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Internet-based versus face-to-face cognitive-behavioral intervention for depression: A randomized controlled non-inferiority trial


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In the past decade, a large body of research has demonstrated that internet-based interventions can have beneficial effects on depression. However, only a few clinical trials have compared internet-based depression therapy with an equivalent face-to-face treatment. The primary aim of this study was to compare treatment outcomes of an internet-based intervention with a face-to-face intervention for depression in a randomized non-inferiority trial. A total of 62 participants suffering from depression were randomly assigned to the therapist-supported internet-based intervention group (n=32) and to the face-to-face intervention (n=30). The 8 week interventions were based on cognitive-behavioral therapy principles. Patients in both groups received the same treatment modules in the same chronological order and time-frame. Primary outcome measure was the Beck Depression Inventory-II (BDI-II); secondary outcome variables were suicidal ideation, anxiety, hopelessness and automatic thoughts. The intention-to-treat analysis yielded no significant between-group difference (online vs. face-to-face group) for any of the pre- to post-treatment measurements. At post-treatment both treatment conditions revealed significant symptom changes compared to before the intervention. Within group effect sizes for depression in the online group (d=1.27) and the face-to-face group (d=1.37) can be considered large. At 3-month follow-up, results in the online group remained stable. In contrast to this, participants in the face-to-face group showed significantly worsened depressive symptoms three months after termination of treatment (t=-2.05, df=19, p<.05). Due to the small sample size, it will be important to evaluate these outcomes in adequately-powered trials. This study shows that an internet-based intervention for depression is equally beneficial to regular face-to-face therapy. However, more long term efficacy, indicated by continued symptom reduction three months after treatment, could be only be found for the online group.
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Research report
Internet-based versus face-to-face cognitive-behavioral intervention
for depression: A randomized controlled non-inferiority trial
Birgit Wagner
, Andrea B. Horn
, Andreas Maercker
Department of Psychosomatic Medicine and Psychotherapy, University of Leipzig, Semmelweisstr. 10, 04103 Leipzig, Germany
Department of Psychology, University of Zurich, Binzmühlestr. 14/17, 8050 Zurich, Switzerland
article info
Article history:
Received 12 April 2013
Received in revised form
20 June 2013
Accepted 21 June 2013
Background and aims: In the past decade, a large body of research has demonstrated that internet-based
interventions can have benecial effects on depression. However, only a few clinical trials have compared
internet-based depression therapy with an equivalent face-to-face treatment. The primary aim of this study
was to compare treatment outcomes of an internet-based intervention with a face-to-face intervention for
depression in a randomized non-inferiority trial.
Method: A total of 62 participants suffering from depression were randomly assigned to the therapist-
supported internet-based intervention group (n¼32) and to the face-to-face intervention (n¼30). The 8 week
interventions were based on cognitive-behavioral therapy principles. Patients in both groups received the same
treatment modules in the same chronological order and time-frame. Primary outcome measure was the Beck
Depression Inventory-II (BDI-II); secondary outcome variables were suicidal ideation, anxiety, hopelessness and
automatic thoughts.
Results: The intention-to-treat analysis yielded no signicant between-group difference (online vs. face-to-face
group) for any of the pre- to post-treatment measurements. At post-treatment both treatment conditions
revealed signicant symptom changes compared to before the intervention. Within group effect sizes for
depression in the online group (d¼1.27) an d th e faceto-face group (d¼1.37) can be considered large. At
3-month follow-up, results in the online group remained stable. In contrast to this, participants in the face-to-
facegroupshowedsignicantly worsened depressive symptoms three months after termination of treatment
(t¼2.05, df¼19 , po.05).
Limitations: Due to the small sample size, it will be important to evaluate these outcomes in adequately-
powered trials.
Conclusions: This study shows that an internet-based intervention for depression is equally benecial to regular
face-to-face therapy. However, more long term efcacy, indicated by continued symptom reduction three
&2013 Elsevier B.V. All rights reserved.
1. Introduction
Depression is a one of the most common mental disorders among
adults. It is associated with signicant impairments in health and
functional status, as well as with high economic and personal costs
(Andrews et al., 2001).Theearlyageofonset,highprevalencerateand
often long-term nature of depression make it a major public health
problem that generates large direct and indirect costs for the
depressed person as well as for society (Richards, 2011). In Europe
for the year 2010 the annual cost of depression per patient was
estimated at 3034 with an estimated number of 30.3 million people
affected (Olesen et al., 2012). These costs are incurred despite the fact
that the vast majority of people suffering from depression do not
access treatment (Collins et al., 2004). Barriers to assessing effective
treatment include fear of stigma, lack of time, long waiting times,
geographic distance to mental health services, or unwillingness to
disclose psychological problems (Collins et al., 2004). Internet-based
interventions may help to overcome these obstacles. Andersson and
Cuijpers found a strong inuence of therapist support on treatment
outcome in their 2009 meta-analysis of 12 internet-based randomized
controlled trials for depression, (Andersson and Cuijpers, 2009).
Computerized interventions with therapist support showed a mean
between-group effect size of d¼.61, which is comparable with face-to-
face treatment for depression, whereas interventions with little or no
therapist contact had signicantly smaller treatment effect sizes,
averaging d¼.25. A recently published meta-analysis, including data
from 25 controlled trials, supports these previous ndings and found
effect sizes ranging from d¼.10 to d¼1. 20 ( Johansson and Andersson,
2012). The authors categorized the studies by type of human contact.
Category 0 was used for no human contact at all throughout the
treatment, category 1 for therapist contact only before treatment,
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Journal of Affective Disorders
0165-0327/$- see front matter &2013 Elsevier B.V. All rights reserved.
Trial registration: Australian New Zealand Clinical Trial Registry:
Corresponding author. Tel.: +49 175 2452177.
E-mail address: (W. Birgit).
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎
category 2 was contact only during the treatment and nally category
3 was where therapist contact took place before, during and after the
intervention. The effect sizes were d¼.21, .44, .56 and .76. These
results indicate that higher levels of human contact yield larger effect
sizes. This matches other ndings of a signicant correlation between
the amount of therapist time in minutes per participant and the
between-group effect sizes of internet-based interventions (Palmqvist
et al., 2007). Moreover, studies on entirely self-guided programs have
shown not only reduced treatment effects, but also substantial
attrition rates of up to 41% (Christensen et al., 2006a,2006b;Clarke
et al., 2005,2002;Kaltenthaler et al., 2008). In summary, it can be
concluded that therapist-assisted online programs for depression yield
medium to large effect sizes.
However the question remains of whether internet-based thera-
pies for depression are equally as benecial for patients as standard
face-to-face treatments. Only a few studies have directly compared
computerized interventions with face-to-face interventions. Spek and
colleagues evaluated an 8 week internet-based intervention for non-
typical subthreshold depression in people aged 50 and older com-
pared to 10 weekly face-to-face group sessions and a waiting-list
condition (Spek et al., 2007). No signicant treatment effect differences
were found between the face-to-face group intervention and the
internet-based intervention. Other studies have evaluated and com-
pared online versus face-to-face therapies for tinnitus (Kaldo et al.,
2008), social phobia (Andrews et al., 2011), panic disorder (Bergstrom
et al., 2010), spider phobia (Andersson et al., 2009), and relaxation
(Carlbring et al., 2007) and found no signicant differences between
the two settings. Only one study, evaluating an intervention for body
image and eating disorders, showed a signicant difference between
the two groups (Paxton et al., 2007). Post-treatment improvements
were larger in the face-to-face than in the internet-based intervention.
Although there is mounting evidence that internet-based interven-
tions for depression are effective and there is support for the
assumption that therapist guided interventions are favorable over
unguided interventions, to our knowledge, no randomized controlled
trial for depression has been conducted to compare treatment efcacy
in the two treatments (online vs. face-to-face) in an experimental
Recognizing that time-intensive psychotherapies present an impor-
tant barrier to mental health care use, the present study used non-
inferiority methodology to compare the efcacy of a brief 8-week
internet-based CBT intervention with high therapist involvement for
depression with a face-to-face CBT intervention. The Internet-based
intervention was not self-guided and the treatment consisted of
structured writing assignments with an individualized feedback from
the therapist. The treatment manual was based on a German CBT
treatment manual for depression (Hautzinger, 2003). Patients from
timeframe and the time of contact between therapist and patient was
equal for both groups. Primary outcomes of the study were depres-
related outcomes (e.g. suicidal ideation, hopelessness and negative
automatic thoughts). It was predicted that participants in both groups
would have signicant reduced symptoms of depression after treat-
ment and that the improvements would be maintained at a 3-month
follow-up assessment. Further, it was hypothesized that the groups
would not differ signicantly at either post-treatment or 3-month
follow-up for both primary and secondary outcomes.
2. Methods
2.1. Ethics statement
Ethical approval for the trial was given by the institutional
review board at the University of Zurich. Signed informed consent
was given by all participants by fax or post. The protocol for this
trial and supporting CONSORT checklist are available as supporting
information; see Checklist S1 and Protocol S1.
2.2. Participants and recruitment
Potential participants were recruited in the area of Zurich, Switzer-
land, through advertisements in newspapers, the depression website
of the university, local internet news forums and depression self-help
groups, advertisements in supermarkets and pharmacies, and local
press releases. Potential participants were informed that they would
be randomized either to the online or the face-to-face group, and that
Internet access was a prerequisite. Applicants indicated their interest
in the study by contacting the intake coordinator via e-mail or
telephone. The intake coordinator sent a reply e-mail with a patient
information sheet and the inclusion and exclusion criteria. Inclusion
Table 1
Demographic and descriptive characteristics of the online and face-to-face groups at baseline.
Online group (n¼32) Face-to-face group (n¼30) Group comparison
Age (M, SD) 37.25 (11.41) 38.73 (11.41) t¼,50, p¼.61
Age range 2067 1962
Gender (% female) 78% 50% χ
(1)¼5.35, po.05
Educational level (%) χ
(32)¼1.46, p¼.48
Primary education 16 27
Secondary education 37 27
University 47 47
Marital status (%) χ
(3)¼2.62, p¼.45
Single 59 57
Partnership/married 19 20
Divorced 16 7
Widowed 6 17
Professional status (%) χ
(4)¼4.4, p¼.52
Full-time work 72 67
Sick leave 3 10
Unemployed 22 20
Retired 0 3
No current antidepressants (%) 91 70 χ
(3)¼4.82, p¼.18
BDI-score, pre-test (M, SD) 22.96 (6.07) 23.41 (7.63) t¼,25, p¼.80
BDI pre-test (1117) in % 16.7 23.3 χ
(2)¼.44, p¼.80
BDI pre-test (1829) in % 63.3 56.7
BDI pre-test (30) in % 20 20
Completer in % 78 93
W. Birgit et al. / Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎2
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
criteria were a score of at least 12 on the Beck Depression Inventory
(BDI-II) (Beck et al., 1996) and age 18 years or older. Potential
participants were excluded if they were (1) currently receiving
treatment elsewhere, (2) also suffering from substance abuse or
dependence, (3) had been on antidepressant medication for less than
4 weeks, (4) not uent in German. Further exclusion criteria were high
risk of suicide, psychotic symptoms, post-traumatic stress disorder,
anxiety, phobia and bipolar disorder. Applicants who indicated that
they met the study requirements entered an online screening proce-
dure, data from which were later used as pre-test measures. After
condentiality issues had been addressed, eligible applicants returned
a signed informed consent formwhich informed them about poten-
tial risks and benets of study participationby fax or post. Participants
were randomly assigned by the study coordinator to either the
internet-based intervention group or the face-to-face group after
recruitment to the study. Excluded applicants were informed imme-
diately about other available forms of treatment. Applicants excluded
because of a high risk of suicide were contacted by telephone by the
study coordinator. Demographic characteristics of the sample are
presented in Table 1. A total 191 respondents applied for the
treatment. The numbers of patients and reasons for exclusion are
specied in the owchart (see Fig. 1). Participants were recruited
between November 2008 and February 2010.
2.3. Procedure
Primary and secondary outcome measures were collected at pre-
treatment, post-treatment and 3-month follow-up. All measures for
both intervention groups were administered through online diagnos-
tics. A number of studies have shown that online format question-
naires produce results as valid as pen-and-paper questionnaires (Fidy,
2008;Hollandare et al., 2010). The 62 applicants included in the study
were randomized by a true random-number service (http://www. using a 1:1 ratio, with 32 participants randomly allocated
Randomization was performed by the study coordinator and was
not stratied by any participant characteristics.
2.4. Interventions
Online and face-to-face intervention groups received a brief
(8 weeks) cognitive-behavioral therapy (CBT) program for depression
(Hautzinger, 2003). This German manual is based on the cognitive
theory of depression of Beck and colleagues (Hautzinger et al., 2006).
The program involved the following CBT modules: (1) introduction,
(2) behavioral analysis, (2) planning of activities, (3) daily structure,
(7) relapse prevention. The life-review module at the mid-treatment
time-point aimed to encourage participants to revisit past experi-
ences and to activate positive memories and individual resources in
order to achieve a balance between positive and negative memories
(Preschl et al., 2012). Further, patients in both groups were given the
same psychoeducation and received the treatment modules in the
same chronological order. Patients in the face-to-face condition
attended one-hour weekly treatment sessions for 8 weeks with their
allocated psychologist in the Department of Psychopathology and
Clinical Intervention at the University of Zurich. They were also given
weekly homework assignments (e.g., daily structure diaries, negative
thoughts log).
The online intervention was given as a guided intervention
with intensive therapist contact, based on the principles applied in
a number of previous studies (Lange et al., 2003;Ruwaard et al.,
2007;Ruwaard et al., 2009;Wagner et al., 2006). The internet-
based treatment manual was derived from the same cognitive-
behavioral treatment modules for depression as the face-to-face
intervention (Hautzinger, 2003). The therapist time involved
responding to texts, requiring 2050 min per text, depending on
the therapist's experience with internet-based therapies.
Applied to participate
(N= 191)
Did not respond after enrolling
(n= 68)
(n= 123)
Randomly allocated
(n= 62)
Excluded (n= 61) due to:
Co-existing psychiatric disorder (n
= 16)
High suicide risk (n= 8)
Bipolar disorder (n= 6)
Low symptom severity (n = 16)
No informed consent (n= 15)
Face-to-face group
(n= 30)
Online group
(n= 32)
Completed post-test
(n= 28)
Completed post-test
(n= 25)
3-month follow-up
(n= 20)
3-month follow-up
(n= 17)
Online group (n= 7):
Problems with writing (n= 2)
No reason (n= 1)
Preferred face-to-face (n= 1)
Technical problems (n= 1)
Improved (n = 1)
Face-to-face group (n= 2):
No reason (n= 1)
Improved (n = 1)
Fig. 1. Flowchart of patient participation.
W. Birgit et al. / Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎ 3
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
Each scheduled writing assignment lasted 45 min and patients
were given two writing assignments in each week of the 8-week
treatment period. Therapists provided individual written feedback
within one working day, along with instructions for the next
writing assignment. Model responses for the therapists were
available, but they also had the option to provide their own
commentary or supportive feedback on their patients'texts.
2.5. Therapists
The therapists were six psychologists and psychotherapists. All
psychologists were trained in psychotherapy and CBT for depres-
sion specically for this study. The therapists were given special
training in therapeutic writing for the online treatment and
received regular supervision (face-to-face and online), with thera-
pists in both groups receiving the same amount of supervision. All
but one of the therapists was involved in both treatment
2.6. Outcome measures
All outcome measures were collected at pre-treatment, post-
treatment and 3-month follow-up.
2.7. Primary outcome measure
The primary outcome measure of this study was depression
assessed with the German version (Hautzinger et al., 2006) of the
Beck Depression Inventory-II (BDI; (Beck et al., 1996)), comprised
of 21 multiple-choice items assessing specic symptoms of
depression. Symptom severity was dened for mild or moderate
depression (BDI score: 1117); moderate to severe depression (BDI
score: 1829); and severe depression (BDI score 30) (Hautzinger
et al., 2006). Recovery was dened as BDI-II at post-treatment
measurement of 10.
2.8. Secondary outcome measures
2.8.1. Suicidal ideation
Suicidal ideation was assessed with the Beck Suicide Ideation
Scale (BSI) (Beck et al., 1997), a 21-item inventory developed to
measure the intensity and recurrence of suicidal ideation in adults.
The BSI is one of the few well-validated self-report measures of
suicidal ideation The rst 5 items make up a brief subscale
measuring the presence of suicidal thoughts, either recently (in
the last 6 months) or ever in one's life. The BSI has a suggested
cutoff score of 3.
2.8.2. Anxiety
Anxiety was assessed using the Anxiety subscale of the German
version of the Symptom Checklist created by Derogatis (Franke,
1995). This 10-item subscale covers various symptoms of anxiety,
including cognitive and somatic correlates of anxiety and a cut-off
score of .64 has been validated to screen for anxiety (Geiser et al.,
2.8.3. Hopelessness
Hopelessness was measured with the Scale for the Assessment
of Hopelessness (Krampen and Beck, 1994), a German adaptation
of the American Hopelessness Scale (Beck et al., 1974). The Scale
for the Assessment of Hopelessness assesses the negative expecta-
tions of a person referring to himself, to his environment, or his
future life.
2.8.4. Automatic thoughts
The German version of the Automatic Thoughts Questionnaire-
Revised (ATQ-R, (Kendall et al., 1989), German version (Pössel
et al., 2005)) measures positive and negative automatic thoughts
which are specic to depressive thinking. The German ATQ-R
includes the three subscales (1) negative thoughts, (2) well-being,
and (3) self-condence.
2.8.5. Treatment satisfaction
The overall treatment satisfaction was asked with a one-item
question (How satised have you been with the intervention?)
on a 10-point Likert scale (1¼very dissatised, 10¼extremely
2.9. Exclusion criteria
2.9.1. Risk of psychosis
Risk of psychosis was measured using the Dutch Screening
Device for Psychotic Disorder (Lange et al., 2000), a seven-item
inventory that is a good predictor of psychotic episodes. Because
no data are yet available from a German norm group, the Dutch
norm data were used. A score of 13 has been identied as
providing a cutoff for identifying risk of psychosis.
2.9.2. Phobia
The German version of the Symptom Checklist by Derogatis
(Franke, 1995) was also used to test for phobias. The Phobia
subscale contains seven items assessing severity of phobic
2.9.3. Post-traumatic stress
The Post-Traumatic Stress Scale 10 (Maercker, 1998), a
short screening instrument tapping DSM-III symptoms of post-
traumatic stress disorder, was used to measure symptoms of post-
traumatic stress.
2.10. Statistical analysis
Statistical analyses were performed with the Statistical Package
for the Social Sciences (SPSS), version 19.0 for MAC. Independent
t-tests and χ
-tests were used to estimate baseline between-group
differences in demographics and pre-treatment measures. To
evaluate recovery, the BDI-II was used. Changes of prevalence of
depression at pre-treatment and post-treatment were calculated
and analyzed with χ
-tests. Differences between the online and
face-to-face interventions were primarily investigated by mixed-
design ANOVAs with time as the within-subject factor and group
as the between-subject factor. All post-treatment and 3-month
follow-up analyses were based on an Intention to treat (ITT)
design. Missing data were addressed by carrying forward the rst
available data (baseline observation carried forward; BOCF) prin-
ciple. To examine the magnitude of change in mean symptoms
between baseline and post-treatment and between baseline and
3-month follow-up, we calculated effect sizes using Cohen'sdfor
repeated measures. An effect size of d¼.80 for a psychological
treatment is typically considered large (Cohen, 1988). Finally,
t-tests and χ
-tests were used to identify any differences between
dropouts and completers.
3. Results
Table 1 shows baseline sociodemographic characteristics of
participants in the online and face-to-face conditions. There were
no signicant differences for most of the baseline variables,
however, despite randomization, there was a signicantly higher
W. Birgit et al. / Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎4
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
percentage of women in the online group (78%) versus 50% in the
face-to-face group, χ
(1)¼5.35, po.05. The BDI baseline score
was M¼22.96 (S.D.¼6.07) for the online group and M¼23.41
(S.D.¼7.63) for the face-to-face group. Symptom severity was high
in the total sample: 20% of the participants met criteria for mild or
moderate depression (BDI score: 1117); 60% suffered of moderate
to severe depression (BDI score: 1829); and 20% showed a BDI
score 30. There was no signicant difference between the two
interventions groups regarding symptom severity (see Table 1).
Only a small subgroup (19%) was taking antidepressants at the
beginning of the study, and 53% reported that they had previous
experience of psychotherapy.
3.1. Intention-to-treat
Analysis of the change in the primary and secondary outcome
measures was conducted using an intention to treat (ITT) analysis.
Table 2 presents means and standard deviations of baseline, post-
treatment, and 3-month follow-up scores for all outcome measures in
both treatment conditions. The ITT analysis yielded no signicant
between-group difference (online vs. face-to-face group) at any of the
pre- to post-treatment measurements. At post-treatment both treat-
ment conditions revealed signicant symptom changes compared to
pre-intervention levels. The online group showed signicant symptom
reduction for depression (t¼6.98, df¼30, po.001), anxiety (t¼5.60,
df¼31, po.001), hopelessness (t¼5.57, df¼31, po.001), self-esteem
(t¼3.25, df¼31, po.01), and automatic negative thoughts (t¼3.29,
df¼31, po.01). Only suicidal ideation (t¼1.18 , d f ¼30, p¼.24) did not
undergo any signicant change from pre- to post-treatment in the
online group. Similar results and patterns of change were found in the
face-to-face group, with the exception of a signicant change in
suicidal ideation (t¼2.45, df ¼29, po.05). The between-group effect
sizes at post-treatment were small (see Table 2), while within-group
effect sizes for the primary outcome depression in the online group
(d¼1.27)andtheface-to-facegroup(d¼1.37) can be considered large.
Furthermore, as no signicant changes were found in any of the
outcome measurements at 3-month follow-up compared to immedi-
ately after treatment in the online group, there is an indication that the
outcomes remain stable after the treatment. In contrast to this,
participants in the face-to-face group showed a signicant increase
in depressive symptoms from post-treatment to 3-month follow-up
(t¼2.05, df¼19, po.05), although all secondary outcome measures
remained stable.
A MANOVA for repeated measures showed no signicant
interaction effect on any primary or secondary outcomes from
pre- to post-test, but a signicant difference for group effect from
post-treatment to 3-month follow-up for anxiety (F(1,33)¼5.33,
po.05), automatic negative thoughts (F(1,33)¼4.70, po.05) and a
nearly signicant effect for depression (F(1,34)¼2.88, p¼.09).
Pre-treatment to 3-month follow-up within-group effect sizes
on the BDI were more favorable for the online-group (d¼2.00),
compared to the face-to-face group (d¼1.04) (see Fig. 2). The
corresponding secondary outcomes in the online group for anxiety
(d¼1.13) versus the face-to-face group (d¼.55); hopelessness
(d¼1.14) versus the face-to-face group (d¼.65); and negative
automatic thoughts (d¼1.59) versus the face-to-face group
(d¼.52) revealed higher within-group effect sizes for the out-
comes of the online group at 3-month follow-up. Further, treat-
ment satisfaction and psychotherapy utilization comparisons
between the two intervention groups are detailed in Table 3.
3.2. Attrition
Seven (22%) participants in the online group and two (7%)
participants in the face-to-face group failed to nish the treatment
(see Fig. 1). Of these nine persons, three stopped treatment
without giving any reason and could no longer be reached; two
participants stated that writing was not the right approach for
them or that they were no longer interested in the treatment
program; one participant of the online group preferred face-to-
face therapy and therefore discontinued study participation; two
experienced sufcient improvement during the course of treat-
ment and one participant discontinued because of technical
problems. Completion rates revealed an almost signicant differ-
ence between the two groups, χ²(1)¼2.88, p¼.08. Non-completers
in the face-to-face group did not differ from completers in any of
Table 2
Results of mixed design ANOVAs and effect sizes for the online and face-to-face groups at baseline, post-treatment, and 3-month follow-up: intention-to-treat analysis.
Time Online group Face-to-face group Between-group difference Effect size, d
Effect size, d
3-month Follow-up
BDI Pre 22.96 6.07 23.41 7.63 F(1,59)¼.01, p¼.92 .00 .61
Post 12.41 10.03 12.33 8.77 F(1,34)¼2.88, p¼.09
FU 9.28 7.50 14.47 9.33
Suicidal ideation Pre 3.24 4.87 4.40 6.48 F(1,59)¼.23, p¼.63 .02 -
Post 2.27 4.78 2.40 4.80
FU - - - -
SCL-anxiety Pre .95 .52 .92 .56 F(1,58)¼.24, p¼.62 .29 .44
Post .48 .51 .63 .52 F(1,33)¼5.33, po.05
FU .44 .37 .63 .48
Hopelessness Pre 27.59 6.70 28.36 6.41 F(1,60)¼.30, p¼.58 .13 .47
Post 20.40 8.66 21.53 8.52 F(1,34)¼2.44, p¼.12
FU 19.18 7.90 23.20 9.07
Self-condence Pre 7.31 3.65 7.00 2.25 F(1,60)¼.46, p¼.49 .19 .10
Post 9.40 4.30 8.63 3.64 F(1,34)¼.28, p¼.59
FU 9.25 3.13 8.90 3.69
Negative thoughts Pre 35.56 8.08 37.13 9.28 F(1,59)¼.07, p¼.79 .02 .70
Post 26.62 15.95 26.27 9.03 F(1,33)¼4.70, po.05
FU 21.81 9.15 30.52 15.01
Well-being Pre 7.93 2.36 7.63 2.18 F(1,60)¼1,07, p¼.30 .27 .35
Post 12.25 5.13 10.93 4.59 F(1,34)¼1. 71, p¼.19
FU 12.06 4.66 10.50 4.24
Note. Online group: n¼25 (reduced to n¼17 at 3-month follow-up due to dropout); face-to-face group: n¼28 (reduced to n¼20 at 3-month follow-up due to dropout).
W. Birgit et al. / Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎ 5
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
the demographic variables. In the online group the only demo-
graphic difference found was that dropouts were older than
completers, t(30)¼2.33, po.05. Regarding symptom levels, no
signicant differences were found between dropouts and com-
pleters on any of the baseline measures.
3.3. Treatment recovery
Recovery was dened as BDI-II at post-treatment measurement
of 10. No difference was found between groups with regard to
recovery after treatment χ²(1)¼.06, p¼.80 or at 3-month follow-
up χ²(1)¼2.94, p¼.08. At post-treatment, 53% in the online-group
and 50% in the face-to-face group showed a clinically signicant
change. Although the increases were not signicant, at 3-month
follow-up the online group showed a increased recovery rate of
57% while the face-to-face group's recovery rate had decreased to
42%. In the online-group 80% of the participants who reported
mild depression at baseline received signicant clinical change at
posttreatment, compared to 100% in the face-to-face group. In the
online group of the moderate to severe depressive participants
67% achieved clinical change compared to 40% in the face-to-face
group. In the severe depressed group (BDI 30) the online-group
achieved 40% signicant change, while the face-to-face group
achieved 33%. However, no signicant difference could be found
between the two interventions groups in any of the depression
4. Discussion
The aim of this non-inferiority randomized controlled trial was
to test an internet-based intervention against a comparable,
traditional face-to-face therapy for depression. To our knowledge
this is the rst randomized controlled trial for depression compar-
ing both treatment forms with equivalent treatment modules and
treatment length. We assumed equal effects for the two condi-
tions. The main nding of this trial is that the internet-based
intervention is indeed equally as effective as face-to-face therapy
for depression. This is in line with previous studies comparing
face-to-face therapy with online interventions for other mental
conditions (Andrews et al., 2011;Bergstrom et al., 2010;Kaldo
et al., 2008;Spek et al., 2007). Furthermore, both interventions
showed large within group effect sizes at post-treatment for both
depressive symptoms and secondary outcomes, which conrms
ndings of Nieuwsma and colleagues that a brief intervention for
depression can be effective and comparable to standard duration
of psychotherapy (Nieuwsma et al., 2012). The within group effect
size in the online-group ranged from d¼.91 to d¼1.27 for
depression, anxiety and hopelessness. These effect-sizes conrm
the ndings of Johansson et al. (2012), who found the largest effect
sizes for interventions with a high therapist involvement in their
review. The high therapist involvement in our study therefore
seems to play a major role compared to treatment effects of self-
guided interventions for depression (Johansson and Andersson,
However, analysis revealed that from post-treatment to
3-month follow-up a difference between the internet-based inter-
vention and the face-to-face group could be found. Symptom
reductions were maintained for all primary and secondary out-
comes for the online group three months after treatment. In
contrast to this, participants in the face-to-face group signicantly
worsened from post-treatment to 3-month follow-up in terms of
depressive symptoms. Further, signicant differences were found
for the face-to-face group from post-treatment to the 3-month
follow-up for symptoms of anxiety and automatic negative
thoughts and a nearly signicant effect was observed for depres-
sion. Altogether it appears that the treatment effects from pre-
treatment to 3-month follow-up were larger in the online group
than in the face-to-face group. Moreover, at the 3-month follow-
up more participants in the online group indicated clinically
signicant changes than in the face-to-face group. Reasons for
this might include that the online intervention has less personal
guidance and therefore puts a stronger focus on self-responsibility
to conduct the treatment modules and homework assignments
than the face-to-face intervention. This might evoke a stronger,
longer-lasting sense of self-efcacy in handling negative thoughts
and depressive behavior. Further, no signicant difference could be
found regarding treatment satisfaction in both groups. 96% of the
participants in the online group described the contact between
therapist and themselves as personal, compared to 91% in the face-
to-face group. This conicts with previous ndings of Kaldo et al.
(2008), who found that the credibility rating of their internet-
based intervention was signicantly lower than that for the face-
to-face group intervention. Interestingly, there was an almost
Table 3
Treatment satisfaction and psychotherapy utilization at postmeasurement.
Online group (n¼25) Face-to-face group (n¼28) Group comparison
Treatment satisfaction (010), (M, SD) 7.88 (1.66) 6.83 (2.03) t¼.36, p¼.11
Treatment duration (%)
Too short 32 57 χ
(1)¼3.37, po.06
Good 68 43
Contact between therapist and patient (%)
Personal 96 91 χ
(1)¼1.11, po.57
Impersonal 4 4
Do not know 0 4
Started psychotherapy by 3-month follow-up (%) 25% 20% χ
(1)¼.04, po.83
Pretest Posttest 3 MFU
Online Face-to-face
Fig. 2. Online intervention in comparison to a face-to-face group measured with
the Beck Depression Inventory (BDI-II) at pretest, posttest and 3-months-follow-up,
including standard error.
W. Birgit et al. / Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎6
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
signicant difference regarding opinions about the treatment
duration in our study. While only 32% in the online group
experienced the treatment as too short, about 57% in the inter-
vention group found the duration of the 8-week program as too
short and wished that the treatment could have continued longer.
When looking at the clinical signicant change in the different
subgroups of symptom severity, we found that in both treatments
participants with mild to moderate depressive symptoms showed
a higher percentage of recovery. Least recovery was found in the
severely depressed subgroup. Further, we could not nd any
statistical difference between the two interventions and the
symptom severity subgroups.
Although these preliminary results provide some evidence that
online interventions might be as effective as face-to-face inter-
ventions, it remains unclear whether the factors that are respon-
sible for symptom reduction in face-to-face therapy operate in the
same way in online therapeutic settings. Therapeutic factors such
as missing face-to-face contact, decreased social presence and
increased anonymity were originally seen as disadvantages of
internet-based interventions. However, for a specic group of
patients, it might be exactly these factors that offer an advantage
in comparison to conventional therapies. Online participants
might be more focused on the structured treatment manual as
they are responsible for continuation of the intervention, for
example by completing homework assignments. This might lead
to a greater treatment manual adherence than in face-to-face
therapy. DeRubeis and Feeley (1990) differentiated between two
types of adherence to cognitive-behavioral therapies, concrete and
abstract adherence. Concrete adherence involves methods to
support use by the patients of cognitive-behavioral tools such as
cognitive restructuring worksheets, homework assignments and
behavioral techniques. In contrast to this, abstract adherence to
CBT involves broader discussions of therapy-relevant issues with
focus put upon understanding the patients'situation and beliefs
and conversations about the patients'wellbeing and therapy
progress. In internet-based interventions there is a clear focus on
concrete adherence to CBT through use of homework assignments,
psychoeducation and behavioral observation techniques. Only a
small part of the intervention involves abstract adherence, such as
conversations about the patient's current personal situation or
broader discussion of disorder-relevant topics. Face-to-face CBT,
even when highly structured as in our study, still gives the patients
more opportunities to discuss problematic current situations,
alongside pure adherence to the treatment modules. The compar-
ison of abstract and concrete adherence in face-to-face versus
online therapies should be addressed by future research.
Another important factor for therapeutic interventions is the
therapeutic alliance between therapist and patient throughout the
intervention. The therapeutic alliance has traditionally been seen
as a key element contributing to the treatment success of face-to-
face psychotherapy (Horvath and Symonds, 1991). A number of
studies have found that the therapeutic alliance signicantly
inuences symptoms of depression as an outcome measure
(Krupnick et al., 1996). Internet-based interventions involve less
therapeutic contact and are usually restricted to purely text-based
and computer-mediated communication. Initial assumptions that
internet-based therapeutic relationships are less stable or less
positively experienced by the patients have not been conrmed,
however. A number of studies have revealed that therapeutic
relationships in an online setting are consistently rated as posi-
tively and as stable as in face-to-face settings by study participants
(Knaevelsrud and Maercker, 2006;Preschl et al., 2011;Wagner
et al., 2012). However, the working alliance in internet-based
interventions seems to be less predictive of treatment outcome
than in face-to-face interventions and the role of the therapist
seems to be less prominent than in face-to-face treatments.
The drop-out rates in our studyseven (22%) participants in the
online group and two (7%) participants in the face-to-face group
was more favorable for the face-to-face group. This may indicate
that the more anonymous online therapeutic relationship is less
stable than a face-to-face relationship. Face-to-face interventions
involve more social control and it might seem, for a number of
participants, inappropriate to dropout of therapy once they get to
know the psychotherapist personally. It is easier for patients in
online interventions to stop therapeutic communication by simply
disappearing. A study of online romantic relationships revealed
that avoidance behavior and discontinuity are more likely in
online relationships than in face-to-face relationships (Merkle
and Richardson, 2000).
4.1. Limitations
This trial has a number of limitations, which need to be
addressed. First, all primary and secondary outcome measures
were administered as self-rated questionnaires in an online set-
ting. We aimed to conduct a fully internet-based treatment for the
online-group with no personal contact either on the telephone or
face-to-face, therefore we decided to conduct the diagnostic
procedure entirely through the internet for both groups. However,
a structured clinical interview would have allowed a better quality
of diagnosis of depression. Second, only one follow-up assessment,
at three months, was conducted, therefore we cannot draw any
conclusions about the truly long-term effects of the two treat-
ments. Furthermore, the sample used in this study was small, self-
referred, relatively well educated, and more than half of the
participants already had experience of psychotherapy. Therefore
it is unclear whether the results of this study can be generalized to
predict efcacy in people who are referred by health professionals
or who are less educated. Even though the results regarding the
3-months follow-up are surprisingly in favor of the online-
intervention group, it is important to acknowledge that only about
two thirds of the participants completed the 3-months-follow-up.
Future studies should enroll larger and more heterogeneous
samples. Finally, due to our strict exclusion criteria regarding co-
morbidity and psychosis, a number of applicants were excluded
from the study, which also limits the breadth of the conclusions.
A number of studies to date have evaluated the efcacy of tailored
internet-based interventions for patients with comorbidities
(Carlbring et al., 2011;Johansson et al., 2012) and have found
encouraging results suggesting that individually tailored interven-
tions are superior to non-tailored interventions. A recently con-
ducted trial explicitly developed for patients with suicidal
ideations concluded that this patient group should be considered
for future research on internet-based interventions (van Spijker
et al., 2010).
5. Conclusion
Depression has become a very prevalent und costly disorder
and in most countries therapeutic services do not manage to meet
the needs presented by this growing demand. This trial gives
preliminary results that a brief internet-based intervention for
depression is as effective as comparable face-to-face interventions.
Internet-based intervention may be the solution for tackling this
epidemic in a more cost-effective way than traditional face-to-face
therapies. However, further research is needed to replicate these
ndings and possible differences in underlying mechanisms
between online and face-to-face interventions need to be
W. Birgit et al. / Journal of Affective Disorders (∎∎∎∎)∎∎∎∎∎∎ 7
Please cite this article as: Birgit, W., et al., Internet-based versus face-to-face cognitive-behavioral intervention for depression: A
randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
Role of funding source
This research was supported by the Selo Foundation, Switzerland.
Conict of interest
The authors declare no conict of interest.
The authors would like to thank Barbara Preschl, Jenni Keel, Luigina Di Lorenzo,
and Regula Usteri, who served as therapists in the study. This study was co-funded
by the Werner Selo Foundation.
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randomized controlled non-inferiority trial. Journal of Affective Disorders (2013),
... Various studies have shown that therapeutic alliance can be established in an online setting similar to face-to-face counseling (Brown, 2012;Cook & Doyle, 2002;Simpson, 2001;Wagner et al., 2014). Clients and psychotherapists can develop therapeutic alliance via video conferencing (Simpson & Reid, 2014). ...
Full-text available
Online counseling has been increasingly becoming widespread. Nevertheless, the number of studies investigating counselees' online counseling experience in-depth is scarce, especially in Turkey, despite the proliferation of the service. Therefore, this phenomenological study aims to understand the experiences of participants who received online counseling regarding satisfaction and therapeutic alliance. For this purpose, semi-structured interviews were conducted with six participants. Thematic analysis was applied to explore their satisfaction with online counseling and therapeutic alliance. The results showed that three themes emerged for satisfaction with online counseling; improvements in the symptoms, the flexibility of the online counseling, and limitations of online counseling. Thematic analysis of therapeutic alliance revealed the themes; goals, tasks, and bond. As a result, the participants reported high satisfaction with the online sessions, and they were able to establish a therapeutic alliance with their counselor in setting goals, seeking solutions to problems, and bonding. Overall, most of the participants reported positive experiences with online counseling in terms of improvement in their situation and establishing significant relationships with their counselor over the Internet.
... As counseling interventions can be delivered either in-person or online, clients have the option to choose whether or not to use online counseling technology as part of the counseling process (American Counseling Association, 2014;Woo et al., 2020). Online-delivered intervention appears to be just as supportive as in-person-delivered intervention (Bettger et al., 2020;Josephine et al., 2017;Kim et al., 2018;Langarizadeh et al., 2017;Navarro et al., 2019;Reynolds Jr et al., 2013;Richards & Richardson, 2012;Wagner et al., 2014). Online counseling is defined in multiple ways in the literature. ...
A growing problem among university students is academic burnout, and it has become more prevalent in recent years. With the increasing demand for school mental health services, online counseling interventions are gradually being regarded as a reasonable way to provide assistance to students with regard to burnout reduction. It was the researcher's aim to find out whether an online counseling intervention would be able to reduce academic burnout in a Nigerian undergraduate sample. A total of 80 Nigerian undergraduates were participants of this study. In this study, the participants were randomly assigned to the intervention group (n=40) and to the control group (n=40) by using a simple randomization procedure. For the collection of data, the Oldenburg Burnout Inventory for Students and the Students School Burnout Inventory were utilised. The researcher applied repeated measures ANOVA to examine within and between groups' mean differences, reported η² to establish treatment effect size, and conducted posthoc analyses where mean differences existed using Holm's approach. The results of the study demonstrate that the online counseling intervention for academic burnout was effective in reducing the level of academic burnout among Nigerian undergraduates. As online counseling can help students reduce academic burnout, this finding opens the door to reach a larger number of burned-out students, such as those who live off-campus or are afraid of social stigma when seeking mental health care. This intervention has the potential to significantly improve student mental health related to burnout in other higher education settings like polytechnics. It is recommended that students should be encouraged to receive psychological supports and care from the university's counseling centre, not just during crises, but as a regular process of mental health support.
Rates of mental health disorders among adolescents and younger adults are on the rise [19] with the lack of widespread access remaining a critical issue [9]. It has been shown that teletherapy, defined as therapy delivered remotely with the use of a phone or computer system [20], may be a viable option to replace in-person therapy in situations where in-person therapy is not possible [21]. This research seeks to address the need for mental health therapy in situations where patients do not have access to traditional in-person care. This research investigates the use of Augmented Reality (AR) technology to provide a similar experience to in-person care while remaining remote. AR is an innovative technology that allows for virtual “holograms” to appear in the real world, blending the real and virtual worlds together. Exploratory research was focused on creating a minimum viable product (MVP) that could facilitate the basic level interaction between two or more users in a virtual space. By doing so, this paper served as a formative step toward developing AR teletherapy prototypes for larger scale studies. A user study was also conducted at a mid-size university (N = 48) to solicit opinions on AR technology as it relates to mental health therapy.
Background The global estimate of the number of children in institutional care is around 5 million, with around 1 million of these children living in Europe. In Germany, about 75,000 children and adolescents find themselves in the foster care system and about 93,000 additional children and adolescents are living in institutions. Traumatic experiences and neglect in childhood are highly prevalent among these youth in care and are related to severe long-term effects. Childhood maltreatment and abuse can increase the risk of future victimization experiences. Although youth in care are at risk of victimization or revictimization, no specific evidence-based prevention program has been designed to address these specific needs. Objective This study aims to evaluate the efficacy of a newly developed 6-module internet-based prevention program of victimization for youth in care, named EMPOWER YOUTH. Methods In a randomized controlled trial, the intervention group will be compared to a waiting-list control group with an unblinded 1:1 allocation ratio. Assessments will take place before randomization (baseline) and at follow-up 18 weeks after baseline (ie, 12 weeks after finishing the last module of the program). The primary endpoint is the number of victimization, and online and offline bullying experiences (composite score) at the 18-week follow-up. Secondary endpoints are risk-taking behavior, aggressive tendencies, empathy, prosocial behavior, depressiveness, and loneliness at follow-up. The expected outcome requires a sample size of 156 subjects to achieve a power of 80%. Assuming a 30% dropout rate at follow-up, we require 225 participants to be allocated to the trial. Participants are youth in care, that is, adolescents in foster care, adopted adolescents, or young care leavers aged 14 to 21 years. Results Ethical approval was granted by the Ethics Committee of the Medical School Berlin in March 2021 (MSB-2021/55). Recruitment started in September 2021 and is planned until November 2022. The results are expected to be published in January 2023. Conclusions Given the increased likelihood for future victimization experiences among youth in care, there is a strong need for a low-threshold intervention specifically for this high-risk age group. There are no existing nationwide mental health programs exclusively for youth in care in Germany. Trial Registration German Clinical Trials Register DRKS00024749; International Registered Report Identifier (IRRID) DERR1-10.2196/34706
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Global rates of depression have increased significantly since the beginning of the COVID-19 pandemic. It is unclear how the recent shift of many mental health services to virtual platforms has impacted service users, especially for the male population which are significantly more likely to complete suicide than women. This paper presents the findings of a rapid meta-analytic research synthesis of 17 randomized controlled trials on the relative efficacy of virtual versus traditional face-to-face cognitive behavioral therapy (CBT) in mitigating symptoms of depression. Participants’ aggregated depression scores were compared upon completion of the therapy (posttest) and longest follow-up measurement. The results supported the noninferiority hypothesis indicating that the two modes of CBT delivery are equally efficacious, but the results proved to be significantly heterogeneous indicating the presence of moderating effects. Indirect suggestive evidence was found to support moderation by gender; that is, depressed males may benefit more from virtual CBT. Perhaps, this field’s most telling descriptive finding was that boys/men have been grossly underrepresented in its trials. Future trials ought to oversample those who have been at this field’s margins to advance the next generation of knowledge, allowing us to best serve people of all genders, those who live in poverty, Indigenous, Black, and other Peoples of Colour, as well as any others at risk of being marginalized or oppressed in contemporary mental health care systems.
The current qualitative study aimed to investigate psychologists' experiences of teletherapy throughout the COVID‐19 pandemic, with a particular focus on teletherapy's impact on therapeutic relationships. Fifteen participants, consisting of clinical and counselling psychologists employed by the Health Service Executive (HSE) in Ireland, participated in semi‐structured interviews conducted via telephone. Constructionist thematic analysis yielded three prominent themes: (i) psychologists experienced a lack of control over therapeutic boundaries with their clients, such as the setting and time in which sessions take place. Teletherapy also encroaches on psychologists' personal boundaries from having aspects of their home lives becoming associated with clients' trauma; (ii) psychologists have to work much harder to make the connection with clients due to the substantial loss of information, including non‐verbal cues, transmitted across teletherapeutic platforms; and (iii) psychologists themselves feel uncontained from being insufficiently supported by the HSE, which impacts on their ability to contain high‐risk clients via teletherapy. Various aspects of the therapeutic relationship, including therapeutic boundaries between psychologists and their clients, and psychologists' ability to make the connection and foster containment with their clients, were impacted by teletherapy. Psychologists need to feel heard, supported and appreciated to ensure optimal delivery of teletherapy in future.
The onset of the 2020 COVID‐19 pandemic necessitated a rapid transition of mental health services from in‐person to telehealth counselling. Despite the far‐reaching impact of this transition, we know little about the impact of this transition on outcomes for clients working with counsellor trainees. The present study utilised longitudinal data collected from a counselling training centre at a major U.S. university to compare client ratings of depression, anxiety and working alliance across 1) in‐person services delivered (i.e. pre‐pandemic) and 2) telehealth services delivered after the pandemic began (i.e. intra‐pandemic) among the same group of clients. Results support our hypothesis that changes in clients' self‐reports would be generally equivalent across in‐person and telehealth services. Depression and anxiety symptoms decreased, and working alliance tended to increase during both in‐person and telehealth care; however, these trends were only statistically significant during telehealth services. Limitations related to sample size (N = 15 clients; up to 17 sessions per client) and low statistical power are discussed. Nonetheless, this study supports the growing body of literature supporting the efficacy of telehealth counselling services. We provide suggestions for future telehealth research and discuss implications for counsellor training.
There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular machine learning models to the prediction of treatment prognosis, with a particular focus on digital interventions. Assuming that the prediction of treatment prognosis will involve modeling a complex combination of interacting features with measurement error in both the predictors and outcomes, our simulations suggest that to optimize complex prediction models, sample sizes in the thousands will be required. Machine learning methods capable of discovering complex interactions and nonlinear effects (e.g., decision tree ensembles such as gradient boosted machines) perform particularly well in large samples when the predictors and outcomes have virtually no measurement error. However, in the presence of moderate measurement error, these methods provide little or no benefit over regularized linear regression, even with very large sample sizes (N = 100,000) and a non-linear ground truth. Given these sample size requirements, we argue that the scalability of digital interventions, especially when used in combination with optimal measurement practices, provides one of the most effective ways to study treatment prediction models. We conclude with suggestions about how to implement these algorithms into clinical practice.
Background Health planning should be based on data about prevalence, disability and services used. Aims To determine the prevalence of ICD-10 disorders and associated comorbidity, disability and service utilisation. Method We surveyed a national probability sample of Australian households using the Composite International Diagnostic Interview and other measures. Results The sample size was 10 641 adults, response rate 78%. Close to 23% reported at least one disorder in the past 12 months and 14% a current disorder. Comorbidity was associated with disability and service use. Only 35% of people with a mental disorder in the 12 months prior to the survey had consulted for a mental problem during that year, and most had seen a general practitioner. Only half of those who were disabled or had multiple comorbidity had consulted and of those who had not, more than half said they did not need treatment. Conclusions The high rate of not consulting among those with disability and comorbidity is an important public health problem. As Australia has a universal health insurance scheme, the barriers to effective care must be patient knowledge and physician competence.
Zusammenfassung. Theoretischer Hintergrund: Nach Beck sind negative automatische Gedanken eine wichtige Ursache fur Depressionen. Fragestellung: Wie gut sind die Gutekriterien des “Fragebogen positiver und negativer automatischer Gedanken“ (FAG)? Methode: Die psychometrischen Kennwerte des FAG wurden anhand von zwei Stichproben der Allgemeinbevolkerung (n = 352 und n = 171) und einer Patientenstichprobe (n = 59) ermittelt. Ergebnisse: Die drei Skalen des FAG klaren zusammen ca. 60% der Varianz nach VARIMAX-Rotation auf und zeigen Interne Konsistenzen zwischen r = .76-.93. In Abhangigkeit vom Ausmas an Depression, Manie und Angst zeigen sich unterschiedliche Muster positiver und negativer automatischer Gedanken in den Skalen des FAG. Schlussfolgerungen: Mit dem FAG liegt ein zeitokonomisches und reliables Instrument zur Erfassung von positiven und negativen automatischen Gedanken vor.
Results of 24 studies (based on 20 distinct data sets) relating the quality of the working alliance (WA) to therapy outcome were synthesized using meta-analytic procedures. A moderate but reliable association between good WA and positive therapy outcome was found. Overall, the quality of the WA was most predictive of treatment outcomes based on clients' assessments, less so of therapists' assessments, and least predictive of observers' report. Clients' and observers' rating of the WA appear to be more correlated with all types of outcomes reported than therapists' ratings. The relation of WA and outcome does not appear to be a function of the type of therapy practiced, the length of treatment, whether the research is published, or the number of participants in the study. (PsycINFO Database Record (c) 2012 APA, all rights reserved)