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Computer Therapy for the Anxiety and Depressive Disorders Is Effective, Acceptable and Practical Health Care: A Meta-Analysis


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Depression and anxiety disorders are common and treatable with cognitive behavior therapy (CBT), but access to this therapy is limited. Review evidence that computerized CBT for the anxiety and depressive disorders is acceptable to patients and effective in the short and longer term. Systematic reviews and data bases were searched for randomized controlled trials of computerized cognitive behavior therapy versus a treatment or control condition in people who met diagnostic criteria for major depression, panic disorder, social phobia or generalized anxiety disorder. Number randomized, superiority of treatment versus control (Hedges g) on primary outcome measure, risk of bias, length of follow up, patient adherence and satisfaction were extracted. 22 studies of comparisons with a control group were identified. The mean effect size superiority was 0.88 (NNT 2.13), and the benefit was evident across all four disorders. Improvement from computerized CBT was maintained for a median of 26 weeks follow-up. Acceptability, as indicated by adherence and satisfaction, was good. Research probity was good and bias risk low. Effect sizes were non-significantly higher in comparisons with waitlist than with active treatment control conditions. Five studies comparing computerized CBT with traditional face-to-face CBT were identified, and both modes of treatment appeared equally beneficial. Computerized CBT for anxiety and depressive disorders, especially via the internet, has the capacity to provide effective acceptable and practical health care for those who might otherwise remain untreated. Australian New Zealand Clinical Trials Registry ACTRN12610000030077.
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Computer Therapy for the Anxiety and Depressive
Disorders Is Effective, Acceptable and Practical Health
Care: A Meta-Analysis
Gavin Andrews
*, Pim Cuijpers
, Michelle G. Craske
, Peter McEvoy
, Nickolai Titov
1School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia, 2Department of Clinical Psychology, Vrije Universiteit Amsterdam,
Amsterdam, The Netherlands, 3Department of Psychology, University of California Los Angeles, Los Angeles, California, United States of America, 4Centre for Clinical
Interventions, Perth, Western Australia, Australia, 5School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
Depression and anxiety disorders are common and treatable with cognitive behavior therapy (CBT), but access
to this therapy is limited.
Review evidence that computerized CBT for the anxiety and depressive disorders is acceptable to patients and
effective in the short and longer term.
Systematic reviews and data bases were searched for randomized controlled trials of computerized cognitive
behavior therapy versus a treatment or control condition in people who met diagnostic criteria for major depression, panic
disorder, social phobia or generalized anxiety disorder. Number randomized, superiority of treatment versus control (Hedges
g) on primary outcome measure, risk of bias, length of follow up, patient adherence and satisfaction were extracted.
Principal Findings:
22 studies of comparisons with a control group were identified. The mean effect size superiority was
0.88 (NNT 2.13), and the benefit was evident across all four disorders. Improvement from computerized CBT was maintained
for a median of 26 weeks follow-up. Acceptability, as indicated by adherence and satisfaction, was good. Research probity
was good and bias risk low. Effect sizes were non-significantly higher in comparisons with waitlist than with active
treatment control conditions. Five studies comparing computerized CBT with traditional face-to-face CBT were identified,
and both modes of treatment appeared equally beneficial.
Computerized CBT for anxiety and depressive disorders, especially via the internet, has the capacity to provide
effective acceptable and practical health care for those who might otherwise remain untreated.
Trial Registration:
Australian New Zealand Clinical Trials Registry ACTRN12610000030077
Citation: Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N (2010) Computer Therapy for the Anxiety and Depressive Disorders Is Effective, Acceptable and
Practical Health Care: A Meta-Analysis. PLoS ONE 5(10): e13196. doi:10.1371/journal.pone.0013196
Editor: Bernhard T. Baune, James Cook University, Australia
Received June 18, 2010; Accepted September 7, 2010; Published October 13, 2010
Copyright: ß2010 Andrews et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: GA, NT, PM, MC hold a grant for research on Internet Treatment from the Australian National Health and Medical Research Council registration
#630560. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail:
Anxiety disorders and major depressive disorders are common,
costly and debilitating [1,2]. Remarkably, less than half the people
with these disorders see a physician and only a quarter receive
appropriate treatment [3]. Effective treatments for these disorders
exist (i.e., selective serotonin reuptake inhibitors (SSRIs) and
cognitive behavior therapy (CBT) [4,5]. However, the public
health impact of these remedies is limited for a number of reasons.
Specifically, these disorders often are unrecognized [3,6], the
efficacy of SSRIs may be limited to very severe cases [7], CBT is
not widely available, in part because of insufficient numbers of
adequately trained clinicians [8], and patients do not or cannot
adhere to the costs and demands of face-to-face CBT treatment.
Almost one third of individuals attending an anxiety disorders
clinic did not start treatment [9], and attrition from randomized
controlled trials for anxiety and depression can reach 50% [10].
Internet and computer-based delivery formats could improve
access to CBT. There have been two recent meta-analyses of
internet-based and other computerized psychological treatments
for depression and anxiety states [11,12]. They included studies of
participants at risk, with sub-threshold symptoms, or with DSM
disorders. In anxiety states, the effect size superiority over control
conditions was large (23 studies, Cohen’s d = 1.1), and in
depressive states the effect size was moderate (12 studies,
d = 0.41). Two transdiagnostic programs included in these meta-
analyses, one aimed at panic and phobias – Fearfighter [13] – and
the other aimed at depression and anxiety states - Beating the
Blues [14] – were sufficiently powerful to be recommended for
routine use in the UK National Health Service [15].
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Recent research on computerized CBT delivered over the
internet (iCBT) or by computer in the clinic (cCBT) has
emphasized programs in which a predetermined syllabus presents
the principles and methods of CBT in a series of lessons, usually
with homework assignments and supplementary information. The
majority of newer programs are designed for individual anxiety or
depressive disorders. Computerized CBT can be self-guided,
supported by reminders from a non-clinical technician or practice
nurse, or guided by a clinician who makes telephone calls, sends
emails or posts comments on a private forum. The major
advantages of iCBT are accessibility and convenience for both
patients and clinicians, but equally important is that treatment
fidelity in both iCBT and cCBT is guaranteed by the
computerized delivery. If these treatments are to become part of
health care we need to know if such programs benefit patients who
meet criteria for anxiety or depressive disorders in the short- and
long-term, and if they are acceptable to such patients.
We restricted the present review to studies designed as
randomized controlled trials of computerized CBT for participants
who met diagnostic criteria for either major depressive disorder,
social phobia, panic disorder with or without agoraphobia, or
generalized anxiety disorder (GAD). Computerized CBT was
required to be the major intervention that was compared to treatment
as usual, or to control conditions such as placebo or waitlist. We
confined the analysis of outcome to self report measures of the
principal characteristic of each disorder; to the magnitude and
stability of the outcome; and to the acceptability of computer
therapy as estimated from the level of adherence to the course and
the satisfaction upon completion.
This review was registered (
12610000030077.aspx). All English language randomized con-
trolled trials of iCBT or cCBT that used participants who met
DSM criteria (established by structured diagnostic interview) for
either major depression, social phobia, panic disorder or GAD,
and that compared iCBT or cCBT with treatment as usual,
placebo or waitlist control groups, were included. All papers
analysed were either published or in press and the investigators
had copies of all final manuscripts.
Information sources
The search strategy followed that of the previous meta-analyses
[11,12] that used a database of studies on psychological treatment
[16] ( and other general data bases to
include RCTs of computer-aided psychotherapy that were
published after the cut off dates for previous meta-analyses (from
March 2008 for anxiety disorders and January 2009 for
depression). The search was conducted on the 31
of December
2009. A total of 2670 abstracts were examined from the following
databases: PubMed (N = 308), Cochrane Database of Systematic
Reviews and Register of Controlled Trials (N = 719), Cinahl
(N = 88), PsychINFO (N = 78), Medline (N = 171), Social Sciences
Citation Index (N = 1155), and Embase (N = 155). We identified
abstracts by combining terms indicative of psychological treatment
and depression, anxiety, and anxiety disorders (both MeSH terms
and text words). In addition, these terms were paired with the
terms ‘internet or computer or online’ to identify papers relating to
internet or computer treatment in particular. Reference lists for all
identified reviews and meta-analyses of computer-aided psycho-
therapy, as well as those of included studies, for the time period of
interest were also examined. Finally, we wrote to researchers to
identify any unpublished studies meeting the inclusion criteria.
Study selection
All studies of adults with the relevant diagnoses that randomized
subjects to computerized CBT versus treatment as usual or control
condition were included. We additionally examined studies in
which computerised CBT was compared with face to face CBT.
Items extracted in each study were as follows: Number of subjects
randomized; basic results (details of treatment condition, details of
control group, significant differences in outcome, Hedges g and
number needed to treat (NNT), adequacy of bias minimization
scored 0 = complete minimization, 5 = no minimization (ade-
quacy of sequence generation, allocation concealment, adequate
blinding, missing data addressed, no selective reporting [17]);
follow-up duration and stability, acceptability to participants
(percent adherent to the full course, percent satisfied). These
acceptability and bias ratings were independently conducted by
two researchers, with differences resolved following discussion.
We followed a described method [12, p197–198]. In brief, we
calculated the effect size (Hedges’ g) indicating the difference
between the two conditions at post-test, as the difference between
the mean of the treatment condition and the mean of the control
condition, divided by the pooled standard deviation and adjusted
for small sample bias [18]. We only used instruments that related
to the principal measure of the disorder to generate a mean effect
size. Because the effect size is not easy to interpret from a clinical
point of view, we also calculated the NNT by transforming the
effect sizes based on Z scores using the formulae provided by
Kraemer and Kupfer [19]. The NNT is defined as the number of
patients one would expect to treat to have one more successful
The effect sizes for each study were pooled according to the
random effects model, and differences between subgroups of
studies tested using the mixed effects model. As indicators of
heterogeneity of pooled effect sizes, we calculated I
, which
indicates the heterogeneity in percentages, and we tested whether
the level of heterogeneity was significant using the Q statistic.
Small study bias was tested by inspecting the funnel plot on the
primary outcome measures (effects on depression or anxiety at
post-test) and by a trim-and-fill procedure [20], which yields an
estimate of the pooled ES after taking bias into account. All
analyses were conducted using the computer program Compre-
hensive Meta-Analysis (version 2.2.021) [21].
The previous meta-analyses [11,12] were taken as having been
comprehensive for the period covered by their search strategy.
Nine studies included in those meta-analyses met the new
inclusion criteria, (focus on one of the four specified diagnoses,
iCBT or cCBT the principal treatment). Thirteen additional
studies were identified making 22 studies in all. Minimization of
research bias was assessed [17]. All studies reported data using the
intention to treat method and all used self report measures of the main
outcome thereby obviating the need for blinding. Three studies only
met these basic criteria, 13 studies also met the method of sequence
generation or allocation concealment criteria and six studies satisfied all 5
Results of the meta-analysis of the 22 studies [22–43] are
displayed in Table 1: grouped by diagnosis, listing author and date
of publication, N randomized, effect size of intervention compared
iCBT Anxiety and Depression
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to control condition (Hedges g), NNT, risk of bias; length of follow-
up; and adherence and patient satisfaction as a proxy for
acceptability. Summary data are in Table 2 and a funnel plot of
studies ranked by disorder shows the confidence limits around the
effect sizes for each study (Figure 1).The overall effect size
superiority of computerized CBT over control group across all
four disorders was 0.88 and the confidence limits did not include
zero (p,0.001). Similar results were obtained for major depression
(g =0.78, 95% CI 0.59–0.96), social phobia (g = 0.92, 95% CI
0.74–1.09), panic disorder (g = 0.83, 95% CI 0.45–1.21) and GAD
(g =1.12, 95%CI 0.76–1.47). Heterogeneity was non-significant
for each disorder and for all studies together. There was a small,
non-significant indication for small sample bias (adjusted effect size
g = 0.80). Although the effect size for studies using a waitlist
control group (g = 0.94; 95% CI: 0.81–1.07) was somewhat higher
than for treatment as usual and other control groups (g = 0.75;
95% CI: 0.51–0.98), this difference was not significant (p.0.1).
Fourteen of the 22 studies reported follow-up data that range
from 4 to 52 weeks post-treatment (median 26 weeks), and in none
was there evidence of relapse. Adherence and satisfaction are
indicators of acceptability of computerised CBT to patients. All
studies measured one or both. Adherence was good, and a median
of 80% of people who began these programs completed all lessons
(range 48%–100%). Ten of the 23 studies provided data on patient
satisfaction and a median of 86% (range 70%–100%) of patients
reported that they were satisfied or very satisfied.
There were two studies [25,27], in which computerized CBT
was also compared to face-to-face CBT for depression and three
Table 1. Selected characteristics and results of randomized controlled studies examining the effects computerized and internet-
based cognitive behaviour therapy for adult depression and anxiety disorders.
Study Conditions N g NNT Bias Risk F-U Adhere/Satisf
Andersson, 2005
iCBT +therapist support .waitlist +discussion group 75 0.87 2.16 0 26w 63/-
Kessler, 2009
iCBT +therapist support .TAU by GP 297 0.61 2.99 0 16w 73/-
Perini, 2009
iCBT +therapist support .waitlist 48 0.56 3.25 1 - 74/82
Selmi 1990
cCBT .waitlist 36 1.26 1.59 2 9w 100/-
Titov 2010
iCBT +therapist support .waitlist 141 0.99 1.94 1 - 70/87
Wright 2005
cCBT +therapist support .waitlist 45 1.10 1.77 1 26w 87/-
Carlbring, 2001
iCBT .waitlist 41 0.99 1.94 1 - 80/85
Carlbring, 2006
iCBT .waitlist 60 1.13 1.74 0 39w 80/97
Klein, 2001
iCBT .Self-monitoring control 23 0.39 4.59 2 - 90/-
Klein, 2006
iCBT .Information control 55 1.49 1.41 1 13w 90/-
Richards, 2006
iCBT .Information control 32 0.74 2.50 0 13w 82/-
Wims 2010
iCBT +therapist support .waitlist 59 0.28 6.41 1 4w 79/-
Andersson, 2006
iCBT .waitlist 64 0.76 2.44 0 52w 56/-
Berger et al. 2009
iCBT .waitlist 52 0.64 2.86 1 - 90/85
Botella et al. 2009
iCBT .waitlist 52 1.07 1.82 2 52w 48/-
Carlbring, 2007
iCBT .waitlist 57 1.07 1.82 1 52w 93/-
Furmark et al 2009
iCBT +therapist support .waitlist 120 0.67 2.75 0 52w 97/70
Titov, 2008 I
iCBT +therapist support .waitlist 105 0.94 2.02 1 26w 78/100
Titov, 2008 II
iCBT +therapist support .waitlist 88 1.18 1.68 1 26w 81/100
Titov, 2008 III
iCBT +therapist support .waitlist 98 1.02 1.89 1 - 77/-
Titov 2009
iCBT +therapist support .waitlist 48 1.08 1.81 1 - 75/85
Robinson 2010
iCBT +therapist support .waitlist 150 1.13 1.74 1 - 74/87
N, number randomized; g, Hedges g; NNT number needed to treat; Bias risk (0 =no risk, 5 = high risk) inadequacy of sequence generation, no allocation concealment,
inadequate blinding, missing data not addressed, selective reporting; F-U, follow-up in weeks; Adhere/satisfaction, percent adhering to whole course/percent satisfied
with course; iCBT, CBT over the internet; cCBT, CBT over computer in clinic; GAD, Generalized Anxiety Disorder.
Table 2. Summary results of meta-analyses examining the
effects of internet- and computerized CBT for depression and
anxiety disorders.
Disorder N g 95% CI Z
MDD 6 0.78 0.59–0.96 8.20 *** 0 2.39
Social phobia 8 0.92 0.74–1.09 10.28 *** 0 2.07
Panic 6 0.83 0.45–1.21 4.27 *** 49.77 2.26
GAD 2 1.12 0.76–1.47 6.19 *** 0 1.75
All disorders 22 0.88 0.76–0.99 15.04 *** 7.84 2.15
iCBT Anxiety and Depression
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comparison trials, not included in the main meta-analysis as there
was no control group, in which the comparison between
computerised CBT and face to face CBT was in patients with
depression or panic disorder [44–46] (total number of patients in
the five studies was 567; 300 in the computerized and 267 in the
face-to-face conditions). The effect size indicating the difference
between computerized-treatments and face-to-face treatments was
non-significant g = 0.09 in favour of computerized treatments
(95% CI: 20.34,17), with zero heterogeneity. In the computer
condition therapist time was reduced compared to face-to-face
therapy for depression by 50% [27] and 79% [44], and in panic
disorder by 35% [46] and 70% [45]. Treatment satisfaction was
reported as good in both computerised and face-to-face treatment
groups [27,45,46].
Twenty two RCTs of computerised CBT for major depression,
social phobia, panic disorder or generalized anxiety disorder
showed superiority in outcome over control groups. The effect
sizes are substantial, and the results indicate both short term and
long term benefits. Furthermore, patients adhered to and were
satisfied with computerised CBT, despite the significantly reduced
amount of contact with the clinician. Thus, computerised CBT is
an efficacious and acceptable treatment, and by increasing
convenience and reducing clinician time that would otherwise be
required by face-to-face treatment, it offers increased access to
treatment for those suffering from anxiety and depression.
The results come from 9 different groups working indepen-
dently in 7 different countries. Similar results were obtained for
each disorder and heterogeneity was non-significant for each
disorder and for all studies together. It is as though there is a core
set of CBT skills that is of benefit in the internalising disorders
included in this analysis.
Most patients had been recruited as volunteers, largely after
media publicity, but a minority were referred by their clinician.
This raises the question, ‘are these patients comparable to patients
who seek face-to-face treatment?’ In a large study (n = 774),
internet patients with one of these four disorders were as severe
when assessed by symptom, distress and disability measures as
those attending a face-to-face clinic, and both groups were
significantly more severe than cases identified in an epidemiolog-
ical survey [47]. Another index of severity is treatment history.
Three studies reported this. In one study of iCBT for depression in
a primary care setting, three quarters of patients had a history of
previous episodes [23]. The chronicity was similar in two iCBT
studies for depression in community volunteers. In the first [24]
70% had sought prior help and 51% were currently taking
medication for their depression. In the second study [26] help
seeking and medication rates were comparable and 72% said their
onset of depression was before the age of 21, 78% said they had
had more than 5 episodes and 78% said that they had had no
remission in the last 2 years. Thus, it appears that participants in
these trials resemble people who attend regular clinics. There were
few data on treatment history in the studies of anxiety disorders.
The mean effect size, indicating the superiority of the comput-
erized intervention over the control group, was 0.88, NNT 2.15. The
most common control group was waitlist, with treatment for them
delayed until the intervention group had completed treatment.
Placebo or active treatment control groups are preferable, but are
Figure 1. Effect sizes of Computerised CBT versus control conditions at post-test.
iCBT Anxiety and Depression
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difficult to arrange when there is no face to face contact with the
participants. Interventions compared to waitlist controls have shown
increased effect sizes compared to interventions compared to the
treatment as usual studies [12] and the null finding in the present
meta-analysis may be due to insufficient power. There were no
studies comparing computerised CBT and medication. Five studies
compared internet therapy directly with face-to-face CBT for
depression or panic disorder, and while all found strong pre-post
treatment effects, none found differences between the two modes of
delivery. We conclude that computerized CBT, with clinician or
technician assistance which can be as brief as one hour per patient,
can work as well as face-to-face CBT.
Adherence to computerized CBT was good; in the median
study, 80% of individuals who began these programs completed all
stages. This rate of completion suggests that computerized CBT
was well accepted by participants. The programs contained
between five and nine ‘lessons’. Conceivably, some participants
who do not complete all the lessons may have gained all they need
from the program. More research is needed regarding the tailoring
of computerized programs to the needs of individuals. Ten of the
22 studies provided data on patient satisfaction; in the median
study 86% of patients were satisfied or very satisfied with the
computerized CBT program. Participants noted the advantages of
computerized therapy, including convenience (such as completion
of the program in the evening when there are no competing
demands), ability to proceed at one’s own pace to master the
material, low cost and privacy. We conclude that computerised
CBT is acceptable to patients.
There is a need for more extensive follow-up assessment as only
14 of the 22 studies provided follow-up data, at a median 26 weeks
(range 4–52). As with face-to-face CBT [5], the benefits lasted and
no significant relapse was reported.
The majority of studies identified measures of distress, disability,
quality of life, or work force participation as secondary outcome
measures. While changes in these secondary outcome measures
were not as large as in the primary outcome measures, they were
significant and demonstrate that internet treatment has the
capacity to change health status not merely reduce specific
symptoms. One study pooled data from three RCTs of social
phobia and showed significant improvements in comorbid
symptoms of depression and generalized anxiety even though
the treatment was focused solely on the social phobia [48].
The benefits described are substantial yet the content of the
programs is relatively simple and the therapist or technician
contact brief. For example in the Andersson [22] study (g = 0.87),
the treatment group had access to five weekly text ‘lessons’ about
recovering from depression – behavioural activation, cognitive
restructuring, sleep and physical health, and relapse prevention
and future goals. This raises an issue of whether we presently
conceptualise the nature of these four disorders correctly, either as
related to temperament [2] or to neurotransmitter abnormalities
[49] neither of which could be expected to yield to relatively brief
sessions of skills based teaching about controlling worrying
thoughts and confronting feared situations. The mechanism by
which these programs produce benefit needs to be explored.
In sum, the 22 identified computerized CBT programs
generated a large effect size superiority over control groups with
maintenance of gains at follow-up and good patient adherence and
satisfaction. As the programs become more sophisticated, the
clinician or technician time required seems to be decreasing to the
order of 10 minutes per week per patient [26,43,50].
Is it possible to integrate these internet services with existing
mental health services so that people who do not recover with
internet therapy can, in a stepped care design, receive face to face
care? We now, it seems, are beginning to know enough about the
efficacy, applicability and potential cost savings from the internet
programs for people with anxiety and depressive disorders to begin
to integrate these internet services with existing mental health
Author Contributions
Conceived and designed the experiments: GA PC MGC PM NT.
Performed the experiments: PC PM. Analyzed the data: GA PC MGC
PM NT. Contributed reagents/materials/analysis tools: NT. Wrote the
paper: GA PC MGC PM NT.
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iCBT Anxiety and Depression
PLoS ONE | 6 October 2010 | Volume 5 | Issue 10 | e13196
... MoodGYM helps in identifying negative thoughts and teaches practical strategies for managing the negative thoughts and beliefs to reduce the dysfunctional thinking of MDD patients. Several studies have attested to the efficacy of MoodGYM for MDD for both outpatients and inpatients in clinical settings [33,[43][44][45][46][47]. According to the World Health Organization (WHO), it is an international priority to increase the coverage of interventions and evidence-based treatments for TRD globally [48]. ...
... However, even though this study failed to find an additive value for iCBT to rTMS in regard to the management of TRD symptoms, it does not invalidate the full use and therapeutic efficacy of iCBT in the management of TRD, as there is evidence that supports the use of iCBT for the management of depression and resistant depression [33,44,46,[72][73][74][75], with claims of efficacy equivalent to those of CBT delivered by trained personnel [44,72]. ...
... However, even though this study failed to find an additive value for iCBT to rTMS in regard to the management of TRD symptoms, it does not invalidate the full use and therapeutic efficacy of iCBT in the management of TRD, as there is evidence that supports the use of iCBT for the management of depression and resistant depression [33,44,46,[72][73][74][75], with claims of efficacy equivalent to those of CBT delivered by trained personnel [44,72]. ...
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Background: Treatment-resistant depression (TRD) is considered one of the major clinical challenges in the field of psychiatry. An estimated 44% of patients with major depressive disorder (MDD) do not respond to two consecutive antidepressant therapies, and 33% do not respond to up to four antidepressants. Over 15% of all patients with MDD remain refractory to any treatment intervention. rTMS is considered a treatment option for patients with TRD. Likewise, iCBT is evidence-based, symptom-focused psychotherapy recommended for the treatment of TRD. Objective: This study aimed to evaluate the initial comparative clinical effectiveness of rTMS treatment with and without iCBT as an innovative intervention for the treatment of participants diagnosed with TRD. Methods: This study is a prospective two-arm randomized controlled trial. Overall, 78 participants diagnosed with TRD were randomized to one of two treatment interventions: rTMS sessions alone and rTMS sessions plus iCBT. Participants in each group were made to complete evaluation measures at baseline, and 6 weeks (discharge) from treatment. The primary outcome measure was baseline to six weeks change in mean score for the 17-item Hamilton depression rating scale (HAMD-17). Secondary outcomes included mean baseline to six-week changes in the Columbia suicide severity rating scale (CSSRS) for the rate of suicidal ideations, the QIDS-SR16 for subjective depression, and the EQ-5D-5L to assess the quality of health in participants. Results: A majority of the participants were females 50 (64.1%), aged ≥ 40 39 (50.0%), and had college/university education 54 (73.0%). After adjusting for the baseline scores, the study failed to find a significant difference in the changes in mean scores for participants from baseline to six weeks between the two interventions under study on the HAMD-17 scale: F (1, 53) = 0.15, p = 0.70, partial eta squared = 0.003, CSSRS; F (1, 56) = 0.04 p = 0.85, partial eta squared = 0.001, QIDS-SR16 scale; F (1, 53) = 0.04 p = 0.61, partial eta squared = 0.005, and EQ-5D-VAS; F (1, 51) = 0.46 p = 0.50, and partial eta squared = 0.009. However, there was a significant reduction in means scores at week six compared to baseline scores for the combined study population on the HAMD-17 scale (42%), CSSRS (41%), QIDS-SR16 scale (35%), and EQ-VAS scale (62%). Conclusion: This study did not find that combined treatment of TRD with rTMS + iCBT (unguided) was superior to treatment with rTMS alone. Our findings do not support the use of combined treatment of rTMS + iCBT for the management of TRD disorders.
... Although a vast majority of resilience interventions are delivered via face-to-face, innovative approaches such as bibliotherapy, online webinars or phone coaching are also being used (Joyce et al. 2018). A meta-analysis of 64 trials examining Internet-delivered Cognitive Behavioural Therapy modules or lessons reported positive benefits for those with major depression, panic disorder, social anxiety disorder or generalised anxiety disorder (Andrews et al. 2010). ...
Full-text available
This study evaluated two forms of a resilience intervention amongst college students during the COVID-19 pandemic. Utilising a randomised controlled trial design, it examined the impact of a synchronous and asynchronous resilience interventions versus a control group that did a journaling intervention. Outcomes measured included coping behaviour, non-reactivity, wellbeing, stress, depression and anxiety. Participants consisted of Filipino college students randomly assigned to three groups: synchronous online resilience group (n = 135), asynchronous resilience group (n = 121) and control group (n = 127). Results revealed that students who went through the online synchronous resilience reported a significant reduction in depression at post-intervention compared to those who went through an asynchronous intervention. Post-intervention scores for nonreactivity were also higher in the synchronous group compared to both asynchronous and journaling groups. Effect sizes were small to moderate. This study suggests that online resilience interventions are viable means to address the mental health needs of students, especially in countries with limited mental health resources.
... 13,14 A way of increasing access to CBT-i is to provide it via the Internet (ICBT-i), which in many studies has been proven effective, 15,16 as has CBT for depression (ICBT-d). 17 There are, however, very few randomized controlled follow-ups of more than 6-12 months for depression and insomnia treatments. ...
... In another approach to evaluating the efficacy and durability of a therapist-supported method for computer-assisted cognitive-behavioral therapy (CCBT) in comparison to standard cognitive-behavioral therapy (CBT), scientists assigned 154 medication-free patients with a major depressive disorder to either 16 weeks of standard CBT (up to 20 sessions of 50 minutes each) or CCBT using the "Good Days Ahead" program. The study findings indicate that a method of CCBT that blends Internet-delivered skill-building modules with about 5 hours of therapeutic contact was non-inferior to a conventional course of CBT that provided over 8 additional hours of therapist contact (Thase, Wright, Eells, Barrett, Wisniewski, and Balasubramani, 2018;Andrews, Cuijpers, Craske, McEvoy and Titov, 2010). A similar result has been found in another study where scientists have solid evidence for the efficacy of CCBT when the use of a therapeutic computer program is supported by a clinician or other helping professionals. ...
Full-text available
Machine learning has a new landscape for humanity in the area of artificial intelligence (AI). Artificial intelligence (AI) approaches have recently been developed to support mental health professionals, primarily psychiatrists, psychologists, and clinicians, with decision-making based on patients' historical data (e.g., clinical history, behavioral data, social media use, etc.). This article reviews developments in artificial intelligence (AI) technologies and their current and potential applications in clinical psychological practice. Issues associated with AI in the context of clinical practice, the potential risk for job loss among mental health professionals, and other ramifications associated with the advancement of AI technology are discussed. The advancement of AI technologies and their application in psychological practice have important implications that can be expected to transform the mental health care field. Psychologists and other mental health care professionals have an essential part to play in the development, evaluation, and ethical use of AI technologies.
... The advantages of DMHIs include accessibility, cost-effectiveness, and personalization [7], which can address key barriers for mental health recovery such as low helpseeking and stigma associated with mental health problems [8]. Effectiveness of DMHIs has been generally reported in diverse populations (e.g., children, young people, older adults, university students, healthcare workers, people with neurodevelopmental disabilities) and on diverse mental health experiences [2,[9][10][11][12]. DMHIs have become an important domain in medical internet research [13][14][15]. ...
Demand for digital health interventions is increasing in many countries. The use of recorded mental health recovery narratives in digital health interventions is becoming more widespread in clinical practice. Mental health recovery narratives are first-person lived experience accounts of recovery from mental health problems, including struggles and successes over time. Helpful impacts of recorded mental health recovery narratives include connectedness with the narrative and validation of experiences. Possible harms include feeling disconnected and excluded from others. Diverse narrative collections from many types of narrators and describing multiple ways to recover are important, to maximize the opportunity for service users to benefit through connection, and to minimize the likelihood of harm. Mental health clinicians need to know whether narrative collections are sufficiently diverse to recommend to service users. However, no method exists for assessing diversity and inclusivity of existing or new narrative collections. We argue assessing diversity and inclusivity is the next frontier in mental health recovery narrative research and practice. This is important but methodologically and ethically complex. In this viewpoint article, we evaluated one diversity and two inclusivity assessment methods. The diversity assessment method used Simpson’s Diversity Index. The two inclusivity assessment methods were based on comparator demographic rates and arbitrary thresholds. These methods were applied to four narrative collections as a case study. Refinement needs to be made regarding a narrative assessment tool, practicality and cultural adaptation.
Although digital mental health (MH) interventions are no longer new, adapting to an online learning environment involves a considerable learning curve. Numerous detailed models focused on instructional design and learning theory for online formats are available, as well as expert forums and articles on how to develop online content, but most of these resources fall short because they do not adequately address the key ingredients for MH interventions or the broad needs of the populations served by these programs. The goal of this chapter is to provide a practical guide to help subject matter mental health experts and intervention developers make the move to digital delivery. The authors share their experiences and lessons learned working with developers to create digital interventions for youth MH. First, we highlight key considerations that developers should keep in mind when undertaking digital intervention development, including the relative benefits and challenges of adapting an existing evidence-based program versus creating an entirely new program. Then, discuss the “ingredients” that go into making an online MH program and tips for selecting a software development partner. Last, we describe the steps involved in the design and development process and present illustrative case examples. It is important to note that the discussion focuses on the online delivery of asynchronous intervention content, whereby youth access the program directly in a self-paced fashion via any device with a web browser, including a desktop, laptop, tablet, or smartphone.
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After the deleterious effects of the COVID-19 pandemic on healthcare worker mental health, we tested the effectiveness of an interactive chatbot, Vitalk, for improving wellbeing and resilience among healthcare workers in Malawi, a country with few mental health professionals. We conducted a randomized, controlled trial (RCT) to investigate our hypothesis that Vitalk is more effective in improving mental health and resilience outcomes than passive Internet resources. For our 2-arm, 8-week, parallel RCT (ISRCTN Registry: trial ID ISRCTN16378480), we recruited participants from 8 professional cadres from public and private healthcare facilities. The treatment arm used Vitalk; the control arm received links to Internet resources. Of 1,584 participants, 512 completed baseline and endline assessments. Six assessments provided outcome measures for: anxiety (GAD-7); depression (PHQ-9); burnout (OLBI); loneliness (ULCA); resilience (RS-14); and resilience-building activities. We analyzed effectiveness using mixed-effects linear models, effect size estimates, and reliable change in risk levels. Results from mixed-model analyses support our hypothesis. Difference-in-differences estimators showed that Vitalk reduced: depression (-0.68 [95% CI -1.15 to -0.21]); anxiety (-0.44 [95% CI -0.88 to 0.01]); and burnout (-0.58 [95% CI -1.32 to 0.15]). Changes in resilience (1.47 [95% CI 0.05 to 2.88]) and resilience-building activities (1.22 [95% CI 0.56 to 1.87]) were significantly greater in the treatment group. We observed no treatment effect on loneliness. Our RCT produced a medium effect size. This is the first RCT of a mental health app for healthcare workers during the pandemic in Southern Africa combining multiple mental wellbeing outcomes, and measuring resilience and resilience-building activities. A significant number of participants could have benefited from mental health support (1 in 8 reported anxiety and depression; 3 in 4 suffered burnout; and 1 in 4 had low resilience). Such help is not readily available in Malawi. Vitalk has the potential to fill this gap.
Background: Only 11%-40% of those with a mental disorder in Germany receive treatment. In many cases, face-to-face psychotherapy is not available because of limited resources, such as an insufficient number of therapists in the area. New approaches to improve the German health care system are needed to counter chronification. Web-based interventions have been shown to be effective as stand-alone and add-on treatments to routine practice. Interventions designed for a wide range of mental disorders such as transdiagnostic interventions are needed to make treatment for mental disorders more accessible and thus shorten waiting times and mitigate the chronification of mental health problems. In general, interventions can be differentiated as having either a capitalization (CAP) focus-thus drawing on already existing strengths-or a compensation (COMP) focus-trying to compensate for deficits. Up to now, the effectiveness of transdiagnostic web-based interventions with either a CAP or a COMP focus has not yet been evaluated. Objective: This study is the first to examine the effectiveness of two transdiagnostic web-based interventions: (1) the activation of resilience and drawing on existing strengths (CAP: Res-Up!) and (2) the improvement of emotion regulation (COMP: REMOTION), compared with care as usual (CAU) in routine outpatient psychotherapy. Methods: Adults with at least 1 mental health disorder will be recruited at 4 outpatient centers in Germany. Participants will then be randomized equally into 1 of the 2 intervention groups Res-Up! (CAP) and REMOTION (COMP) or into the control group (CAU). Assessments will be made at baseline (T0), at 6 weeks after treatment start (T1), and at 12 weeks after treatment start (T2). A primary outcome will be symptom severity (Brief Symptom Inventory-18). Secondary outcomes will focus on emotion regulation and resilience. Results: Participant recruitment and data collection started in April 2020 and were ongoing as of July 2022. We expect participants to benefit more from the interventions than from the CAU control on the dimensions of symptom severity, resilience, and emotion regulation. Furthermore, we expect to find possible differences between CAP and COMP. The results of the study are expected in 2023. Conclusions: This randomized controlled trial will compare CAU with the transdiagnostic web-based interventions Res-Up! and REMOTION, and will thus inform future studies concerning the effectiveness of transdiagnostic web-based interventions in routine outpatient psychotherapy. Trial registration: NCT04352010; International registered report identifier (irrid): DERR1-10.2196/41413.
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This review summarizes the current meta-analysis literature on treatment outcomes of CBT for a wide range of psychiatric disorders. A search of the literature resulted in a total of 16 methodologically rigorous meta-analyses. Our review focuses on effect sizes that contrast outcomes for CBT with outcomes for various control groups for each disorder, which provides an overview of the effectiveness of cognitive therapy as quantified by meta-analysis. Large effect sizes were found for CBT for unipolar depression, generalized anxiety disorder, panic disorder with or without agoraphobia, social phobia, posttraumatic stress disorder, and childhood depressive and anxiety disorders. Effect sizes for CBT of marital distress, anger, childhood somatic disorders, and chronic pain were in the moderate range. CBT was somewhat superior to antidepressants in the treatment of adult depression. CBT was equally effective as behavior therapy in the treatment of adult depression and obsessive-compulsive disorder. Large uncontrolled effect sizes were found for bulimia nervosa and schizophrenia. The 16 meta-analyses we reviewed support the efficacy of CBT for many disorders. While limitations of the meta-analytic approach need to be considered in interpreting the results of this review, our findings are consistent with other review methodologies that also provide support for the efficacy CBT.
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Meta-analysis collects and synthesizes results from individual studies to estimate an overall effect size. If published studies are chosen, say through a literature review, then an inherent selection bias may arise, because, for example, studies may tend to be published more readily if they are statistically significant, or deemed to be more “interesting” in terms of the impact of their outcomes. We develop a simple rank-based data augmentation technique, formalizing the use of funnel plots, to estimate and adjust for the numbers and outcomes of missing studies. Several nonparametric estimators are proposed for the number of missing studies, and their properties are developed analytically and through simulations. We apply the method to simulated and epidemiological datasets and show that it is both effective and consistent with other criteria in the literature.
Selective serotonin reuptake inhibitors (SSRIs) are now considered by most experts to be the first-line pharmacological treatment for panic disorder based on their low rate of side effects, lack of dietary restrictions, and absence of tolerance and withdrawal symptoms. Similarly, SSRIs are an attractive first-line treatment for social anxiety disorder. The pharmacological treatments of choice for generalized anxiety disorder are buspirone and antidepressants, including SSRIs and venlafaxine. Both buspirone and antidepressants provide a promising alternative to benzodiazepines. Benzodiazepines, although effective for all these disorders, carry with them the risk of physiological dependence and withdrawal symptoms and ineffectiveness for comorbid depression. Their greatest utility at present seems to be as an initial or adjunctive medication for patients with disabling symptoms requiring rapid relief and for those unable to tolerate other medications. Chronic treatment with benzodiazepines is generally safe and effective but should probably be reserved for patients who are nonresponsive or intolerant to other agents. Controlled trials are necessary to determine whether patients with specific phobias respond to pharmacological agents, particularly serotonin reuptake inhibitors.
Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:617-627), an author’s name was inadvertently omitted from the byline on page 617. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Wai Tat Chiu, AM; Olga Demler, MA, MS; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” Also on that page, the affiliations paragraph should have appeared as follows: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Drs Kessler, Chiu, Demler, and Walters); Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Bethesda, Md (Dr Merikangas). On page 626, the acknowledgment paragraph should have appeared as follows: We thank Jerry Garcia, BA, Sara Belopavlovich, BA, Eric Bourke, BA, and Todd Strauss, MAT, for assistance with manuscript preparation and the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on the data analysis. We appreciate the helpful comments of William Eaton, PhD, Michael Von Korff, ScD, and Hans-Ulrich Wittchen, PhD, on earlier manuscripts. Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
In randomized clinical trails (RCTs), effect sizes seen in earlier studies guide both the choice of the effect size that sets the appropriate threshold of clinical significance and the rationale to believe that the true effect size is above that threshold worth pursuing in an RCT. That threshold is used to determine the necessary sample size for the proposed RCT. Once the RCT is done, the data generated are used to estimate the true effect size and its confidence interval. Clinical significance is assessed by comparing the true effect size to the threshold effect size. In subsequent meta-analysis, this effect size is combined with others, ultimately to determine whether treatment (T) is clinically significantly better than control (C). Thus, effect sizes play an important role both in designing RCTs and in interpreting their results; but specifically which effect size? We review the principles of statistical significance, power, and meta-analysis, and commonly used effect sizes. The commonly used effect sizes are limited in conveying clinical significance. We recommend three equivalent effect sizes: number needed to treat, area under the receiver operating characteristic curve comparing T and C responses, and success rate difference, chosen specifically to convey clinical significance.
When there is publication bias, studies yielding large p values, and hence small effect estimates, are less likely to be published, which leads to biased estimates of effects in meta-analysis. We investigate a selection model based on one-tailed p values in the context of a random effects model. The procedure both models the selection process and corrects for the consequences of selection on estimates of the mean and variance of effect parameters. A test of the statistical significance of selection is also provided. The small sample properties of the method are evaluated by means of simulations, and the asymptotic theory is found to be reasonably accurate under correct model specification and plausible conditions. The method substantially reduces bias due to selection when model specification is correct, but the variance of estimates is increased; thus mean squared error is reduced only when selection produces substantial bias. The robustness of the method to violations of assumptions about the form of the distribution of the random effects is also investigated via simulation, and the model-corrected estimates of the mean effect are generally found to be much less biased than the uncorrected estimates. The significance test for selection bias, however, is found to be highly nonrobust, rejecting at up to 10 times the nominal rate when there is no selection but the distribution of the effects is incorrectly specified.
Previous research has established Internet-based cognitive behavioural therapy (CBT) for panic disorder (PD) as effective in reducing panic severity and frequency. There is evidence, however, that such programs are less effective at improving overall end-state functioning, defined by a PD clinician severity rating of ≤2 and panic free. In order to test the effect on end-state functioning of the incorporation of stress management material within a CBT program for PD, 32 people with PD were randomised to either Internet-based CBT (PO1), Internet-based CBT plus stress management (PO2) or an Internet-based information-only control condition (IC). Both CBT treatments were more effective at posttreatment assessment than the control condition in reducing PD severity, panic and agoraphobia-related cognition, negative affect and self-ratings of health. PO2 was more effective than PO1 at posttreatment assessment on PD severity and general anxiety, although at 3-month follow-up these differences were no longer apparent. This study provides further support for the efficacy of Internet-based CBT for PD and suggests that although the incorporation of stress management material confers short-term advantages over a standard program, it is not associated with any longer term improvements on panic severity and related cognitions, negative affect, general wellbeing and end-state functioning.