REVIEW ARTICLEInternet-based cognitive behaviour therapy f.docx

REVIEW ARTICLE
Internet-based cognitive behaviour therapy for symptoms
of depression and anxiety: a meta-analysis
VIOLA SPEK1 ,2*, PIM CUIJPERS 3, IVAN NYKLÍČEK1,
HELEEN RIPER4,
JULES KEYZER 2 A N D VICTOR POP 1,2
1 Department of Psychology and Health, Tilburg University,
The Netherlands; 2 Diagnostic Centre Eindhoven,
The Netherlands; 3 Department of Clinical Psychology, Vrije
Universiteit Amsterdam, The Netherlands;
4 Trimbos-instituut, Netherlands Institute of Mental Health and
Addiction, The Netherlands
ABSTRACT
Background. We studied to what extent internet-based cognitive
behaviour therapy (CBT)
programs for symptoms of depression and anxiety are effective.
Method. A meta-analysis of 12 randomized controlled trials.
Results. The effects of internet-based CBT were compared to
control conditions in 13 contrast
groups with a total number of 2334 participants. A meta-
analysis on treatment contrasts resulted in
a moderate to large mean effect size [fixed effects analysis
(FEA) d=0.40, mixed effects analysis
(MEA) d=0.60] and significant heterogeneity. Therefore, two

sets of post hoc subgroup analyses
were carried out. Analyses on the type of symptoms revealed
that interventions for symptoms of
depression had a small mean effect size (FEA d=0.27, MEA
d=0.32) and significant heterogeneity.
Further analyses showed that one study could be regarded as an
outlier. Analyses without this study
showed a small mean effect size and moderate, non-significant
heterogeneity. Interventions for
anxiety had a large mean effect size (FEA and MEA d=0.96)
and very low heterogeneity. When
examining the second set of subgroups, based on therapist
assistance, no significant heterogeneity
was found. Interventions with therapist support (n=5) had a
large mean effect size, while inter-
ventions without therapist support (n=6) had a small mean
effect size (FEA d=0.24, MEA
d=0.26).
Conclusions. In general, effect sizes of internet-based
interventions for symptoms of anxiety were
larger than effect sizes for depressive symptoms; however, this
might be explained by differences
in the amount of therapist support.
INTRODUCTION
Cognitive behaviour therapy (CBT) is a widely
used and effective form of therapy for a wide
range of psychological disorders, including
depression and anxiety disorders (Hollon et al.
2006). In the industrialized societies, the internet
has become integrated into the daily lives of
a large part of the population. The number of
people using the internet is still rising. Internet

use has even spread among the groups that
are not usually the first to use a new technology,
namely women, elderly people and minority
groups (Lamerichs, 2003). The expansion of the
internet offers new treatment opportunities.
CBT is very suitable for adaptation to a com-
puter format. It is a structured treatment ap-
proach with the aim of developing new types of
behaviour and cognition.
Internet-based CBT has advantages over tra-
ditional CBT for both clients and health care.
* Address for correspondence: Viola Spek, Tilburg University,
PO Box 90153, 5000 LE Tilburg, The Netherlands.
(Email: [email protected])
Psychological Medicine, 2007, 37, 319–328. f 2006 Cambridge
University Press
doi:10.1017/S0033291706008944 First published online 20
November 2006 Printed in the United Kingdom
319
The anonymity and accessibility of the internet
make it very suitable for offering and receiving
help with psychological problems. Clients who
are treated on the internet can avoid the stigma
incurred by seeing a therapist (Gega et al. 2004).
They can obtain treatment at any time and
place, work at their own pace, and review the
material as often as desired. In internet-based
treatment, clients are guided by programs to
work on their problems. The level of therapist

involvement can vary from no assistance, or
minimal therapist contact by email or telephone,
to the amount of involvement as seen in classic
individual therapy. Thus, it may be possible to
reduce the therapist time while maintaining ef-
ficacy (Wright et al. 2005). Furthermore, it may
be possible to reach people through the internet
who might otherwise not receive treatment for
their problems.
Because internet-based interventions seem to
form a very promising line of treatment, it is
important to acquire more knowledge about
the effectiveness of such interventions. In the
past few years, the number of randomized stu-
dies examining the effects of internet inter-
ventions on mood and anxiety disorders has
grown rapidly. This study aimed to integrate
the results of these studies in a meta-analysis
of randomized controlled trails examining the
effects of internet-based cognitive behavioural
programs, with or without minimal therapist
assistance, for mood and anxiety disorders.
METHOD
Criteria for considering studies for this review
Types of studies
Only randomized controlled trials were included
in this review. Both published and unpublished
studies were included. We included only studies
that compared internet-based CBT with control
groups such as waiting-lists, treatment as usual,
and placebos. Studies that compared internet-

based CBT with active treatments were ex-
cluded.
Types of participants
As we also included prevention studies, there
were no limitations in (minimal) significance
of symptoms. Only studies with participants
above 18 years old were included. Studies with
children or adolescents were excluded. Both
clinical patients and subjects recruited from the
community were included.
Types of interventions
Internet-based CBT is defined as a standardized
CBT treatment that the participant works
through more or less independently on the
internet. Studies are included if there is no
therapist support, or if there is limited support,
which is defined as contact that is supportive
or facilitative regarding the course material.
No traditional relationship between therapist
and participant is developed; the therapist only
supports the working through of the standar-
dized treatment.
We selected only internet-based treatment
and excluded computer-based treatment that
did not involve the internet as the study designs
are too different. In studies on computer-based
treatment, participants usually have to go to a
particular computer to receive treatment (e.g.
Marks et al. 2003; Proudfoot et al. 2003). They
have to make appointments and are expected to

comply with these appointments. For internet-
based treatment, there is no need to make an
appointment. Participants can have treatment
whenever they want. This seems to be an im-
portant advantage, but there is also a disad-
vantage. There is no social control on using the
intervention and treatment sessions can be
postponed infinitely. Furthermore, participants
in internet-based treatment are really on their
own. In computer-based treatments, there is
often someone present to help the participant
with technical problems, and the amount of
personal attention, however little, that is given
to the subject might keep the participant more
involved in the study. Internet-based studies
can seem quite impersonal to participants, as we
sometimes heard from people who participated
in internet-based trials. These differences may
substantially affect the amount of treatment that
people take.
We included studies with interventions aimed
at treatment or prevention of symptoms of
depression or anxiety. We followed the DSM-IV
classification in mood and anxiety disorders;
however, we applied no restrictions regarding
the inclusion criteria applied by the authors of
the studies. All symptoms were measured with
validated questionnaires.
320 V. Spek et al.
Types of outcome measures

As we were interested in the effects of internet-
based CBT on symptoms of depression and
anxiety, we used only those instruments that
explicitly measure depression or anxiety. The
following types of outcome measures were
included: (1) self-rating scales measuring symp-
toms of depression or anxiety; and (2) clinician-
ratedscales.Otheroutcomemeasures,measuring
intermediate outcomes, such as cognition, were
not included. All outcome measures included,
except two used in one study (Klein & Richards,
2001), are validated instruments.
Search strategy for identification of studies
Studies were retrieved through systematic
literature searches in the databases of PubMed
(1990 to February 2006), PsycINFO (1990 to
February 2006), and the Social Science Citation
Index. Searches were conducted with key words
and text words, in which words indicative
of internet treatment (computer, internet) were
combined with words indicative of mood or
anxiety disorders or problems or treatment
(mood, depression, anxiety, treatment) and CBT
(cognitive therapy, computer-based therapy).
Literature dating from before 1990 was ex-
cluded because the rapid changes in computers
and software packages mean that internet-based
treatments dating from before 1990 cannot be
compared with the current treatment programs.
We also checked reference lists of retrieved
papers, and of earlier reviews in the field
(Ritterband et al. 2003; Andersson et al. 2004;
Tate & Zabinski, 2004). We contacted the cor-

responding authors of all included papers to
obtain information about any other published
or unpublished studies they were aware of.
Study selection
The retrieved papers were assessed indepen-
dently on inclusion criteria by two of the au-
thors (H.R. and V.S.) to guarantee an error-free
inclusion procedure (Fig. 1). When the two dis-
agreed on inclusion of a paper, they discussed
the differences until agreement was reached.
Methodological quality assessment
The methodological quality of the studies
was assessed using three basic criteria: (1)
Reviewed papers
(n = 28)
No RCT
(n = 5)
Read abstracts and references:
PubMed (26 hits)
PsycINFO (126 hits)
Earlier reviews
Reference lists
Corresponding authors
Included studies (n = 12)
No internet-based treatment
(n = 3)

No CBT
(n = 2)
No self-help
(n = 3)
No symptoms of mood or
anxiety disorders (n = 2)
Active control condition
(n = 3)
FIG. 1. Flow chart of study selection.
Internet-based cognitive behaviour therapy 321
Table 1. Selected characteristics of the studies
First author
and year of
publication
Recruitment;
main inclusion
criterion
Intervention:
number of
modules;
therapist
involvement N
Outcome

measures Analyses
Control
group
TAU
allowed Follow-up
Attrition
rate (%)
Post-treatment
comparison Aim
Effect
size
Clarke
(2002)
Community
recruitment and
clinical patients;
No
7; None 299 CES-D ITT TAU Yes, in both
groups
4, 8, 16, 32
weeks
34 Intervention v. CTR T 0.0
Clarke
(2005)

Community
recruitment and
clinical patients;
No
7; None 255 CES-D ITT TAU Yes, in all
groups
5, 10, 16 weeks 34 Intervention+postcard
reminders v.
intervention+phone
reminders v. TAU
T 0.3
(mail)
0.2
(phone)
Christensen
(2004)
Community
recruitment;
Cut-off on
KPDS
5; None 525 CES-D ITT Attention
placebo
No 6 weeks 17 Intervention v. psycho
education v. placebo
T 0.4
Andersson

(2005)
Community
recruitment;
Cut-off on
CIDI-SF
5; Monitoring
and feedback
117 BDI,
MADRS
ITT Participation
in online
discussion
group
Yes, stable
medication
allowed
Post-treatment
and 6 months
27 Intervention with
participation in online
discussion group v.
participation in online
discussion group
T 0.9
Patten
(2003)

Community
recruitment;
No
4; None 786 CES-D Unclear Psycho-
education
Unclear Post-treatment
and 3 months
3 Intervention v. psycho
education
P 0.0
Klein
(2001)
Community
recruitment;
Panic disorder
Unclear; None 22 PARF,
DRF
CO Self-
monitoring
Unclear Post-treatment 4 Intervention+self-
monitoring v. self-
monitoring
T 0.4
Klein
(2006)

Community
recruitment;
Panic disorder
6; Monitoring
and feedback
55 Clinician
rating PD
and AP,
no. of PA,
PDSS,
DASS
ITT Therapist-
assisted CBT
manual and
information
only
No Post-treatment
and 3 months
16 Intervention v.
information
T 1.5
Carlbring
(2001)
Community
recruitment;
Panic disorder

6; Monitoring
and feedback
41 BSQ, MI,
BAI
ITT Waiting-list Yes, if stable
and if not
CBT
Post-treatment 12 Intervention v.
waiting-list
T 1.0
Carlbring
(in press)
Community
recruitment;
Panic disorder
10; Monitoring
and feedback+
short weekly
phone calls
60 BSQ, MI,
BAI
ITT Waiting-list Yes, if stable
and if not
CBT
Post-treatment
and 9 months

5 Intervention v.
waiting-list
T 1.1
Andersson
(in press)
Community
recruitment;
Social phobia
9; Monitoring and
feedback+6
hours of group
sessions
64 BAI, SPSQ,
LSAS-SR,
SPS
ITT Waiting-list Yes, but only
stable
medication
Post-treatment
and 1 year
3 Intervention v.
waiting-list
T 0.8
3
2

2
V
.
S
p
ek
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a
l.
foreknowledge of treatment assignment is pre-
vented; (2) assessors of outcomes are blinded
for treatment assignment; (3) completeness of
follow-up data (Higgins & Green, 2005). In
most studies it was impossible to conceal treat-
ment conditions from participants because of
the kind of control conditions used (i.e. waiting-
list), so this was not assessed.
Treatment comparisons
Internet-based treatments with or without
minimal therapist support were compared with
control groups.
Meta-analysis
First, we examined the effects of internet-based
interventions compared to control conditions.
We calculated effect sizes (d) by subtracting
(at post-test) the average score of the control
group (Mc) from the average score of the ex-

perimental group (Me) and dividing the result
by the pooled standard deviations of the exper-
imental and control group (S.D.ec). An effect size
of 0.5 thus indicates that the mean of the ex-
perimental group is half a standard deviation
larger than the mean of the control group. Effect
sizes of 0.56 to 1.2 can be assumed to be large,
while effect sizes of 0.33 to 0.55 are moderate,
and effect sizes of 0 to 0.32 are small (Lipsey &
Wilson, 2001).
In the calculations of effect sizes we only used
those instruments that explicitly measure de-
pression or anxiety (Table 1). When means and
standard deviations were not reported, we used
other statistics (F value, p value) to calculate
effect sizes. If more than one measure was used,
the mean of the effect sizes was calculated, so
that each study (or contrast group) only had one
effect size. In some studies, more than one ex-
perimental condition was compared to a control
condition. In these cases, the number of subjects
in the control condition was divided equally
over the experimental conditions so that each
subject was used only once in the meta-analyses.
To calculate pooled mean effect sizes, we used
the computer program Comprehensive Meta-
analysis, version 2.2.021 (Biostat, Englewood,
NJ, USA).
Because it was not known before analyses
whether we could expect heterogeneity among
the studies, we used both the fixed effects model
(FEM) and the random effects model (REM) toH

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calculate the pooled effect size. Heterogeneity
was calculated with the Q-statistic and the
I2-statistic. A significant Q rejects the null
hypothesis of homogeneity and indicates that
the variability among the effect sizes is greater
than what is likely to have resulted from subject-
level sampling error alone (Lipsey & Wilson,
2001). We also calculated I2, which describes
the percentage of total variation across studies
that is due to heterogeneity rather than chance.
An I2 value of 25% is associated with low het-
erogeneity, 50% is associated with moderate
heterogeneity, and 75% is associated with high
heterogeneity (Higgins et al. 2003).
Post hoc subgroup analyses were conducted
both with the fixed effects analysis (FEA) and
the mixed effects analysis (MEA), as im-
plemented in the Comprehensive Meta-analysis
software. In the FEA, the FEM is used to cal-
culate the effect sizes for each subgroup of stud-
ies, and also for the difference between the

subgroups. In the MEA, the REM is used to
calculate the effect size for each subgroup, while
the FEM is used to test the difference between
the subgroups of studies.
Description of studies
A total of 28 studies were retrieved. Of these,
16 studies did not meet the inclusion criteria
and were excluded. A total of 12 trials with
2334 subjects were included. Five studies fo-
cused on depression (four on treatment and
one on prevention). Seven studies were aimed at
anxiety disorders (four on treatment of panic
disorder, one on prevention of anxiety dis-
orders, one on social phobia, and one on sub-
clinical post-traumatic stress disorder). Control
conditions varied from care-as-usual to an
internet-based placebo condition. One of the
five studies on interventions for depression
aimed at prevention. The total number of sub-
jects participating in the depression trials in-
cluded was 1982. In none of the studies were
subjects required to meet diagnostic criteria
for a depressive disorder. In only one of the five
treatment studies (Andersson et al. 2005) thera-
pists monitored progress and gave feedback to
participants; the other studies had no therapist
involvement. Control conditions differed widely
across studies: from care-as-usual (Clarke et al.
2002) to an attention placebo (Christensen et al.
2004). The four included studies on panic
disorder had a total number of 178 participants.
There was one study (Klein & Richards, 2001)
in which the intervention was strictly self-help.

Control conditions varied from waiting-lists to
information about panic disorder (Klein et al.
2006). One study evaluated an intervention
for social phobia: 64 participants were ran-
domized to either an internet-based CBT for
social phobia or to a waiting-list (Andersson
et al. 2006). With two 3-hour group exposure
sessions and individual feedback on homework,
this is the most extensive intervention reviewed
here. One trial was designed to investigate the
efficacy of a preventive cognitive behavioural
intervention for people at risk of developing
anxiety disorders. Eighty-three participants
with elevated anxiety sensitivity were random-
ized to either an intervention group or a wait-
ing-list control group. One paper reported the
comparison of an intervention for subclinical
post-traumatic stress disorder to a waiting-list.
In this study 33 participants were randomized.
Selected characteristics of the included studies
are summarized in Table 1.
Methodological quality of included studies
The quality of the included studies was reason-
able to good. Foreknowledge of treatment as-
signment was prevented in all studies. In most
studies all outcome measures were self-reported
by participants. In two studies some outcome
measures were not self-reported; in one study
assessors of outcomes were blinded for treat-
ment assignment (Patten, 2003), and in another
it was unclear whether the assessors of outcomes
were blinded for treatment condition (Klein
et al. 2006). Drop-out rates varied between 3%
and 34%.

RESULTS
A fixed effects meta-analysis on all contrasts was
conducted (Fig. 2, Table 2), resulting in a mean
effect size of 0.24 [95% confidence interval (CI)
0.16–0.33], while the REM resulted in a mean
effect size of 0.51 (95% CI 0.28–0.74). The hy-
pothesis of homogeneity was rejected because
a significant Q value was found (Q=58.65, I2=
79.5%). We examined possible sources of het-
erogeneity through post hoc subgroup analy-
ses. A subgroup analysis based on the aim of
the intervention (prevention or treatment) still
324 V. Spek et al.
showed high heterogeneity among treatment
studies (n=11, Q=39.77, I2=74.9%) but not
among prevention studies (n=2, Q=1.43, I2=
30.2%). Treatment studies were then further
divided into two sets of subgroups: one set based
on the symptoms that were treated and one
set based on the inclusion of support in the
interventions. These divisions are depicted in
Fig. 3, for purposes of clarity prevention studies
are not included in this figure.
The studies on depression (n=5) had a mean
effect size of 0.27 (95% CI 0.15–0.40) according
to the FEA and 0.32 (95% CI 0.08–0.57)
according to the MEA. The Q value was 13.37

Study name
(1st-named author)
Statistics for each study
S.D. (means) and 95% CI
S.D.
(means) S.E.
Lower
limit
Upper
limitVariance Z value p value
Andersson (2006) 0·769 0·259 0·067 0·261 1·276 2·967 0·003
Carlbring (2001) 0·991 0·327 0·107 0·350 1·632 3·032 0·002
Christensen (2004) 0·365 0·106 0·011 0·157 0·574 3·437 0·001
Clarke (2002) 0·000 0·116 0·013 –0·227 0·227 0·000 1·000
Clarke (2005) Mail 0·310 0·184 0·034 –0·050 0·670 1·690
0·091
Clarke (2005) Phone 0·247 0·181 0·033 –0·108 0·601 1·364
0·173
Hirai (2005) 0·812 0·401 0·161 0·026 1·597 2·026 0·043
Kenardy (2003) 0·293 0·234 0·055 –0·166 0·751 1·251 0·211
Klein (2001) 0·400 0·422 0·178 –0·426 1·226 0·949 0·343
Klein (2006) 1·516 0·373 0·139 0·785 2·248 4·063 0·000
Patten (2003) 0·000 0·072 0·005 –0·141 0·141 0·000 1·000
0·195Fixed
Model
0·046 0·002 0·105 0·284 4·264 0·000
–4·00 –2·00 0·00 2·00 4·00

Favours control Favours treatment
FIG. 2. Meta-analysis. Standard difference between means
indicates the effect size, with the standard error, variance, and
95%
confidence interval (lower limit and upper limit); the Z value
and associated p value indicate whether the effect size differs
significantly from zero. The squares in the figure indicate the
weight of the particular study in the meta-analysis.
Table 2. Meta-analyses of studies examining the effects of
internet-based
psychological treatment of mood and anxiety disorders
Ncomp d 95% CI Q I
2 (%)
Difference between
subgroups
All contrasts 13 FEM 0.24 0.16 to 0.33 58.65* 79.5
REM 0.51 0.28 to 0.74
Type of intervention
Treatment studies 11 FEA 0.40 0.29 to 0.51 39.77* 74.9 *
MEA 0.60 0.35 to 0.86
Prevention studies 2 FEA 0.03 x0.11 to 0.71 1.43 30.2
MEA 0.06 x0.17 to 0.30
Disorder
Depression 5 FEA 0.27 0.15 to 0.40 13.37 70.1 *
MEA 0.32 0.08 to 0.57

Depression without outliera 4 FEA 0.22 0.09 to 0.35 5.75 47.8
MEA 0.22 0.03 to 0.41
Anxiety 6 FEA 0.96 0.69 to 1.22 5.10 2.0
MEA 0.96 0.69 to 1.22
Support
No support 6 FEA 0.24 0.11 to 0.37 8.02 37.6 *
MEA 0.26 0.08 to 0.44
Support 5 FEA 1.00 0.75 to 1.24 3.24 0
MEA 1.00 0.75 to 1.24 3.24
Ncomp, Number of comparisons; CI, confidence interval; FEM,
fixed effects model; REM, random effects model; FEA,
subgroup analysis
based on the fixed effects model; MEA, subgroup analysis based
on the mixed effects model.
a Outlier is study of Andersson et al. (2005).
* Significant at p<0.05.
and I2 was 70.1%, indicating considerable het-
erogeneity. However, further analyses showed
that one study (Andersson et al. 2005) could be
regarded as an outlier. Analyses without this
study showed a mean effect size of 0.22 for
both the FEA and the MEA (95% CI 0.09–0.35
and 0.03–0.41 respectively) and moderate, non-
significant heterogeneity (Q=5.75, I2=47.8%).

For anxiety studies (n=6), both the FEA and
the MEA resulted in an effect size of 0.96 (95%
CI 0.69–1.24), a Q value of 5.10, and an I2 of
2.0%. As heterogeneity in depression studies
was caused by one outlier that was also the only
depression treatment with therapist support, we
conducted other subgroup analyses based on
therapist support (Fig. 3). These showed low
heterogeneity in both subgroups: Q=8.02, I2=
37.6% for studies without support (n=6) and
Q=3.24, I2=0% for studies with support (n=
5). Interventions without support had a pooled
mean effect size of 0.24 (95% CI 0.11–0.37) in
the FEA and 0.26 (95% CI: 0.08–0.44) in the
MEA, which is small. Interventions with sup-
port had a large pooled mean effect size: 1.00
(95% CI 0.75–1.24) in both the FEA and the
MEA and no heterogeneity (I2 was 0).
DISCUSSION
When looking at all studies in this meta-analysis
of internet-based CBT for symptoms of de-
pression and anxiety, we found a moderate
overall mean effect size and significant hetero-
geneity. Subsequently, when looking at preven-
tion and treatment studies separately, a small
effect size and non-significant heterogeneity
were found for prevention studies. Treatment
studies showed a large mean effect size and
significant heterogeneity. Therefore, treatment
studies were divided into two sets of subgroups,
one based on the symptoms that were addressed
and another based on the inclusion of support
in the interventions. The first set of subgroup

analyses showed a large mean effect size with
non-significant heterogeneity for anxiety treat-
ment. The analyses on treatment for depression
showed a small mean effect size with significant
heterogeneity, which was mainly explained by
one outlier. After the exclusion of this study,
a small mean effect size with non-significant
heterogeneity was demonstrated. In the second
set of subgroup analyses, treatment with sup-
port showed a large mean effect size and no
All contrasts (n = 13)
FEM d = 0·24
REM d = 0·51
Q =58·65*
Treatment studies (n = 11)
FEA d = 0·40
MEA d = 0·60
Q = 39·77*
Depression (n = 5)
FEA d = 0·27
MEA d = 0·32
Q = 13·37*
(1 contrast with support;
4 contrasts without support)
Anxiety (n = 6)
FEA d = 0·96
MEA d = 0·96

Q = 5·10
(4 contrasts with support;
2 contrasts without support)
Support (n = 5)
FEA d = 1·00
MEA d = 1·00
Q = 3·24
(1 contrast depr. symptoms;
4 contrasts anxiety)
Without support (n = 6)
FEA d = 0·24
MEA d = 0·26
Q = 8·02
(4 contrasts depr. symptoms;
2 contrasts anxiety)
Depression without outlier
(n = 4)
FEA d = 0·22
MEA d = 0·22
Q = 5·75
(4 contrasts without support)
FIG. 3. Flow chart of post hoc analyses. * Significant at p<0.05.
326 V. Spek et al.

heterogeneity. Treatment without support
showed a small mean effect size and non-
significant heterogeneity.
A large effect for treatment with support was
also found in one of the studies by Carlbring
et al. (2005), in which internet-based self-help
with therapist support proved to be as effective
as traditional individual CBT. In this meta-
analysis, the only study with a high effect size in
the depression treatment studies subgroup was
shown to be an internet-based intervention with
therapist support.
These results suggest that it is not so much the
type of problem (symptoms of depression or
anxiety) that differentiates between large and
small effect sizes but rather the distinction
between whether support is added or not. How-
ever, because of the substantial differences in the
design of the studies that were included (differ-
ences in symptoms and differences in treatment),
future studies are needed to support this hy-
pothesis.
This meta-analysis has several limitations.
Because internet-based CBT is a relatively new
area of research, the number of studies that met
the inclusion criteria was small. This first meta-
analysis included studies on interventions for
symptoms of depression and anxiety, which is
a fairly broad range of symptoms. Therefore,
heterogeneity was found and subgroup analyses
had to be carried out. As a consequence, power

declined.
A second limitation is the distribution of
numbers of subjects across studies. The stud-
ies on depression all had large numbers of sub-
jects; the studies on anxiety disorders all had
small numbers of subjects. This means that
power differed largely across studies. Finally,
studies used different inclusion criteria for
participants. In only five of the 11 studies
included was the presence or absence of a dis-
order established. Three studies had a cut-off
score on a questionnaire as the main inclusion
criterion. Three studies had no such inclusion
criteria.
Despite these limitations, our study indicates
that internet-based interventions, especially
those with therapist support, are effective. More
research is needed to further evaluate the ef-
fectiveness of internet-based CBT. If it can be
proved that internet-based treatment is effective,
it could be a very promising line of treatment,
reaching people who otherwise would not re-
ceive treatment.
ACKNOWLEDGEMENTS
This study was supported by a grant from ZON-
MW, the Netherlands Organization for Health
Research and Development.
DECLARATION OF INTEREST
None.

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Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.
A Meta-Analysis of the Effects of Internet- and Computer-
Based Cognitive-Behavioral Treatments for Anxiety
Mark A. Reger
Madigan Army Medical Center and University of Washington

School of Medicine
Gregory A. Gahm
Madigan Army Medical Center
Internet-and computer-based cognitive-behavioral treatments
have been introduced as novel approaches to deliver standard,
quality treatment that may reduce barriers to care. The purpose
of this review is to quantitatively summarize the literature
examining the treatment effects of Internet- or computer-based
treatment (ICT) on anxiety. Nineteen randomized controlled
ICT trials were identified and subjected to fixed and random
effects meta-analytic techniques. Weighted mean effect sizes
(Cohen’s d) showed that ICT was superior to waitlist and
placebo assignment across outcome measures (ds5.49–1.14).
The effects of ICT also were equal to therapist-delivered
treatment across anxiety disorders. However, conclusions were
limited by small sample sizes, the rare use of placebo controls,
and other methodological problems. In addition, the number of
available studies limited the opportunity to conduct analyses by
diagnostic group; there was preliminary support for the use of
ICT for panic disorder and phobia. Large, well-designed,
placebo-controlled trials are needed to confirm and extend the
results of this meta-analysis. & 2008 Wiley Periodicals, Inc. J
Clin Psychol 65: 53–75, 2009.
Keywords: Internet; computer; cognitive-behavioral therapy;
meta- analysis; anxiety
Dozens of randomized, controlled clinical trials have
demonstrated that cognitive- behavioral therapy (CBT)
improves symptoms of anxiety (Deacon & Abramowitz, 2004;
Scott, 2001). Despite this body of research, multiple factors still
limit the utility
Correspondence concerning this article should be addressed to:
Gregory Gahm, Ph.D., Director, Telehealth and Technology
Directorate, Defense Centers of Excellence for Psychological
Health and Traumatic Brain Injury, Bldg. 9040, Fitzsimmons
Drive, Attn: OMAMC 9933A, Tacoma, WA 98431- 1100; e-
mail: [email protected]

JOURNAL OF CLINICAL PSYCHOLOGY, Vol. 65(1), 53-75
(2009) & 2008 Wiley Periodicals, Inc. Published online in
Wiley InterScience (www.interscience.wiley.com). DOI:
10.1002/jclp.20536
m
m
54 Journal of Clinical Psychology, January 2009
of efficacious psychotherapies. Many individuals avoid or delay
seeking treatment because the very nature of their psychological
disorder limits travel outside the home (e.g., agoraphobia) or
because of geographic or mobility limitations. In addition,
clinicians who are trained to provide efficacious treatments may
be in short supply (Scott, 2001); this problem is surely
exacerbated in rural locations with poor access to treatment
resources. Among therapists who practice CBT, training and
follow- through vary widely (Sholomskas et al., 2005). When
high-quality treatment resources are available, a fear of stigma
can influence an individual’s decision to access care (Hoge et
al., 2004; Wells, Robins, Bushnell, Jarosz, & Oakley-Browne,
1994). Despite improved outreach and educational efforts,
treatment stigma continues to affect young populations
(Chandra & Minkovitz, 2006), and the problem may be
exacerbated in some ethnic minority groups (e.g., African
Americans; Das, Olfson, McCurtis, & Weissman, 2006), and
special occupational groups such as military personnel and
others (Carter, Buckey, Greenhalgh, Holland, & Hegel, 2005;
Hoge et al., 2004). Thus, additional efforts are needed to
continue to reduce barriers to treatment.
Internet- and computer-based treatments have been proposed as
a delivery approach that may minimize several barriers to care
while providing the opportunity to deliver standardized,
empirically supported treatment. There is significant interest in
a diverse array of Internet and computer-based treatments
(ICTs). For example, computer-based approaches can be used to
facilitate communication between patients and providers. Some
methods seek to simply ‘‘extend’’ the benefits of traditional

face-to-face therapy between sessions with the use of e-mail or
other Internet-based communications. Alternatively,
technological equipment can be used as the primary
communication device to support therapy across geographical
distances (e.g., video teleconferencing; Modai et al., 2006).
Technology also can be used to automate or manualize routine
aspects of treatment. For example, software can be developed to
train patients in basic cognitive-behavioral skills such as
relaxation training, or in cognitive restructuring (Litz, Engel,
Bryant, & Papa, 2007). Standard personal computers (PCs) can
be used to train patients on a single CBT skill or to deliver an
entire standardized treatment plan. As technological advances
continue at an unprecedented rate, the use of highly interactive
software that utilizes audio, video, animations, and advanced
graphics is possible. Branching logic can be used to create
response-dependent experiences that attempt to shape treatment
based on the needs of an individual patient (Carter et al., 2005).
Specific programs differ in their degree of interactivity, ranging
from primarily text-based approaches to multimedia experiences
that extend what is possible through traditional self-help
manuals.
Specialized software and equipment also have been explored as
possible tools to facilitate psychotherapy. Virtual reality
immerses a participant into a realistic, computer-generated
world. Hardware such as head mounted displays (HMDs),
motion tracking, tactile feedback, olfactory stimuli, and
naturalistic navigation devices help deliver three-dimensional
virtual environments in a believable manner. A growing body of
literature is exploring the use of virtual reality to facilitate a
variety of therapeutic goals (e.g., exposure; Difede et al., 2007).
Interest also is growing in the use of other technologies for
therapeutic goals, including cell phones, handheld video
devices, personal digital assistants, bulletin boards, Internet
chat rooms, blogs, and so on (e.g., Bang, Timpka, Eriksson,
Holm, & Nordin, 2007). Different technologies, by their nature,
imply varying levels of therapist involvement. Some ICTs are

proposed as pure ‘‘self-help’’ solutions while
Journal of Clinical Psychology DOI: 10.1002/jclp
A Meta-Analysis of Internet-Based CBT 55
others assume regular, face-to-face clinician contact that
utilizes a technological tool to accomplish some therapy goals.
While the nature of ICTs differ, in general they may have
potential to reduce some barriers to care (Andersson,
Bergstrom, Carlbring, & Lindefors, 2005; Emmelkamp, 2005).
ICTs may improve access to treatment for individuals who
require psychological services, but whose transportation options
are limited due to anxiety, other mental health problems,
physical disabilities, or other medical complications. ICT also
has the potential to improve access to care in rural areas, and
may provide the opportunity to reach individuals with mental
disorders who would otherwise avoid treatment because of a
fear of stigma. Furthermore, early evidence has suggested that
computer-based CBT can be cost-effective (McCrone et al.,
2004). Although ICT may be contraindicated in certain cases, it
may provide a flexible tool to address a variety of treatment
goals for a subgroup of patients.
The purpose of this review is to quantitatively summarize the
literature that has examined the treatment effects of Internet- or
computer-based CBT on anxiety. For the purposes of this
article, we focused on Internet- and computer-based approaches
that utilized software on a standard PC to automate the delivery
of CBT training (In the sections that follow, the term ICT is
used to refer to this subgroup of technological approaches.)
Several qualitative reviews of this literature have been
conducted and have suggested, in general, that ICT may be a
viable option for some patients (e.g., Andersson et al., 2005;
Emmelkamp, 2005). However, a quantitative analysis provides a
clearer understanding of the magnitude of the treatment effects,
and allows for a comparison of ICT to treatment as usual,
placebo, or waitlist assignment. Efforts also can address the
variability noted in the current research literature.
The present review examines the effects of ICT on five types of

clinical measures (Depression, Anxiety, General Distress,
Dysfunctional Thinking, and Functioning/ Quality of Life) in
individuals with anxiety using meta-analytic methodologies.
Method
Selection of Studies
We attempted to include all studies on the effects of Internet- or
computer-based CBT for anxiety published through 2007. A
preliminary search for articles was conducted using PubMed,
PsychInfo, and the Cochrane Central Register of Controlled
Trials with keywords identified through a search of the database
index terms. For example, the Medical Subject Heading (MeSH)
terms used for indexing articles for MEDLINE/ PubMed were
searched, and the following search terms were identified:
Internet, computers, computer-assisted therapy, behavior
therapy, and anxiety disorders (which includes phobic disorders,
panic disorder, PTSD, etc.). Terms from the index were selected
to be intentionally broad to maximize the chances of identifying
all articles. For example, behavior therapy includes multiple
subcategories such as cognitive therapy, cognitive behavior
therapy, relaxation techniques, and so on. Reference lists of all
relevant articles were searched manually to locate any other
studies that were not identified by the preliminary search.
Corresponding authors of relevant articles were e- mailed to
request any additional unpublished manuscripts or data they
may possess. One ‘‘in press’’ article was identified.
All studies were included that met the following eligibility
criteria: (a) randomized controlled trial (RCT) of individual
CBT delivered with Internet or computer-based software; (b)
included adult participants with anxiety; (c) administered
outcome measures assessing levels of anxiety, depression,
general distress, dysfunctional
thinking, or functioning/quality of life; and (d) reported
sufficient information about the study results to allow
computation of an effect size (ES) estimate. Studies were

excluded when the computer hardware used to conduct the
treatment required more than a PC and an Internet connection.
Thus, studies utilizing equipment such as a virtual reality head
mount display were excluded. Three studies were identified that
appeared to meet inclusion criteria, but did not report sufficient
statistical information for the computation of an ES. Letters
were sent to the corresponding authors of these studies
requesting additional information; one response was received,
allowing the study to be included (Klein & Richards, 2001). The
final results of the literature search produced 19 studies that
met the aforementioned criteria. A list of excluded studies is
available upon request. Participant characteristics and ICT
treatment details for each study are presented in Tables 1 and 2,
respectively.
Methodological Quality
The methodological quality of each included primary study was
rated utilizing a rating scheme recommended in a recent review
of systems available for rating the strength of scientific
evidence (West et al., 2002). The methodological ratings of the
RCTs were based on four broad categories (study population,
interventions, measurement of effect, and data presentation) that
were divided into specific weighted criteria based on each
criterion’s importance for validity and clinical relevance (van
der Heijden, van der Windt, Kleijnen, Koes, & Bouter, 1996).
Specific criteria were modified when the characteristics of
interest were specific to medical RCTs in the original system.
The detailed criteria utilized in this study are available upon
request. Briefly, studies were rated based upon (a) patient
selection [possible points (pp) 5 4], (b) randomization
procedure (pp 5 5), (c) sample size (pp 5 15), (d) comparability
of groups (pp 5 6), (e) management of dropouts (pp 5 6), (f)
rates of loss-to-follow-up (pp 5 5), (g) description of treatments
(pp 5 8), (h) management of co-interventions (pp54), (i)
blinding of patients (pp54), (j) relevant outcome measures
(pp56), (k) blinded outcome measurement (pp53), (l) duration of
follow-up (pp54), and (m) adequate analysis and presentation

(pp58). Thus, RCTs were rated on a total of 78 possible points.
Data Coding
The following information was extracted from the published
articles for each coded effect: (a) number of participants and
their demographics, (b) participants’ diagnosis, (c) nature of
ICT, (d) type of control group used [waitlist, placebo, or
therapist-delivered treatment as usual (TAU)], (e) outcome
measures administered, (f) summary statistics required for
computation of ESs, and (g) amount of clinician contact during
ICT.
Outcome measures were categorized into the following five
domains: Depression, Anxiety, General Distress, Dysfunctional
Thinking, and Functioning/Quality of Life. Each measure was
coded in a single domain that was judged to best represent the
primary area measured. Measures of functional abilities and
quality of life were grouped together based on research showing
that functional skills are a primary component of quality of life
(Quilty, Van Ameringen, Mancini, Oakman, & Farvolden,
2003). Table 3 lists the tests that were included in each outcome
domain.
Table 1
Participant Characteristics in Primary Studies
Study N Andersson
69% had at least some college
–
Recruited by means of newspaper and magazine articles, and an
Internet link
Social Phobia
et al., 2006
Carlbring et al., 2005
Recruited from a waitlist of individuals interested in Internet
treatment.
Met DSM-IV criteria for a diagnosis of panic disorder

as confirmed by the SCID; panic disorder was
the primary problem with a duration of at least 1 year.
All met DSM-IV criteria for a diagnosis of panic disorder; panic
disorder was the primary problem with a duration
of at least 1 year; all participants had at least one panic attack
or limited symptoms
Carlbring, Westling, Ljungstrand, Ekselius, & Andersson, 2001
–
Recruited with advertising and Web links.
Gilroy, Kirkby, Daniels, Menzies, & Montgomery, 2000 Grime,
2004
45 33.1
–
Recruited via newspaper advertisements and public notices.
attack during the pretreatment baseline. Specific Phobia
(Spiders) based on the CIDI; minimum duration of phobia of 1
year.
Heading
et al., 2001 Hirai & Clum, 2005
–
Kenardy, Dow, et al., 2003
186 36.8
Recruited by means of referrals from general practitioners and
through the media.
Participants met DSM-IV criteria for panic disorder, confirmed
by the SCID; current episode durationZ3 months; all considered
panic their main problem.
Elevated anxiety sensitivity (Anxiety
Sensitivity Index 4 24).
Kenardy, 83 McCafferty, &
Rosa, 2003
Kline & Richards, 2001 22 Klein, Richards, & 55 Austin, 2006
19.9
All were first-year university psychology students.
M age 64 37.3 49 35.0 41 34.0
M education/ alternative

Recruitment
Diagnosis/symptoms
48 39.0 40 34.9 27 29.4
–
Recruited through a London NHS occupational health
department. Recruited through newspaper and notice board
advertisements
Missed 10 or more days of work due to stress, anxiety, or
depression in the past 6 months and GHQ-12 Z4
Met DSM-IV criteria for Specific Phobia
(Spiders) based on CIDI-Auto 2.1.
45% were university students 33% had some postsecondary
education All were first year psychology students 11.7
Recruited with advertisements, and from the introductory
psychology class student pool
All participants met at least the reexperiencing and avoidance
criteria for PTSD (DSM-IV).
40.8 ––
–
Recruited by contacts to a study Web site.
Met DSM-IV criteria for a primary diagnosis of panic disorder
Primary diagnosis of panic disorder as assessed by the ADIS-IV
TABLE 1. Continued
M age M education/ Study N alternative
Recruitment
Recruited through the Internet or advertisements;
Diagnosis/symptoms
Knaevelsrud & 96 35 Maercker, 2007
44% had university degree –
participants had a primary DSM-IV diagnosis of panic disorder,
as assessed by the ADIS-IV. Recruited undergraduate
psychology students
Sx of PTSD (70% scored above a cut-point for PTSD on the
IES).

Lange, van de Ven, 25 22.0 & Schrieken, 2001
All participants had experienced a traumatic event (including
loss of a beloved one) at least 3 months prior to the study and
were excluded for ‘‘not (suffering) from (PTSD) or pathological
grief.’’ Symptoms of PTSD; 90% scored above IES cut-off for
PTSD. All participants had
Lange, van de Ven, 101 39.0 Schrieken, &
Emmelkamp, 2003
–
Participants were recruited through the Interapy Web site.
Litz, Engel, Bryant, & 45 39.2 Papa, 2007
Marks, Kenwright, 90 38.0 McDonough, Whittaker,
– 11.0 years
Recruited through advertisements and Web sites
experienced a traumatic event (including events such as sudden
loss of a beloved one, divorce, loss of job) at least 3 months
prior to the study.
PTSD
& Mataix-Cols, 2004
Orbach, Lindsay, 58 23.7 & Grey, 2007
Richards, Klein, 32 36.6 & Austin, 2006
Zetterqvist, 63 39.2 Maanmies,
Strom, &
Andersson, 2003
3.1 years at University 13.2 years
Participants responded to notices in general practitioner
practices or phobia self-help groups, or were referred by health
professionals. e-mail invitation circulated to graduate students
All participants met DSM-IV criteria for agoraphobia without
panic disorder, panic disorder with agoraphobia, social phobia,
and/or simple phobia as measured by semistructured interview.
Test anxiety
–
Recruited from visitors to a Web site or through local and
national print and electronic media Recruited through
newspaper articles or through a Web site for health.

Met DSM-IV criteria for a primary diagnosis of panic disorder
as assessed by the AIDI-IV.
Stress. Levels of depression and anxiety were ‘‘checked’’
on screening instruments.
Note. N represents the no. of participants from each primary
study included in the meta-analysis. Participants met diagnostic
criteria for each disorder listed unless otherwise specified. CBT
5 cognitive-behavioral treatment; GHQ 5 General Health
Questionnaire; PTSD 5 posttraumatic stress disorder; TAU 5
treatment as usual; CIDI 5 Composite International Diagnostic
Interview; ADIS 5 Anxiety Disorders Interview Schedule; IES 5
Impact of Event Scale.
Table 2
Characteristics of Treatment and Control Groups in Primary
Studies
Study
Nature of ICT
Control group(s)
Nature of placebo –
Clinician contact with ICT group
Andersson et al., 2006
Internet-delivered self-help program divided into nine modules.
Topics included an introduction, a model of social phobia and
CBT, modifying cognitive distortions, conducting behavioral
experiments, setting goals, exposure and reality testing, shifting
focus, listening skills, setting boundaries and assertiveness,
perfectionism and self-confidence, and relapse prevention. In
each module, participants posted a message in a discussion
forum about a specific topic. PC: M of 7.5 sessions.
Waitlist
Individual feedback via e-mail on questions at the end of each
module (approximately 3 hr per participant). Participants
invited to complete two 3-hr group exposure sessions; 59%
completed second session.

Internet-delivered self-help program divided into six modules,
including psychoeducation, breathing retraining, modifying
interpretations of physical symptoms, interoceptive exposure, in
vivo exposure, and relapse prevention and assertiveness
training. Each module ended with five to eight questions on
which participants received e-mailed feedback. PC: All
Waitlist
–
No personal contact; individual feedback via e- mail on
questions at the end of each module; e- mail to inquire about
progress; M contact time (via e-mail) 5 90 min
Completers 5 100%.
Internet-delivered self-help program divided into 10 modules,
including (1–2) psychoeducation and socialization, (3)
breathing retraining and hyperventilation test, (4–5) cognitive
restructuring, (6–7) interoceptive exposure, (8–9) exposure in
vivo, (10) relapse prevention and assertiveness training.
Participants also posted at least one message in an online
discussion group during each module. Additioanally, each
module ended with five to eight questions and an
TAU
–
Individual feedback was provided via e-mail on homework for
each module; participants could send unlimited e-mail (M 5
15.4); M therapist time 5 150 min per participant
TABLE 2. Continued
Study
Nature of ICT
Control group(s)
Nature of placebo
Gilroy et al., 2000

interactive multiple-choice quiz. Feedback on homework was
provided via e-mail. PC: M of 7.4 sessions.
Computer-aided vicarious exposure in which participants used a
computer mouse to direct a screen figure into scenarios with
pictures of spiders, or plastic, dead, or live spiders. A game
score is calculated, and an anxiety thermometer rises as the
figure approaches the spiders. Beating the Blues: Eight sessions
of interactive computerized CBT. Cognitive components explore
automatic thoughts, thinking errors, and distraction, challenging
unhelpful thinking, core beliefs, and attributional style.
Behavioral components include activity scheduling, task
breakdown, problem solving, sleep management, relaxation
training and biofeedback, planning and prioritizing, and graded
exposure. PC: M of 6.4 sessions.
Placebo, Progressive muscle relaxation delivered with an TAU
audiotape. Authors cited evidence showing that
Five min in the initial session to ensure participant was
comfortable using the program
Grime, 2004
TAU –
ICT was administered in a private room in the Occupational
Health Department. The author reviewed weekly progress
reports to monitor for adverse events. Treatment and control
groups were allowed to continue conventional care.
Heading
et al., 2001
Computerized symbolic modeling treatment in which
participants guide a computer figure into an elevator. An
onscreen thermometer displays increasing anxiety/panic as the
figure approaches, enters, remains in, or travels in the elevator,
but a score on the screen increases when the computer figure is
in these situations. PC 5 100%.
Waitlist, – TAU
Therapist worked with the patient for the first 5 min of session.
Hirai & Clum, 2005
Internet-based, cognitive-behavioral self-change program

composed of information, breathing retraining, muscle
relaxation, imagery-induced relaxation, cognitive restructuring,
and written exposure modules.
Waitlist –
Contact was made only to prompt participants to take
assessments or mastery tests or to provide information about the
timeline for completion of the program. Technical assistance
was allowed.
relaxation is ineffective for specific phobia.
Kenardy, Dow et al, 2003
Palmtop computer program included a self- statement module, a
breathing control module, and a situational and interoceptive
exposure module. PC: M of 5.9 sessions.
Waitlist, TAU
–
The computer-based treatment augmented therapist-delivered
CBT. Total therapist contact time was 6 hr.
Kenardy, McCafferty, & Rosa, 2003
Online Anxiety Prevention Program included psychoeducation,
relaxation training, interoceptive exposure, cognitive
restructuring, and relapse prevention. Each session required
participants to practice skills and record progress. PC: accessed
software M of 7.76 times with 90.4 min per access.
Waitlist
–
None noted
Klien et al., 2001
Internet-based program focused on the nature, causes, and
management of panic. Topics included negative self-statements,
errors in thinking, and techniques for overcoming cognitive
errors.
Placebo
Self-monitoring
No therapeutic contact. Investigators assisted participants in

accessing and navigating the program, and checked to ensure
they were accessing it during the treatment phase
Klien et al., 2006
Panic Online (delivered via the Internet) included an
introductory module, four learning modules, and a relapse-
prevention module. Treatment methods included controlled
breathing, cognitive restructuring, and interoceptive and
situational exposure.
Interapy was an online standardized treatment that consisted of
three treatment phases (Self- Confrontation, Cognitive
Restructuring, Social Sharing and Farewell Ritual). Each phase
began with an information section and then required
participants to write essays on given topics.
Placebo, TAU
Information control in which participants were instructed to
reread Internet-based informational program about panic.
Clinical student called controls weekly to assess panic and
encourage self-monitoring. Received no active CBT.
Individualized e-mail support and feedback; actual therapist
time: M 5 332.5 min per participant
Knaevelsrud & Maercker, 2007
Waitlist
–
At midpoint and end of each treatment phase, therapists
provided Internet-based feedback and instructions tailored to
patients’ needs. Provided recognition of patients work, positive
feedback, motivation, and encouragement to voice concerns.
Assisted patients in focusing on painful memories during self-
confrontation phase
Lange et al., 2001
three treatment phases (Actualization/Confrontation, Cognitive
Reappraisal, Sharing and Farewell Ritual). Each phase began
with an information section and then required participants to
write essays on given topics.
Waitlist

–
During the middle of each treatment phase, therapists provided
participants with feedback on their essays via the Interapy Web
site, and provided instructions on how to proceed.
TABLE 2. Continued
Study
Nature of ICT
Control group(s)
Nature of placebo –
Lange et al., 2003
three treatment phases (Actualization/Confrontation, Cognitive
Reappraisal, Sharing and Farewell Ritual). Each phase began
with an information section and then required participants to
write essays on given topics. PC: Completers 5 100%. DE-
STRESS was an Internet-delivered program composed of the
following components: self- monitoring triggers, developing a
hierarchy of trauma triggers, stress management, graduated self-
guided, in vivo exposure, trauma writing sessions, a review of
progress, and relapse prevention.
Waitlist
During the middle of each treatment phase, therapists provided
participants with feedback on their essays via the Interapy Web
site, and provided instructions on how to proceed.
Litz et al., 2007
Placebo
Self-monitored daily non-trauma-related concerns and hassles
and wrote online about these experiences. Psychoeducational
materials were available, but no skills training or prescriptions
for proactive steps were provided
Study therapist assisted with hierarchy of stressful situations
and provided training in stress management and cognitive
reframing; called participants to assess readiness to complete

trauma narrative
Marks et al., 2004
FearFighter was a computer-based system with Placebo,
Computer-guided self-relaxation techniques with up to 20 min
of clinician contact per session focused on relaxation
Protocol included 20 min of coaching, progress discussion, and
treatment advice during each of six sessions; actual mean total
therapist time per participant was 76 min.
Orbach
et al., 2007
Placebo
Internet-based program consisting of four modules, including
psychoeducation, relaxation training (based only on music and
breathing), a thought diary, and ‘‘brain puzzles.’’
No therapeutic contact. Investigators introduced the Internet-
based programs to participants (some by phone and some face-
to face).
Richards et al., 2006
Panic Online was an Internet-delivered program with four
learning modules and introductory and relapse prevention
modules. CBT methods included controlled breathing,
progressive
Placebo
Information-Only Controls received no active CBT. They
completed assessments and received weekly e-mail to check on
panic status and provided minimal support. Controls were
E-mail contact to guide participants through the program. E-
mailed weekly feedback based on weekly panic summary
information entered in the program. M therapist time 5 342.8
min.
nine steps including (1–3) introduction, rationale, and use of a
‘‘co-therapist;’’ (4) identifying triggers and writing problem
statements; (5) developing exposure homework; (6) panic
coping skills; (7) practicing coping during exposure; (8) review,
feedback, and modifying goals; and (9) troubleshooting.
Internet-based treatment program consisting of six modules,

including psychoeductaion, relaxation training, rational
thinking, study skills training, and two modules on controlling
stress. PC: M of 5.2 sessions
TAU
Zetterqvist et al., 2003
Internet-based stress-management program consisting of six
treatment modules, including relaxation training, problem
solving, time management, and cognitive and behavioral
restructuring. An optional information section included sleeping
problems, nutrition and eating habits, exercise, work stress, and
social life and assertiveness. PC: M of 4.2 sessions
Waitlist –
E-mail feedback on submitted forms and correspondence on
individual issues of stress management or technical problems.
muscle relaxation, cognitive restructuring, and interoceptive
and situational exposure. A second group used Panic and Stress
Online, which was the same except that it included six learning
modules on stress.
encouraged to reread information on a panic resource program
that contained no active CBT.
Note. PC 5 Patient Compliance
Table 3
Measures Categorized by Clinical Domain
Beck Depression Inventory Montgomery–Asberg
4 3 2 2 2 1 1 1 1
Body Sensations Questionnaire Beck Anxiety Inventory
Fear Questionnaire
Impact of Events Scale
4 4 4 4 3 3
Anticipatory Fear Rating Scale 1 Anxiety Hierarchy
Questionnaire
Brief Symptom Inventory-Anxiety 1 Generalized Anxiety

Rating Scale 1 Leibowitz Social Anxiety Scale 1 Main Problem
and Goals Rating 1
Depression measures
Test k
Anxiety measures Test k
Test k Weekly Panic Attack Frequency 2
Depression Rating Scale Depression, Anxiety, Stress Scales-
Depression Scale
Hospital Anxiety and Depression
Body Vigilance Scale Mobility Inventory for
Scales-Depression Subscale Symptom Checklist-90-Depression
Agoraphobia
Behavioral Assessment Test Clinician Agoraphobia Rating
Clinician Panic Disorder Rating Depression, Anxiety, Stress
Scales-
2 2 2
Scales
Panic Frequency 1 Panic Frequency and Severity 1
Subscale
Brief Symptom Inventory-
Depression
Center for Epidemiological Studies
Anxiety Scale
2
Ratings 1 PTSD Symptom Scale-Interview
Depression Scale
Generalized Depression Rating
Scales-Anxiety
Panic Disorder Severity Scale
Phobic Targets
Spider Questionnaire
State-Trait Anxiety Inventory
Symptom Checklist-90-Anxiety Subscale
Subjective Units of Distress 2
Version 1 Social Interaction Anxiety Scale

Social Phobia Scale 1 Social Phobia Screening
Scale
Profile of Mood States-
2 2 2 2 2 2
Depressiveness
Questionnaire 1 Stressful Responses Questionnaire 1 Test
Anxiety Inventory 1
Domain
General Distress
Domain
Dysfunctional Thinking Functioning/QOL
Test k Depression, Anxiety, Stress Scales-
Test k Test k
Stress Scale
Symptom Checklist-90
2 2
Agoraphobic Cognitions Questionnaire
6 Quality of Life Inventory 3 Work and Social Adjustment Scale
3 2 WHO Quality of Life Questionnaire 1
Scale
Profile of Mood States
Outcome Study Self-Report Form
1 1 1
Anxiety Sensitivity Index
Anxiety Sensitivity Profile 2 Active Coping with Trauma Scale-
Questionnaire-Modified 1 Note. k 5 No. of journal articles
included in the meta-analysis that used the measure.
General Strategy 1 Attributional Style Questionnaire 1
Catastrophic Cognitions
To compare the effects of ICT to the effects of waitlist, placebo
or TAU, studies were further divided based on type of control
group that was utilized. Ten RCTs included a waitlist control

group, seven included a placebo, and seven articles utilized a
TAU control group (Five articles compared ICT to both
waitlist/placebo and TAU control groups.) Placebos included
both activities without active CBT features that the participants
thought may be helpful and ‘‘attention placebos.’’ TAU was
defined as traditional face-to-face treatment provided by a
therapist.
All participants were initially analyzed together (Any Anxiety
Disorder). Studies were then subdivided on the basis of specific
anxiety disorders when at least two primary studies were found
that had examined a specific diagnosis. Coding was possible for
studies of posttraumatic stress disorder (PTSD; five primary
studies), panic disorder (six primary studies), and phobia (three
studies).
Statistical Analysis
For each study, Cohen’s d was calculated as the posttreatment
difference between the control and treatment group means
divided by the pooled SD. Formulas were adjusted so that a
positive d indicated that the ICT group showed less
symptomatology than did the control group on any given
assessment instrument. Thus, each ES represents the
posttreatment difference between the ICT and control groups on
a given clinical measure (e.g., Beck Depression Inventory) in
SD units.
To adjust ESs for sample size, the weight given to each study
ES was inversely proportional to the conditional variance in the
study (Hedges & Olkin, 1985). When a study contributed more
than one individual ES to an analysis (e.g., administration of
multiple measures), a mean was calculated such that each
primary study contributed only a single ES, as recommended by
Rosenthal (1991). Weighted mean ESs were calculated
separately for each type of control-group comparison (waitlist,
placebo, or TAU) by each type of clinical measure, as described
earlier.
The goal of all fixed effects meta-analyses is to aggregate only
the data that are thought to share a common population ES. The

test statistic Q was used, as outlined by Hedges and Olkin
(1985), to test the hypothesis that the observed variance in
study ESs that make up a mean ES is within the range that can
be reasonably expected by chance if all studies share a common
population ES. Random effects models (Hedges & Olkin, 1985)
were used when Q was significant (po.05), indicating
heterogeneity of the variance.
The test statistic Q also was used as an analogue to the analysis
of variance for ESs, as outlined by Hedges and Olkin (1985).
ESs were compared between studies that enrolled participants
with a Diagnostic and Statistical Manual of Mental Disorders
(DSM) diagnoses and studies that enrolled subclinical,
nondiagnosed participants. In addition, we compared ESs for
studies with and without face-to-face clinician contact during
ICT. The procedures followed here differ from an ANOVA in
that an estimate of unsystematic error was incorporated in the
weights of the ESs. Therefore, there was no need to use the
separate error term required by an F test. Since each comparison
included only two mean ESs, no contrasts were needed.
Results
Methodological Quality of the Primary Studies
Of a total possible method score of 78 for each study, only eight
studies scored above 40, indicating that methodological
problems persisted in many of the studies (data
available upon request). In general, studies proved to be
methodologically sound with respect to (A) patient selection,
(D) comparability of groups, (G) description of treatments, (J)
relevant outcome measures, and (M) adequate analysis and
presentation. The most prevalent limitations to the primary
studies included (C) small sample sizes, (F) high rates of loss-
to-follow-up, (I) unblinded patients, and (K) unblinded
assessment of outcome measures.
Tests of Homogeneity of Variance
The within-group homogeneity of the ESs was tested, and the

results are presented in Table 4. Thirteen of the 17 tests were
not significant; thus, despite differences in study
characteristics, most of the planned study groupings were
statistically appropriate to combine under the assumptions of a
fixed effects analysis. More specifically, when results from all
outcome measures were combined, Q statistics were significant
for TAU-controlled studies (po.05); heterogeneity was not
detected for waitlist or placebo-controlled ICT studies. When
ESs were disaggregated by type of outcome measure, Q was
significant for Anxiety Measures for both waitlist- and TAU-
controlled studies. The Q test for Dysfunctional Thinking
measures also was significant for waitlist-controlled studies.
Random effects analyses were conducted when Q was
significant.
Mean ESs
Mean ESs were calculated for five types of clinical outcome
measures (Depression, Anxiety, General Distress, Dysfunctional
Thinking, and Functioning/Quality of Life) within each of four
diagnostic groups (Any Anxiety Disorder, PTSD, Panic
Disorder, and Phobia). In addition, to compare the effects of
ICT to the effects of waitlist, placebo and TAU, studies with
different control groups were aggregated separately. These data
are shown in Table 4, which also includes the variance and
confidence intervals (CI) for the mean weighted ESs.
Although classification systems for ESs facilitate
communication, they are based on arbitrary distinctions between
magnitudes. The importance of a ‘‘small’’ ES depends on the
nature of the question. In addition, the number of studies
identified in this review is small. However, for the purposes of
facilitating discussion, Cohen’s (1988) classification system is
used to describe the results. In his system, ESs of .20, .50, and
.80 are classified as ‘‘small,’’ ‘‘moderate,’’ or ‘‘large,’’
respectively. These terms are used later to describe the results
in the current study.
Following treatment, participants receiving ICT showed fewer
symptoms than did the waitlist and placebo controls across all

types of clinical measures and when all outcomes were analyzed
together (po.05). The ESs were moderate to large in almost all
cases. In addition, the benefits of ICT were equivalent or
superior to TAU in all analyses. Participants showed fewer
symptoms of depression following ICT compared to TAU, but
this finding requires replication as it was based on only three
RCTs.
ESs within specific diagnostic groups were analyzed separately
for available data. Four waitlist-controlled studies examined
participants with symptoms of PTSD. A moderate, significant
weighted mean ES of .75 (95% CI 5 .49, 1.01) was observed in
these studies across outcome measures. A large, mean ES of 1.2
(CI 5 .87, 1.58) was observed across outcomes after
synthesizing the results from two waitlist-controlled studies of
panic disorder. In addition, analysis of the outcomes in three
waitlist-
Table 4
Weighted Mean Effect Sizes (ESs) by Type of Measure
ICT vs. Waitlist ICT vs. Placebo
ICT vs. TAU
All measures
M ES (95% CI) .76 (.60, .92) .86 (.61, 1.11)
k 10 7 7
Variance
Q
.0069 15.23
.0164 5.80
.03 ( .35, .41) .0377
Anxiety measures
M ES (95% CI)
k 10 6 7
Variance
Q
.77 (.56, .98) .0115

.88 (.70, 1.31) .0192
.00 ( .38, .38) .0376
Depression measures M ES (95% CI)
18.08 .89 (.69, 1.08)
6.83
13.46
.57 (.22, .92) .0325 .0323
Dysfunctional Thinking
M ES (95% CI) 1.14 (.43, 1.85) .70 (.26, 1.15) .25 ( .02, .53)
k434
.49 (.14, .84) k843
Variance
Q
General Distress
M ES (95% CI) k42– Variance .0151 .0708 – Q 2.82 .29 –
Functioning/QOL M ES (95% CI)
.0100 6.02
1.67 0.95 .58 –
.48 (.24, .72)
Variance
Q
Q 4.20 .27 4.99
Note. k 5 no. of studies in the analysis; ICT 5 Internet- or
computer-based treatment; TAU 5 treatment as usual.
Value differs from 0, po.05. Where Q is significant, results are
reported from random effects analyses. Mean ESs are presented
for groups with two or more studies; dashes indicate insufficient
data available.
controlled studies that examined participants with phobia
revealed a moderate, mean ES of .66 (CI 5 .30, 1.02).
Studies that utilized a placebo control were aggregated for panic
disorder and phobia. Three studies examined participants with
panic disorder and, together, revealed a large, mean ES of .93
(CI 5 .49, 1.38). For phobia, the mean ES for three studies was
.81 across outcome measures (CI 5 .44, 1.19).
Among TAU-controlled studies, data were available to analyze

three studies of panic disorder. There was no difference
between ICT and TAU outcomes in these studies (ES 5 .30, CI 5
.001, .61). There were insufficient data available to examine
other results for specific diagnostic groups.
Effects of Participant or Treatment Characteristics
Among the 10 waitlist-controlled ICT studies, four studied
participants who met full DSM diagnostic criteria for an anxiety
disorder while six studied participants who met partial criteria
or were below diagnostic thresholds. There was no significant
.1314 17.42
.0513 .28
.0196 1.25
.57 (.23, .91) k334
.71 (.29, .1.14)
Variance .0307 .0473 .0261
13.12
.02 ( .33, .30)
difference between the mean weighted ES for the diagnosed
groups (ES5.93; CI 5 .66, 1.20) and subclinical groups across
outcome measures (ES 5 .66; CI 5 .45, .86).
Only one placebo- and one TAU-controlled study used
subclinical samples, so these analyses were not repeated for
these groups.
To examine the impact of clinician contact on treatment
outcomes, waitlist- controlled ICT studies were divided into two
groups based on whether ICT participants had face-to-face
clinician contact (k 5 3) or no clinician contact except via e-
mail, in some cases (k 5 7). There was no significant difference
in ESs for those with clinician contact (ES 5 .91; CI 5 .61, 1.21)
and those without clinician contact (ES 5 .70; CI 5 .50, .89).
Similarly, among placebo-controlled studies, there was no
difference between ICT studies with (ES5.91; CI5.61, 1.21;
k53) and without (ES5.85; CI551, 1.18; k54) clinician contact
as a part of treatment. The same nonsignificant pattern emerged

when TAU-controlled studies with (ES 5 .04; CI 5 .22, .31; k 5
5) and without (ES 5 .26; CI 5 .17, .68; k 5 2) clinician contact
were compared.
Publication Bias
To evaluate the impact of publication bias on the results of the
meta-analysis, a funnel plot was constructed to display the ICT
ESs against their respective sample sizes. The resulting
scatterplot should resemble an inverted funnel since the
precision of an ES estimate increases as the sample size
increases. Publication bias is suspected when a ‘‘hole’’ in the
area representing smaller ESs creates a degree of asymmetry in
the funnel plot.
Since both reporting bias and retrieval bias are of concern
(Greenhouse & Iyengar, 1994), all individual ESs from all
studies were plotted (Fig. 1). No publication bias was detected.
Figure 1. Funnel plot of all ICT effect sizes by the size of the
sample. Journal of Clinical Psychology DOI: 10.1002/jclp
Discussion
The results of this meta-analysis provide preliminary support
for the use of Internet- and computer-based CBT for the
treatment of anxiety. The benefits of ICT were superior to
waitlist or placebo assignment, although the number of placebo-
controlled studies was small (n 5 7), which limits conclusions.
The clinical effects of ICT were equal to traditional therapist-
delivered treatment in the few studies available for analyses
(n57). One of the primary conclusions of this systematic review
is that additional high-quality research is needed in this field.
Placebo- controlled studies were rare, and many of the studies
suffered from small sample sizes and high dropout rates.
In addition, conclusions are limited by the fact that it was not
possible to conduct these comparisons by diagnostic group in
most cases. Where sufficient data were available, results
provided tentative support for ICT. For participants with panic
disorder, ICT may be superior to no treatment (i.e., waitlist
control) and to placebo assignment; the benefits of ICT also

were equivalent to TAU in the few studies available. For
participants with a phobia, posttreatment symptoms were lower
following ICT compared to both waitlist and placebo
assignments. For participants with putative PTSD, there was
preliminary support for ICT compared to no treatment. There
was one well-designed placebo-controlled study for PTSD that
generally supported ICT (Litz et al., 2007), but it was a small
proof-of-concept trial. Therefore, there are limited data to
support the use of ICTs for PTSD at this time. Sufficient data
were not available to compare ICT to controls for other
diagnostic groups. Large placebo-controlled trials that examine
participants with well-defined mental disorders are needed to
confirm the results of this meta-analysis.
Results demonstrating that the effects of ICT were equivalent or
superior to TAU were unexpected. Failure to detect superior
effects of TAU did not appear to be related to a problem with
power, as ICT showed slightly higher ESs in the majority of the
nonsignificant analyses. Since six of the seven TAU-controlled
studies utilized CBT approaches in the TAU conditions, it is
unlikely that the equivalence of ICT is due to poor TAU
treatment approaches. However, while empirically supported
CBT techniques were employed, most studies did not appear to
utilize empirically supported manualized treatments. Thus, the
efficacy of specific TAU treatments is unknown in some cases.
It is possible that differences in treatment ‘‘dose’’ (e.g., number
of ‘‘sessions’’) impacted comparisons of ICT and TAU. The use
of TAU control groups implies that efforts were made in the
RCTs to provide equivalent therapist-delivered ‘‘doses’’ of the
same treatment; many authors were explicit about such
attempts, but at least one trial was not properly controlled. In
addition, it may be difficult in practice for a therapist to provide
treatment in a format that properly parallels computerized
modules. Therefore, ICT’s superior treatment of depression may
reflect a dose–response effect. Alternatively, a structured,
computer presentation of treatment techniques may be more
efficacious than a presentation by a therapist who may use

clinical judgment to modify empirically supported procedures.
However, it is unclear why ICT’s superior treatment outcomes
might apply only to depression; since only three RCTs were
included in this analysis, replication of this finding in future
RCTs would be helpful.
The results also showed that there was no difference in ESs
between participants with anxiety disorders and participants
with subclinical symptoms. These preliminary results suggest
that ICT may be useful in stepped-care models of
treatment. Stepped-care models are based on the assumption
that the same treatment approaches are not indicated for all
clients (Bower & Gilbody, 2005). Some clients may require
only psychoeducation while others might require careful, face-
to-face risk management by a therapist. Multiple options for
incorporating ICT into stepped-care treatment approaches exist,
including use of ICT for pure ‘‘self-help’’ delivery (i.e., no
therapist contact), minimal therapist contact, or treatment as
usual with ICT used as an adjunct to care.
In addition, there was no difference in treatment outcome
between participants who received face-to-face clinician contact
compared to those with no clinician contact. Although minimal
therapist contact may not significantly improve treatment
outcome, there may be other reasons for maintaining contact
with patients during ICT. For example, clinician contact may
improve treatment compliance, and certainly patients at risk for
self-harm must be followed by a clinician, regardless of the use
of ICT. It also is possible that treatment outcome may differ by
‘‘dose’’ of clinician contact. It was not possible to reliably code
amount of clinician contact as a continuous variable in the
meta-analysis due to the variability in study reporting formats.
Future studies should further evaluate the impact of clinician
contact on treatment outcome.
Although ICT may be efficacious, this meta-analysis does not
address the ethical, legal, and philosophical questions related to

the use of ICT. ICT does not yet have general acceptance in
many countries. Questions are still being raised regarding
privacy, confidentiality, risk management issues, liability, and
patient contra- indications. These areas of concern are
contrasted with the potential benefits that this methodology
offers. Although a thorough review of these issues is beyond the
scope of this article (for reviews, see Fenichel et al., 2002;
Hsiung, 2001), recent concerns have been raised that specific
subgroups of the population may experience elevated fears
about treatment stigma (Hoge et al., 2004). Military personnel,
police officers, firefighters, pilots, and others may fear real and
perceived consequences to pursuing psychological services
(Carter et al., 2005; Hoge et al., 2004). Significant numbers of
individuals may prefer ICT to walking into a counseling center,
sitting in a waiting room, and talking with a therapist. The
assumption that a traditional therapist is generally preferred
over ICT may be inaccurate for significant subgroups of the
population, but this possibility requires additional study.
ICT also may be useful as a research tool to explore the
essential components of CBT. ICT allows researchers to add,
modify, or delete specific treatment interventions while truly
leaving all other treatment components unchanged for a
comparison group. For example, Schneider, Mataix-Cols,
Marks, and Bachofen (2005) recently examined the differences
between Internet-guided treatment of panic and phobia with or
without exposure instructions. Thus, ICT may provide a tool to
help refine CBT theory and practice.
Additional research is needed on a variety of topics related to
ICT. Many of the studies reviewed here used small sample
sizes, and there were insufficient data to compare the effects of
ICT to placebo or TAU in some analyses. In addition,
participants in the primary studies were generally well
educated, and all could presumably use a computer. It is
unknown how well these results generalize to the general
population. Further research is needed on the relationship
between the effects of ICT and a client’s age, education,

computer familiarity, and socioeconomic status to inform
potential ICT treatment procedures. Demographic variables also
may be directly or indirectly associated with treatment
compliance through a variety
of mechanisms such as cultural preferences, language barriers,
literacy, or access to technology. Population-based studies
addressing access to, and use of, various forms of technology
will be helpful in guiding the development of tools that are
likely to be utilized by target populations.
There are several limitations of the current study. As noted
earlier, the conclusions from this study are limited by the
methodological problems observed in the primary studies.
Large, well-designed, randomized, placebo-controlled trials of
homogenous clinical samples are needed. In addition, every
meta-analysis struggles with the conflict between the goal of
data synthesis versus the problem of between-study variability.
The current meta-analysis suffers from variability in participant
characteristics, treatment approaches, clinical scales, and other
methodological differences between studies. Grouping tests by
clinical domain reduces only some of the variability because
many measures tap multiple domains, and each measure may
assess different aspects of a given domain. Development of a
standardized battery of measures that can be used across
Internet- and computer-based treatment studies may be helpful.
Despite the apparent differences between studies, however,
there was statistical support for the decision to combine these
studies into the various groups in the majority of the analyses.
Tests of the homogeneity of the variance suggested that the
mean ESs do estimate a single population ES in most cases.
Where this was not true, a random effects approach was used, as
is typical for cases where heterogeneity is detected (Hedges &
Olkin, 1985).
The importance of developing cost-effective approaches to
delivering empirically supported treatments for anxiety that

reduce barriers to care is likely to grow as awareness of mental
health issues continues to improve. The results of this study
provide preliminary evidence that support additional research
on the benefits of Internet- and computer-based CBT. As society
becomes increasingly comfortable with, and reliant upon, the
use of computers and the Internet for more of their routine
business and healthcare, opportunities to apply ICT approaches
are likely to continue to grow.
Note: References marked with an asterisk indicate studies
included in the meta- analysis.
References:
Reger, M. A., & Gahm, G. A. (2009). A meta-analysis of the
effects
of Internet and computer-based cognitive-behaviour
treatments
for anxiety. Journal of Clinical Psychology, 65(1),
5375.
doi:10.1002/jclp.20536
Ketty work
Read the following articles from the South University Online
Library and the Internet:
· Reger, M. A., & Gahm, G. A. (2009). A meta-analysis of the
effects of Internet and computer-based cognitive-behaviour
treatments for anxiety. Journal of Clinical Psychology, 65(1),
5375.doi:10.1002/jclp.20536
· Spek, V., Cuijpers, P., Nyklicek, I., Riper, H., Keyzer, J., &
Pop,V. (2007). Internet-based cognitive-behavior therapy for

symptoms of depression and anxiety: A meta-
analysis.Psychological Medicine, 37(3),
219328.doi:10.1017/S0033291706008944
The articles quantitatively summarize the literature examining
the treatment effects of Internet or computer-based treatment
(ICT) on anxiety.
Review the articles and create a two to three page report in a
Microsoft Word document on the efficacy of internet or
computer-based treatment on community-dwelling individuals
with symptoms of depression.
Your report should include answers to the following:
· How effective is ICT compared to person-to-person therapy?
· How effective is ICT compared to having no therapy?
· Should computer-aided therapy actually be considered a form
of therapy even though no therapist is present? Why or why
not?
· What are the benefits and drawbacks of computer-aided
therapy?
The document should adhere to the following guidelines:
· Include an APA formatted cover page in your work.
· The font size used should be 12-point font, Times New Roman
or Courier.
· The document should have double spacing.
· The margin should be one inch on all sides.
Support your responses with examples.
Cite any sources in APA format.
Submission Details
Name your document
SU_PSY4300_W2_A2_LastName_FirstInitial.doc.
Submit your document to the W2 Assignment 2 Dropbox by
Tuesday, April 25, 2017.
Assignment 2 Grading Criteria
Maximum Points
Explained how effective is ICT compared to person-to-person

therapy.
10
Analyzed how effective is ICT compared to having no therapy.
10
Justified whether a computer-aided therapy can actually be
considered a form of therapy even though no therapist is
present.
15
Summarized the benefits and drawbacks of computer-aided
therapy.
10
Used correct spelling, grammar, professional vocabulary, and
APA format.
5
Total:
50

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