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Balance—a pragmatic randomized controlled trial of
an online intensive self-help alcohol intervention
Håvar Brendryen1, Ingunn Olea Lund1, Ayna Beate Johansen1, Marianne Riksheim1,
Sverre Nesvåg1,2 & Fanny Duckert1
SERAF—Norwegian Centre for Addiction Research, University of Oslo, Oslo, Norway1and Alcohol and Drug Research Western Norway, Stavanger University
Hospital, Stavanger, Norway2
ABSTRACT
Aims To compare a brief versus a brief plus intensive self-help version of ‘Balance’, a fully automated online alcohol
intervention, on self-reported alcohol consumption. Design A pragmatic randomized controlled trial. Participants in
both conditions received an online single session screening procedure including personalized normative feedback.
The control group also received an online booklet about the effects of alcohol. The treatment group received the
online multi-session follow-up program, Balance. Setting Online study in Norway. Participants At-risk drinkers
were recruited by internet advertisements and assigned randomly to one of the two conditions (n=244).
Measurements The primary outcome was self-reported alcohol consumption the previous week measured 6 months
after screening. Findings Regression analysis, using baseline carried forward imputation (intent-to-treat), with
baseline variables as covariates, showed that intervention significantly affected alcohol consumption at 6 months
(B =2.96; 95% confidence interval =0.02–5.90; P=0.049). Participants in the intensive self-help group
drank an average of three fewer standard alcohol units compared with participants in the brief self-help group.
Conclusions The online Balance intervention, added to a brief online screening intervention, may aid reduction in
alcohol consumption compared with the screening intervention and an educational booklet.
Keywords Behaviour change intervention, early intervention, harmful drinking, hazardous drinking, mobile
telephone text messages.
Correspondence to: Håvar Brendryen, SERAF, Postboks 1039 Blindern, 0315 Oslo, Norway. E-mail: haavabre@medisin.uio.no
Submitted 18 December 2012; initial review completed 22 February 2013; final version accepted 30 September 2013
INTRODUCTION
Online alcohol interventions can be important in com-
bating alcohol misuse [1–10], but more insight is needed
into how different types of online intervention should
be implemented into the real world [7,11,12]. One
possibility is to chain the brief and intensive self-help
intervention formats together into a stepped-care frame-
work [13]. This may exhaust the potential of online
automated interventions, prior to introducing more
resource-intensive treatment requiring person-to-person
communication. Therefore, the feasibility and effective-
ness of this public health approach to alcohol misuse
needs to be investigated within a naturalistic online
setting.
Intensive self-help interventions are often based on
cognitive–behavioural and self-control principles, and
may comprise goal-setting, self-monitoring, refusal skills,
behavioural contracting, emotion regulation and relapse
prevention [5–10]. Such interventions are usually multi-
session programs and pre-suppose multiple visits for the
user to take full advantage of the treatment. In contrast,
standard practice for brief interventions, which is the
most common format for online self-help, is to provide a
screening with personalized normative feedback, and
sometimes motivational enhancement components,
within a 5–20-minute session [1–4,14]. Brief interven-
tions require little from the users, and it is a viable and
effective treatment option alone, but effect sizes tend to be
small [3]. However, brief interventions also represent an
opportunity for recruiting select people to intensive self-
help. A recent meta-analysis found that the effect sizes
were higher in trials of intensive self-help interventions
compared to trials of brief interventions [4]. In previous
RESEARCH REPORT
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doi:10.1111/add.12383
© 2013 Society for the Study of Addiction Addiction,109, 218–226
trials, intensive self-help was compared to passive control
conditions [5–7] (i.e. neither personalized normative
feedback nor motivational enhancement was provided to
the control group), or the recruitment or treatment pro-
cedure required either contact beyond the internet or
person-to-person communication [8–10]. To date, no
trials have compared brief intervention alone to brief plus
intensive self-help in an online setting [1–4]. This paper
reports on a pragmatic randomized controlled trial com-
paring two versions of the online intervention ‘Balance’
[15]: the brief intervention-only versus the full version
comprising both the brief and intensive self-help. We
hypothesized improved treatment outcome in the inten-
sive self-help condition compared to brief intervention
only, 2 and 6 months after baseline.
METHODS
Participants and procedure
Participants were recruited by banner advertisements in
online newspapers. When clicking on the advertisement,
participants were routed to a website with information on
the study. They were informed that participants would be
assigned randomly to groups receiving different online
tools that all started with screening and feedback on
alcohol habits. They had the opportunity to explore the
screening and feedback before deciding to participate in
the study. They were informed that providing an e-mail
and telephone number during the screening implied
consent to participate. Participants were informed that by
consenting they would receive additional follow-up,
including surveys after 2 and 6 months. There was no
reimbursement for participation. From the information
page the participants were routed to the online screening
procedure. After the personalized normative feedback,
participants were asked to provide their e-mail address
and telephone number if they wanted further informa-
tion and follow-up, and consented to participate in the
study. Trial participants thus signed up in the same way
that people in a non-research setting would sign up for
the treatment. To be eligible for inclusion into the study
the participant had to be an at-risk drinker aged 18 years
or older; complete the baseline assessment with no
missing items; and provide a valid e-mail address and
Norwegian mobile telephone number. At-risk drinking
was defined as a score of 3 or higher on The Fast Alcohol
Screening Test (FAST) [16]. A computerized automatic
simple randomization procedure was performed through-
out the recruitment period (from April to November
2011), assigning 244 participants either to the intensive
or the brief self-help condition. The randomization took
place immediately after each participant had provided
an e-mail address. An e-mail was sent to participants
randomized to the intensive self-help condition on the
consecutive day with a link and an invitation to the
first follow-up session, whereas participants rando-
mized to the brief self-help condition were routed to the
booklet.
Data were collected using online questionnaires at
baseline and at 2 and 6 months post-baseline times. At
follow-up points, an e-mail with a link to the question-
naire was sent to participants. Two e-mail reminders
were sent to non-responders at both points. At the
6-month follow-up, participants not responding to
e-mails were contacted by telephone. The telephone inter-
viewer was blinded to allocation. Participants in the
experimental condition logged on to the online sessions
with a unique username and password, allowing for
a continuous, unobtrusive and reliable measure of
program exposure [17,18]. Apart from the telephone
interview, there was no person-to-person interaction
between participant and experimenters.
Interventions
In a previous paper [15], we described the treatment
rationale of ‘Balance’, an online intervention that
combines the brief and intensive self-help formats. It
starts with a screening and feedback session. The
purpose is to provide personalized normative feedback;
support an informed choice of whether to change or not;
and recruit individuals to the intensive self-help inter-
vention. The feedback compared the reported drinking
habits to the recommended gender-matched low-risk
drinking guidelines. Additionally, they were compared
with national gender-matched averages; for example,
‘the average Norwegian male consumes more than 5
drinks less than once a month’, and ‘two thirds of Nor-
wegian males report they have never experienced a
blackout’. At the end of the feedback session, users iden-
tified as engaging in at-risk drinking are recommended
to sign up for the intensive self-help program. The inten-
sive self-help comprises 62 online sessions that are
released one by one in a predetermined sequence during
6 months.
The control group received an e-booklet, issued by the
Norwegian Directorate of Health that covers general
information about alcohol and potential risks and harms
of drinking. Neither the screening session nor the booklet
contains advice on how to achieve a change in drinking
behaviour. The rationale for delivering the booklet was
not to improve the brief intervention, but to provide
something that appeared as a plausible follow-up to the
control participants. This was necessary because the
participants, prior to the screening, were told that they
would receive more alcohol-related information and
follow-up after they signed up for the study.
RCT of an online alcohol intervention 219
© 2013 Society for the Study of Addiction Addiction,109, 218–226
The intensive self-help program was delivered to the
treatment group through multiple interactive sessions,
reminder e-mails and mobile telephone text messages
(Table 1). The central concept of Balance is to support
continued self-regulation throughout the behaviour
change process [15,19]. There are four key aspects of the
program; the first is focus on goal-setting and tracking of
alcohol consumption on a day-to-day basis.The second is
on relapse prevention; for example, when clients report
drinking more than their pre-set target, they receive per-
sonalized content aimed at preventing a full-blown
relapse [20]. The third is emotion regulation [21], where
content and assignments from positive psychology and
from cognitive behavioural therapy are used [22–24].
Finally, the intervention covers alcohol education (i.e. the
same topics as in the booklet provided to the control
group).
Balance uses tunnelled information architecture
[25]. The program withholds and gradually releases ses-
sions in a predetermined sequence. This facilitates learn-
ing moments that are short and many rather than few
and lengthy, and are in accordance with the need for
continuous self-regulation. The awareness of one’s own
attempt to self-regulate and change behaviour will be
stimulated by the distributed and frequent contact
points, which represent a tacit way of telling the clients
that behaviour change is a process that calls for sus-
tained effort [26–28]. In a recent trial, participants
randomized to a freedom of choice condition were more
satisfied but learned less compared to participants in
a tunnel design condition [29], demonstrating that
restricting the freedom of choice can improve learning
in e-health interventions. Although restricting choice, a
tunnel design does not dictate passive users, as it is well
suited to foster interactive dialogues. Hence, the sessions
include interactive tasks, cognitive behavioural assign-
ments and quizzes. As part of a lapse prevention system,
participants are given the option of scheduling a sup-
portive mobile telephone text message tailored to their
reported need on that particular day (motivation, mood,
or self-efficacy).
An average session consists typically of 1000 words,
split between 10 and 15 screens. Each session takes from
3 to 10 minutes to complete, depending on the clients’
depth of processing and speed of reading. Completing the
62 follow-up sessions thus requires up to 10 hours of
reading. Unless actively withdrawing, the participant will
receive an e-mail reminder each time a new session is
released. There is a danger that the many e-mails create
annoyance among participants with low motivation to
complete the program. The treatment rationale of the
intervention is described thoroughly in a separate paper
[15]. The Norwegian version of the program is currently
publicly available [30].
Measures at baseline
Total weekly alcohol consumption was defined as the
sum of drinks reported for each of the previous 7 days.
The scale for each day ranged from zero to 10, giving a
weekly consumption range from zero to 70. In Norway,
a standard alcohol unit is equivalent to 12 g of pure
alcohol.
The Fast Alcohol Screening Test (FAST) is a brief
version of The Alcohol Use Disorder Identification
Test (AUDIT) [16]. The four items of FAST was admin-
istered at baseline. The first item of FAST, which
measures frequency of binge episodes during the last
year, was modified to reflect the Norwegian official
guidelines for low-risk drinking. A binge drinking
episode was defined as more than four or more than
six drinks in one session for females and males, respec-
tively. FAST also includes items that assess how often
during the last year responders have experienced
memory problems, failed to do what is normally
expected from them due to alcohol consumption, and if
anyone has been concerned about their drinking. FAST
scores range from zero to 16, each item score ranges
from zero to 4 and a score of 3 or higher suggests at-risk
drinking.
Gender and age were the only socio-demographic
characteristics that were assessed. A broader baseline
assessment was not included due to practical and meth-
odological issues. By preserving the screening procedure
in the way it was already used in practice, ecological
validity would be higher compared to a more complex
screening assessment.
Table 1 Overview of the online sessions across program phase.
Program structure and phases
Brief intervention
Single session with screening (i.e. FAST) and personalized
normative feedback
Active behaviour change phase
Fifty-six sessions: one new session available daily for 8
weeks. Daily logging of alcohol consumption and
self-efficacy. Supportive mobile telephone text messages
available on demand
Follow-up phase
Six sessions: one session per week for 4 weeks (four sessions),
then once every fourth week for the remaining period
(two sessions); supportive mobile telephone text messages
available on demand
For each interactive online-session an e-mail reminder was sent to the
subjects. Within the 6 months of follow-up, the Balance intervention
comprises a total of 62 follow-up sessions (not counting the screening
session). FAST =Fast Alcohol Screening Test.
220 Håvar Brendryen et al.
© 2013 Society for the Study of Addiction Addiction,109, 218–226
Measures at 2- and 6-month follow-up
Total weekly alcohol consumption was the primary and
only outcome reported herein. This was measured in the
same way at the 2- and 6-month follow-up as at baseline.
Analyses
All the analyses were based on the standard alpha level of
0.05 (two-tailed). Assuming an effect size of Cohen’s
d=0.35, a sample size of 260 was necessary to achieve
an 80% chance (power) of detecting an effect at the
P<0.05 level of significance. Linear regression analyses
were used to compare outcomes across conditions for
each of the two follow-up points. The 6-month follow-up
is considered the prime outcome. The primary compari-
sons applied the intent-to-treat principle, in which all
missing values were substituted with baseline values.
Complete case analyses were performed as secondary
comparisons. Three linear regression models were per-
formed. The first model included experimental condition
as the only predictor; the second model included baseline
weekly alcohol consumption as a covariate; and the third
model included all the baseline variables (i.e. baseline
alcohol consumption, FAST, age and gender).
RESULTS
There were 276 unique registrations, 32 of which did not
fulfil the inclusion criteria (Fig. 1). This left 244 partici-
pants to be randomized into two experimental conditions.
Table 2 shows participant characteristics at baseline.
The response rate of the web surveys fell from the 2- to
the 6-month follow-up. With additional telephone-based
follow-up at the 6-month point, the total response
rate increased (Table 3). Significantly more participants
in the control group responded to the survey both
at 2 months (χ2=19.8, P<0.001) and 6 months
(χ2=11.0, P<0.001). There were no significant differ-
ences between non-responders and responders on any
baseline variables at any of the follow-up assessments for
the sample as a whole, or for experimental conditions
separately.
Table 4 shows the results of the series of regression
analyses comparing alcohol consumption per week
between the control and treatment groups. While the
results at two months was inconclusive, the final model
for the main comparison at 6 months showed that
intervention affected alcohol consumption significantly
[B =2.96; 95% confidence interval (CI) =0.02–5.90;
P=0.049]. Specifically, participants in the intensive
Assessed for eligibility
(n = 276)
Randomised (n = 244)
Excluded (n = 32):
Not at-risk drinker
(n = 20)
No valid e-mail
(n = 6)
No valid mobile
phone number
(n = 2)
< 18 years (n = 4)
Allocated to brief plus intensive
self-help
(n = 125)
Allocated to brief self-help
(n = 119)
Lost to follow-up
2 months (n = 65)
6 months (n = 52)
Lost to follow-up
2 months (n = 28)
6 months (n= 25)
Intent-to-treat analyses (n = 125)
Complete case analyses at 2
months (n = 60)
Complete case analyses at 6
months (n = 73)
Intent-to-treat analyses (n = 119)
Complete case analyses at 2
months (n = 91)
Complete case analyses at 6
months (n = 94)
Analyses Follow up Allocation Enrolment
Figure 1 Flowchart
RCT of an online alcohol intervention 221
© 2013 Society for the Study of Addiction Addiction,109, 218–226
self-help group drank approximately three drinks fewer
compared to participants in the brief self-help group. This
effect corresponds to a Cohen’s dof 0.20, which is con-
sidered a small effect size. The mean and standard devia-
tions for drinking outcome across condition at 2 and 6
months are reported in Table 2. Of the 244 people in the
study, 109 reported drinking 10 or more drinks on any
day during the three assessment weeks, suggesting a
ceiling effect.
Treatment adherence
As randomization took place at the end of the screening
session, all randomized participants completed the nor-
mative feedback session. For technical reasons, we have
no data for the control group’s use of the e-booklet. Of the
125 participants allocated to the active treatment condi-
tion, half (n=63) dropped out, completing fewer than
three sessions. Of the 63 ‘early dropouts’, 38 did not even
log on to the first follow-up session. Of the 62 ‘stayers’ (≥3
sessions), 31 completed 20 sessions or more and 10 indi-
viduals stayed for the full 6 months of the program (62
sessions). In other words, treatment dose was distributed
unequally. Moreover, post-hoc analyses showed a substan-
tial overlap between being an early treatment dropout
and not responding to the questionnaires: at the 2-month
follow-up, the measurement attrition rates were 75
versus 29% for the early dropouts and the stayers, respec-
tively. At the 6-month follow-up, the measurement attri-
tion rates were 56 versus 27% for the early dropouts and
stayers, respectively. The response rates among those
completing three sessions or more was similar to that
observed in the control group. Treatment adherence and
its relation to baseline characteristics and treatment
outcome will be analysed in detail in a subsequent paper.
DISCUSSION
In this study we compared two types of fully automated
online self-help interventions: a traditional brief inter-
vention, which included personalized normative feed-
back, and an intensive self-help program that also
included the brief component. The results partially sup-
ported the hypothesis that adding an intensive self-help
program to an online brief intervention would increase
its effectiveness; that is, an effect was apparent, but statis-
tical significance depended upon analytical strategy,
follow-up time and covariates.
At the 2-month follow-up the evidence was inconclu-
sive, indicating reduced drinking in the intensive group
when examining the complete case analysis, but no dif-
ference when using an intent-to-treat approach. By the
6-month follow-up, the intensive group showed improved
outcomes with both analytical approaches. None the less,
the intent-to-treat analyses were significant only when
controlling for baseline characteristics, and the effect size
was small. However, the intervention had no apparent
negative effects on alcohol consumption, indicating inter-
vention safety regardless of follow-up time, analysis and
control for covariates. Due to limitations on time and
budget, we were not able to recruit as many participants
as we planned to, meaning that this trial can be slightly
underpowered to detect true effects. Nevertheless, the
primary outcome comparison of this trial, using intent-
to-treat analysis with baseline carried forward imputa-
tion 6 months after baseline, showed that participants in
the intensive self-help condition had reduced their weekly
consumption by three drinks more than the people who
received the brief intervention only.
Currently, the most common format for online alcohol
programs is brief intervention [1–4]. Brief interventions
are effective, but effect sizes tend to be small [3]. In a
previous trial [14], two additional sessions of screening
and feedback did not enhance the effect of brief interven-
tion. In the current trial, instead of giving more of the
same, intensive self-help was provided after a screening
and feedback session. The current trial adds to this litera-
ture by providing preliminary evidence in support of this
Table 2 Baseline characteristics and drinking outcome at 2
and 6 months.
Treatment Control
n=125 n =119
Baseline
n(%)
Female 38 (30) 43 (36)
Mean ±SD
Age (years) 39 ±14 37 ±13
FAST 6.3 ±3.0 6.2 ±2.8
Drinks/week 19.8 ±14.0 19.4 ±12.8
Two months
Drinks/week, complete case 14.9 ±15.6 17.3 ±13.0
Drinks/week, intent-to-treat 16.3 ±13.5 18.1 ±13.4
Six months
Drinks/week, complete case 12.7 ±12.0 17.3 ±16.0
Drinks/week, intent-to-treat 15.4 ±13.6 17.9 ±15.7
Fast Alcohol Screening Test (FAST) ranges from 0 to 16; a score greater
than 2 indicates at-risk drinking. SD =standard deviation.
Table 3 Number of responders in treatment (n=125) and
control group (n=119) at specified data collections.
Data collection Treatment Control
Two months (web) 60 (48.0%) 91 (76.5%)
Six months (web) 44 (35.2%) 78 (65.5%)
Six months (telephone) 29 (23.2%) 16 (13.4%)
Six months (web+telephone) 73 (58.4%) 94 (79.0%)
222 Håvar Brendryen et al.
© 2013 Society for the Study of Addiction Addiction,109, 218–226
Table 4 Summary of regression analyses for experimental condition and baseline variables predicting drinks per week 6 and 2 months after baseline.
Two months post-baseline Six months post-baseline
Intent-to-treat (n =244) Complete case (n =151) Intent-to-treat (n =244) Complete case (n =167)
Independent variable B 95% CI PB 95% CI PB 95% CI PB 95% CI P
Model 1
Condition 1.83 −1.56 5.22 0.29 2.36 −2.26 6.99 0.31 2.55 −1.15 6.25 0.18 4.59 0.15 9.03 0.43
Model 2
Condition 2.06 −0.40 4.51 0.10 4.19 0.35 8.03 0.033 2.77 −0.18 5.71 0.07 5.07 1.02 9.12 0.014
Drinks/week at
baseline
0.69 0.60 0.78 <0.001 0.57 0.44 0.71 <0.001 0.66 0.55 0.77 <0.001 0.47 0.31 0.63 <0.001
Model 3
Condition 2.22 −0.25 4.68 0.078 4.46 0.62 8.31 0.023 2.96 0.02 5.90 0.049 5.11 1.08 9.15 0.013
Drinks/week at
baseline
0.64 0.52 0.76 <0.001 0.52 0.33 0.70 <0.001 0.58 0.43 0.73 <0.001 0.34 0.14 0.55 0.001
FAST 0.29 −0.26 0.85 0.29 0.26 −0.61 1.14 0.55 0.45 −0.21 1.11 0.18 0.55 −0.36 1.46 0.23
Age 0.08 −0.02 0.17 0.10 0.14 −0.01 0.28 0.7 0.00 −0.11 0.11 0.94 0.06 −0.09 0.21 0.45
Gender −0.09 −2.79 2.61 0.95 −0.54 −4.67 3.58 0.80 3.24 0.02 6.47 0.049 4.70 0.28 9.13 0.037
A positive score on the beta weightfor experimental condition indicates a lower consumption level in the treatment group compared to the control group; that is, a beta weight of 5 would mean that subjects in the intervention group
reported drinking five fewer drinks a week compared to the control group. A positive score on the beta weight for gender indicate that males drink more than females. The intent-to-treat analyses are based on a baseline carried
forward imputation strategy. CI =confidence interval; FAST =Fast Alcohol Screening Test.
RCT of an online alcohol intervention 223
© 2013 Society for the Study of Addiction Addiction,109, 218–226
treatment approach. The findings thus support continued
research on the use of intensive, online treatment sched-
ules that engage participants and that can exhaust the
potential of automated components, prior to introducing
more resource intensive options.
The public health impact of these findings must be
viewed relative to the potential reach and cost of the
treatment. Online self-help represents a simple and eco-
nomic way to make treatment available for large groups,
offering many advantages to person-to-person treatment,
which is resource-intensive and available to few. Lowered
alcohol consumption reduces the risk of mortality and
health problems [31,32], and if enough at-risk drinkers
are willing to use the treatment, even small effect sizes
can translate into substantial public health benefits. A
similar stepped-care model has already been applied in
online settings to improve the cost-effectiveness within
the Dutch health-care system [13]. A detailed cost–
benefit analysis is beyond the scope of this paper, but by
succeeding in providing a treatment that may well be
effective when offered to a subgroup of at-risk drinkers
recruited from the general population, the current study
adds to the feasibility of this public health approach to
reduce risky drinking.
Limitations
The generalizability of the findings is a concern, as
recruitment was conducted through self-selection, and a
restricted set of variables were assessed at baseline. This
limits the exploration of sample representativeness, and
additional information is thus needed to determine more
precisely for whom the intervention will be effective.
Another concern is that drinks per day, measured with a
scale ranging from zero to 10, may have resulted in an
underestimation of the standard deviations and average
alcohol use.
Issues related to blinding and responder reactivity
were sources of concern in the current experiment. Spe-
cifically, balancing validity and ethics, with the aim of
making the recruitment and treatment delivery as realis-
tic and close to real-life dissemination as possible, was a
challenge. Blinding of participants to treatment is impos-
sible, and blinding of treatment provider does not apply to
computer interventions. Blinding of allocation to a
control group is possible, however, and to avoid resentful
demoralization among participants in the control group,
the scope and content of the follow-up and the number of
experimental conditions was not disclosed to the partici-
pants. However, individuals in the intensive condition
were not informed about the intensity of the treatment,
which may have influenced both how they perceived the
treatment and subsequent attrition.
A significant proportion of participants assigned to
intensive self-help dropped out early, as only half the par-
ticipants completed three or more sessions, which is in
line with previous trials [6,8,33]. This may simply reflect
the low participation threshold, or it may suggest that
intensive online self-help is not a universally acceptable
format. Future research should investigate how intensive
self-help programs can be improved to increase program
engagement across a broader spectrum of at-risk
drinkers.
Measurement attrition was high but comparable to
similar studies [7,8,34], and higher in the treatment
group than the control group. The high rate of measure-
ment attrition in the treatment group, particularly the
rate observed among early dropouts from treatment,
could be an iatrogenic effect of the treatment. The many
reminder e-mails may have triggered annoyance and
teaching participants to ignore all project-related e-mails,
including the survey reminders. This interpretation is in
accordance with unsolicited feedback from two partici-
pants who dropped out early; one expressed that the
number of reminders was ‘way too many’, and the other
explained that after a few days without internet connec-
tion, he decided to ignore the project as he felt discour-
aged by ‘all the unread e-mails’ in his inbox. Regarding
outcome analyses, however, the attrition problem was
controlled for statistically by applying baseline carried
forward imputation and intent-to-treat analysis as
primary outcome comparison, and including the baseline
variables as covariates in the final model. Despite using a
conservative imputation strategy for the main analysis, a
statistically significant difference between conditions
emerged.
A key strength of this study is the combination of
recruitment from the general public, low participation
threshold, few exclusion criteria, no participation com-
pensation and no person-to-person contact during either
recruitment or treatment. These conditions are close
to those of a real-world implementation of automated
online interventions, which signifies high ecological
validity. Moreover, the similarity to real-world implemen-
tation strengthens the viability of this public health
approach to reduce alcohol misuse.
CONCLUSIONS
This study provides partial evidence for the benefit of
intensive self-help over and above traditional, brief inter-
ventions, and adds promise to the joining of two distinct
intervention formats, the brief and the intensive, into an
online stepped-care setting. This preliminary evidence
suggest that intensive self-help can be added to online
brief interventions to exhaust the potential of online
automated interventions prior to introducing more
resource intensive treatment.
224 Håvar Brendryen et al.
© 2013 Society for the Study of Addiction Addiction,109, 218–226
Clinical trial registration
Clinical trial registration details: ClinicalTrials.gov
Identifier: NCT01754090
Declarations of interest
In 2009, H.B. received payments from The Workplace
Advisory Centre for Issues Relating to Alcohol, Drugs and
Addictive Gambling, a non-profit organization working
with prevention and recovery of addictions. The advisory
centre developed and funded the current intervention,
and is currently implementing it across Norway. H.B.
has no other competing interests. I.O.L., A.B.J., M.R.,
S.N. and F.D. declare no financial interests in the current
intervention, or any other conflicting interests.
Acknowledgements
This trial was funded by the Norwegian Research Council
and the Norwegian Centre for Addiction Research. The
intervention was funded by The Workplace Advisory
Centre for Issues Relating to Alcohol, Drugs and Addic-
tive Gambling.Trial results are owned by the University of
Oslo; that is, there are no contractual constraints regard-
ing publication from any of the sponsors. We are grateful
to Pål H. Lillevold for technical assistance and Njål
Andersen for English language editing and proofreading.
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Supporting information
Additional Supporting information may be found in the
online version of this article at the publisher’s web-site:
Appendix S1 Balance screenshots.
Appendix S2 Treatment manual for Balance, an online
intervention provided to the intensive self-help condition.
Appendix S3 Treatment manual for an online brief inter-
vention provided to the comparison group in the current
trial.
226 Håvar Brendryen et al.
© 2013 Society for the Study of Addiction Addiction,109, 218–226
