 308 of these were eligible for study participation and were randomly allocated in an unblinded manner to either self-help with chat (n=114), self-help without chat (n=101), or a waiting list control group (n=93). The fully automated self-help intervention consisted of eight modules designed to reduce cannabis use, and was based on the principles of motivational interviewing, self-control practices, and methods of cognitive behavioral therapy. Additional individual chat counseling sessions were based on the same therapeutic principles. The sessions were conducted by trained counselors and addressed participants' personal problems. The main outcomes were the frequency (number of days) and quantity of cannabis use (number of standardized joints) per week, as entered into the consumption diary at baseline and at the 3-month follow-up. Secondary outcomes included self-reported symptoms of cannabis use disorder, severity of cannabis dependence, risky alcohol use, and mental health symptoms. Intervention participation and retention were extracted from the user progress data and the consumption diary, respectively. Results: Can Reduce participants were older (U=2.296, P=.02) and reported a greater number of cannabis use days at baseline than patients who entered outpatient treatment with cannabis as their main problem substance (data from the Swiss treatment demand monitoring statistics were used; chi-square [df 2]=4.0, P=.046). Participants in the self-help with chat study arm completed a mean of 3.2 modules and 27 out of 114 (23.7%) of the participants received at least one chat session. Participants in the self-help without chat study arm completed similar numbers of self-help modules. A total of 117 of 308 participants (38.0%) completed the 3-month follow-up assessment. The change in the mean number of cannabis use days per week at 3 months differed between self-help without chat (mean change 0.7, SD -0.2) and self-help with chat (mean change 1.4, SD -0.5; beta=-0.75, SE=0.32, t=-2.39, P=.02, d=0.34, 95% CI 0.07-0.61), as well as between self-help with chat and waiting list (mean change 1.0, SD -0.8; beta=0.70, SE=0.32, t=2.16, P=.03, d=0.20, 95% CI -0.07 to 0.47). However, there were no differences between self-help without chat and waiting list (beta=-0.05, SE=0.33, t=-0.16, P=.87, d=-0.14, 95% CI -0.43 to 0.14). Self-reported abstinence was significantly different in the self-help without chat study arm (2.0%) than in the self-help with chat study arm (8.8%; beta=-1.56, SE=0.79, P=.05, odds ratio [OR]=0.21, 95% CI 0.02-2.33). There were no significant differences between the study arms with respect to the secondary outcomes. Conclusions:Web-based self-help interventions supplemented by brief chat counseling are an effective alternative to face-to-face treatment and can reach a group of cannabis users who differ in their use and sociodemographic characteristics from those who enter outpatient addiction treatment. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 59948178.
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Original Paper
A Web-Based Self-Help Intervention With and Without Chat
Counseling to Reduce Cannabis Use in Problematic Cannabis
Users: Three-Arm Randomized Controlled Trial
Michael P Schaub1, PhD; Andreas Wenger1, MSc; Oliver Berg2, MD; Thilo Beck2, MD; Lars Stark2, MA; Eveline
Buehler1, MSc; Severin Haug1, PhD
1Swiss Research Institute for Public Health and Addiction (ISGF), associated to the University of Zurich and World Health Organization Collaborating
Center, Zurich, Switzerland
2Arud Center for Addiction Medicine, Zurich, Switzerland
Corresponding Author:
Michael P Schaub, PhD
Swiss Research Institute for Public Health and Addiction (ISGF)
associated to the University of Zurich and World Health Organization Collaborating Center
Konradstrasse 32
Zurich, 8031
Switzerland
Phone: 41 444481165
Fax: 41 444481170
Email: michael.schaub@isgf.uzh.ch
Abstract
Background: After alcohol and tobacco, cannabis is the most widely used psychoactive substance in many countries worldwide.
Although approximately one in ten users develops serious problems of dependency, only a minority attend outpatient addiction
counseling centers. A Web-based intervention could potentially reach those users who hesitate to approach such treatment centers.
Objective: To test the efficacy of a Web-based self-help intervention with and without chat counseling—Can Reduce—in
reducing the cannabis use of problematic cannabis users as an alternative to outpatient treatment services.
Methods: Altogether, 436 participants were recruited by various online and offline media for the Web-based trial. A total of
308 of these were eligible for study participation and were randomly allocated in an unblinded manner to either self-help with
chat (n=114), self-help without chat (n=101), or a waiting list control group (n=93). The fully automated self-help intervention
consisted of eight modules designed to reduce cannabis use, and was based on the principles of motivational interviewing,
self-control practices, and methods of cognitive behavioral therapy. Additional individual chat counseling sessions were based
on the same therapeutic principles. The sessions were conducted by trained counselors and addressed participants' personal
problems. The main outcomes were the frequency (number of days) and quantity of cannabis use (number of standardized joints)
per week, as entered into the consumption diary at baseline and at the 3-month follow-up. Secondary outcomes included self-reported
symptoms of cannabis use disorder, severity of cannabis dependence, risky alcohol use, and mental health symptoms. Intervention
participation and retention were extracted from the user progress data and the consumption diary, respectively.
Results: Can Reduce participants were older (U=2.296, P=.02) and reported a greater number of cannabis use days at baseline
than patients who entered outpatient treatment with cannabis as their main problem substance (data from the Swiss treatment
demand monitoring statistics were used; chi-square [df 2]=4.0, P=.046). Participants in the self-help with chat study arm completed
a mean of 3.2 modules and 27 out of 114 (23.7%) of the participants received at least one chat session. Participants in the self-help
without chat study arm completed similar numbers of self-help modules. A total of 117 of 308 participants (38.0%) completed
the 3-month follow-up assessment. The change in the mean number of cannabis use days per week at 3 months differed between
self-help without chat (mean change 0.7, SD -0.2) and self-help with chat (mean change 1.4, SD -0.5; beta=-0.75, SE=0.32,
t=-2.39, P=.02, d=0.34, 95% CI 0.07-0.61), as well as between self-help with chat and waiting list (mean change 1.0, SD -0.8;
beta=0.70, SE=0.32, t=2.16, P=.03, d=0.20, 95% CI -0.07 to 0.47). However, there were no differences between self-help without
chat and waiting list (beta=-0.05, SE=0.33, t=-0.16, P=.87, d=-0.14, 95% CI -0.43 to 0.14). Self-reported abstinence was
significantly different in the self-help without chat study arm (2.0%) than in the self-help with chat study arm (8.8%; beta=-1.56,
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SE=0.79, P=.05, odds ratio [OR]=0.21, 95% CI 0.02-2.33). There were no significant differences between the study arms with
respect to the secondary outcomes.
Conclusions: Web-based self-help interventions supplemented by brief chat counseling are an effective alternative to face-to-face
treatment and can reach a group of cannabis users who differ in their use and sociodemographic characteristics from those who
enter outpatient addiction treatment.
Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 59948178;
http://www.isrctn.com/ISRCTN59948178 (Archived by WebCite at http://www.webcitation.org/6bt01gfIr)
(J Med Internet Res 2015;17(10):e232) doi:10.2196/jmir.4860
KEYWORDS
cannabis; Internet; chat; Web based; self-help; cognitive behavioral therapy; motivational interviewing; counseling; self-control;
behavioral self-management
Introduction
Web-based self-help programs that aim to reduce cannabis use
might help to reach cannabis users who do not want to enter
available outpatient addiction counseling services due to their
fear of being stigmatized or their need to distance themselves
socially from drug counselors [1]. Moreover, the limited opening
hours of many outpatient addiction services might act as a
barrier to care for some users [1]. It has been estimated that
approximately 22% of Europeans between 15 and 64 years of
age have tried cannabis. A total of 6.8% of Europeans report
using cannabis in the preceding month and an estimated 3
million report daily cannabis use [2]. Switzerland has the
third-highest national prevalence of cannabis use in Europe; the
12-month prevalence rate is 5.7% (men 7.8%, women 3.7%)
and the 30-day prevalence rate is 2.7% (men 3.7%, women
1.7%) [3]. The age group with the highest prevalence is between
15 and 24 years of age; this group has a 12-month prevalence
rate of 19.9%, and nearly one in five members from this group
uses cannabis daily [3]. Daily cannabis use is associated with
greater risks of developing cannabis dependence, poor mental
and physical health, lower educational achievement, and
decreased cognitive functioning [4]. The risks of cannabis
dependence [5] and problems with cannabis use [6] are
considerably higher in cannabis users with early rather than late
onset of use.
Treatment demand statistics from Swiss in- and outpatient
addiction treatment centers demonstrated a linear
increase—from 2006 (9.9%) to 2012 (14.7%)—in new treatment
entry cases for whom cannabis was the main problem substance
[7]. The main group seeking treatment for cannabis use disorder
mainly consists of adolescents and young adults between the
ages of 15 and 24 years old (71.6%) and are predominantly male
(82.5%) [8]. In Europe, cannabis is the main problem substance
for almost 40% of all individuals entering addiction treatment
for the first time and has been a more frequent problem than
opioids since 2006 [9]. It has been estimated that about 50% of
problematic cannabis users will develop cannabis dependence
[5] and many of these exhibit mental health problems; however,
most of them are not yet in treatment. Raising awareness of
cannabis-related risks to physical health might also encourage
users to reduce or quit cannabis use [10]. In general, the
principle of stepped care (ie, noninvasive, low-cost interventions
in which therapeutic intensity can be enhanced according to
need) appears to be an appropriate means for problematic
cannabis users to lower their ever-increasing health care costs
[11], and this consideration is of interest in Switzerland and
other industrialized countries suffering from exorbitant health
costs.
An initial meta-analysis included diverse studies that mainly
investigated computer- and some Web-based interventions to
reduce cannabis consumption and found a small overall effect
size (g=0.16, 95% CI 0.09-0.22, P<.001) at posttreatment. There
have now been three studies on the efficacy of Web-based
interventions to reduce cannabis use in problematic users. First,
the German Quit the Shit program [12] is based on principles
of self-regulation and self-control and is a solution-focused
approach. This program is structured into weekly personalized
feedback sessions based on participants’ consumption diary
entries, and intake and termination chats; the total allowed
program time is 50 days. Tossmann et al [12] recruited a total
of 1292 cannabis users and found significant reductions in
cannabis use in their intention-to-treat (ITT) analyses, but with
high attrition rates. Second, a distinct version of the program
was developed that consisted of one comprehensive chat session
with motivational interviewing (MI) [13] in the intervention
group (n=33) versus a technical information chat in the control
group (n=34). No significant differences in cannabis use were
found between the study groups [14]. Third, the Australian
program, Reduce Your Use: How to Break the Cannabis Habit
[15], is a fully automated self-help intervention consisting of
six modules that aim to reduce the symptoms of cannabis use
disorders and which is based on cognitive behavioral therapy
(CBT) [16,17], MI [13], and behavioral self-management (BSM)
[18]. Its efficacy was tested in a randomized controlled trial
(RCT) and compared to a psychoeducative control condition
that also consisted of six modules (n=225). The frequency of
cannabis use and the quantity of cannabis consumed were both
reduced to a greater extent in the intervention group than in the
control group at 6 weeks and at the 3-month follow-up. They
achieved considerably higher participation rates at the 3-month
follow-up than the German Quit the Shit program (54% in the
intervention and 52% in the control condition) [12].
The combination of a fully automated self-help intervention
based on the approaches of Rooke et al [15], together with
additional individual chat sessions to reduce cannabis use, could
potentially increase the efficacy of interventions for problematic
cannabis users—in the sense that the use is harmful to the user
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or others—as has been demonstrated for the reduction of alcohol
use in problematic alcohol users [19].
Thus, the current study aims to investigate and compare the
efficacy of Web-based self-help interventions—in combination
with or without tailored chat counseling based on CBT, MI, and
BSM—in reducing cannabis use in problematic cannabis users.
Methods
Participants
Study participants were recruited by a press release, several
websites from local outpatient treatment centers, and from
nightlife prevention websites that were linked to the Can Reduce
website [20]. In addition, advertisements were placed in Internet
forums and recruitment flyers were distributed to Swiss
addiction service centers and practitioners in the Canton of
Zurich. Moreover, two major Swiss commuter newspapers and
one Swiss weekend newspaper published extensive reports on
the Can Reduce interventions in their print media and websites.
The collaboration of the Swiss Research Institute for Public
Health and Addiction (ISGF) and the Arud Centers for Addiction
Medicine (ARUD) as the responsible study institutions was
clearly stated in all recruitment channels.
Study inclusion and exclusion criteria are depicted in Table 1.
In addition to the email addresses in the registration process,
participants were asked to provide their telephone numbers in
case they could not be reached online for the 3-month follow-up
[1]. The participant information and informed consent page
from the Can Reduce website is provided in Multimedia
Appendix 1.
Table 1. Inclusion and exclusion criteria and rationales.
RationalesParticipant criteria
Inclusion criteria
To ensure a minimal age of participationMinimum age of 18 years
To ensure understanding of interventionsRead and understand German
To ensure participationInternet access and a valid email address
To include at least occasional usersUsing cannabis at least once a week over the 30 days prior to study entry
Exclusion criteria
To avoid exacerbation of serious symptoms of these severe
psychiatric disorders
Current serious psychiatric disorders or history of psychosis, schizophrenia, bipolar
type I disorder, or significant current suicidal or homicidal thoughts
To avoid confounding treatment effectsOther pharmacological or psychosocial treatments for cannabis use disorders
To avoid serious complications resulting, for example, from
withdrawal symptoms
For women: pregnancy and breastfeeding
Preparatory Work
The Web-based self-help intervention, Can Reduce, was based
on classical CBT approaches for treating cannabis dependence
[17], MI approaches [13], and BSM [18]. A detailed description
of the intervention can be found in the study protocol by Schaub
et al [1]. This randomized controlled trial was registered with
the International Standard Randomized Controlled Trial Number
(ISRCTN) registry (ISRCTN59948178).
Can Reduce is the first self-help intervention for problematic
cannabis users in Switzerland. It was developed by the authors
of this publication from the ISGF and the ARUD. Both
institutions are located in the Canton of Zurich, Switzerland.
Study participation was free of charge. The self-help part of
Can Reduce was developed according to the experiences of an
earlier study in problematic cocaine users [21,22] and the Global
Drug Survey cannabis meter [23], and was piloted for
acceptability and usability. The piloting was organized into two
steps. In the first step, we piloted Can Reduce with
cannabis-using students from the University of Zurich. In the
second step, we combined this with additional chat sessions
with two trained psychiatrists from ARUD and four of their
problematic cannabis-using patients. This pilot phase resulted
in some minor changes in the interventions.
Ethical Review and Informed Consent
The protocol of the RCT was approved by the Ethics Committee
of the Canton of Zurich (KEK-StV-Nr. 15/13) and was carried
out in compliance with the Helsinki Declaration. Before giving
informed consent, participants were informed of the following:
(1) the rationale of the study, (2) study inclusion and exclusion
criteria (see Table 1), (3) the three different arms and their 1:3
chance of being allocated to one of the arms, (4) the potential
risks of participation, (5) safety arrangements during and after
the study phase [19], (6) the inability of Can Reduce (with or
without chat counseling) to replace face-to-face therapy for
problematic cannabis use/abuse, (7) the circumstances under
which they should contact their general practitioner or a
professional from a medical advisory group; an emergency list
that would be accessible at all times via an instant help button
was provided as well, (8) the approval of the study by the Ethics
Committee of the Canton of Zurich and their declaration of no
objection (nihil obstat), and (9) their right to withdraw from the
study at any time without consequences except for the loss of
further compensation. Informed consent was accepted when
participants clicked on all consent fields of the informed consent
page and submitted the consent by clicking the submission
button (see Multimedia Appendix 1).
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Study Arms and Contents
There were three different study arms. The first consisted of the
Web-based self-help intervention, Can Reduce, in combination
with up to two individual chat counseling sessions based on MI
and CBT approaches that considered the data the participants
entered into the self-help intervention and individual requests.
The second study arm consisted of the same intervention but
without chat counseling. Study arms 1 and 2 received weekly
automated motivational emails to remind the user to log in and
fill out the consumption diary. Study arm 3 consisted of a
classical waiting list and people in this arm received access to
the self-help intervention after 3 months.
The following modules, organized into three main parts, were
offered as a Web-based self-help intervention (study arms 1 and
2) and—as long as the participant did not feel an urgent need
to skip to a specific module—it was recommended that they
should be worked through in the order shown in Textbox 1
within the planned 6 weeks of intervention.
Textbox 1. Modules for the Can Reduce Web-based self-help intervention.
Part 1: Introduction
Registration process
Explanation of the "standard cannabis joint" concept and choice of the personal standard cannabis joint (see Figure 1), the cannabis consumption
diary, and the automated reminder emails
Examination of the pros and cons resulting from a change in cannabis consumption patterns and further principles of motivational interviewing
to address motivation, followed by setting an appropriate target value for overall cannabis use, which is to be reached by the end of the intervention
Explanation of the My Can Reduce folder
Explanation of the emergency button for immediate responses to frequently asked questions and access to emergency contacts
Part 2: Key Modules (participants are encouraged to complete these modules in the order presented below; see Figure 2)
Module 1: Strategies for goal achievement
Module 2: Identifying risk situations
Module 3: Dealing with cannabis craving
Module 4: Dealing with relapses
Part 3: Further Modules (participants are encouraged to complete at least two, in any order)
Module 5: Tobacco smoking during the reduction in cannabis use
Module 6: Saying "no" to foster refusal skills
Module 7: Dealing with burdens
Module 8: Preserving achievements
Figures 1and 2show screenshots of the Can Reduce Web-based
intervention website. The following were also provided: a
glossary that explained the terms, definitions, and concepts used
in the intervention; a knowledge base about the history of
cannabis use; the effects and risks of cannabis use; concurrent
mental health problems; and the enhanced risks when cannabis
is mixed with tobacco and smoked, as in a previously developed
and positively evaluated cannabis group smoking cessation
program [10,24] (study arm 1 and 2). The knowledge base also
included harm reduction techniques with recommendations for
the use of cannabis [25,26].
The additional (up to two) chat counseling sessions with a
scheduled duration of 20 to 30 minutes in study arm 1 supported
behavioral change according to MI, discussed the modules of
the Web-based self-help part based on MI and CBT, and
reviewed the development of the consumption diary. Invitations
to chat sessions were sent by the counselors according to a
predefined procedure between weeks 1 and 2 for the first and
between weeks 4 and 6 for the second chat session. The chats
took place within the website in a small box at the bottom right
corner, while keeping the content of the webpage in view (see
Figure 2). It was initially planned that the structure of these chat
sessions should be fixed [1]. However, as a result of the
counselor supervision sessions, the structure of the chat session
was made more flexible and more dependent on the participants'
needs and served as a checklist for the counselors in order to
ensure that they covered all of the relevant contents.
The chat counselors received quarterly supervision sessions and
consisted of trained MI counselors, mainly psychologists or
psychiatrists with advanced or completed further education,
with at least one year of experience in treating cannabis-abusing
patients face to face. Specific quality standards were developed
for addiction chat counseling and implemented for this study
in the chat counselor supervision based on the study on the
development of a European Union framework for minimum
quality standards and benchmarks in drug demand reduction
treatment quality standards [27] and the Swiss national addiction
counseling quality standards [28].
To optimize and manage their interactions with clients,
counselors had access to a specific user management area to
add arranged chat dates, define statuses, and add personal
comments about their clients. With this tool, counselors could
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follow their clients’ progress in reducing their cannabis use
through clearly arranged charts, and look up previous chat
histories. Specific lists helped counselors track their clients (eg,
a list with all users, my clients, or my upcoming chat sessions).
The Web-based self-help intervention and the subsequent
tailored chat counseling aimed to reduce cannabis use. However,
those participants who sought cannabis abstinence were also
encouraged to make step-by-step reductions until full abstinence
was reached. In accordance with the counselor supervision
group, we deviated from the study protocol [1] by introducing
the option to dispense with a second chat session if a participant
and his/her counselor agreed that another chat would not be
needed.
Participants randomized to the waiting list had the opportunity
to participate in the Web-based self-help intervention 3 months
after registration.
Figure 1. Screenshot of the Can Reduce Web-based intervention, showing the decision on the standard cannabis joint prior to the first consumption
diary entry.
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Figure 2. Main menu of the Can Reduce Web-based intervention's study arm 1 with self-help plus chat counseling that took place within the website
in a small box at the bottom right corner.
Detailed Study Hypotheses
This study aimed at comparing the efficacy of a Web-based
self-help intervention alone or combined with chat counseling
in the reduction of the cannabis use of problematic cannabis
users within a three-arm randomized controlled trial with
assessments at baseline and 3-month follow-up (see Multimedia
Appendix 2 for the CONSORT-EHEALTH checklist [29]).
We hypothesized that Web-based interventions—which are
more interactive—would be more effective than less interactive
interventions in reducing cannabis use among problematic
cannabis users. We tested the following detailed study
hypotheses with respect to the main outcome (ie, the reduction
of the weekly cannabis used between the baseline and the
3-month follow-up):
1. Tailored chat-based counseling in combination with
Web-based self-help for the reduction of cannabis use (study
arm 1) is more effective than the waiting list control
condition (study arm 3).
2. Web-based self-help for the reduction of cannabis use (study
arm 2) is more effective than the waiting list control
condition (study arm 3).
3. Chat-based counseling in addition to Web-based self-help
for the reduction of cannabis use (study arm 1) exhibits a
trend to be more effective than Web-based self-help alone
(study arm 2).
Measurement Instruments
The primary outcome measure was the recorded quantity of
cannabis use in the previous 7 days, quantified in individually
standardized cannabis joint sizes, and as specified in the
consumption diary (see Table 2 and Schaub et al for further
details [1]). In the first step, participants chose between three
different cannabis forms presented in photographs—low-potency
cannabis plant, high-potency cannabis plant, or cannabis resin
(see Figure 1). In the second step, five different standard joints
for each category were presented (1/10 g, 1/6 g, 1/4 g, 1/3 g,
1/2 g; pictures came from the Global Drug Survey cannabis
meter [23]); these joints were either pure cannabis or cannabis
mixed with tobacco. A standard tobacco cigarette, a ruler with
centimeter and millimeter scales, the fraction amount in grams,
and an open 10 cm paper prepared to roll a joint and containing
the cannabis plant-/resin-tobacco mixture or pure cannabis were
presented. Participants chose which picture most closely
approximated the cannabis joints they most often smoke. The
chosen picture was placed in the individual consumption diary
(see Figure 1), and participants were asked to convert the
quantities of cannabis they smoked into units relative to that
picture if they exceptionally consumed cannabis in forms other
than their common standard joint. As this kind of outcome
assessment has not previously been used in an efficacy trial, we
also considered the number of cannabis use days in the last 7
days as a primary outcome [1].
The following secondary outcome instruments were applied:
1. The Cannabis Use Disorders Identification Test (CUDIT),
which is a 10-item questionnaire [30] that was constructed by
adapting the Alcohol Use Disorders Identification Test [31].
To cover the length of the trial, this instrument was adapted to
focus on the last 3 months in its planned assessments (baseline
and 3-month follow-up).
2. The Severity of Dependence Scale (SDS), which is a five-item
questionnaire that measures the severity of cannabis dependence.
Each of the five items is scored on a 4-point scale (0-3). The
total score is obtained by adding the ratings on all five items.
High scores indicate high levels of dependency [32].
3. The Cannabis Withdrawal Scale (CWS) [33], which is a
19-item questionnaire containing statements that describe
cannabis withdrawal symptoms within the last 24 hours on an
11-point scale (0-10).
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4. The Cannabis Craving Symptoms questionnaire (CCS-7),
which is a seven-item questionnaire [34] derived from the
Marijuana Craving Questionnaire [35]. Each item is rated on a
7-point scale (1-7).
5. The Fragebogen Substanzanamnese (FDA), which is a
questionnaire that ascertains the number of years of consumption
over the lifetime, the past month’s consumption, and the manner
of consumption for the Diagnostic and Statistical Manual of
Mental Disorders’ substances of abuse. This measure was
derived from the Europe Addiction Severity Index [36].
6. The short version of the Mental Health Inventory (MHI-5)
[37], which is a validated and user-friendly self-assessment
questionnaire that assesses recent mental distress and
self-reported diagnoses of depression.
None of the secondary outcome instruments has yet been
specifically validated for Internet use. Intervention satisfaction
for all modules, the diary, the chat, the knowledge base, the
instant help, and the overall satisfaction was ascertained on a
4-point scale, ranging from not at all useful to very useful.
Finally, intervention participation was assessed for completed
modules each time a participant pressed the back to the main
menu button at the very end of a module. Retention was
calculated as the percentage of days per week a user entered
any number of cannabis use in the diary.
Table 2. Study measurements and instruments.
3-month follow-up6 weeks3 weeks1 weekBaselineAssessments/instruments
xSociodemographics
xxMHI-5a
xxxxxQuantity of cannabis useb
xxxxxFrequency of cannabis useb
xxCUDITc
xxxSDSd
xxxFDAe
aMental Health Inventory (MHI-5).
b7-day point prevalence values of the quantity (in common standard joints) and frequency (the number of days on which cannabis is used) of cannabis
use were derived from the consumption diary for the preceding 7 days.
cCannabis Use Disorders Identification Test (CUDIT).
dSeverity of Dependence Scale (SDS).
eFragebogen Substanzanamnese (FDA).
Sample Size
Based on results of the study of Rooke et al [15], we expected
small to medium effect sizes of at least 0.30 (Cohen’s d) for the
reduction in the quantity of cannabis used and the frequency of
cannabis use between study arm 2 (Web-based self-help without
chat counseling) and study arm 3 (waiting list control) between
baseline and follow-up assessment, and greater effects between
study arms 1 and 3. We estimated a sample size of 89 in each
study group that would have 80% power (Ftest, alpha = 5%)
to detect these differences, as based on calculations with
G*Power software version 3.1. Therefore, we aimed to recruit
a total of 267 participants [1]. We had no reference values for
the expected differences in effects between study arms 1 and 2
and thus planned an exploratory study of effect sizes in case we
failed to reach significance for these study arm comparisons.
Randomization and Allocation
Once participants had completed their baseline assessment, they
were randomized by a computer program in a 1:1:1 ratio to one
of three parallel groups. As the participant information offered
full transparency on the three study arms in our nonblinded
design, we anticipated a risk that some participants might
register another account, in an effort to change their assignment
and access a different study arm. In that case, the participant
remained in the initially assigned study arm for the rest of the
day, as based on his or her IP address.
Statistical Methods
Data were analyzed according to the intention-to-treat principle.
For the ITT analyses, in departure from the study protocol, we
applied multiple imputation procedures of R (R Foundation for
Statistical Computing, Vienna, Austria) in Amelia II that have
been demonstrated to outperform other imputation methods
[38]. For each study arm, we performed 50 separate imputations
using the following as imputation variables: sex, age, education,
origin, years of cannabis use, number of finished modules, the
baseline variables for frequency and quantity of cannabis use,
alcohol use in the last 30 days (risky and normal), SDS, CUDIT,
and MHI-5. Baseline measurements were compared between
the three study arms and study participants were compared with
people entering addiction treatment according to data from the
Swiss addiction, care and therapy information (act-info)
monitoring statistics. Depending on the scale of the
corresponding outcome, Mann-Whitney U tests, chi-square
tests, or analyses of variance (ANOVA) were calculated via
SPSS version 22.0 (IBM Corporation). The calculation of the
changes between baseline and the 3-month follow-up was
modified from the protocol, as there were a considerable number
of missing values at the 6-week assessment. Regression analyses
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in R were used for the calculated differences between 3-month
follow-up and baseline, using the corresponding baseline
variables as control variables. Results from the imputed dataset
were cross-checked with the nonimputed dataset in the latter
analyses. In departure from the study protocol, we dispensed
with analyzing the Cannabis Withdrawal Scale and the Cannabis
Craving Symptoms questionnaire data [1], as very low numbers
of questionnaires were completed at intervention weeks 3 and
6. In the study dropout analysis, we conducted regression
analyses to investigate the interaction effect of relevant baseline
characteristics (ie, sociodemographic and consumption
characteristics) between those who did and those who did not
provide a 3-month follow-up. These analyses were conducted
for the total sample and for each study arm separately. Similar
analyses were conducted in the subgroup analyses.
Results
Participant Flow
Figure 3 provides an overview of the trial flow. Recruitment
started in the beginning of June 2014 and ended on February
28, 2015, after exceeding the total estimated number of 267
participants. Of the 436 Can Reduce registrants recruited, 308
(70.6%) were allocated to one of the three study arms.
Three months after the baseline assessment, participants were
invited by email to log in and complete the final study
assessment; they were reimbursed with 40 (via an online
voucher or an online charitable donation). The follow-up
assessment was performed in three steps. First, participants
were invited via email to participate in the assessment. Up to
three reminders were sent. Those participants who failed to
complete the 3-month follow-up despite these reminders were
contacted via telephone and offered an interview by study
collaborators. Those participants who refused a telephone
interview were offered an interview on the primary outcome
only. Finally, 117 out of 308 participants (38.0%) could be
followed up with.
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Figure 3. CONSORT-EHEALTH trial flowchart: overview of the participant flow for this trial.
Participants’Baseline Characteristics
Table 3provides an overview of the participants’characteristics
and comparisons between the three study arms at baseline
assessment. In comparison with participants whose main
problem substance was cannabis in the Swiss treatment
monitoring statistics (act-info) in 2013 [7], Can Reduce
participants demonstrated a similar gender distribution (75.3%
males in Can Reduce vs 82.4% act-info, U=1.342, P=.18),
tended to be older within the age groups between 20 and 69
years old (U=2.296, P=.02), and reported a higher number of
cannabis use days in the 7 days prior to intervention start (70.9%
daily use in Can Reduce vs 41.4% act-info; 20.2% 4-6 days per
week Can Reduce vs 10.5% act-info; 5.2% 2-3 days per week
Can Reduce vs 21.7% act-info; 3.8% 1 day a week Can Reduce
vs 26.3% act-info; chi-square [df 2]=4.0, P=.046).
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Table 3. Baseline characteristics of participants.
P
χ2, ANOVAd, or
Kruskal-Wallis testTotal (n=308)
Study arm 3c
(n=93)
Study arm 2b
(n=101)
Study arm 1a
(n=114)Characteristics
.12
χ22=4.3 (n=308)
Sex, n (%)
76 (24.7)17 (18)24 (23.8)35 (30.7)Female
232 (75.3)76 (82)77 (76.2)79 (69.3)Male
.15F2,308=1.94029.8 (10.0)31.0 (11.1)30.2 (9.2)28.4 (9.6)Age in years, mean (SD)
.14
χ22=3.9 (n=308)
Age range, n (%)
54 (17.5)18 (19)12 (11.9)24 (21.1)20 years
63 (20.5)13 (14)19 (18.8)31 (27.2)21-25 years
64 (20.8)19 (20)29 (28.7)16 (14.0)26-30 years
50 (16.2)15 (16)18 (17.8)17 (14.9)31-35 years
35 (11.4)11 (12)10 (9.9)14 (12.3)36-40 years
18 (5.8)7 (8)5 (5.0)6 (5.3)41-45 years
24 (7.8)10 (11)8 (7.9)6 (5.3)46+ years
.57
χ210=8.6 (n=308)
Highest education, n (%)
12 (3.9)5 (5)3 (3.0)4 (3.5)Not specified
41 (13.3)11 (12)12 (11.9)18 (15.8)Primary school
122 (39.6)41 (44)38 (37.6)43 (37.7)Apprenticeship
49 (15.9)17 (18)13 (12.9)19 (16.7)Secondary school
57 (18.5)13 (14)26 (25.7)18 (15.8)Technical college
27 (8.8)6 (7)9 (8.9)12 (10.5)University
.23
χ26=8.1 (n=308)
Origin, n (%)
127 (41.2)42 (45)33 (32.7)52 (45.6)Canton of Zurich
162 (52.6)48 (52)61 (60.4)53 (46.5)Other cantons
16 (5.2)3 (3)5 (5.0)8 (7.0)Germany
3 (1.0)0 (0)2 (2.0)1 (0.9)Other countries
.69
F2,308=0.37
19.6 (6.1)19.1 (6.2)19.7 (6.4)19.8 (5.8)CUDITe, mean (SD)
.69
F2,308=0.37
7.5 (3.4)7.3 (3.2)7.5 (3.6)7.7 (3.5)SDSf, mean (SD)
.90
F2,308=0.11
54.3 (20.5)55.1 (22.6)53.9 (20.0)54.0 (19.3)MHI-5g, mean (SD)
Number of years of substance use,
mean (SD)
.04h
F2,305=3.29
10.9 (8.4)12.6 (10.0)10.9 (7.6)9.6 (7.4)Cannabinoids
.97
F2,228=0.03
2.6 (5.7)2.7 (6.4)2.6 (5.3)2.5 (5.6)Risky alcohol usei
.52F2,222=0.671.1 (3.4)0.8 (1.8)1.4 (3.8)1.1 (4.3)Cocaine
.40F2,208=0.910.8 (2.4)0.6 (2.0)1.1 (3.1)0.7 (2.0)Amphetamines
Substance use in the last 30 days, n
(%)
N/Aj
Not computable (no vari-
ance)305 (99.0)93 (100)100 (99.0)112 (98.2)Cannabinoids
.28
χ22=2.6 (n=226)
97 (31.5)31 (33)26 (25.7)40 (35.1)Risky alcohol usei
.74
χ22=0.6 (n=215)
20 (6.5)5 (5)8 (7.9)7 (6.1)Tranquilizers
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P
χ2, ANOVAd, or
Kruskal-Wallis testTotal (n=308)
Study arm 3c
(n=93)
Study arm 2b
(n=101)
Study arm 1a
(n=114)Characteristics
.26
χ22=2.7 (n=223)
31 (10.1)10 (11)14 (13.9)7 (6.1)Cocaine
.90
χ22=0.2 (n=221)
43 (14.0)14 (15)13 (12.9)16 (14.0)Amphetamines
.76
χ22=0.6 (n=210)
14 (4.5)4 (4)4 (4.0)6 (5.3)Hallucinogens
.40
χ22=1.8 (n=201)
1 (0.3)0 (0)1 (1.0)0 (0)Heroin
.40
χ22=1.9 (n=197)
5 (1.6)3 (3)1 (1.0)1 (0.9)Methadone
.21
χ22=3.1 (n=198)
6 (1.9)1 (1)1 (1.0)4 (3.5)Others
aSelf-help with chat.
bSelf-help without chat.
cWaiting list.
dAnalysis of variance (ANOVA).
eCannabis Use Disorders Identification Test (CUDIT) scores range from 0 to 40 with a cutoff of >8 for a cannabis use disorder.
fSeverity of Dependence Scale (SDS) scores range from 0 to 15 with a cutoff of 4 for cannabis dependence.
gMental Health Inventory (MHI-5): higher values represent improved symptoms. MHI-5 values range from 0 to 100 with a cutoff of <70 for clinically
relevant symptoms.
hP<.05, represents a significant value.
iRisky alcohol use was defined as five or more standard drinks per day on at least three days per week. A standard drink was defined as 5 cl spirits,
15-20 cl wine, or 33-45 cl beer.
jNot applicable (N/A).
Intervention Participation and Retention
Figure 4depicts the module completion by participants in study
arms 1 and 2. Participants in the self-help with chat study arm
completed a mean of 3.2 modules and 27 out of 114 (23.7%)
of the participants received at least one chat session. Participants
in the self-help without chat study arm completed similar
numbers of self-help modules (U=-1.189, P=.23). Participants
in study arm 1 more frequently completed the consumption
diary than those in study arm 2 during their recommended 6
intervention weeks (U=-2.375, P=.02; see Figure 5). Of the 27
users in study arm 1 who received chat counseling sessions, 23
(85%) received one session and 4 (15%) received two sessions.
Figure 4. Module completion rate for study arms 1 (self-help with chat) and 2 (self-help without chat).
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Figure 5. Study retention based on the weekly completion of the consumption diary for study arms 1 (self-help with chat) and 2 (self-help without
chat) between baseline and week 6, including 3-month follow-up completion rate.
Main Outcomes
Figure 6 depicts the mean numbers of cannabis use days per
week and Figure 7 the mean weekly quantity of cannabis used
in standard joints according to the consumption diary, between
baseline and follow-up for all three study arms and based on
the nonimputed dataset.
The differences in cannabis use between baseline and the
3-month follow-up, as expressed by the mean number of
cannabis use days per week and based on the imputed data,
differed between self-help without chat versus self-help with
chat (beta= -0.75, SE = 0.32, t=-2.39, P=.02, d=0.34, 95% CI
0.07-0.61), and between self-help with chat versus waiting list
(beta= 0.70, SE = 0.32, t=2.16, P=.03, d=0.20, 95% CI -0.07
to 0.47), but not between self-help without chat versus waiting
list (beta= -0.05, SE = 0.33, t=-0.16, P=.87, d=-0.14, 95% CI
-0.43 to 0.14). In contrast, we only observed one trend to a
significant difference in the weekly quantity of standard joints
in the comparison of self-help with chat versus waiting list in
the imputed dataset (beta = 4.73, SE = 2.50, t=1.89, P=.06,
d=0.09, 95% CI -0.19 to 0.36; see Tables 4 and 5).
Figure 6. Cannabis use days per week according to the consumption diary between baseline and 3-month follow-up for all three study arms based on
the nonimputed dataset.
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Figure 7. Weekly quantity of cannabis used in number of standardized cannabis joints between baseline and 3-month follow-up for all three study arms
based on the nonimputed dataset.
Secondary Outcomes
There were no significant differences in the group comparisons
in the secondary outcomes (see Tables 4 and 5). We observed
slight improvements in mental health (MHI-5), cannabis use
disorders (CUDIT), and severity of dependence (SDS) in all
three groups (see Table 4; pre/post comparisons not reported).
Assessment of the intervention satisfaction was completed by
only a few participants at 6 months past baseline and we
therefore omit group comparisons. Not surprisingly, those who
remained in the active study arms rated their satisfaction as high
(eg, intervention satisfaction in general [19/308, 6.2%]: very
satisfied 42% [8/19], quite satisfied with most of the intervention
42% [8/19], quite unsatisfied with most of the intervention 11%
[2/19], quite unsatisfied 5% [1/19]).
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Table 4. Number of participants and mean and standard deviation changes from the imputed (50 imputations) and complete case datasets between
baseline and 3-month follow-up.
Study arm 3
(waiting list)
(n=93), mean (SD)
Study arm 2
(self-help without chat)
(n=101), mean (SD)
Study arm 1
(self-help with chat)
(n=114), mean (SD)
Outcomes
Follow-upBaselineFollow-upBaselineFollow-upBaseline
Frequency of cannabis use a
5.3 (1.8)6.3 (1.0)5.3 (1.8)6.0 (1.6)4.6 (2.1)6.0 (1.6)Imputed data
5.3 (2.5)6.7 (0.9)5.5 (2.3)6.1 (1.7)3.8 (3.0)6.1 (1.6)Complete cases
Quantity of cannabis use b
18.6 (17.7)25.8 (18.7)14.4 (11.8)23.1 (23.1)13.3 (12.0)22.3 (14.8)Imputed data
20.7 (23.7)23.6 (13.2)14.2 (13.3)25.1 (25.2)10.9 (13.8)23.0 (15.1)Complete cases
CUDIT c
16.6 (6.4)19.1 (6.2)15.6 (6.7)19.7 (6.4)16.6 (7.1)19.8 (5.8)Imputed data
16.0 (7.2)19.1 (6.2)13.0 (7.4)19.7 (6.4)12.6 (8.4)19.8 (5.8)Complete cases
SDS d
6.3 (3.3)7.3 (3.1)6.2 (3.1)7.5 (3.6)6.3 (3.3)7.7 (3.5)Imputed data
5.9 (3.8)7.3 (3.1)6.0 (3.3)7.5 (3.6)5.3 (3.8)7.7 (3.5)Complete cases
MHI-5 e
59.4 (19.4)55.1 (22.6)60.4 (19.1)53.9 (20.0)58.1 (18.2)53.9 (19.3)Imputed data
64.6 (18.3)55.1 (22.6)63.4 (20.4)53.9 (20.0)62.4 (19.8)53.9 (19.3)Complete cases
Alcohol use in the last 30 days (risky)
3.3 (4.0)4.5 (7.9)2.2 (3.0)2.4 (5.0)2.8 (2.8)3.4 (6.2)Imputed data
2.1 (4.7)4.5 (8.7)1.0 (2.6)2.5 (5.8)1.6 (2.6)3.4 (7.0)Complete cases
aBased on the weekly number of cannabis use days according to the consumption diary.
bBased on the weekly number of standard cannabis joints according to the consumption diary.
cCannabis Use Disorders Identification Test (CUDIT) scores range from 0 to 40 with a cutoff of >8 for a cannabis use disorder.
dSeverity of Dependence Scale (SDS) scores range from 0 to 15 with a cutoff of 4 for cannabis dependence.
eMental Health Inventory (MHI-5): higher values represent improved symptoms. MHI-5 values range from 0 to 100 with a cutoff of <70 for clinically
relevant symptoms.
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Table 5. Results for the between-study armacomparisons from the linear (and logistic) regression models and calculated effect sizes based on the
imputed dataset (50 imputations).
Cohen's d (95% CI)PtSEbetaCharacteristics
Frequency of cannabis use b
<.001-6.760.58-3.95(Intercept)
0.20 (-0.07 to 0.47).03 c
2.160.320.70Arm 1 vs arm 3
-0.14 (-0.43 to 0.14).87-0.160.33-0.05Arm 2 vs arm 3
<.001-5.790.56-3.25(Intercept)
0.34 (0.07 to 0.61).02-2.390.32-0.75Arm 2 vs arm 1
Quantity of cannabis use d
<.001-6.462.24-14.50(Intercept)
0.09 (-0.19 to 0.36).061.892.504.73Arm 1 vs arm 3
0.06 (-0.22 to 0.35).121.562.423.77Arm 2 vs arm 3
<.001-5.091.92-9.78(Intercept)
0.01 (-0.26 to 0.28).69-0.392.43-0.96Arm 2 vs arm 1
CUDIT e
<.001-6.461.61-10.39(Intercept)
0.09 (-0.18 to 0.37).850.191.290.24Arm 1 vs arm 3
0.21 (-0.07 to 0.49).320.991.201.19Arm 2 vs arm 3
<.001-6.051.68-10.14(Intercept)
-0.12 (-0.39 to 0.14).410.821.160.95Arm 2 vs arm 1
SDS f
<.001-7.340.64-4.68(Intercept)
0.08 (-0.19 to 0.36).960.050.580.03Arm 1 vs arm 3
0.07 (-0.21 to 0.35).860.170.560.10Arm 2 vs arm 3
<.001-7.190.65-4.65(Intercept)
0.02 (-0.25 to 0.28).900.130.550.07Arm 2 vs arm 1
MHI-5 g
<.001-10.154.33-43.91(Intercept)
0.01 (-0.27 to 0.28).780.283.440.96Arm 1 vs arm 3
-0.09 (-0.38 to 0.19).69-0.403.42-1.38Arm 2 vs arm 3
<.001-10.374.14-42.95(Intercept)
0.11 (-0.16 to 0.38).48-0.713.28-2.34Arm 2 vs arm 1
Alcohol use in the last 30 days (risky)
<.001-5.090.56-2.84(Intercept)
-0.10 (-0.38 to 0.17).610.520.630.32Arm 1 vs arm 3
-0.16 (-0.44 to 0.12).251.140.730.83Arm 2 vs arm 3
<.001-5.460.46-2.52(Intercept)
0.06 (-0.20 to 0.33).400.840.610.51Arm 2 vs arm 1
aStudy arm 1: self-help with chat; study arm 2: self-help without chat; study arm 3: waiting list.
bBased on the weekly number of cannabis use days according to the consumption diary.
cSignificant and borderline significant differences and effect sizes are in italics.
dBased on the weekly number of standard cannabis joints according to the consumption diary.
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eCannabis Use Disorders Identification Test (CUDIT) scores range from 0 to 40 with a cutoff of >8 for a cannabis use disorder.
fSeverity of Dependence Scale (SDS) scores range from 0 to 15 with a cutoff of 4 for cannabis dependence.
gMental Health Inventory (MHI-5): higher values represent improved symptoms. MHI-5 scores range from 0 to 100 with a cutoff of <70 for clinically
relevant symptoms.
Dropout Analysis
Dropouts at follow-up did not differ from completers with
respect to the following baseline variables: gender (t=1.34,
P=.16), age (t=-0.24, P=.81), years of cannabis use (t=0.18,
P=.86), frequency of cannabis use in the preceding 30 days
(t=0.22, P=.83), the weekly number of standardized cannabis
joints used (t=1.20, P=.42), the SDS (t=-1.52, P=.13), the
CUDIT (t=0.49, P=.63), alcohol use in the preceding 30 days
(t=1.20, P=.23), risky alcohol use in the preceding 30 days
(t=1.56, P=.12), and the MHI-5 (t=0.40, P=.69).
Significantly more participants could be followed up who
received at least one chat session compared to those who could
not be contacted at the 3-month follow-up (17.0% vs 5.5%,
chi-square [df 2]= 7.5, P=.001).
Dropouts did not differ between the three study arms with
respect to gender (F2=0.04, P=.96), age (F2= 1.13, P=.27),
years of cannabis use (F2= 0.81, P=.79), frequency of cannabis
use in the preceding 30 days (F2= 0.91, P=.59), the standardized
cannabis use quantity (F2= 0.93, P=.60), the SDS (F2= 1.20,
P=.23), the CUDIT (F2= 0.94, P=.58), alcohol use in the
preceding 30 days (F2= 0.57, P=.97), risky alcohol use in the
preceding 30 days (F2= 0.48, P=.98), and the MHI-5 (F2=
1.00, P=.47) at baseline.
Nonintended Results
Although not intended as an outcome measure, we also offered
cannabis abstention in the study protocol for those participants
who wished to achieve this [1]. Self-reported 7-day point
prevalence abstinence was significantly higher in self-help with
chat (8.8%) than in the self-help without chat study arm (2.0%;
beta = -1.56, SE = 0.79, P=.05, odds ratio [OR] = 0.21, 95% CI
0.02-2.33), but not between the self-help study arm with chat
and the waiting list control group (4.3%; beta = 0.76, SE = 0.61,
P=.21, OR = 2.14, 95% CI 0.86-5.30; see Tables 6 and 7).
Table 6. Number of participants in three study arms at each time point.
Study arm 3
(waiting list)
(n=93), n (%)
Study arm 2
(self-help without chat)
(n=101), n (%)
Study arm 1
(self-help with chat)
(n=114), n (%)
Study time point
N/Aa
12 (11.9)9 (7.9)Week 1
N/A9 (8.9)8 (7.0)Week 6
4 (4)2 (2.0)9 (8.8)Follow-up
aNot applicable (N/A).
Table 7. Self-reported abstinence between groups and with the corresponding logistic regression.
ORa(95% CI)PtSEbetaAbstinence at follow-up
.061.880.020.04(Intercept)
2.14 (0.86-5.30).211.250.610.76Arm 1 vs arm 3
0.45 (0.11-1.78).36-0.910.88-0.80Arm 2 vs arm 3
<.001-7.070.33-2.34(Intercept)
0.21(0.02-2.33).05 b
-1.980.79-1.56Arm 2 vs arm 1
aOdds ratio (OR).
bBorderline significant difference is shown in italics.
Subgroup Analyses
Participants in study arm 1 who received at least one chat session
exhibited lower changes in their entries in the consumption
diary. This meant that they took longer to complete the
consumption diary and exhibited higher retention (change in
mean 0.3 vs 0.5; beta = -0.28, SE = 0.12, P=.03, 95% CI -0.66
to -0.53) than those who did not receive the chat session in study
arm 1. In line with this, they completed twice as many modules
(mean 5.4, SD 2.8 vs mean 2.5, SD 2.1; t=5.45, df = 96, P<.001,
95% CI 1.88-4.00). Regarding cannabis use, these two
subgroups did not differ in their reduction in frequency (change
in mean 3.3 vs 1.9; beta = -1.38, SE = 0.93, P=.14, 95% CI
-3.19 to 0.44) or quantity (change in mean 15.2 vs 10.6; beta =
-4.61, SE = 4.44, P=.30, 95% CI -13.32 to 4.10).
Participants in study arm 1 who did not receive a chat session
for whatever reason did reduce their frequency of cannabis use
more (change in mean 1.9) than participants in study arm 2
(change in mean 0.7) who did not have the possibility for a chat
session due to their allocation (beta = -1.97, SE = 0.60, P=.001,
95% CI -3.14 to -0.80). However, they did not differ with respect
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to the reduction in the quantity of cannabis used (change in
mean 10.6 vs 10.3; beta = -0.33, SE = 6.48, P=.96, 95% CI
-13.03 to 12.37). There were no significant differences between
these two groups with respect to module completion (mean 2.5,
SD 2.1 vs mean 2.9, SD 2.4; t=-1.18, df = 159, P=.23), but those
in study arm 2 showed lower changes in their entries in the
consumption diary (change in mean 0.4 vs 0.5; beta = -0.28, SE
= 0.12, P=.03, 95% CI -0.66 to -0.53) compared to those who
did not receive the chat session in study arm 1.
Additional Help and Adverse Events
At the 3-month follow-up, 88.0% of participants (103/117)
stated that they had not contacted any other treatment services
(7 participants in study arm 1, 2 in study arm 2, and 5 in study
arm 3). A total of 5.1% (6/117) had contacted a psychiatrist,
2.6% (3/117) a family doctor, 1.7% (2/117) a psychologist,
1.7% (2/117) a different Internet counseling service, and 1
person (0.9%) a drug counselor. During the whole study period,
5 out of 308 (1.6%) participants contacted one of the outpatient
addiction clinics from the ARUD Centers for Addiction
Medicine. None of them had to be treated as an emergency case
or had to be referred to an inpatient treatment service. Moreover,
none of the involved counselors or researchers are aware of any
adverse or serious adverse event related to the Can Reduce study
that was reported by other addiction counseling services.
Discussion
Principal Findings
The Can Reduce study could reach a different group of cannabis
users who do not enter outpatient addiction treatment services.
They are older and consume much more cannabis than outpatient
service users. The finding that we reached cannabis users with
more entrenched problems (eg, daily users) is not consistent
with the common perception that those using online
interventions have less severe problems than those entering
outpatient services. We assume that this finding was most
probably due to an age effect. Older users consume longer and
possibly also more than younger ones but might feel more
stigmatized if they enter an outpatient addiction service, due to
their greater responsibilities and roles in social relationships, at
work, and in society in general.
Can Reduce participants allocated to the self-help with chat
study arm reduced their frequency of cannabis use more than
those in the other two arms. Even cannabis abstinence was
higher among those who received additional chat counseling
relative to those who received self-help only at follow-up. There
was a trend (P=.06) for a greater reduction in quantity of
cannabis use in those who received chat versus those in the
waiting list group and only a weak tendency (P=.12) for the
comparison of those with self-help only versus waiting list.
Hence, adding one to two chat counseling sessions that are
tailored to the self-help participant data and are based on the
same therapy approaches as the self-help part can be worthwhile.
As only one-quarter received at least one chat session, the
question arose as to what was actually responsible for the
superiority of the self-help with chat study arm. The subgroup
analyses showed that those participants in study arm 1 who did
not receive a chat session reduced their frequency of cannabis
use more than those who received self-help only from the
beginning (study arm 2). Thus, even an invitation to a chat
session and the knowledge that there is a possibility to have a
chat appointment might have improved this main outcome for
cannabis use. To the best of our knowledge, there are no similar
studies in the literature that have reported a comparable effect.
However, our result is in line with the first point of the
Supportive Accountability model [39] that argues that human
support increases adherence—and potentially
outcomes—through accountability to a coach who is seen as
trustworthy, benevolent, and having expertise. We took care
that our chat counselors were perceived as possessing these
attributes in the respective chat study arm.
However, those participants who actually received at least one
chat counseling session in study arm 1 still performed better in
their reduction of cannabis use and completed more self-help
modules than their counterparts who did not receive a chat
session in the same study arm. This result is in line with a further
point of the Supportive Accountability model [39] expecting
better outcomes due to a reciprocal relationship, through which
the patient can derive explicit benefits. However, this finding
could also be related to a selection bias. Those who actually
received at least one chat appointment with their counselor
could be a selected group of more compliant and possibly more
structured participants who could profit best from their allocated
intervention.
If we compare the current results with former studies about the
reduction of cannabis use with similar therapeutic approaches,
it stands out that participants in the Can Reduce self-help
without chat study arm performed worse than those in the
Australian Reduce Your Use study [15], in which greater effects
were achieved in the reduction of the quantity (d=0.06 vs d=0.25
in the Australian sample) and frequency of cannabis use days
(d=-0.14 vs d=0.33). This Australian study enrolled cannabis
users of a similar age range, but included more females (38.6%
vs 24.7%) and users with less severe cannabis consumption at
baseline. This may also be the reason that we did not observe
greater effects in the Severity of Dependence Scale, in contrast
to the Australian study (ITT: d=0.07 vs d=0.33). However, the
Australian study provided videos of a real person who provided
continuous MI during almost all parts of the intervention. This
clearly might have been an advantage compared to our version
with only written MI. Another possibility that could potentially
increase the engagement of self-help participants might be to
provide a personal companion with whom the participants could
identify, as we attempted in a similar ongoing trial with
problematic cocaine users [40]. The effects in the Can Reduce
self-help plus chat study arm were smaller for the quantity of
cannabis used (ITT: d=0.09 vs d=0.25) and similar for the
frequency of cannabis use days (ITT: d=0.34 vs d=0.33)
compared to the Australian self-help trial [15]. Participants in
the Can Reduce self-help with chat study arm performed better
than those in the more recent German Quit the Shit study with
respect to the reduction in the frequency of cannabis use days
(ITT: d=0.34 vs d=0.20) [11]. The German study recruited
younger participants (mean age 24.2 years, SD 5.8 vs mean age
29.8 years, SD 10.0).
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We observed a borderline significant effect in the abstention
rates between the self-help with chat and the self-help without
chat study arms. As we did not initially expect that enough
participants would maintain their abstinence, we omitted
abstinence as an outcome measure in the study protocol [1].
Abstinence rates were not reported in the German Quit the Shit
studies [12,14], but comparable differences between study arms
with respect to 3 months of abstinence were achieved in this
study (8.8%) and in the Australian study (5.8%) [15].
Setting a goal for cannabis consumption was implemented as
described in the study protocol [1]. In the introduction to the
consumption diary, we recommended that participants should
plan to reduce their cannabis use by at least 20 to 30% in the
first week and then continue with this strategy in subsequent
weeks if they succeeded. For participants who did not succeed,
we recommended that they created more modest goals until
their final aim was achieved. During the analyses of the
consumption diary patterns, we realized that there was a
considerable subgroup of participants who preferred to abstain
from cannabis even in the first week. Experiences from the chat
counseling sessions showed that, although the counselors in the
corresponding study arm strengthened this procedure in the
self-help intervention part, some participants argued that they
had learned from previous experience that they were much more
successful in stopping a potentially addictive behavior than in
reducing it. In this case, the counselors tried to encourage them
to abstain and to assist them in the maintenance of their
abstinence. However, this also resulted in some cases with a
new challenge. There was a substantial number of participants
in this subgroup who very quickly abstained from their cannabis
use and who did not log in again, although they were reminded
by automated reminder emails and/or their chat counselor, and
who then could not be reached at the follow-up assessment.
Possible strategies to prevent such early missing cases due to
abstention could be specific reminder emails sent automatically
and/or by introducing the chat counselor at an earlier stage.
Strengths and Limitations
The strengths of the Can Reduce study are that the intervention
is theory based and pretested, that this Web-based intervention
was able to reach cannabis users who otherwise would not have
sought help, and that we were able to disentangle the effects of
chat counseling additional to self-help for the reduction in
cannabis use in frequent cannabis users, three-quarters of whom
used cannabis daily. This study also possesses limitations that
merit consideration. First, we did not biologically validate
cannabis consumption for financial reasons, as we did not want
to limit participation to participants who were willing to provide,
for example, saliva samples, and as we did not want to limit
external validity. Second, we did not succeed in attaining a
better 6-week follow-up as intended in the study protocol, which
limits the explanatory power of the short-term effects of Can
Reduce. However, the 3-month follow-up rate (117/308, 38.0%)
was comparable to similar studies with problematic cannabis
users in Europe [12,14], but rather low compared to
Internet-based randomized controlled trials for the improvement
of nonaddiction-related problems. Moreover, we used the most
reliable imputation method available to handle missing data
[38] at follow-up. Third, due to ethical legislations, we had to
limit the minimal participation age to 18 years, as younger
participants would have needed parental informed consent, and
we expected that the overwhelming majority of minors would
avoid participation under these conditions. Moreover, this would
have been a contradiction with the concept of a maximally
anonymous Web-based intervention for the reduction of
cannabis use. Cannabis is still illegal in Switzerland and
Germany, from where the majority of participants in this study
come from. Fourth, participants were randomly allocated into
three study arms with slightly different sizes and a block
randomization could have prevented this.
Conclusions
In conclusion, the Can Reduce study demonstrated that
Web-based interventions possess the potential to reach heavy
cannabis users who differ from those who enter outpatient
addiction treatment services. We further conclude that offering
brief chat counseling in addition to Web-based self-help can
significantly increase success in the reduction of cannabis use
in the different groups of cannabis users investigated.
Acknowledgments
Funding for this study was provided by Infodrog, the Swiss Office for the Coordination of Addiction Facilities, Switzerland
(Grant No. 5012/13/ZH/Cannabis Control). The funding institution had no role in the development or evaluation of the interventions.
The authors wish to extend particular appreciation to the psychology master's students Emilija Nikolic and Manja Djordjevic for
helping to conduct the telephone follow-up interviews.
Conflicts of Interest
None declared.
Authors' Contributions
MPS was responsible for the study design and the final manuscript. AW and MPS performed the analyses and prepared the first
draft of the paper. All authors developed the intervention of study arms 1 and 2 and SH supervised the analyses. AW programmed
and implemented the study website, Can Reduce. All of the authors approved the final version of the manuscript submitted for
publication.
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Multimedia Appendix 1
Can Reduce participant information and informed consent page.
[PDF File (Adobe PDF File), 110KB - jmir_v17i10e232_app1.pdf ]
Multimedia Appendix 2
CONSORT-EHEALTH checklist V1.6.1 [28].
[PDF File (Adobe PDF File), 853KB - jmir_v17i10e232_app2.pdf ]
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Abbreviations
act-info: addiction, care and therapy information
ANOVA: analysis of variance
ARUD: Arud Centers for Addiction Medicine
BSM: behavioral self-management
CBT: cognitive behavioral therapy
CCS-7: Cannabis Craving Symptoms questionnaire
CUDIT: Cannabis Use Disorders Identification Test
CWS: Cannabis Withdrawal Scale
FDA: Fragebogen Substanzanamnese (questionnaire for the assessment of substance use history)
ISGF: Swiss Research Institute for Public Health and Addiction
ITT: intention-to-treat
MHI-5: Mental Health Inventory
MI: motivational interviewing
N/A: not applicable
OR: odds ratio
RCT: randomized controlled trial
SDS: Severity of Dependence Scale
WL: waiting list
Edited by G Eysenbach; submitted 06.07.15; peer-reviewed by R Tait, B Jonas, U Buchner, N Arnaud, D Reinwand; comments to
author 29.07.15; revised version received 27.08.15; accepted 21.09.15; published 15.10.15
Please cite as:
Schaub MP, Wenger A, Berg O, Beck T, Stark L, Buehler E, Haug S
A Web-Based Self-Help Intervention With and Without Chat Counseling to Reduce Cannabis Use in Problematic Cannabis Users:
Three-Arm Randomized Controlled Trial
J Med Internet Res 2015;17(10):e232
URL: http://www.jmir.org/2015/10/e232/
doi:10.2196/jmir.4860
PMID:
©Michael P Schaub, Andreas Wenger, Oliver Berg, Thilo Beck, Lars Stark, Eveline Buehler, Severin Haug. Originally published
in the Journal of Medical Internet Research (http://www.jmir.org), 15.10.2015. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet
Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/,
as well as this copyright and license information must be included.
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... Most were conducted in the United States (n=22) . Others were conducted in the Netherlands (n=4) [50][51][52][53], Switzerland (n=2) [54,55], Germany (n=1) [56], Korea (n=1) [57], Austria (n=1) [58], Canada (n=1) [59], and New Zealand (n=1) [60]. One study included participants from Germany, Sweden, Belgium, and the Czech Republic [61]. ...
... Studies were based on a variety of conceptual frameworks. Of the 34 studies, 24 used only MI [28-32,34,35,37-39, 41-44,46,47,49,51,53,54,56,59-61] and the remaining 10 used alternative frameworks in conjunction with MI [33,36,40,45,48,50,52,55,57,58] (Multimedia Appendix 1). The most commonly used conceptual framework other than MI was cognitive behavioral therapy (CBT; n=3) [45,50,55]. ...
... Of the 34 studies, 24 used only MI [28-32,34,35,37-39, 41-44,46,47,49,51,53,54,56,59-61] and the remaining 10 used alternative frameworks in conjunction with MI [33,36,40,45,48,50,52,55,57,58] (Multimedia Appendix 1). The most commonly used conceptual framework other than MI was cognitive behavioral therapy (CBT; n=3) [45,50,55]. ...
Article
Background: Motivational interviewing (MI) can increase health-promoting behaviors and decrease health-damaging behaviors. However, MI is often resource intensive, precluding its use with people with limited financial or time resources. Mobile health-based versions of MI interventions or technology-delivered adaptations of MI (TAMIs) might increase reach. Objective: We aimed to understand the characteristics of existing TAMIs. We were particularly interested in the inclusion of people from marginalized sociodemographic groups, whether the TAMI addressed sociocontextual factors, and how behavioral and health outcomes were reported. Methods: We employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews to conduct our scoping review. We searched PubMed, CINAHL, and PsycInfo from January 1, 1996, to April 6, 2022, to identify studies that described interventions incorporating MI into a mobile or electronic health platform. For inclusion, the study was required to (1) describe methods/outcomes of an MI intervention, (2) feature an intervention delivered automatically via a mobile or electronic health platform, and (3) report a behavioral or health outcome. The exclusion criteria were (1) publication in a language other than English and (2) description of only in-person intervention delivery (ie, no TAMI). We charted results using Excel (Microsoft Corp). Results: Thirty-four studies reported the use of TAMIs. Sample sizes ranged from 10 to 2069 participants aged 13 to 70 years. Most studies (n=27) directed interventions toward individuals engaging in behaviors that increased chronic disease risk. Most studies (n=22) oversampled individuals from marginalized sociodemographic groups, but few (n=3) were designed specifically with marginalized groups in mind. TAMIs used text messaging (n=8), web-based intervention (n=22), app + text messaging (n=1), and web-based intervention + text messaging (n=3) as delivery platforms. Of the 34 studies, 30 (88%) were randomized controlled trials reporting behavioral and health-related outcomes, 23 of which reported statistically significant improvements in targeted behaviors with TAMI use. TAMIs improved targeted health behaviors in the remaining 4 studies. Moreover, 11 (32%) studies assessed TAMI feasibility, acceptability, or satisfaction, and all rated TAMIs highly in this regard. Among 20 studies with a disproportionately high number of people from marginalized racial or ethnic groups compared with the general US population, 16 (80%) reported increased engagement in health behaviors or better health outcomes. However, no TAMIs included elements that addressed sociocontextual influences on behavior or health outcomes. Conclusions: Our findings suggest that TAMIs may improve some health promotion and disease management behaviors. However, few TAMIs were designed specifically for people from marginalized sociodemographic groups, and none included elements to help address sociocontextual challenges. Research is needed to determine how TAMIs affect individual health outcomes and how to incorporate elements that address sociocontextual factors, and to identify the best practices for implementing TAMIs into clinical practice.
... It is considered a medical device under European Union guidelines 93/42/EWG and 2007/47/EWG. Randomized clinical trial (RCT) results were published in 2015, demonstrating CANreduce to be effective, though beset by an adherence rate that might be improved by creating internet-based applications responsive to all devices (phones, tablets, computers) and adding psychological support [24]. An updated, adherence-focused guidance enhanced version, CANreduce 2.0, has since been developed [25] and found to be effective [26]. ...
... Adding psychological support (the option for program users to contact an e-coach to resolve doubts or any concerns they have regarding the program or process) is expected to optimize both adherence to and the effectiveness of the program. Previous studies have identified low demand for this type of support, but better results with similar support options, like chat sessions with a counsellor [24]. Study hypotheses will be tested concerning the main outcome: reduction in the number of days of cannabis use over the prior week, comparing assessments at baseline versus 6-week, 3-month, and 6-month follow-up. ...
... It is broadly recommended to be used for outcome evaluation and treatment planning purposes. Secondary outcomes will include the number of days of cannabis use over the last 30 days, again using the TLFB method, and the number of standardized cannabis joints [24] consumed in the previous week. The presence of a cannabis use disorder (CUD) will be measured by the CUDIT-R (Cannabis Use Disorder Identification Test-Revised, CUDIT-R) [38], a validated and quick-touse self-administered questionnaire with eight items that has been found to exhibit excellent psychometric Table 4 First-week e-mail example (translated from Spanish to English for publication purposes only) Study arm 1 and 2 (paragraph in italics ONLY appears in study arm 1) Hello {participant's name }! You've made it through the first week. ...
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Background Cannabis is the most-frequently used illicit drug in Europe. Over the last few years in Spain, treatment demand has increased, yet most cannabis users do not seek treatment despite the related problems. A web-based self-help tool, like CANreduce 2.0, could help these users to control their consumption. Methods This study protocol describes a three-arm randomized controlled trial (RCT) comparing the effectiveness of three approaches, in terms of reducing cannabis use among problematic cannabis users, the first two treatment arms including the Spanish version of CANreduce 2.0 (an adherence-focused, guidance-enhanced, web-based self-help tool) (1) with and (2) without psychological support; and the third group (3) treatment as usual (TAU). Study hypotheses will be tested concerning the primary outcome: change in the number of days of cannabis use over the previous week, comparing assessments at 6 weeks and 3 and 6 months follow-up between groups and against baseline. Secondary outcomes related to cannabis use will be tested similarly. Mental disorders will be explored as predictors of adherence and outcomes. Analyses will be performed on an intention-to-treat basis, then verified by complete case analyses. Discussion This study will test how effective the Spanish version of CANreduce 2.0 (CANreduce-SP) is at reducing both the frequency and quantity of cannabis use in problematic users and whether adding psychological support increases its effectiveness. Trial registration This trial is registered with the Clinical Trials Protocol Registration and Results System (PRS) number: NCT04517474. Registered 18 August 2020, (Archived by archive.is https://archive.is/N1Y64). The project commenced in November 2020 and recruitment is anticipated to end by November 2022.
... Version 1.0 [16] was shown to be more effective reducing cannabis use when supplemented with the option of a brief professional chat (counseling) session, even though this option only was used by about one fifth of users. More precisely, small ES (0.20) were identified for the comparison between the study arm with chat counseling versus a waiting list control group at 3-month follow-up [17]. Hence, the chat invitation that was sent out by a health professional was possibly already sufficient to reduce participants' cannabis use. ...
... Important limitations of previous research are taken into account; for instance, by aiming for a large enough sample size that allows us to detect even small ES. Furthermore, study arm 1 of CANreduce 3.0, which combines CBT and mindfulness, seeks to leverage the established effectiveness of IMI [10][11][12][13] (including previous versions of CANreduce [17,20]) as well as mindfulness-based programs [33,[38][39][40][41][42][43][44][45] for the treatment of SUD (or frequent cannabis use, more specifically). It is possible that adding mindfulness to CANreduce 3.0 is particularly relevant for some subgroups of the population with certain clinical (e.g., co-occurring SUD and depression; [46,47]) or sociodemographic characteristics (e.g. ...
... First, and based upon previous research (e.g. [17]), relatively large drop-out rates are expected. Furthermore, it is possible that adherence to IMI is generally limited, due to the distant nature of such interventions [90]. ...
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Background Though Internet- and mobile-based interventions (IMIs) and mindfulness-based interventions (generally delivered in-situ) appear effective for people with substance use disorders, IMIs incorporating mindfulness are largely missing, including those targeting frequent cannabis use. Methods This paper details the protocol for a three-arm randomized controlled trial comparing a mindfulness-based self-help IMI (arm 1) and cognitive-behavioral therapy (CBT)-based self-help IMI (arm 2) versus being on a waiting list (arm 3) in their effectiveness reducing cannabis use in frequent cannabis users. Predictors of retention, adherence and treatment outcomes will be identified and similarities between the two active intervention arms explored. Both active interventions last six weeks and consist of eight modules designed to reduce cannabis use and common mental health symptoms. With a targeted sample size of n = 210 per treatment arm, data will be collected at baseline immediately before program use is initiated; at six weeks, immediately after program completion; and at three and six months post baseline assessment to assess the retention of any gains achieved during treatment. The primary outcome will be number of days of cannabis use over the preceding 30 days. Secondary outcomes will include further measures of cannabis use and use of other substances, changes in mental health symptoms and mindfulness, client satisfaction, intervention retention and adherence, and adverse effects. Data analysis will follow ITT principles and primarily employ (generalized) linear mixed models. Discussion This RCT will provide important insights into the effectiveness of an IMI integrating mindfulness to reduce cannabis use in frequent cannabis users. Trial registration International Standard Randomized Controlled Trial Number Registry: ISRCTN14971662; date of registration: 09/09/2021.
... Similar research involving 308 subjects also found that online interventions that include chat counseling can also be helpful for people with cannabis use disorder, especially when used by those who don't traditionally utilize outpatient treatment options [12]. ...
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Cannabis remains the most commonly used illicit psychoactive drug and contains substances that affect the brain and body. A range of acute and chronic health problems associated with cannabis use has been identified. Cannabis use disorder is defined as the continued use of cannabis despite clinically significant impairment. It is estimated that 1 in 10 people who use marijuana will become addicted. CUD is a problematic pattern of cannabis use that causes clinically significant impairment. There is not an available medication to successfully treat CUD, but psychotherapeutic models hold promise. Cognitive behavioural therapy, motivational enhancement therapy and contingency management can substantially reduce cannabis use and cannabis-related problems. The legalization of non-medical cannabis use in some high-income countries may increase the prevalence of CUD. Since this approach has not yet been validated for CUD, the improvement of psychosocial treatments with pharmacological therapies should be further explored in future clinical research.
... CANreduce version 1.0 [40,41] had already been shown to be effective at reducing cannabis use by combining an automated self-help program (web-based psychoeducation modules with a consumption diary) with the opportunity for individual chat counseling, both of which were grounded in motivational interviewing, self-control practices, and classical cognitive behavioral therapy. However, there were difficulties with adherence, retention, and high dropout rates. ...
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Background: Prevalence rates for lifetime cannabis use and cannabis use disorder are much higher in people with attention deficit/hyperactivity disorder than in those without. CANreduce 2.0 is an intervention that is generally effective at reducing cannabis use in cannabis misusers. This self-guided web-based intervention (6-week duration) consists of modules grounded in motivational interviewing and cognitive behavioral therapy. Objective: We aimed to evaluate whether the CANreduce 2.0 intervention affects cannabis use patterns and symptom severity in adults who screen positive for attention deficit/hyperactivity disorder more than in those who do not.
... Online self-help interventions may reduce traditional treatment barriers by providing a free and easy-to-use tool with high anonymity, privacy, flexibility, and short or no waiting time; and supporting participants' autonomy and self-efficacy, as it was demonstrated, for example, in the case of substance use and problematic gambling (Baumgartner et al., 2019;Haug, Castro, Wenger, & Schaub, 2018;Herrero et al., 2019;Weisel et al., 2018). However, online interventions have their disadvantages as well, such as high dropout rates (Rooke, Copeland, Norberg, Hine, & McCambridge, 2013;Schaub et al., 2015), or lower levels of adherence due to the "distant" nature of the interventions (e.g., lack of personal relationships, or limited individualization options) (Amann et al., 2018;Haug et al., 2018;Schaub et al., 2016). Thus, online interventions should not be considered as replacements for traditional, face-to-face therapies but as an additional method that may provide treatment for those people who are hesitant or cannot afford to seek traditional treatment. ...
Chapter
The present chapter addresses the many faces of cybersex and describes the mental health challenges of various sexual activities using new technologies. This includes a range of sexual behaviors, from Internet use to sex with robots. In many cases, cybersex use is not problematic and not associated with personal distress or functional impairment. However, in those cases where people lose control over their cybersexual behavior or harm others we discuss diagnostic criteria as well as differential diagnoses and ways to evaluate the given behavior. The chapter also addresses the current state of research regarding the psychobiology as well as pharmacological and psychotherapeutic treatment options of cybersexual behaviors that are associated with mental health issues.
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Cannabis use and Cannabis Use Disorder (CUD) have been increasing. There are no FDA approved medications and evidence-based psychotherapy is limited by insufficient providers, serving very few patients effectively. The lack of resources for prevention and treatment of CUD has resulted in a significant gap between the need for services and access to treatment. The creation of a scalable system to prevent, screen, refer and provide treatment for a chronic, relapsing diagnosis like CUD could be achieved through the application of technology. Many studies have utilized ecological momentary assessments (EMA) in treatment seeking and non-treatment seeking cannabis users. EMA allows for repeated, intensive, longitudinal data collection in vivo . EMA has been studied in cannabis use and its association with affect, craving, withdrawal, other substances, impulsivity, and interpersonal behaviors. EMA has the potential to serve as a valuable monitoring tool in prevention, screening, and treatment for CUD. Research has also focused on the development of internet and application-based treatments for CUD, including a currently available prescription digital therapeutic. Treatment options have expanded to more broadly incorporate telehealth as an option for CUD treatment with broad acceptance and change in regulation following the COVID-19 pandemic. While technology has limitations, including cost, privacy concerns, and issues with engagement, it will be a necessary medium to meet societal health needs as a consequence of an ever-changing cannabis regulatory landscape. Future work should focus on improving existing platforms while ethically incorporating other functions (e.g., sensors) to optimize a public and clinical health approach to CUD.
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Background There has been a lack of systematic exploration of remotely delivered intervention content and their effectiveness for behaviour change outcomes. This review provides a synthesis of the behaviour change techniques (BCT) contained in remotely delivered alcohol and/or substance misuse approaches and their association with intervention promise. Methods Searches in MEDLINE, Scopus, PsycINFO (ProQuest), and the Cochrane Library, included studies reporting remote interventions focusing on alcohol and/or substance misuse among adults, with a primary behaviour change outcome (e.g., alcohol levels consumed). Assessment of risk of bias, study promise, and BCT coding was conducted. Synthesis focussed on the association of BCTs with intervention effectiveness using promise ratios. Results Studies targeted alcohol misuse (52 studies) or substance misuse (10 studies), with predominantly randomised controlled trial designs and asynchronous digital approaches. For alcohol misuse studies, 16 were very promising, 17 were quite promising, and 13 were not promising. Of the 36 eligible BCTs, 28 showed potential promise, with seven of these only appearing in very or quite promising studies. Particularly promising BCTs were ‘Avoidance/reducing exposure to cues for behaviour’, ‘Pros and cons’ and ‘Self-monitoring of behaviour’. For substance misuse studies, three were very promising and six were quite promising, with all 12 BCTs showing potential promise. Conclusions This review showed remotely delivered alcohol and substance misuse interventions can be effective and highlighted a range of BCTs that showed promise for improving services. However, concerns with risk of bias and the potential of promise ratios to inflate effectiveness warrant caution in interpreting the evidence.
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Background Prevalence rates for lifetime cannabis use and cannabis use disorder are much higher in people with attention deficit/hyperactivity disorder than in those without. CANreduce 2.0 is an intervention that is generally effective at reducing cannabis use in cannabis misusers. This self-guided web-based intervention (6-week duration) consists of modules grounded in motivational interviewing and cognitive behavioral therapy. Objective We aimed to evaluate whether the CANreduce 2.0 intervention affects cannabis use patterns and symptom severity in adults who screen positive for attention deficit/hyperactivity disorder more than in those who do not. Methods We performed a secondary analysis of data from a previous study with the inclusion criterion of cannabis use at least once weekly over the last 30 days. Adults with and without attention deficit/hyperactivity disorder (based on the Adult Attention deficit/hyperactivity disorder Self-Report screener) who were enrolled to the active intervention arms of CANreduce 2.0 were compared regarding the number of days cannabis was used in the preceding 30 days, the cannabis use disorder identification test score (CUDIT) and the severity of dependence scale score (SDS) at baseline and the 3-month follow-up. Secondary outcomes were Generalized Anxiety Disorder score, Center for Epidemiological Studies Depression scale score, retention, intervention adherence, and safety. Results Both adults with (n=94) and without (n=273) positive attention-deficit/hyperactivity disorder screening reported significantly reduced frequency (reduction in consumption days: with: mean 11.53, SD 9.28, P<.001; without: mean 8.53, SD 9.4, P<.001) and severity of cannabis use (SDS: with: mean 3.57, SD 3.65, P<.001; without: mean 2.47, SD 3.39, P<.001; CUDIT: with: mean 6.38, SD 5.96, P<.001; without: mean 5.33, SD 6.05, P<.001), as well as anxiety (with: mean 4.31, SD 4.71, P<.001; without: mean 1.84, SD 4.22, P<.001) and depression (with: mean 10.25, SD 10.54; without: mean 4.39, SD 10.22, P<.001). Those who screened positive for attention deficit/hyperactivity disorder also reported significantly decreased attention deficit/hyperactivity disorder scores (mean 4.65, SD 4.44, P<.001). There were no significant differences in change in use (P=.08), dependence (P=.95), use disorder (P=.85), attention deficit/hyperactivity disorder status (P=.84), depression (P=.84), or anxiety (P=.26) between baseline and final follow-up, dependent on positive attention-deficit/hyperactivity disorder screening. Attention deficit/hyperactivity disorder symptom severity at baseline was not associated with reduced cannabis use frequency or severity but was linked to greater reductions in depression (Spearman ρ=.33) and anxiety (Spearman ρ=.28). Individuals with positive attention deficit/hyperactivity disorder screening were significantly less likely to fill out the consumption diary (P=.02), but the association between continuous attention deficit/hyperactivity disorder symptom severity and retention (Spearman ρ=−0.10, P=.13) was nonsignificant. There also was no significant intergroup difference in the number of completed modules (with: mean 2.10, SD 2.33; without: mean 2.36, SD 2.36, P=.34), and there was no association with attention deficit/hyperactivity disorder symptom severity (Spearman ρ=−0.09; P=.43). The same was true for the rate of adverse effects (P=.33). Conclusions Cannabis users screening positive for attention deficit/hyperactivity disorder may benefit from CANreduce 2.0 to decrease the frequency and severity of cannabis dependence and attenuate symptoms of depression and attention deficit/hyperactivity disorder-related symptoms. This web-based program’s advantages include its accessibility for remote users and a personalized counselling option that may contribute to increased adherence and motivation to change among program users. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN) 11086185; http://www.isrctn.com/ISRCTN11086185
Chapter
Dual diagnosis is a leading contributor of disease burden worldwide. Whilst integrated treatment is recommended, there are considerable barriers that may inhibit access to integrated care, including a lack of training and resources. Digital interventions may enable access to support, providing a space for people to engage in treatment when they need it most. This chapter reviews the current literature on the efficacy of digital interventions for dual diagnosis. Computer-based interventions were effective at improving dual diagnosis outcomes; however, the combined effect of computer-based interventions and therapist support was found to be more effective than the effects of computer-based interventions alone. The evidence-base around smartphone applications is lacking, and there are perceived difficulties with this technology in addressing the complexity of issues faced by people with dual diagnosis. Future research should include standardised terminology to describe techniques used within interventions and consider a variety of research methods to understand implementation.
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Web-based self-help interventions that aim to reduce problematic substance use are able to reach "hidden" consumer groups in the general population who often fear stigmatization and thus avoid institutional addiction treatment. In Western European countries, including Switzerland, cocaine is the most widely used psychoactive substance after alcohol, tobacco, and cannabis. Although approximately one in six users develop serious problems of dependency, only a minority seeks help from psychiatrists or in outpatient counseling centers or psychiatric hospitals. Offering web-based therapy treatment may potentially reach users who hesitate to approach institutional treatment services and help them reduce their cocaine use before they get into more serious trouble. The study will use a three-arm randomized controlled trial (RCT) design to test the efficacy of a web-based self-help intervention with or without guided chat counseling compared with that of a waiting list control condition in reducing or stopping cocaine use. The primary outcome measure will be the weekly quantity of cocaine used. Secondary outcome measures will include the number of cocaine use days in the past 30 days, the severity of cocaine dependence, the use of alcohol, tobacco, and/or other illicit drugs, changes in mental health symptoms, and treatment retention. The self-help intervention will consist of eight modules that are designed to reduce cocaine use and depression symptoms. These modules are based on the principles of Motivational Enhancement Therapy and Cognitive Behavioral Therapy, such as Behavioral Self-Management. The three individual chat therapy sessions will be based on the same therapy approaches and will be tailored to participants' self-help data and aim to assist the reinstatement of social rewards and the improvement of social support and relationships. This study will be the first RCT to test the effectiveness of a web-based self-help intervention in combination with or without chat counseling in reducing cocaine use. The expected findings will contribute substantial knowledge that may help design effective guided and unguided web-based treatment for cocaine users. Moreover, the study will elucidate to what extent a therapeutic alliance with cocaine users can be established in a guided Internet-delivered setting. Additionally, the present study will investigate changes in social support with specific guided therapy interventions that aim to ameliorate social support and social perceptions and compare these changes with those in an unguided self-help intervention Current Controlled Trials ISRCTN12205466 . Registered 24 February 2015.
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Background: Cannabis craving can be assessed on four dimensions using an English-language test (MCQ). Objective: We aimed to develop a German-language inventory to assess cannabis craving. Further, wished to we analyzed correlations between craving and data concerning demographics and patterns of consumption. Methods: After translating the short version of the MCQ into German, we validated the inventory by collecting data from three samples of cannabis users. Demographic data and data concerning patterns of consumption were collected. Further, we assessed the subjective rating of current craving. Results: We could not confirm the factor model of the MCQ. We found two factors, corresponding to the concepts of reward and relief craving. Relief craving was positively correlated with the frequency of use. Reward craving was negatively correlated with the period of abstinence. Both dimensions were associated with the subjective rating of current craving. Conclusions: Cannabis craving may be operationalized by at least two dimensions. These dimensions may be reliably assessed by means of the presented inventory.
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Objective: Problematic alcohol use is the third leading contributor to the global burden of disease, partly because the majority of problem drinkers are not receiving treatment. Internet-based alcohol interventions attract an otherwise untreated population, but their effectiveness has not yet been established. The current study examined the effectiveness of Internet-based therapy (therapy alcohol online; TAO) and Internet-based self-help (self-help alcohol online; SAO) for problematic alcohol users. Method: Adult problem drinkers (n 205; 51% female; mean age 42 years; mean Alcohol Use Disorders Identification Test score 20) were randomly assigned to TAO, SAO, or an untreated waiting-list control group (WL). Participants in the TAO arm received 7 individual text-based chat-therapy sessions. The TAO and SAO interventions were based on cognitive– behavioral therapy and motivational interviewing techniques. Assessments were given at baseline and 3 and 6 months after randomization. Primary outcome measures were alcohol consumption and treatment response. Secondary outcome measures included measures of quality-of-life. Results: Using generalized estimating equation regression models, intention-to-treat analyses demonstrated significant effects for TAO versus WL (p .002) and for SAO versus WL (p .03) on alcohol consumption at 3 months postrandom-ization. Differences between TAO and SAO were not significant at 3 months postrandomization (p .11) but were significant at 6 months postrandomization (p .03), with larger effects obtained for TAO. There was a similar pattern of results for treatment response and quality-of-life outcome measures. Conclusions: Results support the effectiveness of cognitive– behavioral therapy/motivational interviewing Internet-based therapy and Internet-based self-help for problematic alcohol users. At 6 months postrandomization, Internet-based therapy led to better results than Internet-based self-help.
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Background: As part of the launch of the first Swiss online portal for addiction several addiction specialists have been trained as online addiction counselors. At the same time the persons involved had to find a consensus on binding quality standards for their institutions and their daily work. Methods: Based on an international literature review we formulated a list of quality standards for online addiction counseling. In an online survey the persons involved in the online portal rated these standards in terms of suitability for minimal and best-practice quality standards. Results: Consensus on quality standards was generally high. Over two thirds of the standards reached more than 70% agreement. The list of minimum and thus binding quality standards involves 13 structural standards, 11 process standards aimed at daily work and 2 outcome standards. The list of best-practice quality standards compromises two additional process and outcome standards each. Conclusions: For the first time binding minimal quality standards for addiction counseling on the internet have been defined. This is an important starting point for introducing quality assurance of online addiction counseling in Switzerland.
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In European countries, including Switzerland, as well as in many states worldwide, cannabis is the most widely used psychoactive substance after alcohol and tobacco. Although approximately one in ten users develop serious problems of dependency, only a minority attends outpatient addiction counseling centers. The offer of a combined web-based self-help and chat counseling treatment could potentially also reach those users who hesitate to approach such treatment centers and help them to reduce their cannabis use.Methods/design: This paper presents the protocol for a three-armed randomized controlled trial that will test the effectiveness of a web-based self-help intervention in combination with, or independent of, tailored chat counseling compared to a waiting list in reducing or enabling the abstention from cannabis use in problematic users. The primary outcome will be the weekly quantity of cannabis used. Secondary outcome measures will include the number of days per week on which cannabis is used, the severity of cannabis use disorder, the severity of cannabis dependence, cannabis withdrawal symptoms, cannabis craving, the use of alcohol, tobacco, and other non-cannabis illicit drugs, changes in mental health symptoms, and treatment retention. The self-help intervention will consist of 8 modules designed to reduce cannabis use based on the principles of motivational interviewing, self-control practices, and methods of cognitive behavioral therapy. The two additional individual chat-counseling sessions in the additional chat condition will be based on the same therapy approaches and tailored to participants' self-help information data and personal problems. The predictive validity of participants' baseline characteristics on treatment retention and outcomes will be explored. To the best of our knowledge, this will be the first randomized controlled trial to test the effectiveness of online self-help therapy in combination or without chat counseling in reducing or enabling the abstention from cannabis use. It will also investigate predictors of outcome and retention for these interventions. This trial is registered at Current Controlled Trials and is traceable as ISRCTN59948178.
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Ziel: Überprüfung der Effektivität einer auf Motivational Interviewing (MI) basierenden Intervention im Online-Chat unter jungen Alkohol- und Cannabiskonsumenten mit ambivalenter Änderungsmotivation. Methodik: Randomisiert-kontrollierte Online-Studie mit Nachbefragungen nach einem und nach drei Monaten. Die Rekrutierung erfolgte über die Selbsttests auf der Website drugcom.de und schloss nur Personen mit problematischem Alkohol- oder Cannabiskonsum ein. Teilnehmer der Interventionsgruppe nahmen am privaten MI-Chat teil. Mitglieder der Kontrollgruppe erhielten im Chat lediglich Sachinformationen über den zuvor genutzten Selbsttest. Ergebnisse: 302 Personen wurden randomisiert und in die ITT-Auswertung einbezogen. Es zeigten sich keine Gruppenunterschiede im Konsum von Alkohol (p ≥ 0.224), Cannabis (p = 0.537) oder in der Änderungsmotivation nach RCQ (p = 0.469). Beide Gruppen senkten ihren Alkoholkonsum im Studienverlauf signifikant und zeigten Verbesserungen der Änderungsbereitschaft. Schlussfolgerungen: Die beschriebene Chat-Intervention erzielt keine Verhaltensänderung bei ambivalent eingestellten Konsumenten. Online-Interventionen für diese Zielgruppe sollten womöglich länger und verbindlicher gestaltet werden.
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Tobacco and cannabis use are strongly interrelated, but current national and international cessation programs typically focus on one substance, and address the other substance either only marginally or not at all. This study aimed to identify the demand for, and describe the development and content of, the first integrative group cessation program FVfor co-smokers of cigarettes and cannabis. First, a preliminary study using expert interviews, user focus groups with (ex-)smokers, and an online survey was conducted to investigate the demand for, and potential content of, an integrative smoking cessation program (ISCP) for tobacco and cannabis co-smokers. This study revealed that both experts and co-smokers considered an ISCP to be useful but expected only modest levels of readiness for participation.Based on the findings of the preliminary study, an interdisciplinary expert team developed a course concept and a recruitment strategy. The developed group cessation program is based on current treatment techniques (such as motivational interviewing, cognitive behavioural therapy, and self-control training) and structured into six course sessions.The program was evaluated regarding its acceptability among participants and course instructors. Both the participants and course instructors evaluated the course positively. Participants and instructors especially appreciated the group discussions and the modules that were aimed at developing personal strategies that could be applied during simultaneous cessation of tobacco and cannabis, such as dealing with craving, withdrawal, and high-risk situations. There is a clear demand for a double cessation program for co-users of cigarettes and cannabis, and the first group cessation program tailored for these users has been developed and evaluated for acceptability. In the near future, the feasibility of the program will be evaluated.Trial registration: Current Controlled Trials ISRCTN15248397.
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This chapter describes selected features of cannabis epidemiology, with a focus upon recent evidence from field studies of cannabis dependence. An epidemiologist's interest in cannabis can be motivated by an appreciation that cannabis smoking represents the most common illegal drug use behavior in the world, with a roughly estimated 140–150 million cannabis users, as compared to rough estimates of 14–15 million for cocaine and 13–14 million for opium, heroin, and other opioid drugs (United Nations, 2002). Based upon recent estimates, projections, and averages for the USA, an estimated 7000–8000 individuals start using cannabis every day and there are 95 million US community residents who have tried cannabis on at least one occasion (Substance Abuse and Mental Health Services Administration, Office of Applied Studies (SAMHSA), 2002c, d). As will be documented later in this chapter, our rough averaged estimate is that some 50–80 recent-onset cannabis users develop a cannabis dependence syndrome each day during the year; some substantial fraction of these cases appear to require clinical intervention services. It is generally possible to dissect epidemiological research in relation to five general rubrics or sub-headings. The first rubric concerns quantification of disease burden, including the burdens associated with mental and behavioral disturbances that do not qualify as formal diseases, as well as the population-averaged “incidence” and individual-level risk of becoming a cannabis user, and the separately estimated population-averaged “prevalence” and individual-level likelihood of being an active or former cannabis user (e.g., see Anthony & Van Etten, 1998, Wu, et al ., 2003, for detailed discussions of the distinctions between incidence and prevalence).
Article
Introduction and Aims. This study aims to evaluate the feasibility and effects of a group cessation program for cannabis and tobacco co-smokers. Design and Methods. Using a repeated-measures design with pre-, post- and six months follow-up assessments, feasibility (intervention utilisation, safety and acceptability) and changes in substance use behaviour and mental health were evaluated. The intervention consisted of five to six group sessions and was based on current treatment techniques (e.g. motivational interviewing, cognitive-behavioural therapy, and self-control training). In total, 77 adults who used cannabis at least once weekly and cigarettes or similar products at least once daily participated in the study. Results. Within nine months, the target sample size was reached. Treatment retention was 62.3%, and only three participants discontinued treatment due to severe problems (concentration problems, sleeping problems, depressive symptoms, and/or distorted perceptions). In total, 41.5% and 23.4% reported abstinence from cigarettes, cannabis or both at the end of treatment and the follow-up, respectively. The individual abstinence rates for cigarettes and cannabis were 32.5% and 23.4% (end of treatment) and 10.4% and 19.5% (follow-up), and 13% (end of treatment) and 5.2% (follow-up) achieved dual abstinence validated for tobacco abstinence. Over the study period, significant decreases in tobacco and cannabis use frequencies and significant improvements in additional outcomes (drinking problems, symptoms of cannabis use disorder, nicotine dependence, depression and anxiety) were achieved. Discussion and Conclusions. The evaluated intervention for co-smokers is feasible regarding recruitment, intervention retention and safety. The promising results regarding substance use and mental health support a randomised controlled trial to evaluate effectiveness.
Article
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