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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. 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. Online study in Norway. At-risk drinkers were recruited by internet advertisements and randomly assigned to one of the two conditions (n = 244). The primary outcome was self-reported alcohol consumption the previous week measured six months after screening. Regression analysis, using baseline carried forward imputation (intent-to-treat), with baseline variables as covariates, showed that intervention significantly affected alcohol consumption at six months (B = 2.96; 95% CI = 0.02-5.90; p = .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. The online Balance intervention, added to a brief online screening intervention may aid reduction in alcohol consumption compared with the screeining intervention and an educational booklet.
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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
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:
Submitted 18 December 2012; initial review completed 22 February 2013; final version accepted 30 September 2013
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
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
© 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.
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
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.
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
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
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
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.
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).
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
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
(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.
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
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
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
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.
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
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
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.
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:
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.
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
226 Håvar Brendryen et al.
© 2013 Society for the Study of Addiction Addiction,109, 218–226
... However, only one study has directly compared a brief online intervention conceptualised as PNF (Personalized Normative Feedback) and a booklet with an unguided online self-help intervention. Although unguided, the latter was very intense, containing 56 daily sessions plus six weekly follow-up sessions, and involved alcohol misusers from the general population [11]. Although only 8 % completed all sessions in the intensive intervention group, there was a superior reduction in alcohol use in this group than in the PNF/booklet group over the medium term (6 months follow-up). ...
... As we aim to compare the "Selge" program with a control condition which provides extensive resources and a self-administered test of alcohol abuse, we expect larger effects. The study that comes closest to our design compared online Personalized Normative Feedback (PNF) plus an intensive, but unguided CBT (56 daily sessions plus six weekly follow up sessions) program versus PNF with an online booklet; it also recruited alcohol misusers from the general population and it generated a Cohens' d = 0.20 [11]. As we are offering a minimal-guidance intervention and comparing it to a self-administered test with routine advice on usual treatment, we expect a Cohens' d = 0.23 for the current study. ...
... This is, amongst others, one reason why we decided to have an active control group consisting of a self-administered test as well as advice on and how to access treatment as usual. Such a direct comparison has only been reported for one other study [11], which contained several methodological limitations. If the "Selge" program turns out to be effective, it will be continuously advertised and implemented as an important part of the continuum of intervention and care for alcohol misuse and dependence in Estonia. ...
Full-text available
Background: Despite an initial steep decrease in alcohol misuse among Estonians through structural intervention means and the scaling up of alcohol counselling in the mid-2000's, most of the country's alcohol misuse indicators remain clearly higher than European averages. Consequently, an online self-help program was launched as part of an initial behavioral intervention initiative to foster progress in alcohol prevention on a population level. Methods: A two-arm randomized controlled trial (RCT) has been designed to compare the efficacy of a culturally-adapted minimal-guidance online self-help program, the 8-week "Selge" online program against a control condition that consists of a self-administered test of alcohol use and advice regarding usual treatment in Estonia. A target sample of 600 individuals will be recruited and randomly assigned to either condition. The program will contain 10 modules based on principles of cognitive behavioural therapy (CBT) and motivational interviewing (MI). Participants in the control group will have access to the full treatment after they complete their final follow-up assessment. The primary outcome will be change in the Alcohol Use Disorders Identification Test (AUDIT) score between the 6-month follow-up and baseline assessments. Secondary outcomes will include the number of standard drinks consumed and alcohol-free days, drinking motives and motivation for change, as well as changes in mental health. Assessments will be completed at baseline, at the end of treatment, and at 6 months follow-up. Data analysis will follow the intention-to-treat principle and employ (generalised) linear mixed models. Discussion: The "Selge" program is the first and only internet program for the intervention of alcohol misuse in Estonia. If proven effective, it will foster progress in the intervention of alcohol misuse in the Estonian population and be implemented as a standard program amidst the continuum of intervention and care. Trial registration: Current Controlled Trials ISRCTN48753339 registered 04/06/2019 retrospectively.
... However, most studies employed designs with non-active control conditions, thus leaving a significant gap regarding superiority of interventions over active control groups [11]. To our knowledge, only one study has directly confirmed effects of an unguided high-intensity intervention based on integrated CBT and brief supportive counseling compared to a PNF/information booklet control condition in the general population, despite severe attrition [12]. ...
... Sample size was calculated to be 298 per group to find an effect of Cohen's d = 0.23 with 80% power. This effect size was based a previous study investigating an unguided self-help intervention, which came closest to the intervention in the current study and reported a Cohen's d = 0.20 [12]. As our intervention was minimally guided, a slightly higher effect size was expected due to the general superiority of guided approaches [7]. ...
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Background and aims: Estonia has one of the highest alcohol-attributable mortality rates within the European Union. The aim of this study was to estimate the efficacy of an on-line self-help intervention to reduce problem drinking at the population level. Design: On-line open randomized controlled trial with an 8-week intervention and an active control group (intervention n = 303, control n = 286). Assessments took place at baseline and at 6 months follow-up. Setting: On- and offline channels were used for population-based recruitment within a nation-wide prevention campaign in Estonia. Participants: Inclusion criteria were age ≥ 18 years, heavy drinking [Alcohol Use Disorders Identification (AUDIT) test score ≥ 8], literacy in Estonian and at least weekly access to the internet; n = 589 participants were randomized (50% male, 1% other; mean age 37.86 years; 45% with higher level of education). Intervention and comparator: The intervention consisted of 10 modules based on principles of cognitive-behavioral therapy and motivational interviewing. The active control group received access to a website with a self-test including personalized normative feedback and information for standard alcohol treatment. Measurements: The primary outcome was AUDIT scores at 6 months follow-up adjusted for baseline scores. Findings: Intention-to-treat analyses were applied. Missing data were addressed by using baseline observation carried forward (BOCF) and multiple imputation by chained equations (MI); 175 completed follow-up in the intervention group and 209 in the control group. AUDIT score at follow-up was significantly smaller in the intervention [BOCF mean = 13.91, standard deviation (SD) = 7.61, MI mean = 11.03, SD = 6.55] than control group (BOCF mean = 15.30, SD = 7.31; MI mean = 14.30, SD = 7.21), with a group difference of -1.38 [95% confidence interval (CI) = -2.58, -0.18], P = 0.02 for BOCF and -3.26 (95% CI = -2.01, -4.51), P < 0.001 for MI. Conclusions: A randomized controlled trial has found that an on-line self-help intervention with minimal guidance was effective at reducing problem drinking in Estonia.
... Brief interventions can consist of self-monitoring exercises combined with personalized feedback and additional modules such as identification of high-risk situations, which help reduce alcohol use in specific situations (eg, the web-based personalized feedback program Drinktest [3]). On the other hand, extended interventions offer a digital form of intensive treatment, including multiple sessions (eg, the self-help alcohol intervention Balance [4]). ...
... Another important feature of DYD is that it is one of the first web-based extended alcohol interventions based on MI techniques and cognitive behavioral therapy (CBT). Other examples are web-based self-help alcohol interventions from the Netherlands [14] and Norway [4]. DYD attempted to translate components of usual face-to-face treatment to a web-based, unguided setting and encourages self-monitoring of drinking behavior in the Drinking Episode Diary. ...
Full-text available
Background: Down Your Drink (DYD) is a widely used unguided online alcohol moderation program for the general public based on cognitive behavioural therapy (CBT) and motivational interviewing (MI), with many opportunities for free-text responses. / Objective: To assess participants’ use of key CBT and MI components, the presence of change and sustain talk within their responses, and whether these are associated with drinking outcomes after three months. / Methods: A secondary data-analysis was conducted on data from the definitive randomized trial of DYD, collected in 2008 (n=503). Past week alcohol use at baseline and three month follow-up was measured with the TOT-AL. Covariates included baseline alcohol use, age, gender, education level and word count of responses. Use of MI and CBT components and presence of change and sustain talk was coded by two independent coders (Cohen’s kappa range = 0.91-1). Linear model regressions on the full sample and a subsample of active users (n=411) are presented. / Results: Most commonly used components were the listing of pros and cons. Number of listed risky situations was associated with lower alcohol use at three months follow-up in both the full sample and the subsample (Badj = −2.10 95%CI −3.88 - −0.31, P = 0.02). Number of listed pros of drinking was only associated with higher alcohol use in the subsample (Badj = 3.12, 95%CI −0.55-6.80, P = 0.095). When the primary measure for alcohol use was log-transformed, number of strategies to deal with risky situations (Badj = 0.03, 95%CI 0.00 - 0.06, P = 0.02) and presence of any change talk (Badj =−0.46, 95%CI −0.86 - −0.06, P = 0.02) also predicted alcohol use. / Conclusions: An unguided online alcohol moderation program can elicit change and sustain talk. Number of noted risky situations can predict alcohol use at three month follow-up. Other components, namely number of strategies to deal with risky situations, number of listed pros, sustain talk and any presence of change talk, are also of interest, but need further research. Clinical Trial: Original trial registration: ISRCTN registry, ID ISRCTN31070347,
... Brief interventions can consist of self-monitoring exercises combined with personalized feedback and additional modules such as identification of high-risk situations, which help reduce alcohol use in specific situations (eg, the web-based personalized feedback program Drinktest [3]). On the other hand, extended interventions offer a digital form of intensive treatment, including multiple sessions (eg, the self-help alcohol intervention Balance [4]). ...
... Another important feature of DYD is that it is one of the first web-based extended alcohol interventions based on MI techniques and cognitive behavioral therapy (CBT). Other examples are web-based self-help alcohol interventions from the Netherlands [14] and Norway [4]. DYD attempted to translate components of usual face-to-face treatment to a web-based, unguided setting and encourages self-monitoring of drinking behavior in the Drinking Episode Diary. ...
Background: Down Your Drink (DYD) is a widely used unguided web-based alcohol moderation program for the general public based on cognitive behavioral therapy (CBT) and motivational interviewing (MI); it provides users with many opportunities to enter free-text responses. Objective: The aim of this study was to assess participants' use of key CBT and MI components, the presence of change talk and sustain talk within their responses, and whether these data are associated with drinking outcomes after 3 months. Methods: An exploratory secondary data analysis was conducted on data collected in 2008 from the definitive randomized trial of DYD (N=503). Past week alcohol use at baseline and 3-month follow-up were measured with the TOT-AL. Covariates included baseline alcohol use, age, gender, education level, and word count of the responses. Use of MI and CBT components and presence of change talk and sustain talk were coded by two independent coders (Cohen κ range 0.91-1). Linear model regressions on the subsample of active users (n=410) are presented along with a negative binomial regression. Results: The most commonly used component was the listing of pros and cons of drinking. The number of listed high-risk situations was associated with lower alcohol use at 3-month follow-up (Badj -2.15, 95% CI -3.92 to -0.38, P=.02). Findings on the effects of the percentage of change talk and the number of listed strategies to deal with high-risk situations were inconsistent. Conclusions: An unguided web-based alcohol moderation program can elicit change talk and sustain talk. This secondary analysis suggests that the number of listed high-risk situations can predict alcohol use at 3-month follow-up. Other components show inconsistent findings and should be studied further.
... White and colleagues (2010) conducted a systematic review of 17 studies evaluating alcohol e-interventions and concluded that online alcohol programs can reduce alcohol use and may prove particularly beneficial to women, young people, and at-risk drinkers. Another study of an alcohol e-intervention with at-risk drinkers found that even six months posttreatment, use of the e-intervention significantly decreased participants' alcohol consumption (Brendryen et al., 2014). Thus, alcohol e-interventions appear effective, even with more vulnerable and at-risk drinkers. ...
Both media and academic reports have highlighted COVID-19's negative impacts on mental health and safety in the United States, yet care and service gaps persist. Evidence suggests that a default to in-person service delivery did not meet clients' needs before the pandemic, and that unmet needs have ballooned since COVID-19 spread throughout the United States due to a combination of increased stress, social isolation, and fewer available services during lockdowns. This article reviews literature on online interventions' utility and effectiveness in preventing and treating problems likely exacerbated under pandemic conditions, including mental health conditions, anger, couple dynamics, parenting, and alcohol misuse. The article also describes barriers to evidence-based e-interventions' wider and more consistent use, highlights some vulnerable populations' unique service needs, outlines service gaps that online programs might effectively mitigate, and offers a path by which social workers can lead an interdisciplinary charge in researching, developing, and implementing e-interventions during the current pandemic and beyond.
Full-text available
Introduction: Individual participant data meta-analyses (IPD-MAs) include the raw data from relevant randomised clinical trials (RCTs) and involve secondary analyses of the data. Performed since the late 1990s, ~50 such meta-analyses have been carried out in psychiatry, mostly in the field of treatment. IPD-MAs are particularly relevant for three objectives: (1) evaluation of the average effect of an intervention by combining effects from all included trials, (2) evaluation of the heterogeneity of an intervention effect and sub-group analyses to approach personalised psychiatry, (3) mediation analysis or surrogacy evaluation to replace a clinical (final) endpoint for the evaluation of new treatments with intermediate or surrogate endpoints. The objective is to describe the interest and the steps of an IPD-MA method applied to the field of psychiatric therapeutic research. Method: The method is described in three steps. First, the identification of the relevant trials with an explicit description of the inclusion/exclusion criteria for the RCT to be incorporated in the IPD-MA and a definition of the intervention, the population, the context and the relevant points (outcomes or moderators). Second, the data management with the standardisation of collected variables and the evaluation and the assessment of the risk-of-bias for each included trial and of the global risk. Third, the statistical analyses and their interpretations, depending on the objective of the meta-analysis. All steps are illustrated with examples in psychiatry for treatment issues, excluding study protocols. Conclusion: The meta-analysis of individual patient data is challenging. Only strong collaborations between all stakeholders can make such a process efficient. An “ecosystem” that includes all stakeholders (questions of interest prioritised by the community, funders, trialists, journal editors, institutions, …) is required. International medical societies can play a central role in favouring the emergence of such communities.
Background Alcohol use is a major contributor to health loss. Many persons with harmful use or alcohol dependence do not obtain treatment because of limited availability or stigma. They may use internet-based interventions as an alternative way of obtaining support. Internet-based interventions have previously been shown to be effective in reducing alcohol consumption in studies that included hazardous use; however, few studies have been conducted with a specific focus on harmful use or alcohol dependence. The importance of therapist guidance in internet-based cognitive behavioral therapy (ICBT) programs is still unclear. Objective This trial aims to investigate the effects of a web-based alcohol program with or without therapist guidance among anonymous adult help-seekers. Methods A three-armed randomized controlled trial was conducted to compare therapist-guided ICBT and self-help ICBT with an information-only control condition. Swedish-speaking adult internet users with alcohol dependence (3 or more International Classification of Diseases, Tenth Revision criteria) or harmful alcohol use (alcohol use disorder identification test>15) were included in the study. Participants in the therapist-guided ICBT and self-help ICBT groups had 12-week access to a program consisting of 5 main modules, as well as a drinking calendar with automatic feedback. Guidance was given by experienced therapists trained in motivational interviewing. The primary outcome measure was weekly alcohol consumption in standard drinks (12 g of ethanol). Secondary outcomes were alcohol-related problems measured using the total alcohol use disorder identification test-score, diagnostic criteria for alcohol dependence and alcohol use disorder, depression, anxiety, health, readiness to change, and access to other treatments or support. Follow-up was conducted 3 (posttreatment) and 6 months after recruitment. Results During the recruitment period, from March 2015 to March 2017, 1169 participants were included. Participants had a mean age of 45 (SD 13) years, and 56.72% (663/1169) were women. At the 3-month follow-up, the therapist-guided ICBT and control groups differed significantly in weekly alcohol consumption (−3.84, 95% Cl −6.53 to −1.16; t417=2.81; P=.005; Cohen d=0.27). No significant differences were found in weekly alcohol consumption between the self-help ICBT group and the therapist-guided ICBT at 3 months, between the self-help ICBT and the control group at 3 months, or between any of the groups at the 6-month follow-up. A limitation of the study was the large number of participants who were completely lost to follow-up (477/1169, 40.8%). Conclusions In this study, a therapist-guided ICBT program was not found to be more effective than the same program in a self-help ICBT version for reducing alcohol consumption or other alcohol-related outcomes. In the short run, therapist-guided ICBT was more effective than information. Only some internet help-seekers may need a multisession program and therapist guidance to change their drinking when they use internet-based interventions. Trial Registration NCT02377726;
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Background: The extent to which eligible individuals in a target population are willing to participate in interventions is important when evaluating the efficacy of public health interventions. Objectives: As part of a process evaluation of an ongoing randomized controlled trial, this study aimed to identify the proportion of risky drinkers who were willing to participate in an alcohol prevention intervention in an occupational health setting, and correlates for such willingness. Methods: Risky drinking employees from 22 companies in Norway were identified through an alcohol screening survey. Risky drinkers' ( N = 779) willingness to complete a health examination and to be randomized into an alcohol prevention intervention (digital or face-to-face intervention, or control) was recorded by personnel from occupational health services. The proportion of employees who were willing to participate was assessed on 31 potential correlates (sociodemographic, alcohol-related, work-related, and lifestyle/daily activity). Adjusted (multiple logistic regression) analyses were utilized to explore associations between potential correlates and willingness to participate. Results: Altogether, 38.1% of employees were willing to participate in prevention interventions. In the adjusted analysis, only 5 out of 31 potential correlates were significantly associated with willingness to participate. Managers were more than twice as willing to participate than workers (OR = 2.17, p < 0.01). Willing employees had less workplace decision latitude (perceived control over workplace decisions and less possibility of utilizing personal skills in the job) (OR = 0.62, p < 0.05), and were more overcommitted with exorbitant work ambition and need for approval (OR = 1.49, p < 0.05). Willing employees had to some extent less alcohol-related impaired work performance (presenteeism, OR = 0.78, p < 0.05), and they spent less time on care activities (OR = 0.84, p < 0.05). Conclusions: Reaching four out of ten with risky drinking habits for prevention interventions strengthens the rationale for targeting this public health problem in occupational health care settings. In particular, this study suggests the importance of ensuring secure commitment among workers, who were less willing til participate than managers. Nevertheless, tailoring recruitment and implementation strategies based on easily identifiable correlates may be onerous.
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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: Due to limited reporting of intervention rationale, little is known about what distinguishes a good intervention from a poor one. To support improved design, there is a need for comprehensive reports on novel and complex theory-based interventions. Specifically, the emerging trend of just-in-time tailoring of content in response to change in target behavior or emotional state is promising. Objective: The objective of this study was to give a systematic and comprehensive description of the treatment rationale of an online alcohol intervention called Balance. Methods: We used the intervention mapping protocol to describe the treatment rationale of Balance. The intervention targets at-risk drinking, and it is delivered by email, mobile phone text messaging, and tailored interactive webpages combining text, pictures, and prerecorded audio. Results: The rationale of the current treatment was derived from a self-regulation perspective, and the overarching idea was to support continued self-regulation throughout the behavior change process. Maintaining the change efforts over time and coping adaptively during critical moments (eg, immediately before and after a lapse) are key factors to successful behavior change. Important elements of the treatment rationale to achieving these elements were: (1) emotion regulation as an inoculation strategy against self-regulation failure, (2) avoiding lapses by adaptive coping, and (3) avoiding relapse by resuming the change efforts after a lapse. Two distinct and complementary delivery strategies were used, including a day-to-day tunnel approach in combination with just-in-time therapy. The tunnel strategy was in accordance with the need for continuous self-regulation and it functions as a platform from which just-in-time therapy was launched. Just-in-time therapy was used to support coping during critical moments, and started when the client reports either low self-efficacy or that they were drinking above target levels. Conclusions: The descriptions of the treatment rationale for Balance, the alcohol intervention reported herein, provides an intervention blueprint that will aid in interpreting the results from future program evaluations. It will ease comparisons of program rationales across interventions, and may assist intervention development. By putting just-in-time therapy within a complete theoretical and practical context, including the tunnel delivery strategy and the self-regulation perspective, we have contributed to an understanding of how multiple delivery strategies in eHealth interventions can be combined. Additionally, this is a call for action to improve the reporting practices within eHealth research. Possible ways to achieve such improvement include using a systematic and structured approach, and for intervention reports to be published after peer-review and separately from evaluation reports.
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Within the last 30 years, a substantial number of interventions for alcohol use disorders (AUDs) have received empirical support. Nevertheless, fewer than 25% of individuals with alcohol-related problems access these interventions. If several intensive psychosocial treatments are relatively effective, but most individuals in need do not access them, it seems logical to place a priority on developing more engaging interventions. Accordingly, after briefly describing findings about barriers to help-seeking, we focus on identifying an array of innovative and effective low-intensity intervention strategies, including telephone, computer-based, and Internet-based interventions, that surmount these barriers and are suitable for use within a stepped-care model. We conclude that these interventions attract individuals who would otherwise not seek help, that they can benefit individuals who misuse alcohol and those with more severe AUDs, and that they can facilitate subsequent help-seeking when needed. We note that these types of low-intensity interventions are flexible and can be tailored to address many of the perceived barriers that hinder individuals with alcohol misuse or AUDs from obtaining timely help. We also describe key areas of further research, such as identifying the mechanisms that underlie stepped-care interventions and finding out how to structure these interventions to best initiate a program of stepped care.
Persuasive technologies pervade much of our everyday lives today in areas from marketing to public health. In the latter case, persuasive technology represents a promising area of application. However, we know much too little about how to design effective interventions to support sustained behaviour change and improved well-being. The purpose of the present paper was to contribute in two ways. First, we want to contribute to current practice in designing such interventions. Second, we try to identify key research questions that could be a point of departure for a more detailed and comprehensive future research program. We do this by means of expressing 28 propositions. In sum, the propositions reflect that the construction of digital interventions should be seen as an iterative process which should take into account both content and design factors. However, we argue that intervention research and practical design experience is not just something that follows basic research at a polite distance, but rather is its inherent complement.
Remember when categorizations for emotional responsiveness were simple—type A vs type B, or introverted vs extroverted? Once you read Handbook of Emotion Regulation, edited by respected Stanford psychologist James J. Gross, you’ll long for those days of simplicity. As stated in the book, the complexity of emotion regulation is like a “riddle wrapped in a mystery inside an enigma” (p 87), words used by Churchill to describe Russia. Although several definitions are presented in the text, emotion regulation generally refers to the modification of emotional reactions in the form of activation, inhibition, or more graded modifications.
The pursuit of happiness is an important goal for many people. However, surprisingly little scientific research has focused on the question of how happiness can be increased and then sustained, probably because of pessimism engendered by the concepts of genetic determinism and hedonic adaptation. Nevertheless, emerging sources of optimism exist regarding the possibility of permanent increases in happiness. Drawing on the past well-being literature, the authors propose that a person's chronic happiness level is governed by 3 major factors: a genetically determined set point for happiness, happiness-relevant circumstantial factors, and happiness-relevant activities and practices. The authors then consider adaptation and dynamic processes to show why the activity category offers the best opportunities for sustainably increasing happiness. Finally, existing research is discussed in support of the model, including 2 preliminary happiness-increasing interventions. (PsycINFO Database Record (c) 2012 APA, all rights reserved)