ArticlePDF Available

Internet-based CBT for patients with depressive disorders in primary and psychiatric care: Is it effective and does comorbidity affect outcome?

Authors:

Abstract and Figures

Internet-based cognitive behavior therapy (ICBT) has proved effective in reducing mild to moderate depressive symptoms. However, only a few studies have been conducted in a regular healthcare setting which limits the generalizability of the results. The influence of psychiatric comorbidity on outcome is not well understood. In the current study, patients with mild to moderate depressive symptoms in primary and psychiatric care were interviewed using the SCID-I and SCID-II to assess psychiatric diagnoses. Those included were randomly allocated to ICBT (n = 48) or to an active control condition (n = 47). Both groups received therapist support. At post-treatment, ICBT had reduced depressive symptoms on the BDI-II more than the active control intervention (p = .021). However, the difference between groups was no longer significant at the 6-, 12- or 24-month follow-ups. The within-group effect size after ICBT (BDI-II) was large (d = 1.4). A comorbid anxiety disorder didn't moderate the outcome, while the presence of a personality disorder predicted significantly less improvement in depressive symptoms. ICBT had a large effect on depressive symptoms in a sample from regular healthcare. It is possible to obtain a large effect from ICBT despite comorbid anxiety, however, including patients with a comorbid personality disorder in the current form of ICBT seems questionable.
Content may be subject to copyright.
Contents lists available at ScienceDirect
Internet Interventions
journal homepage: www.elsevier.com/locate/invent
Internet-based CBT for patients with depressive disorders in primary and
psychiatric care: Is it eective and does comorbidity aect outcome?
Anna-Lena Flygare
a
, Ingemar Engström
b
, Mikael Hasselgren
c
, Markus Jansson-Fröjmark
d
,
Rikard Frejgrim
a
, Gerhard Andersson
d,e
, Fredrik Holländare
c,
a
Centre for Clinical Research, Region Värmland, Älvgatan 49, Karlstad, Sweden
b
University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, 70116 Örebro, Sweden
c
School of Medical Sciences, Örebro University, Örebro, Sweden
d
Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institute, Liljeholmstorget 7b, 117 63 Stockholm, Sweden
e
Department of Behavioural Sciences and Learning, Linköping University, 581 83 Linköping, Sweden
ARTICLE INFO
Keywords:
Depression
Internet
Comorbidity
Personality disorders
Primary healthcare
Psychiatric care
ABSTRACT
Internet-based cognitive behavior therapy (ICBT) has proved eective in reducing mild to moderate depressive
symptoms. However, only a few studies have been conducted in a regular healthcare setting which limits the
generalizability of the results. The inuence of psychiatric comorbidity on outcome is not well understood. In the
current study, patients with mild to moderate depressive symptoms in primary and psychiatric care were in-
terviewed using the SCID-I and SCID-II to assess psychiatric diagnoses. Those included were randomly allocated
to ICBT (n= 48) or to an active control condition (n= 47). Both groups received therapist support. At post-
treatment, ICBT had reduced depressive symptoms on the BDI-II more than the active control intervention
(p= .021). However, the dierence between groups was no longer signicant at the 6-, 12- or 24-month follow-
ups. The within-group eect size after ICBT (BDI-II) was large (d= 1.4). A comorbid anxiety disorder didn't
moderate the outcome, while the presence of a personality disorder predicted signicantly less improvement in
depressive symptoms. ICBT had a large eect on depressive symptoms in a sample from regular healthcare. It is
possible to obtain a large eect from ICBT despite comorbid anxiety, however, including patients with a co-
morbid personality disorder in the current form of ICBT seems questionable.
1. Introduction
It has been found that cognitive behavioral therapy (CBT) is trans-
ferable to Internet format, especially when guided by a therapist
(Andersson, 2016) who provides support, encouragement, and occa-
sionally direct therapeutic activities (Johansson and Andersson, 2012).
Meta-analyses show that guided Internet-based CBT (ICBT) is an ef-
fective treatment for depression (Karyotaki et al., 2018;Richards and
Richardson, 2012). ICBT with guidance seems to be equally eective as
face-to-face therapy (Andersson et al., 2016), and oering ICBT as a
complement to standard care expands the availability of eective psy-
chological treatment, as it enables therapists to increase their case load.
There are clear indications that guided internet-based psychological
treatments are more eective than unguided treatments (Richards and
Richardson, 2012), although there are exceptions (Titov et al., 2014).
The guidance is typically supportive in nature, including encourage-
ment and reinforcement (Holländare et al., 2016;Paxling et al., 2013;
Sanchez-Ortiz et al., 2011) but how dierent aspects of guidance in-
uence outcome is still not fully understood.
Most studies on internet-based psychological treatment have in-
vestigated CBT-based interventions, (Ruwaard et al., 2009;Andersson
et al., 2005;Hedman et al., 2014;Ruwaard et al., 2012;Williams and
Andrews, 2013;Hedman et al., 2012;Dear et al., 2018;Titov et al.,
2016;Johansson et al., 2019;Mathiasen et al., 2018) although one
study compared internet-based psychodynamic treatment for depres-
sion with an active control condition with positive results (Johansson
et al., 2013). One study compared guided ICBT with individualized e-
mail therapy (Vernmark et al., 2010), and moderate to large eect sizes
were found in both groups. Internet-based psychological treatment
programs can thus vary in content and presentation, yet still be
https://doi.org/10.1016/j.invent.2019.100303
Received 12 September 2019; Received in revised form 12 December 2019; Accepted 14 December 2019
Corresponding author at: School of Medical Sciences, Örebro University, Södra Grev Rosengatan 30, 70362 Örebro, Sweden.
E-mail addresses: anna-lena.ygare@liv.se (A.-L. Flygare), ingemar.engstrom@regionorebrolan.se (I. Engström), mikael.hasselgren@oru.se (M. Hasselgren),
markus.jansson-frojmark@ki.se (M. Jansson-Fröjmark), rikard.frejgrim@clarahalsan.se (R. Frejgrim), geran@ibv.liu.se (G. Andersson),
fredrik.hollandare@regionorebrolan.se (F. Holländare).
Internet Interventions 19 (2020) 100303
Available online 29 December 2019
2214-7829/ © 2020 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/).
T
eective.
Much research on ICBT has been conducted in university settings
with nationwide recruitment (Andersson et al., 2013a) or via the media,
and several ecacy studies have found that guided ICBT for depression
is eective (Andersson et al., 2005;Johansson and Andersson, 2012;
Vernmark et al., 2010;Ruwaard et al., 2009;Carlbring et al., 2013;
Warmerdam et al., 2008;Perini et al., 2009;Robinson et al., 2010;
Hollandare et al., 2013;Carlbring et al., 2005;Andersson et al., 2006;
Berger et al., 2011a;Berger et al., 2011b;Botella et al., 2010;Titov
et al., 2011;Heinrich et al., 2016;Ivarsson et al., 2014;Zetterberg
et al., 2019) with a favorable outcome at the 3.5 year follow-up in one
study (Andersson et al., 2013b). Less research has been conducted in
representative clinical settings and few studies have had an active
control condition. However, some studies on ICBT in health care set-
tings have been carried out (Ruwaard et al., 2012;Watts et al., 2012;
Williams and Andrews, 2013;Kessler et al., 2009;Hedman et al., 2014;
Titov et al., 2015;Kivi et al., 2014;Johansson et al., 2019;Mathiasen
et al., 2018;Hadjistavropoulos et al., 2016;Nordgreen et al., 2018)Ina
large eectiveness study by Hedman et al. (2014) 1203 patients were
treated with ICBT for depression in routine psychiatric care in Stock-
holm, Sweden. The eect on depressive symptoms was large with a
within-group eect size (d) of 1.27. Some studies have used active
control conditions, often resulting in smaller between group eects
(Kampmann et al., 2016).
Comorbidity is common among depressed persons, and one meta-
analysis (Friborg et al., 2014) estimated that as many as 45% of the
patients with major depressive disorder (MDD) have a comorbid per-
sonality disorder (PD). There is a widespread perception within clinical
psychiatry that PD has an adverse eect on the outcome of many
treatments, and PD has been found to increase the risk of drop out
(Schindler et al., 2013) and fewer patients with a PD responded to CBT
compared to those without a PD in one study (Fournier et al., 2008).
Meta-analyses have shown that PD is a risk factor for poor outcome in
treatment for depression (Newton-Howes et al., 2006;Newton-Howes
et al., 2014). However, there are exceptions, i.e. RCTs showing that the
eect of CBT on patients with depression and PDs is comparable to that
for patients without a PD (Joyce et al., 2007;Lis and Myhr, 2016;van
Bronswijk et al., 2018).
It has also been found that at least 50% of depressed persons fulll
the diagnostic criteria for a comorbid anxiety disorder (Kessler et al.,
2007). One study demonstrated that a high anxiety level increased the
risk of relapse after CBT for depression (Forand and Derubeis, 2013)
and that focusing on anxiety during sessions reduced the eect on de-
pressive symptoms (Gibbons and DeRubeis, 2008). However, there are
also studies indicating that a comorbid anxiety disorder does not aect
the outcome of CBT for depression (Smits et al., 2009), and in a study
by Kashdan and Roberts (2011), comorbid social anxiety actually in-
creased the eect of CBT for depression.
The aim of this study was to compare the eect of ICBT with the
eect of an active control condition in a clinical sample with a de-
pressive disorder recruited from primary and psychiatric care. An ad-
ditional aim was to investigate the impact of comorbidity.
We hypothesized that, in comparison to the active control condi-
tion, ICBT would lead to reduced depressive symptoms and that im-
provements would be sustained over time. The second hypothesis was
that patients suering from a comorbid anxiety disorder or a person-
ality disorder would improve less.
2. Material and methods
2.1. Design
A randomized controlled trial (RCT) with repeated measurements
was conducted to compare the eects of ICBT with those of an active
control group in terms of changes in depressive symptoms and remis-
sion rates.
2.2. Participants
Participants were recruited in primary care and psychiatric care in
two similarly sized neighboring County Councils in Sweden, Örebro and
Värmland, each with around 280,000 inhabitants. Recruitment started
in 2007 and ended in 2012. Patients received information about the
study from posters in waiting rooms as well as being briefed by primary
and psychiatric care sta. Both referrals from a health professional and
self-referrals were accepted. To qualify, the participant should have
mild to moderate depressive symptoms (1530 on the MADRS-S
(Svanborg and Asberg, 1994)), be at least 18 years old, have internet
access and be uent in Swedish. Reasons for exclusion were ongoing
CBT, starting or adjusting antidepressant medication during the past
month, being suicidal, suering from bipolar disorder, psychosis, and/
or seasonal aective disorder. A demographic description of the parti-
cipants is presented in Table 1.
2.3. Procedure
Patients who expressed an interest in the study received a letter
containing a formal invitation to participate, a written consent form,
and a postage prepaid envelope.
Patients who scored 1530 on the MADRS-S (Svanborg and Asberg,
1994) were called for a SCID-I (First et al., 1998) and a SCID-II (First
et al., 1994) interview to assess psychopathology on the DSM-IV
(American Psychiatric Association, 2000) Axis I and comorbid PDs
(results are shown in Table 2). The face-to-face interview was con-
ducted by a licensed psychologist and lasted for about two hours. The
psychologists were unaware of the study hypotheses and not informed
about group allocation to ensure an independent diagnostic process
before and after treatment.
The participants were randomized to either ICBT or active control.
The randomization sequence had diering block sizes, unknown to the
researchers, which was prepared in advance by a statistician. A num-
bered opaque envelope was opened for each participant after the de-
cision of inclusion was made. The patients were recommended to
complete approximately one module per week, and hence the treatment
in eight weeks. At the start, the participants had access to one module
and subsequently to the other modules in sequence when the treating
psychologist considered that all homework had been completed sa-
tisfactorily. Each module contained questions to answer and return to
the psychologist via the online platform.
To identify deterioration or suicidal tendencies, patients were asked
to complete weekly self-ratings with the MADRS-S. In cases where a
participant rated 4 or higher on MADRS-S item 9 (zest for life) the
treating psychologist contacted the participant for an assessment of
current suicidal ideation and possible referral to further care.
After treatment, all patients were called for a second SCID-I face-to-
face interview. They were also asked to ll out self-ratings (MADRS-S
and BDI-II) on the website at post-treatment and after 6, 12 and
24 months. A ow chart of patient participation is presented in Fig. 1.
Table 1
Demographic description of the participants in the ICBT and active control
group.
ICBT
(n= 48)
Control
(n= 47)
Total
(n= 95)
Female; number (%) 38 (79.2) 34 (72.3) 72 (75.8)
Mean age (SD) 42.9 (11.26) 47.7 (12.74) 45.30 (12.20)
Age range 2068 2368 2068
Patients with earlier depressive
episode(s); number (%)
28 (58.3) 27 (57.4) 55 (57.9)
Patients with ADM at recruitment;
number (%)
20 (41.7) 22 (46.8) 42 (44.2)
Note: ADM = antidepressant medication.
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
2
The protocol was approved by the Regional Ethics Committee in
Uppsala, Sweden (No. 2006:038). No trial registration was made.
2.4. Treatment content
A secure web-based platform was constructed for the study where
participants logged in with unique user names and passwords and
completed their self-ratings on the MADRS-S and BDI-II. The partici-
pants gained access to their treatment material, ICBT, or active control
(in accordance with their random allocation) on the platform. There
was also a messaging service (similar to email), by which the partici-
pants in both groups had most of their contact with the psychologist.
The participants could send messages to the psychologist without re-
strictions and typically did so once a week. Messages were normally
responded to within one working day. At the end of each module there
were mandatory questions that all participants answered in a message
to their psychologist. The psychologist gave feedback on completed
homework, answered questions and gave access to the next module in
their replies. Feedback on homework in the ICBT-group were typically
written to help the participant to understand and to use the strategies in
behavioral activation and cognitive restructuring. In the control group
however, the messages from the psychologist were restricted to support
and did not contain any suggestions about altering behavior or chal-
lenging negative thoughts.
The two treatment materials compared in the study diered in
content but were both divided into modules with questions at the end.
The guided self-help program was a modied version of the material
used in previous RCTs (Andersson et al., 2005;Vernmark et al., 2010).
It consisted of eight text modules including exercises and comprised
39,000 words (114 pages) in total. The material started with an in-
troduction to CBT followed by a module about depression from a CBT-
perspective with a behavioral focus (Martell et al., 2001). There were
two modules on behavioral activation and one on cognitive re-
structuring (Beck, 1979). There was one basic module on sleep and
relaxation (Morin, 1996) and one optional module on more advanced
strategies for improved sleep. The nal module was designed to help
patients in dening long-term goals (Wilson and Murrell, 2004) and
relapse prevention (Gortner et al., 1998).
The material read by the active control group consisted of in-
formation about depression. It was divided into 9 modules without CBT
elements and comprised 23,000 words (49 pages). The material covered
information about the diagnosis of depression, epidemiology, gender
dierences, comorbidity and short descriptions of dierent psycholo-
gical and pharmacological treatments.
2.5. Outcome measures
The self-reported Montgomery-Åsberg Depression Rating Scale
(MADRS-S) (Svanborg and Asberg, 1994) was used to measure de-
pressive symptoms and suicidal thoughts. MADRS-S is a self-adminis-
tered version of Montgomery-Åsberg Depression Rating Scale (MADRS)
(Montgomery and Asberg, 1979) and has been reported to have a high
or moderate correlation with MADRS (Svanborg and Asberg, 1994;
Svanborg and Asberg, 2001;Fantino and Moore, 2009).
MADRS-S is a 9-item measure where the patient is asked to answer
questions by scoring them from 0 to 6. There are four type-statements
for each item (representing scores 0, 2, 4 and 6) to make the scoring
easier. A higher score indicates more severe depression. The maximum
score for MADRS-S is 54 and the cutopoints are; 012 minimal, 1319
mild, 2034 moderate and > 34 severe(Montgomery and Asberg,
1979).
MADRS-S has been tested for reliability and validity, for instance by
Fantino and Moore (2009) who analyzed data from a large group of
patients diagnosed with major depressive disorder. In that study, the
construct validity was found to be satisfactory and Cronbach's alpha
was reported at 0.84. It has been shown that MADRS-S is sensitive to
change in depressive symptoms during treatment (Montgomery and
Asberg, 1979;Fantino and Moore, 2009).
In addition to MADRS-S, the Beck Depression Inventory Second
edition (BDI-II) (Beck et al., 2005) was used to measure degree of de-
pressive symptoms. BDI-II is self-administered and it comprises 21
items. Each item yields a score of 03 which gives a maximum score of
63. Every item consists of a group of statements concerning a specic
symptom that is common in depressions. The patient chooses which
statement that best describes how he or she felt the last two weeks. BDI-
II cutoscores are; 013 minimal, 1419 mild, 2028 moderate and
2963 severe (Beck et al., 2005).
BDI-II is a revised version of BDI, created in 1996 to meet the cri-
teria of depression from Diagnostic and Statistical Manual of Mental
Disorders Fourth Edition (American Psychiatric Association, 2000;
Beck et al., 1996). The reliability and validity of BDI-II have been tested
with good results in several studies, showing satisfactory internal con-
sistency and test-retest reliability, and was found to dierentiate well
between grades of depression with sucient sensitivity to change (Beck
et al., 2005;Garcia-Batista et al., 2018;Kuhner et al., 2007;Osman
et al., 2004;Storch et al., 2004). A comparative study of MADRS-S and
BDI-II applied within primary care was published by (Wikberg et al.,
2015), where the two instruments were found to correlate well, both
with sucient reliability measures. Both the MADRS-S and BDI-II have
been validated for online use (Hollandare et al., 2010).
For the interviews, the SCID I (Structured Clinical Interview for
DSM-IV-Axis I) (First et al., 1998) and SCID II (Structured Clinical In-
terview for DSM-IV-Axis II) (First et al., 1994) were used to obtain a
good diagnostic picture of the participants.
Table 2
Diagnoses of the participants as reported in the SCID interviews at pre-treat-
ment.
ICBT
(n= 48)
Control
(n= 47)
Total
(n= 95)
Depressive disorders
Major depressive disorder 33 (68.8%) 35 (74.5%) 68 (71.6%)
Dysthymic disorder 6 (12.5%) 3 (6.4) 9 (9.5%)
Depressive disorder NOS 2 (4.2%) 2 (4.2%) 4 (4.2%)
MDD in partial remission 8 (16.7%) 7 (14.9%) 15 (15.8%)
Any depressive disorder 48 (100%) 47 (100%) 95 (100%)
Anxiety disorders
GAD 4 (8.3%) 3 (6.4%) 7 (7.4%)
Panic disorder with or without
agoraphobia
7 (14.6%) 7 (14.9%) 14 (14.7%)
Specic phobia 3 (6.3%) 5 (10.6%) 8 (8.4%)
Social phobia 4 (8.3%) 1 (2.1%) 5 (5.3%)
OCD 3 (6.3%) 2 (4.3%) 5 (5.3%)
PTSD 1 (2.1%) 2 (4.3%) 3 (3.2%)
Anxiety disorder NOS 4 (8.3%) 5 (10.6%) 9 (9.5%)
Any anxiety disorder 21 (43.8%) 24 (51.1%) 45 (47.4%)
Personality disorders
Borderline 1 (2.1%) 2 (4.3%) 3 (3.2%)
Obsessive-compulsive 1 (2.1%) 1 (2.1%) 2 (2.1%)
Antisocial 2 (4.2%) 0 (0.0%) 2 (2.1%)
Depressive 2 (4.2%) 2 (4.3%) 4 (4.2%)
Avoidant 0 (0.0%) 3 (6.4%) 3 (3.2%)
Passive-aggressive 0 (0.0%) 1 (2.1%) 1 (1.1%)
Personality disorder NOS 1 (2.1%) 1 (2.1%) 2 (2.1%)
Any personality disorder 7 (14.6%) 7 (14.9%) 14 (14.7%)
Other disorders
Maladaptive stress reaction 3 (6.3%) 2 (4.3%) 5 (5.3%)
Eating disorder 1 (2.1%) 0 (0.0%) 1 (1.1%)
Substance-related disorder 0 (0.0%) 3 (6.4%) 3 (3.2%)
Any other disorder 4 (8.3%) 5 (10.6%) 9 (9.5%)
Note: NOS = not otherwise specied; MDD = Major depressive disorder;
GAD = Generalized anxiety disorder; OCD = Obsessive compulsive disorder;
PTSD = Post traumatic stress disorder. A patient could have more than one
diagnosis.
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
3
2.6. Analyses
A group (ICBT versus control) × time (pre-, post-treatment, 6-, 12-,
and 24-month follow-ups) randomized design based on the intent-to-
treat model was used. Linear mixed models (Brown and Prescott, 1999)
with full information maximum likelihood estimation were employed to
handle missing data because repeated observations for the same in-
dividual are correlated which violates the assumption of independence
(Gueorguieva and Krystal, 2004;Brown and Prescott, 1999). Full in-
formation maximum likelihood estimation has the advantage of pro-
viding accurate estimates with missing data under fairly unrestrictive
missing data assumptions (Hesser, 2015) and is a recommended method
for handling missing data (Schafer and Graham, 2002). Estimated
parameters were obtained using a mixed-model approach employing an
unstructured covariance structure. The alpha level was set at 0.05.
Within and between-group eect sizes (Cohen's d)(Cohen, 1988) were
rst calculated using the estimated means from the mixed model ana-
lysis and are shown in Table 3. We also calculated eect sizes based on
observed data (using the pooled standard deviations) for the ICBT and
control group (Table 4), as well as between ICBT-patients with or
without a PD (Table 5).
For remission rates, clinically signicant change was determined for
BDI-II by using a combination of a lower limit cut-oscore (two stan-
dard deviations from the pretest mean) and a minimum improvement
Fig. 1. Participant owchart (remaining participants are based on MADRS-S ratings).
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
4
score calculated by means of the Reliable Change Index (RCI) for each
individual. The formulas provided by Jacobson and Truax (1991) were
employed. The RCI was calculated using a test-retest reliability of 0.80
for both the MADRS-S (Svanborg and Asberg, 1994) and the BDI-II
(Beck et al., 1996). The RCI cut-owas 1.96 (p< .05). For the
MADRS-S the change is considered reliable and clinically signicant if
the score is below 15 points and has decreased by at least 5 points, and
for the BDI-II when the score is below 13 points and has decreased at
least 10 points. Cut-os using these formulas have been applied in
previous ICBT research (Berger et al., 2011b). In the RCI-analyses,
missing data was replaced with the last known value. The second way
of determining clinical signicance was whether the participants were
diagnosed with a depressive disorder in the SCID interview after
treatment. No assumptions were made about diagnostic status for par-
ticipants who we were unable to interview at post treatment. Chi
2
tests
were used to analyse the dierence in proportions with diagnose be-
tween the groups.
Multiple regression analyses were performed to assess whether co-
morbidity predicted the improvement in depressive symptoms. The
outcome variables were standardized change scores on the MADRS-S
and the BDI-II between pre and post-treatment, calculated with the
formula Z
2
(Z
1
R
12
)(Steketee and Chambless, 1992). The in-
dependent variables, i.e. the presence (or non-presence) of an anxiety
disorder or a personality disorder, were inserted simultaneously into
the model.
3. Results
3.1. Adherence
In the ICBT group, the mean number of modules completed was 5.9
(SD = 2.2) (range 18) out of a maximum of 8 (73.8%). In the control
condition, an average of 6.8 (SD = 3.1) (range 19) modules were
completed out of a maximum of 9 (75.6%).
3.2. Clinical signicance
There was no signicant dierence between the proportions of pa-
tients with the diagnosis of major depression at pre-treatment in the
two groups (χ
2
= 0.38, df = 1, p= .537) nor any dierence between
the proportions of those fullling the criteria for any depressive dis-
order at pre-treatment in the two groups (χ
2
= 0.56, df = 1, p= .81)
(see Table 2). In the ICBT group, the number diagnosed with MDD
dropped from 33/48 (68.8%) participants at pre-treatment to 9/38
(23.7%) after treatment. In the control group, the number diagnosed
with MDD decreased from 35/47 (74.5%) to 19/36 (52.7%). After
treatment, signicantly fewer participants in the ICBT group fullled
the criteria for MDD compared to the control group (χ
2
= 6.653,
df = 1, p= .01). The proportions fullling the criteria for any de-
pressive disorder at post-treatment in the two groups did not dier
signicantly (χ
2
= 1.9, df = 1, p= .168).
From pre- to post-treatment, 56% (n= 27) of the participants in the
ICBT group exhibited reliable change (Jacobson and Truax, 1991)on
the BDI-II compared to 36% (n= 17) in the control group. Although the
dierence was not signicant, a trend favoring ICBT could be observed
(χ
2
= 3.646, df = 1, p= .056). At the 6-month follow-up, 60%
(n= 29) in the ICBT group and 38% (n= 18) in the control group
demonstrated reliable change. At this stage the dierence between the
two groups had decreased (χ
2
= 2.109, df = 1, p= .146). Similar
results were found at the 12-month follow-up; 58% (n= 28) in the
ICBT group and 30% (n= 14) in the control group exhibited reliable
change (χ
2
= 1.212, df = 1, p= .271).
3.3. Changes over time on the BDI-II: ICBT versus active control
The estimated means and statistics for the two groups across time
Table 3
Estimated means (standard deviations) for BDI-II and MADRS-S from pre-treatment to the 24-month assessment, eect sizes within and between groups.
Group Pre
[M (SD)]
Post
[M (SD)]
WG dBG d6-m
[M (SD)]
WG dBG d12-m
[M (SD)]
WG dBG d24-m
[M (SD)]
WG dBG d
BDI-II ICBT 29.5 (7.6) 18.5 (11.8) 1.11 0.23 14.7 (10.4) 1.62 0.18 15.3 (11.8) 1.43 0.08 13.5 (7.5) 2.11 0.00
CONT 27.0 (7.5) 21.2 (11.7) 0.59 16.6 (11.0) 1.10 16.3 (13.7) 0.97 13.5 (15.4) 1.11
MADRS-S ICBT 23.2 (4.2) 14.5 (8.3) 1.32 0.23 12.3 (7.6) 1.78 0.27 12.9 (9.7) 1.38 0.13 11.9 (10.6) 1.40 0.03
CONT 22.9 (4.1) 16.5 (8.9) 0.92 14.4 (8.2) 1.31 14.3 (11.7) 0.98 11.6 (12.0) 1.26
Q9 MADRS-S ICBT 2.1 (1.0) 1.4 (1.1) 0.67 0.09 1.1 (1.1) 0.95 0.09 1.2 (1.3) 0.78 0.07 1.0 (1.7) 0.81 0.06
CONT 2.2 (1.0) 1.5 (1.2) 0.63 1.2 (1.2) 0.91 1.3 (1.6) 0.67 1.1 (1.4) 0.92
Note. BDI-II = Beck Depression Inventory Second Edition, BG = between-group, ICBT = internet-based cognitive behavior therapy, CONT = control group,
d= Cohen's d, M = mean, MADRS-S = Montgomery-Åsberg Depression Rating Scale self-rated version, SD = standard deviation, WG = within-group. All the
within-group eect sizes were calculated based on the pre-treatment scores.
Table 4
Within and between-group eect size (Cohens' d), based on observed data, at
post-treatment and the 6-, 12- and 24-month follow-ups.
Within-group eect size Between-group eect size
ICBT Active control ICBT vs. Active control
MADRS-S
Post-treatment 1.57 1.00 0.31
6-month follow-up 2.02 1.37 0.30
12-month follow-up 1.42 1.25 0.15
24-month follow-up 1.80 1.55 0.01
BDI-II
Post-treatment 1.40 0.65 0.22
6-month follow-up 1.78 1.11 0.20
12-month follow-up 1.48 0.93 0.13
24-month follow-up 1.75 1.20 0.06
Table 5
Mean dierence between participants with and without a personality disorder
(PD) on the MADRS-S and BDI-II as well as between-group (no PD vs. PD) eect
size (Cohens' d), based on observed data, at post-treatment and the 6-, 12- and
24-month follow-ups for the ICBT group.
Mean dierence no PD vs. PD Between-group eect size
MADRS-S
Pre-treatment 0.07 0.02
Post-treatment 6.74 0.97
6-month follow-up 1.77 0.28
12-month follow-up 10.76 1.25
24-month follow-up 5.14 0.62
BDI-II
Pre-treatment 0.80 0.11
Post-treatment 8.06 0.90
6-month follow-up 1.56 0.18
12-month follow-up 9.43 0.94
24-month follow-up 5.93 0.54
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
5
for the BDI-II are presented in Table 3. Mixed-eect models showed a
non-signicant group eect (F = 0.01, df = 94.33, p= .991), a sig-
nicant time eect (F = 63.28, df = 78.06, p< .001), and a sig-
nicant group × time eect on the BDI-II (F = 5.52, df = 78.06,
p= .021) from pre to post-treatment in favor of the ICBT group.
Further analyses were carried out to examine changes from pre-
treatment to 6-, 12- and 24-month follow-ups. Mixed-eect models
showed a non-signicant group eect (F = 0.08, df = 87.72,
p= .782), a signicant time eect (F = 64.09, df = 75.86, p< .001),
and a non-signicant group × time eect on the BDI-II (F = 3.01,
df = 75.86, p= .055) from pre-treatment to the 6-month follow-up.
Mixed eect models also demonstrated a non-signicant group eect
(F = 0.19, df = 81.27, p= .661), a signicant time eect (F = 42.68,
df = 70.61, p< .001), and a non-signicant group × time eect on
the BDI-II (F = 2.44, df = 70.61, p= .071) from pre-treatment to the
12-month follow-up. There was a non-signicant group eect
(F = 0.47, df = 73.61, p= .496), a signicant time eect (F = 90.91,
df = 55.82, p< .001), and a non-signicant group × time eect on
the BDI-II (F = 0.68, df = 55.82, p= .414) from pre-treatment to the
24-month follow-up.
Finally, analyses were carried out to examine changes from post-
treatment to the 6-, 12- and 24-month follow-ups. Mixed-eect models
showed a non-signicant group eect (F = 0.88, df = 74.96, p= .351;
F = 0.36, df = 74.96, p= .553; F = 0.94, df = 68.82, p= .760), a
signicant time eect (F = 10.17, df = 70.61, p= .002; F = 7.48,
df = 67.46 p= .008; F = 15.13, df = 54.75, p< .001), and a non-
signicant group × time eect on the BDI-II (F = 0.18, df = 70.61,
p= .670; F = 0.92, df = 67.46, p= .341; F = 1.51, df = 54.75,
p= .225) from post-treatment to the 6-, 12- and 24-month follow-up
respectively.
3.4. Changes over time on the MADRS-S: ICBT versus active control
The estimated MADRS-S means and statistics for the two groups
over time are presented in Table 3. Mixed-eect models showed a non-
signicant group eect (F = 0.55, df = 85.56, p=.460), a signicant
eect of time (F = 83.11, df = 75.41, p< .001), and a non-signicant
group × time eect on the MADRS-S (F = 1.99, df = 75.41, p= .163)
from pre- to post-treatment.
Further analyses were carried out to examine changes from pre-
treatment to the 6-, 12- and 24-month follow-ups. Mixed-eect models
showed a non-signicant group eect (F = 0.99, df = 76.61,
p= .323), a signicant time eect (F = 74.86, df = 70.63, p< .001),
and a non-signicant group × time eect on the MADRS-S (F = 1.16,
df = 70.63, p= .318) from pre-treatment to the 6-month follow-up.
There was a non-signicant group eect (F = 1.17, df = 71.28,
p= .282), a signicant eect of time (F = 53.09, df = 65.53,
p<.001), and a non-signicant group × time eect on the MADRS-S
(F = 1.09, df = 65.53, p= .358) from pre-treatment to the 12-month
follow-up. Likewise, there was a non-signicant group eect (F = 0.06,
df = 60.65, p= .804), a signicant time eect (F = 89.46, df = 54.04,
p< .001), and a non-signicant group × time eect on the MADRS-S
(F = 0.01, df = 54.04, p= .974) from pre-treatment to the 24-month
follow-up.
Finally, analyses were carried out to examine changes from post-
treatment to the 6-, 12- and 24-month follow-ups. There was a non-
signicant group eect (F = 2.03, df = 74.41, p= .159; F = 1.06,
df = 74.91, p= .306; F = 0.24, df = 68.08, p= .627), a signicant
time eect (F = 4.67, df = 70.99, p= .034; F = 4.05, df = 65.53,
p= .048; F = 10.62, df = 55.02, p= .002), and a non-signicant
group × time eect on the MADRS-S (F = 0.15, df = 70.99, p= .699;
F = 0.56, df = 65.53, p= .457; F = 2.23, df = 55.02, p= .141) from
post-treatment to the 6-, 12- and 24-month follow-ups.
3.5. Changes over time on suicidal thoughts (MADRS-S item 9): ICBT
versus active control
The estimated MADRS-S item 9 (zest for life) means and statistics
for the two groups across time are presented in Table 3. Mixed-eect
models revealed a non-signicant group eect (F = 0.13, df = 91.23,
p= .724), a signicant eect of time (F = 41.40, df = 80.24,
p< .001), and a non-signicant group × time eect on item 9
(F = 0.01, df = 80.24, p= .999) from pre- to post-treatment.
There was a non-signicant group eect (F = 0.05, df = 85.25,
p= .832), a signicant time eect (F = 36.50, df = 74.02, p< .001),
and a non-signicant group × time eect on item 9 (F = 0.07,
df = 74.02, p= .935) from pre-treatment to the 6-month follow-up.
Likewise, there was a non-signicant group eect (F = 0.21,
df = 89.72, p= .649), a signicant time eect (F = 26.20, df = 69.81,
p< .001), and a non-signicant group × time eect on item 9
(F = 0.09, df = 69.81, p= .968) from pre-treatment to the 12-month
follow-up. There was also a non-signicant group eect (F = 0.01,
df = 75.92, p= .932), a signicant time eect (F = 46.07, df = 57.22,
p< .001), and a non-signicant group × time eect on item 9
(F = 0.09, df = 57.22, p= .767) from pre-treatment to the 24-month
follow-up.
Finally, analyses were carried out to examine changes from post-
treatment to the 6-, 12- and 24-month follow-ups. There was a non-
signicant group eect (F = 0.05, df = 75.60, p= .832; F = 0.28,
df = 77.82, p= .597; F = 0.01, df = 68.91, p= .990), a mixed eect
of time (F = 3.98, df = 72.02, p= .050; F = 2.06, df = 68.81,
p= .156; F = 5.85, df = 53.35, p= .019), and a non-signicant group
× time eect on item 9 (F = 0.22, df = 72.02, p= .638; F = 0.01,
df = 68.81, p= .934; F = 0.86, df = 53.35, p= .358) from post-
treatment to the 6-, 12- and 24-month follow-ups.
3.6. Eects of comorbidity
The presence of a personality disorder (PD) was found to sig-
nicantly predict a lower level of improvement in depressive symptoms
between pre- and post-treatment when measured by the MADRS-S
(β=0.342, t(39) = 2.213, p= .033), and when measured by the
BDI-II (β=0.321, t(39) = 2.151, p= .038). The presence of an
anxiety disorder did not signicantly predict the level of improvement
in depressive symptoms when measured by MADRS-S (β=0.051, t
(39) = 0.327, p= .746), or when measured by the BDI-II
(β=0.299, t(39) = 2.009, p= .052) although a trend was ob-
served.
3.7. Eect sizes based on observed data
The eect sizes (Cohen's d) calculated using observed data for both
MADRS-S and BDI-II were large within both the ICBT group and the
control group at post-treatment and small between groups (Table 4).
The between-groups eect sizes (Cohen's d) calculated using observed
data to compare participants with and without PDs in the ICBT group
were large at post-treatment (Table 5).
4. Discussion
The aim of this study was to compare ICBT to an active control
condition in a clinical sample of patients with a depressive disorder
recruited from primary and psychiatric care. Signicantly more ICBT
patients had entered remission after treatment compared to the active
control patients. Internet-based CBT reduced depressive symptoms
(measured by BDI-II) signicantly more than an active control condi-
tion between pre- and post-treatment in a clinical sample. During
follow-up, the dierence in symptom levels between the groups de-
creased and disappeared after two years, however, the improvements
were sustained during two years. The eect size of ICBT in this study
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
6
can be considered large. The eect of ICBT on depressive symptoms was
not signicantly moderated by the presence of a comorbid anxiety
disorder, but was clearly reduced in cases with a personality disorder.
We are not aware of any previous study that has investigated the
eect of comorbid personality pathology on ICBT for depression. In the
present study, we found that on average, patients without PD had al-
most a one standard deviation larger improvement at post-treatment
than those with PD. This is in line with previous research on psy-
chotherapy face-to-face (Newton-Howes et al., 2006;Newton-Howes
et al., 2014), however, we are not aware of any previous studies
showing that patients with PD may also benet far less from internet
based CBT for depression. This result needs to be replicated, preferably
with a larger sample that enables examination of possible dierences in
eect from specic PDs. If this result is replicated it raises the question
if ICBT can be adapted to become an eective treatment for depression
when comorbidity with a PD is present. Including elements from dia-
lectic behavior therapy (DBT) (Linehan, 1993) could be one way of
adapting the treatment for the needs of some patients with a PD since it
has been shown to be an eective face-to-face psychotherapy for pa-
tients with borderline personality disorder (Linehan et al., 2006;Cristea
et al., 2017) and has also shown an eect on depressive symptoms in
cases with comorbidity (Lynch et al., 2007).
The within-group eect size of ICBT in this study was (d) = 1.4,
which should be compared to (d) = 1.23 in the ecacy study by
Andersson et al. (2005), as the treatment content is almost identical.
However, the sample in the study by Andersson et al. (2005) was re-
cruited through the media and the study was conducted in a university
setting. This provides further evidence that ICBT can yield a similar
eect in a health care setting as in previous ecacy studies. The eect
in the present study was similar to the eects in previous eectiveness
studies, e.g. (d) = 1.27 (Hedman et al., 2014), (d) = 1.0 (calculated
with observed means) (Titov et al., 2015), and (d) = 1.09 (Kivi et al.,
2014).
The eect of the active control treatment was surprisingly large in
view of the fact that the modules did not contain any suggestions for
altering behavior or questioning dysfunctional thoughts. One possible
explanation is that therapist support seems to be responsible for a large
proportion of the eect in meta-analyses. The therapists were instructed
to only oer support to the control patients and not provide any gui-
dance resembling CBT techniques to ensure that no CBT elements
would seep into the control treatment. However, recent results reveal
that therapist guidance in ICBT is also mainly supportive (Holländare
et al., 2016) so perhaps the eect of supportive guidance explains why
the control patients also improved over time. Another possibility is that
merely reading the information material increased activity levels,
which could have reduced depressive symptoms.
The eect of ICBT in this study was not signicantly reduced by the
presence of an anxiety disorder, which is in line with a recent meta-
analysis by Karyotaki et al. (2018) which found no dierence in out-
come after ICBT for depression between patients with and without
comorbid anxiety. However, our result showing that the presence of a
PD did reduce the eect of ICBT raises questions about the eect of
other possible comorbidities, e.g., neuropsychiatric disorders or sub-
stance abuse, on the outcome of internet bases CBT. More knowledge
about the eect of comorbidities on ICBT outcome could help guide
clinical decisions and the development of adapted treatments for sub-
groups.
The drop-out rate was higher in the control condition compared to
the ICBT. Since CBT is a well-known form of therapy and the in-
formation condition is not an established intervention, a speculation is
that some of those randomized to information dropped out due to
disappointment with their allocation. Another speculation is that they
might have found the modules or the therapist guidance ineective or
unappealing. The randomized design and long term follow-up are
strengths of this study. The inclusion of more than one depressive dis-
order might increase external validity because the case mix was derived
from a recruitment process within a regular clinical health care setting.
The aim of the recruitment process was that each participant should be
eligible for health care before being asked to participate, making the
sample representative of those who normally seek help for depression.
Additional strengths are the thorough diagnostic process using face-to-
face SCID-I interviews before and after treatment, which were con-
ducted by clinical psychologists who were not aware of the patients'
allocation. Another strength is the use of outcome measures that have
been validated for online use (Hollandare et al., 2010). The limited
sample size did not allow for analyses of the impact on outcome from
dierent personality disorders, and also makes the estimate of the size
of dierences in outcome between patients with and without PD im-
precise (Kapur and Munafo, 2019). Another limitation was missing data
during the follow up which limits statistical power. We did not assess
PD after the intervention which limits our knowledge of diagnostic
status post treatment.
5. Conclusions
In Conclusion, although ICBT reduced depressive symptoms more
than an active control condition directly after treatment, the two in-
terventions seemed to have a similar eect over time. Depressed pa-
tients within primary and psychiatric care, with or without a comorbid
anxiety disorder, can benet from ICBT. Patients with a PD seem to
benet less from ICBT for depression, however this needs to be in-
vestigated further.
Funding
Financial support for the study was provided by two Swedish re-
search funds: Region Örebro County Research Committee [OLL-57/06],
and the Regional Research Council [RFR-73351]. No funding body took
part in designing the study, collecting or analysing the data, inter-
preting the results or writing the manuscript.
Declaration of competing interest
The authors have declared that no conict of interest exists.
References
American Psychiatric Association, 2000. Diagnostic and Statistical Manual of Mental
Disorders, 4th ed. American Psychiatric Press, Washington DC Text Revision.
Andersson, G., 2016. Internet-delivered psychological treatments. Annu. Rev. Clin.
Psychol. 12, 157179.
Andersson, G., Bergstrom, J., Hollandare, F., Carlbring, P., Kaldo, V., Ekselius, L., 2005.
Internet-based self-help for depression: randomised controlled trial. Br. J. Psychiatry
187, 456461.
Andersson, G., Carlbring, P., Holmstrom, A., Sparthan, E., Furmark, T., Nilsson-Ihrfelt, E.,
Buhrman, M., Ekselius, L., 2006. Internet-based self-help with therapist feedback and
in vivo group exposure for social phobia: a randomized controlled trial. J. Consult.
Clin. Psychol. 74, 677686.
Andersson, G., Carlbring, P., Ljótsson, B., Hedman, E., 2013a. Guided internet-based CBT
for common mental disorders. J. Contemp. Psychother. 43, 223233.
Andersson, G., Hesser, H., Hummerdal, D., Bergman-Nordgren, L., Carlbring, P., 2013b. A
3.5-year follow-up of internet-delivered cognitive behavior therapy for major de-
pression. J. Ment. Health 22, 155164. https://doi.org/10.3109/09638237.2011.
608747. (Epub 2011 Sep 29).
Andersson, G., Topooco, N., Havik, O., Nordgreen, T., 2016. Internet-supported versus
face-to-face cognitive behavior therapy for depression. Expert. Rev. Neurother. 16,
5560. https://doi.org/10.1586/14737175.2015.1125783. (Epub 2015 Dec 15).
Beck, A.T., 1979. Cognitive Therapy of Depression. Guilford Press.
Beck, A.T., Steer, R.A., Brown, G.K., 1996. Manual for the Beck Depression Inventory-II.
Psychological Corporation, San Antonio, TX.
Beck, A.T., Steer, R.A., Brown, G.K., 2005. BDI-II, Beck Depression Inventory - Second
Edition. Manual - Svensk Version. Psykologiförlaget, Sandviken.
Berger, T., Caspar, F., Richardson, R., Kneubuhler, B., Sutter, D., Andersson, G., 2011a.
Internet-based treatment of social phobia: a randomized controlled trial comparing
unguided with two types of guided self-help. Behav. Res. Ther. 49, 158169.
Berger, T., Hammerli, K., Gubser, N., Andersson, G., Caspar, F., 2011b. Internet-based
treatment of depression: a randomized controlled trial comparing guided with un-
guided self-help. Cogn. Behav. Ther. 40, 251266.
Botella, C., Gallego, M.J., Garcia-Palacios, A., Guillen, V., Banos, R.M., Quero, S., Alcaniz,
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
7
M., 2010. An internet-based self-help treatment for fear of public speaking: a con-
trolled trial. Cyberpsychol. Behav. Soc. Netw. 13, 407421.
Brown, H., Prescott, R., 1999. Applied Mixed Models in Medicine. John Wiley & Sons,
Chichester.
Carlbring, P., Nilsson-Ihrfelt, E., Waara, J., Kollenstam, C., Buhrman, M., Kaldo, V.,
Soderberg, M., Ekselius, L., Andersson, G., 2005. Treatment of panic disorder: live
therapy vs. self-help via the internet. Behav. Res. Ther. 43, 13211333.
Carlbring, P., Hägglund, M., Luthström, A., Dahlin, M., Kadowaki, Å., Vernmark, K.,
Andersson, G., 2013. Internet-based behavioral activation and acceptance-based
treatment for depression: a randomized controlled trial. J. Aect. Disord. 148 (23),
331337. https://doi.org/10.1016/j.jad.2012.12.020.
Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. Lawrence
Erlbaum Associates.
Cristea, I.A., Gentili, C., Cotet, C.D., Palomba, D., Barbui, C., Cuijpers, P., 2017. Ecacy
of psychotherapies for borderline personality disorder: a systematic review and meta-
analysis. JAMA Psychiat. 74, 319328.
Dear, B.F., Fogliati, V.J., Fogliati, R., Johnson, B., Boyle, O., Karin, E., Gandy, M.,
Kayrouz, R., Staples, L.G., Titov, N., 2018. Treating anxiety and depression in young
adults: a randomised controlled trial comparing clinician-guided versus self-guided
internet-delivered cognitive behavioural therapy. Aust. N. Z. J. Psychiatry 52,
668679.
Fantino, B., Moore, N., 2009. The self-reported Montgomery-Asberg Depression Rating
Scale is a useful evaluative tool in major depressive disorder. BMC Psychiatry 9, 26.
First, M., Spitzer, R., Gibbon, M., Williams, J., Benjamin, L., 1994. Structured Clinical
Interview for DSM-IV Axis II Personality Disorders (SCID II). Biometric Research
Department, New York.
First, M.B., Gibbon, M., Spitzer, R.L., Williams, J.B.W., 1998. Structured Clinical
Interview for DSM-IV Disorders (SCID-I) (Swedish Version). Pilgrim Press, Danderyd.
Forand, N.R., Derubeis, R.J., 2013. Pretreatment anxiety predicts patterns of change in
cognitive behavioral therapy and medications for depression. J. Consult. Clin.
Psychol. 81, 774782. https://doi.org/10.1037/a0032985. (Epub 2013 May 6).
Fournier, J.C., Derubeis, R.J., Shelton, R.C., Gallop, R., Amsterdam, J.D., Hollon, S.D.,
2008. Antidepressant medications v. cognitive therapy in people with depression
with or without personality disorder. Br. J. Psychiatry 192, 124129.
Friborg, O., Martinsen, E.W., Martinussen, M., Kaiser, S., Overgård, K.T., Rosenvinge,
J.H., 2014. Comorbidity of personality disorders in mood disorders: a meta-analytic
review of 122 studies from 1988 to 2010. J. Aect. Disord. 152154, 111.
Garcia-Batista, Z.E., Guerra-Pena, K., Cano-Vindel, A., Herrera-Martinez, S.X., Medrano,
L.A., 2018. Validity and reliability of the Beck Depression Inventory (BDI-II) in
general and hospital population of Dominican Republic. PLoS One 13, e0199750.
Gibbons, C.J., Derubeis, R.J., 2008. Anxiety symptom focus in sessions of cognitive
therapy for depression. Behav. Ther. 39, 117125. https://doi.org/10.1016/j.beth.
2007.05.006. (Epub 2007 Oct 31).
Gortner, E.T., Gollan, J.K., Dobson, K.S., Jacobson, N.S., 1998. Cognitive-behavioral
treatment for depression: relapse prevention. J. Consult. Clin. Psychol. 66, 377384.
Gueorguieva, R., Krystal, J.H., 2004. Move over ANOVA: progress in analyzing repeated-
measures data and its reection in papers published in the Archives of General
Psychiatry. Arch. Gen. Psychiatry 61, 310317.
Hadjistavropoulos, H.D., Nugent, M.M., Alberts, N.M., Staples, L., Dear, B.F., Titov, N.,
2016. Transdiagnostic internet-delivered cognitive behaviour therapy in Canada: an
open trial comparing results of a specialized online clinic and nonspecialized com-
munity clinics. J. Anxiety Disord. 42, 1929.
Hedman, E., Ljotsson, B., Lindefors, N., 2012. Cognitive behavior therapy via the internet:
a systematic review of applications, clinical ecacy and cost-eectiveness. Expert
Rev. Pharmacoecon. Outcomes Res. 12, 745764.
Hedman, E., Ljotsson, B., Kaldo, V., Hesser, H., EL Alaoui, S., Kraepelien, M., Andersson,
E., Ruck, C., Svanborg, C., Andersson, G. & Lindefors, N. 2014. Eectiveness of in-
ternet-based cognitive behaviour therapy for depression in routine psychiatric care. J.
Aect. Disord.,155:4958., https://doi.org/10.1016/j.jad.2013.10.023. (Epub 2013
Oct 26).
Heinrich, S., Rozental, A., Carlbring, P., Andersson, G., Cotter, K., Weise, C., 2016.
Treating tinnitus distress via the internet: a mixed methods approach of what makes
patients seek help and stay motivated during internet-based cognitive behavior
therapy. Internet Interv. 4, 120130.
Hesser, H., 2015. Modeling individual dierences in randomized experiments using
growth models: recommendations for design, statistical analysis and reporting of
results of internet interventions. Internet Interv. 2, 110120.
Hollandare, F., Andersson, G., Engstrom, I., 2010. A comparison of psychometric prop-
erties between internet and paper versions of two depression instruments (BDI-II and
MADRS-S) administered to clinic patients. J. Med. Internet Res. 12, e49.
Hollandare, F., Anthony, S.A., Randestad, M., Tillfors, M., Carlbring, P., Andersson, G.,
Engstrom, I., 2013. Two-year outcome of internet-based relapse prevention for par-
tially remitted depression. Behav. Res. Ther. 51, 719722. https://doi.org/10.1016/j.
brat.2013.08.002. (Epub 2013 Aug 16).
Holländare, F., Gustafsson, S., Berglind, M., Grape, F., Carlbring, P., Andersson, G.,
Hadjistavropoulos, H., Tillfors, M., 2016. Therapist behaviours in internet-based
cognitive behaviour therapy (ICBT) for depressive symptoms. Internet Interv. 3, 17.
Ivarsson, D., Blom, M., Hesser, H., Carlbring, P., Enderby, P., Nordberg, R., Andersson, G.,
2014. Guided internet-delivered cognitive behaviour therapy for post-traumatic
stress disorder: a randomized controlled trial. Internet Interv. 1, 3340.
Jacobson, N.S., Truax, P., 1991. Clinical signicance: a statistical approach to dening
meaningful change in psychotherapy research. J. Consult. Clin. Psychol. 59, 1219.
Johansson, R., Andersson, G., 2012. Internet-based psychological treatments for depres-
sion. Expert. Rev. Neurother. 12, 861870.
Johansson, R., Bjorklund, M., Hornborg, C., Karlsson, S., Hesser, H., Ljotsson, B.,
Rousseau, A., Frederick, R.J., Andersson, G., 2013. Aect-focused psychodynamic
psychotherapy for depression and anxiety through the internet: a randomized con-
trolled trial. PeerJ 1, e102.
Johansson, O., Bjarehed, J., Andersson, G., Carlbring, P., Lundh, L.G., 2019. Eectiveness
of guided internet-delivered cognitive behavior therapy for depression in routine
psychiatry: a randomized controlled trial. Internet Interv. 17, 100247.
Joyce, P.R., Mckenzie, J.M., Carter, J.D., Rae, A.M., Luty, S.E., Frampton, C.M., Mulder,
R.T., 2007. Temperament, character and personality disorders as predictors of re-
sponse to interpersonal psychotherapy and cognitive-behavioural therapy for de-
pression. Br. J. Psychiatry 190, 503508.
Kampmann, I.L., Emmelkamp, P.M., Morina, N., 2016. Meta-analysis of technology-as-
sisted interventions for social anxiety disorder. J. Anxiety Disord. 42, 7184.
Kapur, S., Munafo, M., 2019. Small sample sizes and a false economy for psychiatric
clinical trials. JAMA Psychiatry 76, 676677.
Karyotaki, E., Ebert, D.D., Donkin, L., Riper, H., Twisk, J., Burger, S., Rozental, A., Lange,
A., Williams, A.D., Zarski, A.C., Geraedts, A., Van Straten, A., Kleiboer, A., Meyer, B.,
Unlu Ince, B.B., Buntrock, C., Lehr, D., Snoek, F.J., Andrews, G., Andersson, G., Choi,
I., Ruwaard, J., Klein, J.P., Newby, J.M., Schroder, J., Laferton, J.A.C., Van Bastelaar,
K., Imamura, K., Vernmark, K., Boss, L., Sheeber, L.B., Kivi, M., Berking, M., Titov, N.,
Carlbring, P., Johansson, R., Kenter, R., Perini, S., Moritz, S., Nobis, S., Berger, T.,
Kaldo, V., Forsell, Y., Lindefors, N., Kraepelien, M., Bjorkelund, C., Kawakami, N.,
Cuijpers, P., 2018. Do guided internet-based interventions result in clinically relevant
changes for patients with depression? An individual participant data meta-analysis.
Clin. Psychol. Rev. 63, 8092.
Kashdan, T.B., Roberts, J.E., 2011. Comorbid social anxiety disorder in clients with de-
pressive disorders: predicting changes in depressive symptoms, therapeutic re-
lationships, and focus of attention in group treatment. Behav. Res. Ther. 49, 875884.
https://doi.org/10.1016/j.brat.2011.10.002. (Epub 2011 Oct 12).
Kessler, R.C., Merikangas, K.R., Wang, P.S., 2007. Prevalence, comorbidity, and service
utilization for mood disorders in the United States at the beginning of the twenty-rst
century. Annu. Rev. Clin. Psychol. 3, 137158.
Kessler, D., Lewis, G., Kaur, S., Wiles, N., King, M., Weich, S., Sharp, D.J., Araya, R.,
Hollinghurst, S., Peters, T.J., 2009. Therapist-delivered internet psychotherapy for
depression in primary care: a randomised controlled trial. Lancet 374, 628634.
Kivi, M., Eriksson, M.C., Hange, D., Petersson, E.L., Vernmark, K., Johansson, B.,
Bjorkelund, C., 2014. Internet-based therapy for mild to moderate depression in
Swedish primary care: short term results from the PRIM-NET randomized controlled
trial. Cogn. Behav. Ther. 43, 289298. https://doi.org/10.1080/16506073.2014.
921834. (Epub 2014 Jun 9).
Kuhner, C., Burger, C., Keller, F., Hautzinger, M., 2007. Reliability and validity of the
Revised Beck Depression Inventory (BDI-II). Results from German samples.
Nervenarzt 78, 651656.
Linehan, M., 1993. Cognitive-behavioral Treatment of Borderline Personality Disorder.
The Guilford Press, New York.
Linehan, M.M., Comtois, K.A., Murray, A.M., Brown, M.Z., Gallop, R.J., Heard, H.L.,
Korslund, K.E., Tutek, D.A., Reynolds, S.K., Lindenboim, N., 2006. Two-year rando-
mized controlled trial and follow-up of dialectical behavior therapy vs therapy by
experts for suicidal behaviors and borderline personality disorder. Arch. Gen.
Psychiatry 63, 757766.
Lis, E., Myhr, G., 2016. The eect of borderline personality pathology on outcome of
cognitive behavior therapy. J. Psychiatr. Pract. 22, 270282. https://doi.org/10.
1097/PRA.0000000000000167.
Lynch, T.R., Cheavens, J.S., Cukrowicz, K.C., Thorp, S.R., Bronner, L., Beyer, J., 2007.
Treatment of older adults with co-morbid personality disorder and depression: a
dialectical behavior therapy approach. Int. J. Geriatr. Psychiatry 22, 131143.
Martell, C.R., Addis, M.E., Jacobson, N.S., 2001. Depression in Context: Strategies for
Guided Action. W W Norton & Company Incorporated.
Mathiasen, K., Riper, H., Andersen, T.E., Roessler, K.K., 2018. Guided internet-based
cognitive behavioral therapy for adult depression and anxiety in routine secondary
care: observational study. J. Med. Internet Res. 20, e10927.
Montgomery, S.A., Asberg, M., 1979. A new depression scale designed to be sensitive to
change. Br. J. Psychiatry 134, 382389.
Morin, C.M., 1996. Relief from Insomnia: Getting the Sleep of Your Dreams. Broadway
Books.
Newton-Howes, G., Tyrer, P., Johnson, T., 2006. Personality disorder and the outcome of
depression: meta-analysis of published studies. Br. J. Psychiatry 188, 1320.
Newton-Howes, G., Tyrer, P., Johnson, T., Mulder, R., Kool, S., Dekker, J., Schoevers, R.,
2014. Inuence of personality on the outcome of treatment in depression: systematic
review and meta-analysis. J. Personal. Disord. 28, 577593.
Nordgreen, T., Gjestad, R., Andersson, G., Carlbring, P., Havik, O.E., 2018. The eec-
tiveness of guided internet-based cognitive behavioral therapy for social anxiety
disorder in a routine care setting. Internet Interv. 13, 2429.
Osman, A., Kopper, B.A., Barrios, F., Gutierrez, P.M., Bagge, C.L., 2004. Reliability and
validity of the Beck depression inventoryII with adolescent psychiatric inpatients.
Psychol. Assess. 16, 120132.
Paxling, B., Lundgren, S., Norman, A., Almlov, J., Carlbring, P., Cuijpers, P., Andersson,
G., 2013. Therapist behaviours in internet-delivered cognitive behaviour therapy:
analyses of e-mail correspondence in the treatment of generalized anxiety disorder.
Behav. Cogn. Psychother. 41, 280289. https://doi.org/10.1017/
S1352465812000240. (Epub 2012 May 1).
Perini, S., Titov, N., Andrews, G., 2009. Clinician-assisted internet-based treatment is
eective for depression: randomized controlled trial. Aust. N. Z. J. Psychiatry 43,
571578.
Richards, D., Richardson, T., 2012. Computer-based psychological treatments for de-
pression: a systematic review and meta-analysis. Clin. Psychol. Rev. 32, 329342
(Epub 2012 Feb 28).
Robinson, E., Titov, N., Andrews, G., Mcintyre, K., Schwencke, G., Solley, K., 2010.
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
8
Internet treatment for generalized anxiety disorder: a randomized controlled trial
comparing clinician vs. technician assistance. PLoS ONE [Electronic Resource] 5,
e10942.
Ruwaard, J., Schrieken, B., Schrijver, M., Broeksteeg, J., Dekker, J., Vermeulen, H.,
Lange, A., 2009. Standardized web-based cognitive behavioural therapy of mild to
moderate depression: a randomized controlled trial with a long-term follow-up. Cogn.
Behav. Ther. 38, 206221.
Ruwaard, J., Lange, A., Schrieken, B., Dolan, C.V., Emmelkamp, P., 2012. The eec-
tiveness of online cognitive behavioral treatment in routine clinical practice. PLoS
ONE [Electronic Resource] 7, e40089.
Sanchez-Ortiz, V.C., Munro, C., Startup, H., Treasure, J., Schmidt, U., 2011. The role of
email guidance in internet-based cognitive-behavioural self-care treatment for bu-
limia nervosa. Eur. Eat. Disord. Rev. 19, 342348. https://doi.org/10.1002/erv.
1074. (Epub 2011 Mar 10).
Schafer, J.L., Graham, J.W., 2002. Missing data: our view of the state of the art. Psychol.
Methods 7, 147177.
Schindler, A., Hiller, W., Witthoft, M., 2013. What predicts outcome, response, and drop-
out in CBT of depressive adults? A naturalistic study. Behav. Cogn. Psychother. 41,
365370. https://doi.org/10.1017/S1352465812001063. (Epub 2012 Dec 5).
Smits, J.A., Minhajuddin, A., Jarrett, R.B., 2009. Cognitive therapy for depressed adults
with comorbid social phobia. J. Aect. Disord. 114, 271278. https://doi.org/10.
1016/j.jad.2008.08.008. (Epub 2008 Sep 18).
Steketee, G., Chambless, D., 1992. Methodological issues in prediction of treatment
outcome. Clin. Psychol. Rev. 12, 387400.
Storch, E.A., Roberti, J.W., Roth, D.A., 2004. Factor structure, concurrent validity, and
internal consistency of the Beck Depression Inventory-Second Edition in a sample of
college students. Depress. Anxiety 19, 187189.
Svanborg, P., Asberg, M., 1994. A new self-rating scale for depression and anxiety states
based on the Comprehensive Psychopathological Rating Scale. Acta Psychiatr. Scand.
89, 2128.
Svanborg, P., Asberg, M., 2001. A comparison between the Beck Depression Inventory
(BDI) and the self-rating version of the Montgomery Asberg Depression Rating Scale
(MADRS). J. Aect. Disord. 64, 203216.
Titov, N., Dear, B.F., Schwencke, G., Andrews, G., Johnston, L., Craske, M.G., Mcevoy, P.,
2011. Transdiagnostic internet treatment for anxiety and depression: a randomised
controlled trial. Behav. Res. Ther. 49, 441452.
Titov, N., Dear, B.F., Johnston, L., Mcevoy, P.M., Wootton, B., Terides, M.D., Gandy, M.,
Fogliati, V., Kayrouz, R., Rapee, R.M., 2014. Improving adherence and clinical
outcomes in self-guided internet treatment for anxiety and depression: a 12-month
follow-up of a randomised controlled trial. PLoS One 9.
Titov, N., Dear, B.F., Staples, L.G., Bennett-Levy, J., Klein, B., Rapee, R.M., Shann, C.,
Richards, D., Andersson, G., Ritterband, L., Purtell, C., Bezuidenhout, G., Johnston,
L., Nielssen, O.B., 2015. MindSpot clinic: an accessible, ecient, and eective online
treatment service for anxiety and depression. Psychiatr. Serv. 66, 10431050.
https://doi.org/10.1176/appi.ps.201400477. (Epub 2015 Jul 1).
Titov, N., Fogliati, V.J., Staples, L.G., Gandy, M., Johnston, L., Wootton, B., Nielssen, O.,
Dear, B.F., 2016. Treating anxiety and depression in older adults: randomised con-
trolled trial comparing guided v. self-guided internet-delivered cognitive-behavioural
therapy. BJPsych Open 2, 5058.
Van Bronswijk, S.C., Lemmens, L., Viechtbauer, W., Huibers, M.J.H., Arntz, A., Peeters, F.,
2018. The impact of personality disorder pathology on the eectiveness of cognitive
therapy and interpersonal psychotherapy for major depressive disorder. J. Aect.
Disord. 225, 530538.
Vernmark, K., Lenndin, J., Bjarehed, J., Carlsson, M., Karlsson, J., Oberg, J., Carlbring, P.,
Eriksson, T., Andersson, G., 2010. Internet administered guided self-help versus in-
dividualized e-mail therapy: a randomized trial of two versions of CBT for major
depression. Behav. Res. Ther. 48, 368376.
Warmerdam, L., Van Straten, A., Twisk, J., Riper, H., Cuijpers, P., 2008. Internet-based
treatment for adults with depressive symptoms: randomized controlled trial. J. Med.
Internet Res. 10, e44.
Watts, S., Newby, J.M., Mewton, L., Andrews, G., 2012. A clinical audit of changes in
suicide ideas with internet treatment for depression. BMJ Open 2.
Wikberg, C., Nejati, S., Larsson, M.E., Petersson, E.L., Westman, J., Ariai, N., Kivi, M.,
Eriksson, M., Eggertsen, R., Hange, D., Baigi, A., Bjorkelund, C., 2015. Comparison
between the Montgomery-Asberg depression rating scale-self and the Beck depression
inventory II in primary care. Prim Care Companion CNS Disord 17.
Williams, A.D., Andrews, G., 2013. The eectiveness of Internet Cognitive Behavioural
Therapy (iCBT) for depression in primary care: a quality assurance study. PLoS ONE
[Electronic Resource] 8, e57447.
Wilson, K.G., Murrell, A.R., 2004. Values Work in Acceptance and Commitment Therapy.
Mindfulness and acceptance: Expanding the cognitivebehavioral tradition. Guilford, New
York, pp. 120151.
Zetterberg, M., Carlbring, P., Andersson, G., Berg, M., Shafran, R., Rozental, A., 2019.
Internet-based cognitive behavioral therapy of perfectionism: comparing regular
therapist support and support upon request. Internet Interv. 17, 100237.
A.-L. Flygare, et al. Internet Interventions 19 (2020) 100303
9
... For example, some patients with severe comorbid depression may lack the resources to collaborate with the treatment regimen and may require pharmacotherapy as well as augmentation of psychological treatment (Abramowitz, 2004). Treatment of OCD symptoms with autism may also require adaptations such as extended psychoeducation, regular home-based sessions, highly graded exposures, and increased involvement of social systems (Krebs et al., 2016;Flygare et al., 2020). Available measures for structured diagnostic interview include the Mini-International Neuropsychiatric Interview (M.I.N.I., Sheehan et al., 1998), the Structured Clinical Interview for DSM-5 disorders, (SCID-5, First et al., 2015), or the Diagnostic Interview for Anxiety, Mood, and OCD and Related Neuropsychiatric Disorders (DIAMOND, Tolin et al., 2018). ...
... Complex or serious co-morbid disorders such as body dysmorphic disorder, PTSD, eating disorder, or borderline personality disorder may require dual focused interventions, with primacy and seriousness of symptoms influencing sequencing of interventions (e.g., Thamby and Khanna, 2019;Castle et al., 2021;Fletcher et al., 2020;Mandelli et al., 2020). Neurodevelopmental comorbid disorders such as Tourette's syndrome, chronic tic disorder, and ADHD necessitate adaptations in approach based on the specific available evidence (e.g., Flygare et al., 2020;Mersin et al., 2020). Disorders that interfere with learning during CBT, such as substance abuse, need concurrent or prior treatment. ...
Article
Obsessive-Compulsive Disorder (OCD) is a leading cause of disability world-wide (World Health Organization, 2008). Treatment of OCD is a specialized field whose aim is recovery from illness for as many patients as possible. The evidence-based psychotherapeutic treatment for OCD is specialized cognitive behavior therapy (CBT, NICE, 2005, Koran and Simpson, 2013). However, these treatments are not accessible to many sufferers around the world. Currently available guidelines for care are deemed to be essential but insufficient because of highly variable clinician knowledge and competencies specific to OCD. The phase two mandate of the 14 nation International OCD Accreditation Task Force (ATF) created by the Canadian Institute for Obsessive Compulsive Disorders is development of knowledge and competency standards for specialized treatments for OCD through the lifespan deemed by experts to be foundational to transformative change in this field. This paper presents knowledge and competency standards for specialized CBT for adult OCD developed to inform, advance, and offer a model for clinical practice and training for OCD. During upcoming ATF phases three and four criteria and processes for training in specialized treatments for OCD through the lifespan for certification (individuals) and accreditation (sites) will be developed based on the ATF standards.
... The same conclusion has been drawn by separate systematic reviews (Ahern et al., 2018;Andrews et al., 2018;Josephine et al., 2017). The strength of support for iCBT is bolstered further by evidence that trials included in iCBT meta-analyses include severe and complex presentations of depression (see also Bower et al., 2013;Lorenzo-Luaces et al., 2018a) and that effect sizes from controlled trials generalise to everyday practice (Flygare et al., 2020;Staples et al., 2019). ...
Article
Objective To rebut the claims made in an opinion piece by Anaf and colleagues regarding the recommendations for psychotherapy within the 2020 RANZCP Mood Disorders Clinical Practice Guidelines (CPG). Conclusions The CPG attaches importance to psychological interventions and recommends their administration as first-line in the treatment of depression. The concerns raised by Anaf and colleagues have no basis and are readily dismissed by referring to the guidelines. Therefore, we strongly encourage clinicians to formulate their own views by reading the guidelines for themselves.
... Internetdelivered Cognitive Behavioral Therapy (iCBT) may be beneficial by increasing the availability of care through requiring less therapist time, incurring lower health care costs, bridging geographical distances by providing flexibility in the time and location of treatment delivery, and reducing stigma associated with seeking care. The positive effect of iCBT in adults with anxiety disorders is well-known (Andersson, 2018) and comparable to face-to-face therapy in primary care settings (Eriksson et al., 2017;Flygare et al., 2019). Despite the positive results of iCBT, however, dropout from treatment is common (Eriksson et al., 2017;Christensen et al., 2009;van Ballegooijen et al., 2014). ...
Article
Full-text available
Background: The World Health Organization has declared that primary care should be organized to empower individuals, families, and communities to optimize health. Internet cognitive behavioral therapy (iCBT) tailored by psychologists' initial assessments to meet patients' specific needs have shown promising effects. However, few studies have evaluated patient involvement in decisions during iCBT. Aim: This study aimed to explore the effect of patient-driven iCBT compared to standard iCBT on perceived control over treatment, adherence, and level of anxiety symptoms. A secondary aim was to assess the relationship between changes in empowerment and changes in anxiety symptoms. Method: Participants were patients recruited form primary care and assessed as meeting the criterion for an anxiety disorder. Participants were randomized to patient-driven iCBT (n = 27) or standard iCBT (n = 28). Patient-driven iCBT was adapted to participants' preferences regarding for example focus of treatment program and order of modules. Participants randomized to the control condition received the standard iCBT program for anxiety disorders at the participating unit. The outcome measures were patients' perceived control over treatment, adherence to treatment, symptoms of anxiety, depression and general disability as well as the experience of empowerment. Results: Participants in patient-driven iCBT had statistically higher perceived control over treatment (t(43) = 2.13, p = .04). Symptoms were significantly reduced in both arms with regards to anxiety, depression, and general disability. A significant time per condition interaction effect for anxiety symptoms was observed (df = 45.0; F = 3.055; p = .038), where the patient-driven condition had a significantly larger reduction in anxiety. For both groups a significant correlation of r = -0.47 was found between changes in empowerment and changes in anxiety. Conclusion: Results indicate that iCBT that is patient-driven, may have a greater effect on anxiety, than standard iCBT. The effect on perceived control over treatment might also be larger in patient-driven treatments than in standard iCBT. Internet-based therapies inherently promote as active agents of their own care and might be well suited for promoting perceived control and empowerment. Findings need to be replicated given the small sample size and the explorative nature of the study. Clinical trials registration: NCT04688567.
... Twelve RCTs tested TBIs against attention placebo controls, which consisted of online psychoeducation [24,37,48,76], participation in an online discussion forum [49], unspecific telephone support calls [32], neutral tasks [42], tasks without training contingency [27,54], symptom monitoring plus short check-in telephone calls [81], daily mood diary [44], and a walking and wellness control condition [83]. Depression severity was significantly lower at posttreatment in the TBI group than in the attention placebo group, with substantial heterogeneity (SMD -0. ...
Article
Full-text available
Background Evidence on technology-based psychological interventions (TBIs) for the acute treatment of depression is rapidly growing. Despite extensive research in this field, there is a lack of research determining effectiveness and acceptance of TBIs considering different application formats in people with a formally diagnosed depressive disorder. Objective The goal of the review was to investigate the effectiveness and acceptance of TBIs in people with diagnosed depression with particular focus on application formats (stand-alone interventions, blended treatments, collaborative and/or stepped care interventions). Methods Studies investigating adults with diagnosed unipolar depressive disorders receiving any kind of psychotherapeutic treatment delivered (at least partly) by a technical medium and conducted as randomized controlled trials (RCTs) were eligible for inclusion. We searched CENTRAL (Cochrane Central Register of Controlled Trials; August 2020), MEDLINE, PsycINFO, PSYNDEX, CINAHL (January 2018), clinical trial registers, and sources of grey literature (January 2019). Two independent authors decided about study inclusion and extracted data. We performed random effects meta-analyses to synthesize the data. ResultsDatabase searches resulted in 15,546 records of which 78 completed studies were included. TBIs delivered as stand-alone interventions showed positive effects on posttreatment depression severity when compared to treatment as usual (SMD –0.44, 95% CI –0.73 to –0.15, k=10; I²=86%), attention placebo (SMD –0.51, 95% CI –0.73 to –0.30; k=12; I²=66%), and waitlist controls (SMD –1.01, 95% CI –1.23 to –0.79; k=19; I²=73%). Superior long-term effects on depression severity were shown when TBIs were compared to treatment as usual (SMD –0.24, 95% CI –0.41 to –0.07; k=6; I²=48%) attention placebo (SMD –0.23, 95% CI –0.40 to –0.07; k=7; I²=21%) and waitlist controls (SMD –0.74, 95% CI –1.31 to –0.18; k=3; I²=79%). TBIs delivered as blended treatments (providing a TBI as an add-on to face-to-face treatment) yielded beneficial effects on posttreatment depression severity (SMD –0.27, 95% CI –0.48 to –0.05; k=8; I²=53%) compared to face-to-face treatments only. Additionally, TBIs delivered within collaborative care trials were more effective in reducing posttreatment (SMD –0.20, 95% CI –0.36 to –0.04; k=2; I²=0%) and long-term (SMD –0.23, 95% CI –0.39 to –0.07; k=2; I²=0%) depression severity than usual care. Dropout rates did not differ between the intervention and control groups in any comparison (all P≥.09). Conclusions We found that TBIs are effective not only when delivered as stand-alone interventions but also when they are delivered as blended treatments or in collaborative care trials for people with diagnosed depression. Our results may be useful to inform routine care, since we focused specifically on different application formats, formally diagnosed patients, and the long-term effectiveness of TBIs. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42016050413; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42016050413 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2018-028042
... "the counsellor can't tell if I'm crying," that they appreciated the opportunity to take their time while expressing their feelings, and being able to delete what they had written. Further, a wealth of studies have found compelling evidence that Cognitive Behavioural Therapy (CBT) delivered via videoconference is as effective as in person treatment for PTSD, mood, and anxiety disorders (Andersson, 2016;Flygare et al., 2020). Data suggests internet-based interventions for problematic substance use are also beneficial (Hoch et al., 2016;Tait & Christensen, 2010). ...
Article
Full-text available
Emerging adults, including post-secondary education students, are disproportionately affected by the social and economic impacts of the COVID-19 pandemic. The speed with which society moved in attempt to minimize the spread of the virus left many students with uncertainty and concern about their health, mental health, and academic futures. Considering that post-secondary students are a population at risk, it is important to determine how students respond in the face of the pandemic, and what coping mechanisms or supports will result in improved mental health outcomes. This knowledge will be helpful for post-secondary institutions to understand how COVID-19 has influenced the health and well-being of their students, and may facilitate the implementation of strategies to support their students. This narrative review explores evidence on how COVID-19 has impacted students with the overall goal to provide a set of recommendations to post-secondary institutions to help meet the evolving needs of this population.
... The same conclusion has been drawn by separate systematic reviews (Ahern et al., 2018;Andrews et al., 2018;Josephine et al., 2017). The strength of support for iCBT is bolstered further by evidence that trials included in iCBT meta-analyses include severe and complex presentations of depression (see also Bower et al., 2013;Lorenzo-Luaces et al., 2018a) and that effect sizes from controlled trials generalise to everyday practice (Flygare et al., 2020;Staples et al., 2019). ...
... The same conclusion has been drawn by separate systematic reviews (Ahern et al., 2018;Andrews et al., 2018;Josephine et al., 2017). The strength of support for iCBT is bolstered further by evidence that trials included in iCBT meta-analyses include severe and complex presentations of depression (see also Bower et al., 2013;Lorenzo-Luaces et al., 2018a) and that effect sizes from controlled trials generalise to everyday practice (Flygare et al., 2020;Staples et al., 2019). ...
Article
Background Service users and other stakeholders have had few opportunities to influence the design of their mental health and return-to-work services. Likewise, digital solutions often fail to align with stakeholders’ needs and preferences, negatively impacting their utility. mWorks is a co-design initiative to create a digital return-to-work solution for persons with common mental disorders that is acceptable and engaging for those receiving and delivering the intervention. Objective This study aimed to describe stakeholder perceptions and the involvement of a design process during the prototype development of mWorks. Methods A co-design approach was used during the iterative development of mWorks. Overall, 86 stakeholders were recruited using a combination of purposeful and convenience sampling. Five stakeholder groups represented service users with experience of sick leave and common mental disorders (n=25), return-to-work professionals (n=19), employers (n=1), digital design and system developers (n=4), and members of the public (n=37). Multiple data sources were gathered using 7 iterations, from March 2018 to November 2020. The rich material was organized and analyzed using content analysis to generate themes and categories that represented this study’s findings. Results The themes revealed the importance of mWorks in empowering service users with a personal digital support solution that engages them back in work. The categories highlighted that mWorks needs to be a self-management tool that enables service users to self-manage as a supplement to traditional return-to-work services. It was also important that content features helped to reshape a positive self-narrative, with a focus on service users’ strengths and resources to break the downward spiral of ill health during sick leave. Additional crucial features included helping service users mobilize their own strategies to cope with thoughts and feelings and formulate goals and a plan for their work return. Once testing of the alpha and beta prototypes began, user engagement became the main focus for greater usability. It is critical to facilitate the comprehension and purpose of mWorks, offer clear guidance, and enhance motivational and goal-setting strategies. Conclusions Stakeholders’ experience-based knowledge asserted that mWorks needs to empower service users by providing them with a personal support tool. To enhance return-to-work prospects, users must be engaged in a meaningful manner while focusing on their strengths and resources.
Article
Full-text available
Background: Studies on guided internet-delivered treatment have demonstrated promising results for patients with depressive disorder. Objective: The aim of this study was to provide an overview of this research area and identify potential gaps in the research. Methods: In this scoping review, web-based databases were used to identify research papers published between 2010 and 2022 where guided internet-delivered treatment was administered to participants with depressive disorders, a standardized rating scale of depressive symptoms was used as the primary outcome measure, and the treatment was compared with a control condition. Results: A total of 111 studies were included, and an overview of the studies was provided. Several gaps in the research were identified regarding the design of the studies, treatments delivered, participant representation, and treatment completion. Conclusions: This review provides a comprehensive overview of the research area, and several research gaps were identified. The use of other designs and active control conditions is recommended. Future studies should provide access to treatment manuals, and more replications should be conducted. Researchers should aim to include underrepresented populations and provide reports of comorbidities. Definitions of adequate dosage, reports of completion rates, and reasons for treatment dropout are recommended for future studies.
Article
Background: The COVID-19 pandemic has created an epidemic of distress-related mental disorders such as depression, while simultaneously necessitating a shift to virtual domains of mental health care; yet, the evidence to support the use of virtual interventions is unclear. Objective: The purpose of this study was to evaluate the efficacy of virtual interventions for depressive disorders by addressing three key questions: (1) Does virtual intervention provide better outcomes than no treatment or other control conditions (ie, waitlist, treatment as usual [TAU], or attention control)? (2) Does in-person intervention provide better outcomes than virtual intervention? (3) Does one type of virtual intervention provide better outcomes than another? Methods: We searched the PubMed, EMBASE, and PsycINFO databases for trials published from January 1, 2010, to October 30, 2021. We included randomized controlled trials of adults with depressive disorders that tested a virtual intervention and used a validated depression measure. Primary outcomes were defined as remission (ie, no longer meeting the clinical cutoff for depression), response (ie, a clinically significant reduction in depressive symptoms), and depression severity at posttreatment. Two researchers independently selected studies and extracted data using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Risk of bias was evaluated based on Agency for Healthcare and Research Quality guidelines. We calculated odds ratios (ORs) for binary outcomes and standardized mean differences (SMDs) for continuous outcomes. Results: We identified 3797 references, 24 of which were eligible. Compared with waitlist, virtual intervention had higher odds of remission (OR 10.30, 95% CI 5.70-18.60; N=619 patients) and lower posttreatment symptom severity (SMD 0.81, 95% CI 0.52-1.10; N=1071). Compared with TAU and virtual attention control conditions, virtual intervention had higher odds of remission (OR 2.27, 95% CI 1.10-3.35; N=512) and lower posttreatment symptom severity (SMD 0.25, 95% CI 0.09-0.42; N=573). In-person intervention outcomes were not significantly different from virtual intervention outcomes (eg, remission OR 0.84, CI 0.51-1.37; N=789). No eligible studies directly compared one active virtual intervention to another. Conclusions: Virtual interventions were efficacious compared with control conditions, including waitlist control, TAU, and attention control. Although the number of studies was relatively small, the strength of evidence was moderate that in-person interventions did not yield significantly better outcomes than virtual interventions for depressive disorders.
Article
Full-text available
Depression is one of the most common health problems worldwide but is often undertreated. Internet-delivered cognitive behavioral therapy (ICBT) appears to be an effective treatment option, with the potential to reach a larger proportion of individuals suffering from depression. While many studies have examined the efficacy of ICBT for depression in randomized controlled trials, fewer have focused on the effectiveness of ICBT when used as an integral part of routine health care. In this study the effectiveness of an 8-week ICBT program was examined when delivered in a routine psychiatric setting. A total of 108 patients were referred and 54 were then included and randomized to either ICBT or a waitlist control condition. The sample had a lower education level and a higher proportion of individuals were on sick leave than comparable previous efficacy trials of ICBT for depression conducted in Sweden. Measures assessing depression, anxiety and psychiatric symptoms were administered before and after treatment, follow up was performed at 6- and 12 months after treatment had ended. ICBT resulted in significant reductions of depressive symptoms in the treatment group when compared to a waitlist control group with a large effect size (Cohen's d = 1.6). Treatment gains were maintained at 6- and 12 months after the treatment had ended. In terms of clinical significance, 58% of the sample had improved or recovered after treatment. The study was small, and patients received general psychiatric care after the ICBT treatment had ended which limits the implications. We conclude that ICBT appears to be an effective treatment for depression when delivered as an integral part of routine psychiatric care.
Article
Full-text available
Perfectionism may be a clinically relevant problem on its own or as part of the etiology and maintenance of psychiatric disorders, e.g., anxiety disorders, depression, and eating disorders. Cognitive behavior therapy has been shown to be a promising treatment for managing perfectionism and its associated problems, including when being administered via the Internet, i.e., Internet-based cognitive behavioral therapy (ICBT). In the current study, seventy-eight self-referred participants underwent ICBT after an initial eight-week wait-list period, i.e., second wave of treatment. These were randomized to receive regular support from a therapist (ICBT-support) or ICBT with support on request (ICBT-request), in an eight-week treatment of perfectionism. Assessments of perfectionism were made at pre-, mid-, and post-treatment, as well as six-month follow-up, using the Frost Multidimensional Perfectionism Scale, subscale Concern over Mistakes. Mixed effects models revealed large symptom reductions for both conditions; Concern over Mistakes, Cohen's d = 1.40, 95% Confidence Interval (CI) [0.85, 1.95] for ICBT-support, and d = 1.00, 95% CI [0.51, 1.47] for ICBT-request. The effects were maintained at six-month follow-up and there were no differences between the conditions in terms of the results, opened modules, or completed exercises. A total of 28 out of 70 participants (42.4%; ICBT-support, 37.8%; ICBT-request) were classified as improved at post-treatment. Both types of ICBT may thus be beneficial in treating perfectionism, suggesting that just having the opportunity to ask for support from a therapist, when regular support is not provided, could be sufficient for many participants undergoing ICBT. However, the study was underpowered to detect differences between the conditions. The lack of a cutoff also makes it difficult to differentiate a dysfunctional from a functional population in terms of perfectionism. In addition, the study design could have affected the participants' motivational level from start, given their initial eight-week wait-list period. Recommendations for future studies include recruiting a larger sample size, a clearer cutoff for perfectionism, and the use of a non-inferiority test with a predetermined margin of change.
Article
Full-text available
The Beck Depression Inventory-II (BDI-II) is currently one of the most widely used measures in both research and clinical practice for assessing depression. Although the psychometric properties of the scale have been well established through many studies worldwide, so far there is no study examining the validity and reliability of BDI-II in Republic Dominican. The purpose of the present study was twofold: (a) to examine the latent structure of BDI-II by testing several competing models proposed in the literature; and (b) to provide evidence of validity and reliability of the BDI-II in Republic Dominican. Confirmatory factor analysis indicated that a bifactor model with a general depression factor and three specific factors consisting of cognitive, affective and somatic showed the best fit to the data. Internal reliability was moderate to high for all subscales and for the total scale. Scores on BDI-II discriminated between clinical and general population, supporting for external validity. Practical implications are discussed and suggestions for further research are also made.
Article
Full-text available
Social anxiety disorder (SAD) is a common mental disorder with high persistence when untreated. As access to effective treatment is limited, guided internet-based cognitive behavioral therapy (ICBT) has been proposed as an effective alternative to face-to-face treatment. In this study, we examined the effectiveness of a 14-week therapist-guided ICBT program for patients with SAD undergoing routine care. From 2014 to 2017, 169 patients were included in the study, of which 145 started the treatment. The sample was all general practitioner-referred and had a lower educational level and higher rate of work absence compared to similar effectiveness studies. Regarding social anxiety symptoms, we identified significant within-group effect sizes (post-treatment: d = 1.00–1.10; six-month follow-up: d = 1.03–1.55). We also found significant effects on secondary depression symptoms (d = 0.67). Clinically significant improvement was reported by 66.2% of the participants, and 16.6% had a significant deterioration. Clinical implications of the current study are that guided ICBT for SAD is an effective treatment for the majority of the patients undergoing routine care. Future studies should explore interventions targeting non-responders and deteriorated patients.
Article
In 2013, JAMA Psychiatry published an exciting new finding by Hallak et al¹: patients with schizophrenia who were treated with a single infusion of the antihypertensive agent sodium nitroprusside showed a dramatic, instantaneous, and sustained improvement in psychotic and negative symptoms. The study by Hallak et al¹ was not only double-blind, randomized, and placebo-controlled, but the authors had also presented a prospective power analysis, ensured good interrater reliability, and reported all the relevant aspects of the trial. Understandably, this study generated tremendous excitement and several attempts were made to replicate the finding. In this issue of JAMA Psychiatry, Brown et al² report an equally systematic study that fails to replicate the original finding. In fact, the findings by Brown et al² join those of 2 other prior studies³,4 that also failed to replicate the original finding. A careful analysis of the competing studies fails to reveal an obvious explanation for this discrepancy—except that the original finding was based on a rather small sample of just 10 patients per treatment arm.
Article
Background: Internet-based cognitive behavioral therapy (iCBT) is a promising new treatment method for depression and anxiety. However, it is important to determine whether its results can be replicated in routine care before its implementation on a large scale. Although many studies have demonstrated the efficacy of iCBT under controlled conditions, only a few studies have investigated its effectiveness in routine care. Furthermore, several effects of iCBT such as treatment effects in routine care are unclear.
Article
Background: Internet-delivered cognitive behaviour therapy may increase access by young adults to evidence-based treatments for anxiety and depression. Objective: The aim of this study was to compare the efficacy of an Internet-delivered cognitive behaviour therapy intervention designed for adults aged 18-24 years, when delivered in clinician-guided versus self-guided formats. Design: The intervention, the Mood Mechanic Course, is a transdiagnostic treatment that simultaneously targets symptoms of anxiety and depression using cognitive and behavioural skills. The brief intervention comprised four lessons, delivered over 5 weeks. Following a brief telephone interview, young adults ( n = 191) with symptoms of anxiety and depression were randomly allocated to either (1) clinician-guided treatment ( n = 96) or (2) self-guided treatment ( n = 95). Results: At post treatment, large reductions (average improvement; clinician guided vs self-guided) were observed in symptoms of anxiety (44% vs 35%) and depression (40% vs 31%) in both groups. Significant improvements were also observed in general psychological distress (33% vs 29%), satisfaction with life (18% vs 15%) and disability (36% vs 29%). No marked or consistent differences in clinical outcomes emerged between conditions at post-treatment, at 3-month or 12-month follow-up. Satisfaction was high with both treatment formats, but slightly higher for clinician-guided treatment. Conclusion: These results indicate the potential of carefully developed Internet-delivered cognitive behaviour therapy interventions for young adults with anxiety and depression provided in either self or therapist-guided format. Further large-scale research is required to determine the short- and long-term advantages and disadvantages of different models of support.