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“Take Care of You” – Efficacy of integrated, minimal-guidance, internet-based self-help for reducing co-occurring alcohol misuse and depression symptoms in adults: Results of a three-arm randomized controlled trial

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Background: Depression and harmful alcohol use are two of the top five leading causes of years of life lost to disability in high-income countries. Integrated treatment targeting both at the same time is often considered more complicated and difficult and, therefore, more expensive. Consequently, integrated internet-based interventions could be a valuable addition to traditional care. Methods: A three-arm randomized controlled trial was conducted comparing the effectiveness of (1) an integrated, minimal-guidance, adherence-focused self-help intervention designed to reduce both alcohol use and depression symptoms (AFGE-AD); (2) a similar intervention designed to reduce alcohol use only (AFGE-AO), and (3) internet access as usual (IAU) as a control condition, in at least moderately depressed alcohol misusers from February 2016—March 2020. We recruited 689 alcohol misusers (51.6% males, mean age = 42.8 years) with at least moderate depression symptoms not otherwise in treatment from the general population. Six months after baseline, 288 subjects (41.8%) were reachable for the final assessment. Results: All interventions yielded reduced alcohol-use after six months (AFGE-AD: -16.6; AFGE-AO: -19.8; IAU: -13.2). Those who undertook active-interventions reported significantly fewer standard drinks than controls (AFGE-AD: p = .048, d=0.10; AFGE-AO: p = .004, d=0.20). The two active-intervention groups also reported significantly less severe depression symptoms than controls (AFGE-AD: p = .006, d=0.41; AFGE-AO: p = .008, d=0.43). Testing revealed noninferiority between the two interventions. Conclusions: This study documented sustained effectiveness of the first integrated, fully internet-based self-help intervention developed for the reduction of both alcohol use and depression symptoms in at least moderately depressed adult alcohol misusers recruited from the general population.
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Drug and Alcohol Dependence 225 (2021) 108806
Available online 18 June 2021
0376-8716/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Take Care of You Efcacy of integrated, minimal-guidance,
internet-based self-help for reducing co-occurring alcohol misuse and
depression symptoms in adults: Results of a three-arm randomized
controlled trial
Christian Baumgartner
a
,
*, Michael P. Schaub
a
, Andreas Wenger
a
, Doris Malischnig
b
,
Mareike Augsburger
a
, Dirk Lehr
c
, Matthijs Blankers
d
, David D. Ebert
e
, Severin Haug
a
a
Swiss Research Institute for Public Health and Addiction, University of Zurich, Zurich, Switzerland
b
Institute for Addiction Prevention, Addiction and Drug Coordination Vienna, Vienna, Austria
c
Division of Online Health Training, Leuphana University Lueneburg, Germany
d
Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
e
Department for Sport and Health Sciences, Chair for Psychology and Digital Mental Health Care, Technical University Munich, Munich, Germany
ARTICLE INFO
Keywords:
Alcohol
Depression
Co-occurring disorders
Internet
Cognitive Behavioral Therapy
Motivational interviewing
ABSTRACT
Background: Depression and harmful alcohol use are two of the top ve leading causes of years of life lost to
disability in high-income countries. Integrated treatment targeting both at the same time is often considered
more complicated and difcult and, therefore, more expensive. Consequently, integrated internet-based in-
terventions could be a valuable addition to traditional care.
Methods: A three-arm randomized controlled trial was conducted comparing the effectiveness of (1) an inte-
grated, minimal-guidance, adherence-focused self-help intervention designed to reduce both alcohol use and
depression symptoms (AFGE-AD); (2) a similar intervention designed to reduce alcohol use only (AFGE-AO), and
(3) internet access as usual (IAU) as a control condition, in at least moderately depressed alcohol misusers from
February 2016March 2020. We recruited 689 alcohol misusers (51.6 % males, mean age =42.8 years) with at
least moderate depression symptoms not otherwise in treatment from the general population. Six months after
baseline, 288 subjects (41.8 %) were reachable for the nal assessment.
Results: All interventions yielded reduced alcohol-use after six months (AFGE-AD: -16.6; AFGE-AO: -19.8; IAU:
-13.2). Those who undertook active-interventions reported signicantly fewer standard drinks than controls
(AFGE-AD: p =.048, d=0.10; AFGE-AO: p =.004, d=0.20). The two active-intervention groups also reported
signicantly less severe depression symptoms than controls (AFGE-AD: p =.006, d=0.41; AFGE-AO: p =.008,
d=0.43). Testing revealed noninferiority between the two interventions.
Conclusions: This study documented sustained effectiveness of the rst integrated, fully internet-based self-help
intervention developed for the reduction of both alcohol use and depression symptoms in at least moderately
depressed adult alcohol misusers recruited from the general population.
1. Introduction
Depression and harmful alcohol use are two of the top ve leading
causes of years of life lost to disability (DALYs) in high-income countries
(WHO, 2008). In 2016, harmful alcohol use alone accounted for an
estimated 3.0 million deaths and 131.4 million DALYs, representing 5.3
% of all deaths and 5.0 % of all DALYs (Shield et al., 2020). Taken
together, substance use (including alcohol), depression, and other
mental disorders account for 7.4 % of the total global burden of disease
(Whiteford et al., 2013). There is also substantial co-occurrence of
substance use and other mental disorders; these dual-diagnosis disor-
ders, also called co-occurring disorders (COD), are not the exception,
* Corresponding author: Swiss Research Institute for Public Health and Addiction, associated to the University of Zurich, Konradstrasse 32, Zurich, 8005,
Switzerland.
E-mail address: christian.baumgartner@isgf.uzh.ch (C. Baumgartner).
Contents lists available at ScienceDirect
Drug and Alcohol Dependence
journal homepage: www.elsevier.com/locate/drugalcdep
https://doi.org/10.1016/j.drugalcdep.2021.108806
Received 28 January 2021; Received in revised form 14 April 2021; Accepted 15 April 2021
Drug and Alcohol Dependence 225 (2021) 108806
2
with concomitant alcohol use disorders and depression especially com-
mon (Kessler et al., 2003; Schuckit, 2006). CODs are associated with
considerable adverse outcomes (Sullivan et al., 2005). Alcohol misuse or
abuse is two to three times higher among those who suffer from
depression than in the general population (Swendsen et al., 1998).
Moreover, risky alcohol use is associated with a higher probability of
developing affective disorders (Bott et al., 2005). Treating COD is often
considered more complicated and difcult (Roeloffs et al., 2001) and,
therefore, more expensive than treating either of these two disorders
separately. Since internet-based interventions can be administered with
minimal to no cost, they could be an invaluable addition to traditional
care.
In recent years, increasing numbers of internet-based interventions
have been developed and evaluated. These interventions target various
groups with problematic substance use (Amann et al., 2018; Frohlich
et al., 2018; Schaub et al., 2012, 2019) and addictive behaviors
(Baumgartner et al., 2019; Bothe et al., 2020). These internet-based
interventions have the capacity to reach at-risk individuals in the
early stages of potentially more severe mental health disorders, which
might, in turn, reduce some of the burden placed upon public health
services (Smit et al., 2011).
There have been several successful implementations of internet-
based interventions for adult problem drinkers. A review of nine
studies in 2011 (Riper et al., 2011) found a medium effect size on
alcohol consumption (g =0.44), with greater effects observed with
longer treatments (g =0.61) than with single-session, personalized
normative feedback interventions (g =0.27). Another review of 16
studies in 2014 (Riper et al., 2014b) revealed a small, but signicant
effect size (g =0.20). In a more recent individual-patient data
meta-analysis (Riper et al., 2018) of 19 randomized controlled trials of
internet interventions targeting problem drinking, a signicant decrease
of 5.02 standard drinks (g =0.26) over the preceding seven days was
identied, relative to controls.
Similarly, there has been an increase in the number of internet-based
treatments for depression, which have proven effective in controlled
trials both for adults (Hedman et al., 2012) and adolescents (Ebert et al.,
2015). Wright et al. (2019) report a pooled effect size of g =0.502
post-treatment in their meta-analysis of 40 randomized controlled trials
comparing internet-based and other computerized psychological treat-
ments against control conditions (e.g., waiting list, treatment as usual,
different treatment).
There is growing evidence that combined treatment of both alcohol
misuse and depression may be effective. In a meta-analysis conducted by
Riper and colleagues (Riper et al., 2014a), the combination of Cognitive
Behavioral Therapy (CBT) and Motivational Interviewing (MI), pre-
dominantly delivered via face-to-face therapy, proved effective at
treating subclinical and clinical alcohol use disorders and major
depressive disorders (combined treatment) relative to control condi-
tions, with small overall effect sizes post treatment (g =0.17 for
decreased alcohol consumption and g =0.27 for decreased depression
symptoms, respectively). Interestingly, digital interventions (after a
brief session with a therapist) performed better than face-to-face treat-
ment, with respect to reducing depression symptoms (g =0.73 versus g
=0.23, respectively, p =.030). Additionally, relative to non-integrated
treatment, integrated treatment in patients with dual-diagnosis disor-
ders can reduce treatment duration, increase satisfaction with treatment
(Schulte et al., 2011), and decrease costs. Motivational interviewing is
designed to increase patientsmotivation for change. Lack of motivation
is very common in patients with either alcohol misuse or a depressive
disorder, and low motivation is associated with poor treatment
engagement and poor outcomes (Miller and Rollnick, 2012).
There have been some trials involving computer-based (ofine)
treatment for integrated treatment for alcohol and depression, most
notably the work from Kay-Lambkin and colleagues (Kay-Lambkin et al.,
2017, 2009). The aforementioned trial compared therapist-delivered
treatment plus computer-based treatment (combined treatment) and
therapist-delivered treatment alone with a control condition. The com-
bined treatment had the largest effects. Furthermore, a recent
meta-analysis (Schouten et al., 2021) found signicant effects of digital
interventions (including computer-based, internet-based and
text-message based interventions) on depressive symptoms at 3 months
(g =0.34, p =.030) and non-signicant effects at 6-months (g =0.29, p
=.15). The results for alcohol use were non-signicant at 3-months (g =
0.14, p =.07) and signicant at 6-months (g =0.14, p =.005). Even
though these effects were small, they are promising.
Two integrated, fully internet-based self-help intervention have been
developed for co-occurring depression and problematic alcohol use. One
in youth (Deady et al., 2016) that was shown to be effective for both
outcomes in the short term (post-treatment), when compared against an
attentioncontrol conditionalbeit, not long term (6 months). The other
for students (Wiens, 2002) showed no signicant main results
post-treatment (1 month). However, to our knowledge, no such inter-
vention studies have yet been published that have examined the effects
of combined treatment for the general adult population.
This paper reports the efcacy of a minimal-guidance internet-based
self-help intervention called Take Care of You. The program was devel-
oped in 2016, with multiple RCTs involving variants of the program
currently underway (Frohlich et al., 2018; Kaal et al., 2020; Schaub
et al., 2018). More specically, the current three arm randomized
controlled trial compared the efcacy of 1) a combined internet-based
self-help intervention with adherence-focused guidance enhancement
(AFGE) designed to reduce both alcohol consumption and depression
symptoms (AFGE-AD); 2) an internet-based self-help intervention with
adherence-focused guidance that targets problematic alcohol use only
(AFGE-AO); and 3) an internet as usual (IAU) control condition, all
among adult problem drinkers with co-occurring depression symptoms.
A priori, we expected that the combined intervention (AFGE-AD) would
generate greater improvements in depression symptoms and interven-
tion satisfaction than the alcohol-only intervention (AFGE-AO), but
similar reductions in alcohol-related outcomes (Schaub et al., 2016).
It is essential to note that this study had to be completed during the
COVID-19 pandemic that started December 2019 in the city of Wuhan in
Central China. By 11 March 2020, the World Health Organization
(WHO) had declared COVID-19 a pandemic (WHO, 2020). To deal with
this global pandemic, most countries enacted various lockdown mea-
sures to circumvent the spread of this infectious disease. A review
investigating the psychological burden caused by quarantine highlights
the psychological strain on those who are not allowed to participate in
social life (Brooks et al., 2020). Besides these lockdown measures, the
pandemic created various states of uncertainty, which were felt by a
tremendous number of people, leading to difcult psychological con-
sequences. In one study, the prevalence of depressive symptoms in the
US increased by more than 200 % during the COVID-19 pandemic, from
8.5 % of the population before the pandemic to 27.8 % during it (Ettman
et al., 2020). Similarly, a study in Germany revealed increased symp-
toms related to generalized anxiety (44.9 %), depression (14.3 %),
psychological distress (65.2 %) and COVID-19-related fear (59 %)
(B¨
auerle et al., 2020). It is reasonable to speculate that some people may
drink more alcohol to deal with these negative consequences as a coping
mechanism. These effects may be reected in the heighted online sales
of alcohol by US consumers in March 2020; this included an 240 % in-
crease in internet alcohol sales, including strong liquors (spirits)
increased by 75 %, wines by 66 %, and beers by 42 % (Micallef, 2020). In
their study, Chodkiewicz et al. (2020) found that participants currently
drinking more than before the pandemic started reported worse mental
health than other groups; coped less well with everyday functioning; and
suffered more from depression, low self-esteem, and suicidal thoughts.
Providing widespread help that can even be used even while those
seeking help stay at home could be crucial during such difcult times.
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
3
2. Methods
2.1. Study design
The currently-reported study was a three-arm, randomized
controlled trial that compared two web-based self-help interventions
(AFGE-AD, AFGE-AO) in their ability to reduce problematic alcohol use
and depression symptoms, against each other and against a waiting list
control group in which participants received a baseline assessment,
psycho-educative information, and access to the internet as usual (IAU).
Each intervention lasted for six weeks, followed by an immediate short
assessment of satisfaction (t
1
) for the active interventions, followed by
further follow-up three months after the baseline assessment (t
2
) and a
nal survey six months post baseline (18 weeks post treatment, t
3
). All
participants received email reminders for follow-ups and subsequent
telephone calls if they did not complete the survey. Controls using IAU
were provided access to the intervention provided in study arm 1 after
their 6-month follow-up assessment.
Participants were randomized, by computer, to one of the three
conditions, in a 1:1:1 ratio. Participants in the active interventions did
not know to which active intervention they had been assigned; however,
subjects in the IAU group knew they had been placed on a waiting list.
The study was conducted in accordance with the 2013 Declaration of
Helsinki. The study was approved by the Ethics Committee of the Canton
of Zurich on April 7th, 2015 (KEK-ZH-Nr: 2015-0082) and registered at
Current Controlled Trials, traceable as ISRCTN10323951. A detailed
study protocol was published on May 25th, 2016 (Schaub et al., 2016).
2.2. Recruitment, inclusion, and exclusion criteria
We primarily recruited participants in Switzerland, Germany, and
Austria from February 2016 through March 2020 through two websites
(www.takecareofyou.ch, www.alkcoach.at), advertisements in relevant
internet forums and newspapers (or online versions thereof), and search
engine website advertisements. Inclusion criteria were (1) age 18
years; (2) a score 8 on the Alcohol Use Disorder Identication Test
[AUDIT (Saunders et al., 1993)], indicating no less than at-risk alcohol
use at the present time; (3) a score 10 on the Center for Epidemiologic
Studies Depression Scale [CES-D (Cole et al., 2004)], indicating at least
moderate symptoms of depression, (4) regular access to the internet, and
(5) good command of the German language. Exclusion criteria were (1)
participation in any other psycho-social or pharmacological treatment
for the reduction/cessation of alcohol use or the reduction of depression
symptoms; (2) the use of opioids, cocaine or amphetamine type stimu-
lants over the preceding 30 days and/or cannabis use more than three
times weekly over the preceding 30 days; (3) prior treatment for car-
diovascular problems; (4) past suicidal ideations or plans; and (5) for
female participants: pregnancy or breastfeeding. We had two major
deviations from the study protocol (Schaub et al., 2016). First, due to
regulatory issues, which would have required users to send in
hand-signed informed consent forms which defeats the purpose of
having an anonymous program in the rst place, the Netherlands
dropped out of the study. Second, a mitigation strategy was utilized,
lowering the CES-D cut-off from 16 upwards to 10 upwards, as very few
interested users were eligible to participate in the program with the
higher threshold.
2.3. Sample size calculation
On the basis of expert opinions, we estimated a small effect size of
Cohens d =0.25 for the reduction in the weekly number of standard
drinks between study arms 1 and 3 at six months post randomization.
This resulted in a sample size of n =199 for each study arm (597 in total)
to detect a small effect size with 80 % power and an alpha error of 5 %
(two-tailed testing).
2.4. Treatment arms
Both active interventions consisted of a dashboard and eight psycho-
educative modules based on CBT (Marlatt and Donovan, 2005) and
MI (Miller and Rollnick, 2012)that were designed to reduce prob-
lematic alcohol use. Motivational Interviewing mainly consisted of a
balance of pros and cons exercise for alcohol use reduction and
continuous motivational e-mail messages. Adherence-focused guidance
enhancement in the active interventions was based on the
supportive-accountability model of Mohr et al. for providing guidance
for internet interventions (Mohr et al., 2011). The guidance consisted of
two elements: adherence monitoring and feedback. Adherence moni-
toring included regularly checking whether participants have completed
modules or lled out their consumption diary and then sending them
reminders if they have not. The reminders were formulated in an
encouraging and motivational style.
These elements were incorporated fully automatically through the
program. Feedback was automatically generated based on participants
success or failure of decreasing their consumption according to their
entries in the consumption diary. Participants also had the opportunity
to contact their eCoach for further feedback. The integrated intervention
(AFGE-AD) contained behavioral self-help exercises targeting symptoms
of depression (Hides et al., 2010) and social problem solving (DZurilla
and Nezu, 1982), while AFGE-AO consisted of self-help exercises tar-
geting alcohol misuse only. To minimize additional content as a possible
confounding factor, the corresponding modules in the AFGE-AD inter-
vention were designed to have approximately the same quantity of
material. The modules included stories of six ctional companions who
appeared within the modules at key points, with the goal of encouraging
reection on potential questions raised by the modules. All eight mod-
ules were freely accessible from the start, though it was recommended
that users progress through the modules in the order in which they were
presented and complete 12 modules per week. Table 1 summarizes the
content and structure of the eight psycho-educative modules.
Both active interventions also incorporated a consumption and ac-
tivity diary, weekly (semi-)automated motivational and AFG-based
email feedback, and reminders for users to ll out the dairy and
continue the program. The semi-automated motivational emails were
sent out by an eCoach, depending on certain answers of the participants
in particular modules. These feedback emails included suggestions for
working on a particular module.
Those in the control group were granted access to the internet as
usual (IAU), since it was deemed impossible and unethical to prevent
participants in this group from seeking out other internet support or
face-to-face treatment options during the waiting period. A detailed
description of the interventions and their technical specications is
provided in the study protocol paper (Schaub et al., 2016).
At all times, an instant help- webpage was available to all partic-
ipants, with instructions on what subjects could do if their situation
became unbearable or they felt themselves in an emergency situation.
These instructions contained psycho-educational self-help instructions,
as well as phone numbers for professional healthcare providers and
emergency helplines.
2.5. Measurements
The primary outcome measure of interest was the quantity of alcohol
used over the previous seven days, estimated as the number of standard
drinks and assessed by timeline follow-back, in accordance with the
Timeline Follow-Back (TLFB) method (Sobell and Sobell, 1996). The
measure used in the current study referred to a time frame of 7 rather
than 30 days as the original. The shorter time frame was chosen as it may
be more accurate (Hoeppner et al., 2010). Furthermore, the here-used
measure was presented online rather than in-person. This mode of
presentation does not seem to impact the accuracy of participantsan-
swers and may even make them more comfortable reporting their use
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
4
(Pedersen et al., 2012).
Secondary outcomes included the number of drinking days per week
assessed by the TLFB; the severity of alcohol use disorder, assessed using
the Alcohol Use Disorders Identication Test (AUDIT (Saunders et al.,
1993)); change in depression severity (Centre of Epidemiologic Studies of
Depression Scale [CES-D (Cole et al., 2004)]); a combined alcohol and
depression measure, dened as simultaneously falling below the AUDIT
cut-off of 8 and the CES-D cut-off of 10; years of lifetime consumption of
substances of abuse using the FDA (Fragebogen Substanzanamnese)
derived from the EuropASI (Kokkevi and Hartgers, 1995); mental
distress, rated using the short version of the Mental Health Inventory
(MHI-5 (Rumpf et al., 2001)); quality of life, measured using the
ve-level variant of the ve-dimensional EuroQol instrument (EQ-5D-5
L (Group, 1990)); work ability, measured employing the single-item
Work Ability Index (WAI (Ahlstrom et al., 2010)); client satisfaction
with the treatment program (ZUF-8 (Schmidt and Wittmann, 2002));
and treatment adherence (number of nished modules). Lastly, we
asked all participants if they had used any treatment or help other than
Take Care of You during their six months in the study and, if so, to
identify it from a predened list of services. Table 2 overviews the
measurement items used and times measured. More details regarding
study measures are provided in the study protocol (Schaub et al., 2016).
2.6. Statistical analysis
Data were analyzed according to the intention-to-treat principle
(ITT). To address missing data for the ITT analyses, we applied multiple
imputation procedures using the software package MICE(van Buuren
and Groothuis-Oudshoorn, 2011) in R (version 3.6.1) (R Core Team,
2019), which is a minor deviation from the study protocol, wherein we
suggested using the package Amelia. MICE involves specifying a
multivariate distribution for the missing data and drawing imputations
from their conditional distributions using Markov chain Monte Carlo
techniques. Imputations were performed separately for the three con-
ditions, but using the same set of variables. This has been shown to result
in correct treatment effect estimates in RCTs (Sullivan et al., 2018). All
socio-demographic, as well as primary and secondary outcome variables
that had been assessed in all three intervention groups were included in
the imputation. As recommended, 20 imputation sets were employed
(van Buuren and Groothuis-Oudshoorn, 2011). Reported outcomes used
the ITT results from the imputed datasets, but complete case analysis
(CCA) results also are reported in the attached detailed tables and sup-
plementary table.
For investigating treatment effects, multivariable linear regression
models were generated and tested. Change scores between baseline and
follow-up served as dependent variables for the primary and all sec-
ondary outcomes, with study condition set as the independent variable;
each outcome was adjusted for its baseline value and using external
help. For binary outcomes, logistic regression models were generated
and tested. Individuals lost to follow-up were compared against those
who completed the 6-month assessment (completers) in baseline char-
acteristics across study conditions. The use of linear mixed models
(LMMs), including a time-study arm interaction term (as described in
the protocol paper (Schaub et al., 2016)) was impossible, as the models
did not converge. To investigate whether combined treatment for
depression and alcohol resulted in a similar reduction in alcohol
Table 1
Module contents and comparisons between study arm 1 (AFGE-AD) and study
arm 2 (AFGE-AO).
No AFGE-AD: Alcohol and depression self-help AFGE-AO: Alcohol self-help
only
M1 Introduction
Introductory words (tailored to arm 1)
Pro and Contra of drinking
Core motive for change
Condence of change
Introduction to consumption diary, mood
barometer and planning of positive
activities
Introduction
Introductory words
(tailored to arm 2)
Pro and Contra of
drinking (same as arm 1)
Core motive for change
(same as arm 1)
Condence of change
(same as arm 1)
Introduction to
consumption diary
M2 Strategies for goal achievement
Introduction
Changing habits
Alcohol at home
Alcohol for relaxation
My personal strategies (tailored to arm 1)
Strategies for goal
achievement
Introduction (same as
arm 1)
Changing habits (same as
arm 1)
Alcohol for relaxation
My personal strategies
(tailored to arm 2)
M3 Say Yes
Positive activities
Common problems
Say No
Thanks, I dont drink
Common problems
M4 Worries and Problems
Relation of depression and problems
6-step plan
Identify risk situations
Identify risk situations
Seemingly unimportant
decisions
M5 Craving (same in both arms)
Forms of craving (physical & mental)
Craving and conditioned triggers
How to handle craving
M6 Dealing with slips (same in both arms)
Dene what you consider a slip
How to deal with it
Plan your reaction for future slips
M7 Meeting your Needs
Sleep: tips for better sleep hygiene
Rumination: 6 ways to deal with it
Social contacts: importance of and how to
(re)enforce them
Progressive Muscle
Relaxation
Basic overview
FAQ
Exercise in written words
Guided instructions via
audio le
M8 Preserve success (same in both arms)
Your toughest moments?
Most helpful modules?
Your top 5 strategies?
Table 2
Assessment instruments.
Assessments /
instruments
Baseline
(t
0
)
6 weeks
(t
1
)
3-month
follow-up
(t
2
)
6-month
follow-up
(t
3
)
Socio-demographics X
FDA X
AUDIT X X X
CES-D X X X
Number of weekly
standard drinks
a
X X X
Number of weekly
consumption days
a
X X X
MHI-5 X X X
Suicidal ideations or
plans
X X X
EQ-5D-5L X X X
TiC-P X X X
WAI presenteeism X X X
ZUF-8
b
X
Intervention
adherence
c
a
TLFB =Timeline Follow-Back.
b
This instrument will only be applied to intervention arms 1 and 2.
c
Continuous assessment over 6 weeks.
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
5
consumption as with alcohol treatment alone, a CI approach was used to
estimate the effect size, reecting the difference between the two study
arms, with a two-sided 0.05 level of signicance (Piaggio et al., 2012).
The equivalence margin was set, a priori, at d =0.20, corresponding to
the smallest value indicating a relevant effect (Wiens, 2002).
Several participants were directly or indirectly impacted by the
government-ordered lockdown measures employed mid-March 2020 in
Switzerland, Austria, and Germany. For analytical purposes, we cate-
gorized three groups of participants, in terms of the lockdowns effect on
their time in the study: unaffected: those who nished their program and
follow-up assessments before mid-March (12.3); indirectly affected:
those who nished their program before the lockdown, but had their
nal survey during the lockdown; and directly affected: those who
worked through the program during the lockdown. Subgroup analysis
using complete case data was performed to explore possible effects.
Reported effect sizes were calculated for changes from baseline to
follow-up (d
w
) and between the two intervention groups and controls
(d). As suggested elsewhere, d =0.20 was adopted to indicate a small,
d =0.50 a medium, and d =0.80 a large effect (Cohen, 1988).
3. Results
3.1. Participation ow
Fig. 1 overviews the trial ow. Between February 2016 and August
2020, a total of 1388 people registered online for the program, among
whom 689 were considered eligible and randomized to the three study
arms. Three months after baseline, we were able to reach 327 subjects
(47.5 % of the initial 689); this number dropped to 288 (41.8 %) for the
nal 6-month assessment.
3.2. Baseline characteristics
Of the 689 participants, 356 (51.7 %) were male, and the average age
was 42.8 (SD =11.7). The biggest share (n =271, 39.3 %) were from
Austria, followed closely by 232 (33.7 %) from Switzerland and 178
(25.8 %) from Germany. The average participant had used alcohol
almost daily (5.4 days, SD =1.8) over the preceding seven days. Par-
ticipants in the three different intervention arms differed signicantly in
gender distribution (p =.049) and number of alcohol-consumption years
(p =.017).
Complete case analysis of baseline data and study group comparisons
are summarized in Table 3.
3.3. Primary outcome
Fig. 2 depicts the subjectsdecrease in standard drink consumption
throughout study participation. Three months after baseline, users in
both active interventions reported signicantly greater reductions in
alcohol consumption than IAU controls, by averages of 17.4 (d
w
=0.82,
SD =23.96) and 20.9 standard drinks (d
w
=1.12, SD =23.38),
respectively, compared to 14.2 standard drinks (d
w
=0.42, SD =32.8)
in controls (AFGE-AD: B=− 5.82, P =.013, d =0.11; AFGE-AO:
B=− 8.79, p <.001, d=0.24). These effects persisted six months after
baseline, with participants in both active interventions still experiencing
signicantly greater reductions in their alcohol use than controls: mean
reductions of 16.56 (d
w
=0.72, SD =26.2) and 19.8 (d
w
=0.99, SD =
24.2) versus 13.2 standard drinks (d
w
=0.39, SD =39.8; AFGE-AD:
B=− 6.51, p =.048, d=0.10; AFGE-AO: B=− 9.17, p =.004, d=0.20)
There was no signicant difference between the two active in-
terventions at either the 3-month (p =.206) or 6-month follow-up
assessment (p =.366). Equivalence testing between the two active in-
terventions was not signicant, demonstrating an observed effect size in
excess of the upper bound equivalence margin of d=0.20 (p =.211) but
not the lower bound of the predened margin (p <.001), indicating
noninferiority between the integrated and alcohol-only interventions.
Detailed results for primary outcomes are summarized in Table 4.
3.4. Secondary outcomes
At 6-month follow-up, participants in the AFGE-AO group reported
signicantly fewer consumption days over the previous seven days,
including an average decrease of 1.9 days (d
w
=0.93, SD =2.4) relative
to baseline versus a decrease of 0.9 days (d
w
=0.39, SD =2.3) in con-
trols (B=− 1.01, p =.005, d=0.42). No signicant difference was
detected between the decrease of 1.4 (d
w
=0.66, SD =2.2) in the AFGE-
AD and IAU (p =.120) group subjects. See also Table 5.
A signicant difference was noted in the decrease in alcohol use
disorder severity (AUDIT) between both active interventions and the
control condition (AFGE-AD: M =5.0, SD =6.7, d
w
=0.77, B=− 3.36, p
=.003, d=0.54; AFGE-AO: M=6.2, SD=5.7, d
w
=1.04, B=− 4.48, p <
.001, d=0.80; IAU: M=1.7, SD=5.4 d
w
=0.26); but, again, no signicant
difference was noted between the two active interventions (p =.313).
Similarly, subjects in both active intervention groups had greater
CES-D score reductions averaging 6.6 (d
w
=0.79, SD =9.3) and 7.3
(d
w
=0.84, SD =11.6) than controls, averaging 2.6 (d
w
=0.30, SD =
10.0; AFGE-AD: B=− 3.95, p =.006, d=0.41; AFGE-AO: B=− 4.15, p =
.008, d=0.43); but there again was no signicant difference between the
active interventions (p =.890).
Combining the CES-D and AUDIT measures to see which subjects fell
below threshold levels in both measures, signicantly more participants
in the AFGE-AO group (10.7 %) fell below these two cut-offs than con-
trols (1.1 %, p =.031); however, the 5.9 % rate observed in AFGE-AD
subjects (p =.093) was not statistically greater than the control rate.
A greater decrease in the CES-D score was signicantly associated with a
larger decrease in the AUDIT score (p <.001), and vice versa (p <.001).
All three treatment groups decreased their mean MHI-5 score, with
Fig. 1. CONSORT-EHEALTH trial owchart: overview of participant ow.
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
6
no signicant inter-intervention differences detected. The mean WAI
score increased signicantly in the AFGE-AD group (M=− 1.1, SD =3.0,
p<.001, d=− 0.50) relative to the small increase observed in controls (M
=0.3, SD =2.6). The WAI score also increased in AFGE-AO group
subjects, but this increase was not statistically greater than the increase
in controls (p=.209).
With regards to self-reported TLFB abstinence, 11.8 % and 13.8 % of
AFGE-AD and AFGE-AO participants reported abstinence over the last
seven days, neither of which was signicantly higher than the 8.2 %
abstinence rate observed in controls (AFGE-AD: p =.693; AFGE-AO, p =
.193).
3.5. Adherence & user satisfaction
Participants in the AFGE-AD group completed an average of 3.7 (SD
=2.8) modules versus 3.9 modules (SD =2.9) among AFGE-AO subjects
(t
451
=− 0.91, p =.362). Participants in the IAU group were most likely
to remain in the study (44.0 %), but there was no signicant difference
between the groups (
χ
2
=0.74, p =.690).
There was no signicant difference between the two active in-
terventions in level of user satisfaction (AFGE-AD: M =24.6, SD =3.6,
AFGE-AO: M =22.6, SD =5.1, t
48
=1.62, p =.056).
3.6. Using external services
Forty-six participants (16.5 %) reported using external services for
their alcohol problem over the course of their study participation. The
external service most frequently used was a psychologist, with 19 in-
stances of contact (41.3 % of all external service use), followed by other
unspecied services with 16 (34.8 %). Nine reported seeking other
internet counselling (19.6 %), eight sought the services of local drug
counsellors (17.4 %), six sought care from a psychiatrist (13.0 %), and
ve a general practitioner (10.9 %). There was no signicant difference
between the three intervention arms in the rate of external service use
(
χ
2
=0.92, p =.632).
Table 3
Baseline-Data of participants.
AFGE-AD n =
221
AFGE-AO n =
234
IAU n =
234
Total n =
689
Statistical Analysis (Chi-Square, ANOVA or
Kruskal-Wallis-Test)
Gender, n (%) X
2
(2, N =689) =6.03, p =.049*
Female 105 (47.5) 136 (58.1) 115 (49.1) 333 (48.3)
Male 116 (52.5) 98 (41.9) 119 (50.9) 356 (51.7)
Age, M (SD) 43.6 (11.8) 41.7 (11.4) 43.1
(11.8)
42.8 (11.7) F(2,686) =1.59, p =.204
Highest education, n (%) X
2
(10, N =689) =5.46, p =.855
Primary school 6 (2.7) 3 (1.3) 4 (1.7) 13 (1.9)
Apprenticeship 47 (21.3) 40 (17.1) 42 (17.9) 129 (18.7)
Secondary school 44 (19.9) 62 (26.5) 53 (22.6) 159 (23.1)
Technical college 40 (18.1) 42 (17.9) 45 (19.2) 127 (18.4)
University 79 (35.7) 84 (35.9) 85 (36.3) 248 (36.0)
Not specied 5 (2.2) 3 (1.3) 5 (2.1) 13 (1.9)
Country of origin, n (%) X
2
(6, N =689) =8.47, p =.206
Switzerland 63 (28.5) 87 (37.2) 82 (35.0) 232 (33.7)
Austria 95 (43.0) 91 (38.9) 85 (36.3) 271 (39.3)
Germany 58 (26.2) 55 (26.2) 65 (27.8) 178 (25.8)
Other 5 (2.2) 1 (0.4) 2 (0.9) 8 (1.2)
Alcohol Use Disorder (AUDIT, Range 040), M (SD) 19.7 (6.0) 19.9 (5.5) 19.9 (5.6) 19.8 (5.7) F(2,686) =0.10, p =.908
Centre for Epidemiological Studies Depression Scale (CES-D,
Range 060), M (SD)
22.2 (7.8) 22.9 (7.6) 22.0 (6.9) 22.4 (7.4) F(2,686) =0.77, p =.462
Mental Health Inventory 14.8 (3.6) 15.1 (3.3) 14.7 (3.1) 14.9 (3.3) F(2,686) =0.91, p =.402
(MHI-5, Range 525), M(SD)
EuroQol Health Score 68.8 (16.6) 69.4 (14.7) 68.5
(17.5) 69.0 (16.3) F(2,684) =0.33, p =.722
(EQ-5D-5 L Range 0100), M(SD)
Number of Standard Drinks
1
, M(SD) 34.8 (23.5) 36.0 (23.0) 39.0
(43.4) 36.6 (31.7) F(2,672) =1.08, p =.339
Number of Consumption Days
a
, M(SD) 5.2 (1.9) 5.4 (1.7) 5.4 (1.8) 5.4 (1.8) F(2,682) =0.80, p =.451
Number of Consumption Years, M(SD)
Alcohol 15.3 (10.4) 13.0 (9.3) 15.6
(11.3) 14.6 (10.4) F(2,666) =4.07, p =.017*
Alcohol risky use
b
10.2 (9.6) 8.9 (8.2) 10.7
(10.5) 9.9 (9.5) F(2,639) =1.98, p =.139
Cannabis 2.1 (4.9) 1.9 (3.8) 2.0 (5.6) 2.0 (4.8) F(2,520) =0.08, p=.922
a
Last 7 days.
b
Risky Use is dened as 5 or more standard drinks per day at least 3 days per week. A standard drink is dened as 5 cl spirits, 1520 cl wine or 3345 cl beer, CES-D
cut-off: 10, AUDIT cut-off: 8.
Fig. 2. Decreases in Standard Drink consumption over the preceding 7 days,
according to TLFB (Timeline Follow-Back). At the nal survey (6-months) both
intervention groups (AFGE-AD, AFGE-AO) showed signicant higher decrease
in alcohol standard drinks than controls (*, d =.10, p <.05; **, d =.20, p
<.01).
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
7
3.7. Dropout analysis
Participants who dropped out were signicantly more likely to be
male (
χ
2
=0.74, p =.023), younger (t
581
=2.19, p =.029), and have a
lower educational level (
χ
2
=14.58, p =.012). They also reported at
baseline more standard drinks (t
641
=− 2.54, p =.011) and fewer alcohol
use years (t
571
=2.11, p =.036); scored higher on the AUDIT
(t
649
=− 2.14, p =.032) and CES-D (t
630
=− 2.46, p =.014); reported less
working ability (t
490
=2.09, p =.037) and nished fewer modules (t
483
=10.15, p <.001) than those who completed follow up. Full dropout
analysis is summarized in Table S2.
3.8. Exploratory data analysis: COVID-19
In total, 134 (19.4 %) participants were affected by lockdown mea-
sures. Of these, 71 were affected indirectly (10.3 %) and 63 directly (9.1
%). On average, participants who were not affected by lockdowns
decreased their alcohol use signicantly more than those who were
directly affected by them, decreasing by an average of 14.9 (SD =23.4)
standard drinks versus just 5.4 (SD =17.0) standard drinks (t
38
=2.57, p
=.014) in controls. This reduction in alcohol use in lockdown-
unaffected subjects was not greater, however, than the decrease
observed in those indirectly affected, who reduced their alcohol con-
sumption by an average of 9.3 (SD=19.0) standard drinks (t
65
=1.66, p
=.101).
Unaffected subjects also decreased their AUDIT score by 4.4 (SD =
6.1), which was a signicantly greater reduction than the 1.6 (SD =5.2)
observed by directly affected subjects (t
37
=2.62, p =.012), but not
relative to the 3.5 (SD=4.3) decrease observed in those only indirectly
affected by lockdowns (t
74
=1.16, p =.249). Participants not affected by
the lockdown decreased their CES-D score by 5.4 (SD=9.1), versus those
indirectly affected by 7.6 (SD=7.9) and those directly affected by 2.7
(SD=8.9). The reduction in CES-D score was signicantly greater be-
tween those indirectly and directly affected by lockdowns (t
49
=2.33, p
=.024), but no signicant difference was observed between those who
were unaffected and directly affected (t
32
=1.49, p =.147).
4. Discussion
In this study, we compared two active online interventions one
which offered users treatment of both their depression and their alcohol
misuse (AFGE-AD), and a second which only addressed alcohol issues
(AFGE-AO) against a waiting list control group of subjects who were
allowed internet as usual (IAU) use only. Participants in both of these
active intervention groups reported signicantly fewer standard drinks
than controls, both three and six months after initiation of the six-week
program. These main effects were small at three months (AFGE-AD: d =
.11, AFGE-AO: d =.24), but they were signicant, and this signicant
small effect was maintained through the six-month follow-up survey
(AFGE-AD: d =.10, AFGE-AO: d =.20). Equivalence testing showed
noninferiority between the combined/integrated and the alcohol-only
treatment arms, suggesting that this combined intervention was no
worse than the intervention targeting alcohol use alone, in terms of
reducing the number of standard drinks that individuals consumed.
The effect sizes for alcohol-consumption reduction were smaller than
we expected, but consistent with the effect sizes reported for previous
research examining face-to-face treatment (g =0.19 (Riper et al.,
2014a)), and greater than what was reported for another,
previously-studied integrated internet program (DEAL, d=− 0.09),
which targeted co-occurring depression symptoms and problematic
alcohol use in young adults (Deady et al., 2016). The DEAL project
achieved a large post-treatment effect (d =0.99) that vanished by three
months, whereas our program appeared to maintain its benecial effects
for up to six months. More dramatic results, indicating a medium to
large effect size, were observed in terms of reducing the AUDIT score in
both active intervention groups, relative to controls (AFGE-AD: d =.56,
AFGE-AO: d =.80), which adds further credence to the effectiveness of
these programs, since the AUDIT measures symptoms of both alcohol
dependence and risky use.
Regarding depression symptoms, both active interventions reduced
CES-D scores signicantly more than internet as usual (IAU) (AFGE-AD:
d =.41, AFGE-AO: d =.43). These effects are similar to the one observed
for the DEAL project (d =0.39 (Deady et al., 2016)) and slightly higher
than those reported in the meta-analysis on integrated face-to-face
therapy published by Riper and colleagues (g =0.27 (Riper et al.,
2014a)). The effects achieved in the AFGE-AO group were surprising, as
no content in that intervention was specically tailored to target
depression symptoms. It seems that the reduction in alcohol use alone
might have alleviated moderate depression symptoms, as well. This may
stem from our choice of a non-clinical sample that averaged less severe
depressive disorders than the patient populations evaluated in the
face-to-face intervention studies analysed by Riper et al. (2014a). We
also observed a signicant positive and bidirectional relationship be-
tween reduced depression outcomes and alcohol use, consistent with the
meta-analysis performed by Nunes and Levin (2004). The relationship
between alcohol use and depression symptoms seems to be reciprocal,
whereby increased alcohol use leads to worse negative emotions and
vice versa (Witkiewitz and Villarroel, 2009).
A priori, we hypothesised that the integrated intervention (AFGE-AD)
would reduce depression symptoms more than the alcohol-only inter-
vention (AFGE-AO), which we failed to demonstrate, despite showing
the overall effectiveness of both interventions compared to controls.
Several circumstances could have impacted the integrated programs
success. To begin with, during recruitment we did not specify that we
Table 4
Regression analysis results.
AFGE-AD versus control after 6 months (ITT analysis) AFGE-AO versus control after 6 months (ITT analysis)
Outcome B
a
95 % CI p B
a
95 % CI p
Imputed Data (n =689) Imputed Data (n =689)
Standard Drinks
b
6.52 12.97 0.07 .048* 9.13 15.17 3.09 .004**
Consumption Days
b
0.58 1.32 0.17 .125 0.99 1.69 0.30 .006**
AUDIT 3.39 5.47 1.30 .002** 4.51 5.98 3.04 <.001***
CES-D 3.95 6.72 1.18 .006** 4.15 7.18 1.12 .008**
MHI-5 4.78 11.49 1.93 .156 4.72 11.84 2.39 .184
WAI 1.21 0.53 1.88 <.001*** 0.71 0.43 1.84 .209
Abstinence
b
0.20 1.19 0.79 .693 0.59 1.48 0.30 .139
CES-D & AUDIT
c
1.83 3.97 0.31 .093 2.63 4.69 0.56 .031*
ITT =Intention to Treat; AUDIT =Alcohol Use Disorders Identication Test; CES-D =Centre of Epidemiologic Studies of Depression Scale; MHI-5 =Mental Health
Inventory; WAI =Working Ability Index.
a
Condition as predictors for group effect.
b
Last 7 days according to TLFB.
c
Falling below CES-D cut-off 10 and AUDIT cut-off 8.
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
8
were offering an intervention that targets both alcohol use and depres-
sion. Combined with reducing the CES-D inclusion threshold from a
minimum score of 16 to 10 may have resulted in us recruiting in-
dividuals who not only were not expecting an intervention targeting
depressive symptoms, but also might not actually have been suffering
appreciably from them. Our recruitment might, therefore, have
beneted from clearer communication. Secondly, part of our study was
conducted during the COVID-19 pandemic, which may have had un-
foreseen effects on the results of our program. Little is known about the
effects that such a global crisis can have on a participants response to
such programs, which could differ greatly from normal circumstances.
The participantsmobility may have been limited by either lockdown
measures or fear of infection. This may have reduce social gatherings
with or without alcohol. Over time, we should learn more about such
unprecedented times and its consequences on programs like ours.
Lastly, but maybe most obviously, is that the content tailored to-
wards depression in our integrated program was neither specic nor
effective enough. The content we used was based on face-to-face ther-
apy, adapted, and then integrated into our internet intervention. Using
more established material from previously-successful online in-
terventions might be more effective in future iterations of the program.
Another idea would be to restructure and reorder our program to offer
content for depression earlier on in the program.
Regardless of all these explanations, the combined intervention was
as successful at reducing depression symptoms as the alcohol-only
intervention. Future research should consider offering different op-
tions depending on the severity of depression at baseline. Furthermore,
sub-group analyses could possibly be helpful to identify groups of par-
ticipants for which an integrated program is more effective than an
alcohol only program.
We expected userslevel of satisfaction would be greater with the
integrated than alcohol only intervention, but whatever increased
satisfaction we observed with the former ultimately failed to achieve
statistical signicance, albeit only barely (p =.056). Both interventions
were fairly well received by participants, which also is apparent by the
number of completed modules, with users of the active integrated and
alcohol-only interventions averaging 3.7 and 3.9 modules respectively,
out of a possible eight modules. Relative to other programs, these
numbers are high, as internet interventions often suffer greatly from low
adherence and user retention (Eysenbach, 2005).
5. Limitations
This study had three major limitations. First, we had to readjust the
inclusion criteria to achieve our target sample size and, as such, included
people with less severe depressive symptoms. This means that it is not
clear whether some of the people we included in our analysis even
needed treatment for depression, which in turn may limit both the
overall effectiveness and generalizability of our combined treatment.
Secondly, the study had a high overall attrition rate (58.8 %), which is
common with these kinds of intervention, but introduces more uncer-
tainty. We used multiple imputations in an attempt to deal with any bias
that might have resulted from this. Lastly, all measures were self-
reported, and it is possible that many of our subjects portrayed them-
selves in a better light or answered queries more favorably than they
actually felt, merely to please the study team (Davis et al., 2010). This
said, evidence has been published suggesting that the anonymous nature
of the internet may help people to be more open and honest and,
thereby, provide more accurate self-evaluations (Fullwood et al., 2009).
6. Conclusions
In the context of a three-arm randomized controlled trial, the rst
fully-integrated internet intervention targeting both alcohol misuse and
depression was found to be effective at alleviating both among at least
moderately-depressed, adult alcohol misusers in the general public.
Table 5
Means, standard deviations and achieved effect sizes.
Baseline 3 months after baseline (complete cases) 3 months after baseline (ITT analysis) 6 months after baseline (complete cases) 6 months after baseline (ITT analysis)
Outcome Mean SD Mean SD d
a
95 % CL Mean SD d
a
95 % CL Mean SD d
a
95 % CL Mean SD d
a
95 % CL
IAU (n =234) Followed Up (n =119) Imputed Data (n =234) Followed Up (n =103) Imputed Data (n =234)
Standard Drinks
b
39.02 43.36 25.25 23.04 24.74 21.68 24.92 19.89 25.70 21.06
Consumption Days
b
5.35 1.79 4.64 2.25 4.51 2.28 4.50 2.27 4.54 2.30
AUDIT 19.91 5.57 18.65 6.37 18.79 6.68 18.36 6.73 18.25 7.09
CES-D 22.03 6.86 18.37 10.09 18.88 10.41 17.24 9.49 19.37 10.52
MHI-5 48.31 15.43 40.48 18.39 41.54 18.78 37.70 19.34 40.99 20.63
WAI 6.92 2.63 7.28 2.15 6.92 2.40 7.51 2.61 6.80 3.09
AFGE-AD (n =221) Followed Up (n =105) Imputed Data (n =221) Followed Up (n =90) Imputed Data (n =221)
Standard Drinks
b
34.76 23.50 17.85 21.72 .24 .03 .50 17.56 17.94 .11 .07 .29 19.36 27.12 .18 .11 .46 18.34 21.94 .10 .09 .28
Consumption Days
b
5.23 1.87 3.86 2.24 .30 .03 .56 4.08 2.24 .11 .08 .29 3.80 2.26 .25 .04 .53 3.86 2.29 .21 .03 .39
AUDIT 19.69 6.04 15.02 6.24 .47 .20 .73 15.45 6.18 .51 .31 .68 14.02 6.80 .44 .15 .72 14.69 6.94 .54 .35 .72
CES-D 22.23 7.73 14.77 7.75 .25 .02 .51 15.90 8.12 .35 .16 .53 15.38 8.70 .09 .17 .35 15.56 9.17 .41 .22 .59
MHI-5 49.14 17.95 36.60 18.06 .11 .15 .37 36.91 18.64 .28 .09 .46 36.42 18.57 .00 .28 28 36.67 19.46 .25 .06 .43
WAI 6.81 2.52 7.55 2.21 .17 .43 .10 7.02 2.73 .15 .33 .04 7.99 1.92 .31 .59 .02 7.85 2.06 .50 .68 .30
AFGE-AO (n =234) Followed Up (n =108) Imputed Data (n =234) Followed Up (n =96) Imputed Data (n =234)
Standard Drinks
b
36.01 23.03 13.38 13.92 .52 .25 .78 14.83 13.62 .24 .05 .41 15.01 17.68 .46 .17 .73 15.93 17.30 .20 .02 .38
Consumption Days
b
5.40 1.71 3.47 2.33 .62 .34 .88 3.65 2.35 .36 .17 .53 3.51 2.30 .47 .18 .75 3.52 2.31 .42 .23 .59
AUDIT 19.87 5.51 14.35 6.10 .80 .51 1.05 14.55 6.31 .71 .51 .89 12.96 6.01 1.36 1.03 1.65 13.69 6.39 .81 .61 .99
CES-D 22.85 7.59 15.36 9.35 .36 .10 .62 16.12 9.22 .36 .17 .53 15.06 9.17 .34 .07 .59 15.55 9.68 .43 .24 .61
MHI-5 50.38 16.69 35.76 18.84 .36 .09 .62 37.54 19.26 .30 .11 .48 34.25 18.43 .29 .01 .57 36.97 18.84 .28 .09 .46
WAI 7.11 2.41 7.76 2.45 .19 .45 .07 7.60 2.71 .15 .33 .03 7.83 2.36 .21 .48 .07 7.63 2.64 .21 .39 .03
ITT =Intention to Treat; AUDIT =Alcohol Use Disorders Identication Test; CES-D =Centre of Epidemiologic Studies of Depression Scale; MHI-5 =Mental Health Inventory; WAI =Working Ability Index.
a
Condition as predictors for group effect.
b
Last 7 days according to TLFB.
C. Baumgartner et al.
Drug and Alcohol Dependence 225 (2021) 108806
9
Even though the main effects were small, they could be maintained for
six months, which is promising. Future research can use and build upon
our results to gain additional understanding and subsequently develop
even more effective interventions. Online interventions, like Take Care
of You, can be a valuable addition to the general healthcare system,
given that they generally are very cost-effective and can run automati-
cally with minimal human support. Additionally, the remote nature of
online interventions means that they have the capacity to provide sup-
port to people anywhere, including the comfort of their own homes.
Role of funding source
Funding for this trial was received from the Swiss Foundation for
Alcohol Research (grant No SSA #247) and through internal funds from
the Swiss Research Institute for Public Health and Addiction (ISGF). The
funding sources had no role in the writing of the manuscript or the de-
cision to submit for publication. Content is solely the responsibility of
the authors and does not necessarily represent the ofcial views of the
Swiss Foundation for Alcohol Research or the Swiss Research Institute
for Public Health and Addiction.
Authorscontributions
MPS had the initial idea for this study. CB prepared the rst draft of
the paper and nal manuscript. AW, CB, DDE, DL, MB and MPS devel-
oped the interventions for study arms 1 and 2. AW and CB programmed
and implemented the Take Care of You study websites. DM helped to
develop and adapt the Austrian version of the website. DDE, DM, LS,
MA, MB, and SH provided continuous feedback on the development of
the interventions and the present study paper. CB performed statistical
analysis. CB, MA, MPS, and SH thoroughly revised the rst versions of
the study paper. All authors approved the nal version of the manuscript
submitted for publication. CB is the guarantor.
Declaration of Competing Interest
DE has served as a consultant to/on the scientic advisory boards of
Sano, Novartis, Minddistrict, Lantern, Schoen Kliniken, Ideamed and
German health insurance companies (BARMER, Techniker Kranken-
kasse) and a number of federal chambers for psychotherapy. He is also
stakeholder of the Institute for health training online (formerly GET.ON/
nowHelloBetter), which aims to implement scientic ndings related to
digital health interventions into routine care.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.drugalcdep.2021.10
8806.
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... Content for the ACC was initially drawn from an ICBT program for alcohol misuse developed by a team at the Swiss Research Institute of Public Health and Addiction and made available by Michael Schaub (see Baumgartner et al., 2021). The program was then translated into English under the supervision of Matthew Keough at the University of Manitoba for use with young adults (Frohlich et al., 2018). ...
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Although Internet-delivered cognitive behaviour therapy (ICBT) for alcohol misuse is efficacious in research trials, it is not routinely available in practice. Moreover, there is considerable variability in engagement and outcomes of ICBT for alcohol misuse across studies. The Alcohol Change Course (ACC) is an ICBT program that is offered free of charge by an online clinic in Saskatchewan, Canada, which seeks to fill this service gap, while also conducting research to direct future improvements of ICBT. As there is limited qualitative patient-oriented research designed to improve ICBT for alcohol misuse, in this study, we describe patient perceptions of the ACC post-treatment. Specifically, post-treatment feedback was obtained from 191 of 311 patients who enrolled in the ACC. Qualitative thematic analysis was used to examine post-treatment written comments related to what patients liked and disliked about the course, which skills were most helpful for them, and their suggestions for future patients. The majority of patients endorsed being very satisfied or satisfied with the course (n = 133, 69.6%) and 94.2% (n = 180) perceived the course as being worth their time. Worksheets (n = 61, 31.9%) and reflections of others (n = 40, 20.9%) received the most praise. Coping with cravings (n = 75, 39.3%), and identifying and managing risky situations (n = 58, 30.4%) were reported as the most helpful skills. Several suggestions for refining the course were provided with the most frequent recommendation being a desire for increased personal interaction (n = 24, 12.6%) followed by a desire for wanting more information (n = 22, 11.5%). Many patients offered advice for future ACC patients, including suggestions to make a commitment (n = 56, 29.3%), do all of the work (n = 36, 18.8%), and keep a consistent approach to the course (n = 30, 15.7%). The results provide valuable patient-oriented directions for improving ICBT for alcohol misuse.
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Background and aims: The past-year prevalence of problematic pornography use (PPU) was 1-6% in adult populations. As a result of treatment obstacles and barriers, such as unaffordable treatments, only a minority of problematic pornography users may seek treatment. Having a free, online, self-help program may overcome treatment barriers and may help those individuals who cannot receive traditional or offline treatment for PPU. Although the effectiveness of such online programs reducing substance use and problematic gambling have been reported, no prior study has examined the efficacy of an online self-help intervention aiming to reduce PPU. Methods: This two-armed randomized controlled trial (RCT) will examine the effectiveness of an online self-help program (Hands-off) to reduce PPU, while also considering psychopathological comorbidities. The six-week intervention condition includes six core modules developed to reduce PPU based on motivational interviewing, cognitive behavioral therapy, mindfulness, and wise social-psychological intervention techniques. The target sample size is 242 participants. Self-report questionnaires will be administered at baseline, right after the end of the intervention, at one-month, and three-month follow-ups after the end of the intervention. The primary outcome will be the level of PPU. Secondary outcomes will include pornography use frequency, pornography craving, pornography use-avoidance self-efficacy, sex mindset, sexual satisfaction, negative and positive emotions, and life satisfaction. Data will be analyzed on an intention-to-treat basis using linear mixed models. Results: Results will be reported at conferences and published in a scientific peer-reviewed journal. The participants will be sent a lay-person-friendly summary of the results via e-mail.
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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.
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Background Alcohol use has increased globally, with varying trends in different parts of the world. This study investigates gender, age, and geographical differences in the alcohol-attributable burden of disease from 2000 to 2016. Methods This comparative risk assessment study estimated the alcohol-attributable burden of disease. Population-attributable fractions (PAFs) were estimated by combining alcohol exposure data obtained from production and taxation statistics and from national surveys with corresponding relative risks obtained from meta-analyses and cohort studies. Mortality and morbidity data were obtained from the WHO Global Health Estimates, population data were obtained from the UN Population Division, and human development index (HDI) data were obtained from the UN Development Programme. Uncertainty intervals (UIs) were estimated using a Monte Carlo-like approach. Findings Globally, we estimated that there were 3·0 million (95% UI 2·6–3·6) alcohol-attributable deaths and 131·4 million (119·4–154·4) disability-adjusted life-years (DALYs) in 2016, corresponding to 5·3% (4·6–6·3) of all deaths and 5·0% (4·6–5·9) of all DALYs. Alcohol use was a major risk factor for communicable, maternal, perinatal, and nutritional diseases (PAF of 3·3% [1·9–5·6]), non-communicable diseases (4·3% [3·6–5·1]), and injury (17·7% [14·3–23·0]) deaths. The alcohol-attributable burden of disease was higher among men than among women, and the alcohol-attributable age-standardised burden of disease was highest in the eastern Europe and western, southern, and central sub-Saharan Africa regions, and in countries with low HDIs. 52·4% of all alcohol-attributable deaths occurred in people younger than 60 years. Interpretation As a leading risk factor for the burden of disease, alcohol use disproportionately affects people in low HDI countries and young people. Given the variations in the alcohol-attributable burden of disease, cost-effective local and national policy measures that can reduce alcohol use and the resulting burden of disease are needed, especially in low-income and middle-income countries. Funding None.
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Introduction: The past-year prevalence of problem gambling worldwide averages 2.3%. Switzerland exhibits a slightly lower past-year prevalence rate, of 1.1%, among adults. Only a minority of these adults attend outpatient treatment. Surveyed problem gamblers have explained that they wanted to handle the problem on their own. The option of a web-based self-help programme could potentially reach those users who hesitate to approach treatment centres and help them to reduce or stop their problem gambling. The effectiveness of such web-based interventions has been shown in other countries. Methods and analysis: This two-armed randomised controlled trial (RCT) will examine the efficacy of a web-based self-help intervention, relative to an active control condition with a self-help manual, at reducing problem gambling. The active intervention programme, spanning 8 weeks, consists of nine modules developed to reduce gambling and attenuate psychopathological comorbidity, including depression, anxiety and stress-related disorder symptoms, relying on motivational interviewing and cognitive behavioural therapy. With a target sample size of 352, questionnaire data will be collected at baseline, and at 8 and 24 weeks after baseline. Primary outcomes will be the number of days one has gambled in the last 30 days. Secondary outcomes will include money and time spent on gambling activities, changes in gambling-related problems (Problem Gambling Severity Index, Gambling Symptom Assessment Scale), use of alcohol and cigarettes, and psychopathological comorbidity. All data analysis will comply with the intention-to-treat principle. Ethics and dissemination: The RCT will be conducted in accordance with the Declaration of Helsinki; the consort eHealth Guidelines for studies on medical devices; the European Directive on medical devices 93/42/EEC, Swiss Law and Swiss Regulatory Authority requirements. The study was approved by the ethics committee of the Canton of Zurich. Results will be published in a scientific peer-reviewed journal. Participants will be informed via e-mail about study results via a lay-person-friendly summary of trial findings. Trial registration number: Current Controlled Trials registry (ISRCTN16339434 ).
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Background: In recent years, cocaine use has increased in many countries, but only a minority of users seek treatment. Cognitive behavioral therapy (CBT) is seen as first-choice face-to-face treatment. However, a web-based intervention might serve as an alternative. Aims: To test the efficacy of a web-based self-help intervention, with and without chat counseling, grounded in CBT, at reducing cocaine use in cocaine misusers not in treatment for a substance use disorder. Methods: Subjects were randomly assigned to (1) a self-help intervention with chat support, (2) a self-help intervention without chat support, or (3) a waiting list control group. The fully-automated self-help program consisted of eight modules based on motivational interviewing, self-control practices and CBT. The primary outcome was the quantity of cocaine use per week. Secondary outcomes included frequency of cocaine and other substance use and mental health symptoms. Linear regression analysis was performed to investigate changes in primary and secondary outcomes. Results: In total, 416 users registered online for the trial, of whom 311 completed the baseline assessment. Participants were predominantly male (73%) and averaged 33 years old (SD = 7.6). Despite considerable efforts on our part, only 47 of 311 (15.1%) subjects completed the 6-month follow-up assessment. Frequency of cocaine use and severity of cocaine dependence decreased only in the intervention groups. No significant difference in the primary outcome was observed between the study arms, but several differences in secondary outcomes were observed by complete case analyses. Conclusions: Many cocaine misusers from the general population and not otherwise in treatment could be reached and decreased their cocaine use utilizing a CBT-based online intervention. However, due to the high percentage of dropouts and serious difficulties reaching subjects for follow-up assessments, no conclusions can be drawn regarding study arm differences. Implications for future studies are discussed.
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Background: Since the first cases of the novel coronavirus disease SARS-CoV-2 were reported in December 2019 in China, the virus has spread in most countries. The aim of the present study was to assess initial data on the mental health burden of the German public during the COVID-19 pandemic. Methods: A cross-sectional study was conducted in Germany and collected complete datasets from 15 704 German residents aged 18 years and over. Besides demographics, generalized anxiety (GAD-7), depression (PHQ-2) and psychological distress (DT) were assessed. Furthermore, COVID-19-related fear, trust in governmental actions to face COVID-19 and the subjective level of information regarding COVID-19 were covered. Results: Significantly increased symptoms were highly prevalent in all dimensions: generalized anxiety (44.9%), depression (14.3%), psychological distress (65.2%) and COVID-19-related fear (59%). Females and younger people reported higher mental burden. Trust in governmental actions to face COVID-19 and the subjective level of information regarding COVID-19 are negatively associated with mental health burden. However, the subjective level of information regarding COVID-19 is positively associated with increased COVID-19-related fear. Conclusions: The provision of appropriate psychological interventions for those in need and the provision of transparency and comprehensible information are crucial during the current pandemic.
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The December, 2019 coronavirus disease outbreak has seen many countries ask people who have potentially come into contact with the infection to isolate themselves at home or in a dedicated quarantine facility. Decisions on how to apply quarantine should be based on the best available evidence. We did a Review of the psychological impact of quarantine using three electronic databases. Of 3166 papers found, 24 are included in this Review. Most reviewed studies reported negative psychological effects including post-traumatic stress symptoms, confusion, and anger. Stressors included longer quarantine duration, infection fears, frustration, boredom, inadequate supplies, inadequate information, financial loss, and stigma. Some researchers have suggested long-lasting effects. In situations where quarantine is deemed necessary, officials should quarantine individuals for no longer than required, provide clear rationale for quarantine and information about protocols, and ensure sufficient supplies are provided. Appeals to altruism by reminding the public about the benefits of quarantine to wider society can be favourable.