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A Comprehensive Review and a Meta-Analysisof the Effectiveness of Internet-Based
Psychotherapeutic Interventions
Article in Journal of Technology in Human Services · July 2008
DOI: 10.1080/15228830802094429

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A Comprehensive Review and a
Meta-Analysis of the Effectivenessof Internet-Based Psychotherapeutic
Interventions
Azy Barak
Liat Hen
Meyran Boniel-Nissim
Na’ama Shapira

ABSTRACT. Internet-based psychotherapeutic interventions havebeen used for more than a decade, but no comprehensive review
and no extensive meta-analysis of their effectiveness have been conducted. We have collected all of the empirical articles published up
to March 2006 (n ¼ 64) that examine the effectiveness of online therapy of different forms and performed a meta-analysis of all the studies reported in them (n ¼ 92). These studies involved a total of
9,764 clients who were treated through various Internet-based
psychological interventions for a variety of problems, whose effectiveness was assessed by different types of measures. The overallmean weighted effect size was found to be 0.53 (medium effect),which is quite similar to the average effect size of traditional, faceto-face therapy. Next, we examined interacting effects of various
Azy Barak, Liat Hen, Meyran Boniel-Nissim, and Na’ama Shapira,Faculty of Education, University of Haifa, Aba Hushi Rd.,
Mount Carmel, Haifa, Israel.
We are indebted to Ronnen Fluss for his assistance in analyzing the data.
Address correspondence to Azy Barak, Faculty of Education, Universityof Haifa, Aba Hushi Rd., Mount Carmel, Haifa 31905, Israel. E-mail:azy@construct.haifa.ac.il.
Journal of Technology in Human Services, Vol. 26(2/4) 2008
Available online at http://jths.haworthpress.com# 2008 by The Haworth Press. All rights reserved.
doi: 10.1080/15228830802094429

109

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JOURNAL OF TECHNOLOGY IN HUMAN SERVICES

possible relevant moderators of the effects of online therapy, including type of therapy (self-help web-based therapy versus online communication-based etherapy), type of outcome measure, time of
measurement of outcome (post-therapy or follow-up), type of problem treated, therapeutic approach, and communication modality,
among others. A comparison between face-to-face and Internet intervention as reported on in 14 of the studies revealed no differences in
effectiveness. The findings of this meta-analysis, and review ofadditional Internet therapy studies not included in the meta-analysis,
provide strong support for the adoption of online psychologicalinterventions as a legitimate therapeutic activity and suggest several
insights in regard to its application. Limitations of the findings
and recommendations concerning Internet-based therapy and future
research are discussed.

KEYWORDS. Internet, Internet intervention, meta-analysis, online
therapy, psychotherapy

The Internet has been used for psychotherapeutic interventions formore than a decade. Various terms have been used to denote this special professional activity: etherapy (or counseling), online therapy,
Internet therapy, and cybertherapy, and sometimes it is referred
to as e-health or telehealth, as a part of more general activities.
Although attempts have been made to associate specific terms withmore focused activities (e.g., cybertherapy for the use of virtual
reality software), this terminology has failed in practice, and professionals and laypersons alike normally use different terms interchangeably. There are, however, several major factors that differentiate
among the different therapeutic applications conducted by meansof the Internet. One of these has to do with the online-intervention
method employed—whether it includes human communication(termed here etherapy) or is a self-help, website-based therapy(termed here web-based therapy). A different major factor has to
do with another Internet-enabled capacity—whether an intervention
is delivered in ‘‘real-time’’ (synchronously) or is delayed (asynchronously). A third important factor has to do with mode ofcommunication—whether conducted textually, by audio only, or byvideo (webcam). Other important differentiations have to do withindividual versus group mode and therapeutic approach, terms normally associated with traditional, face-to-face therapies.

Barak et al.

111

From its start, Internet therapy has been criticized and opposed byboth many laypeople (e.g., Skinner & Latchford, 2006) andprofessionals (e.g., Lester, 2006; Wells, Mitchell, Finkelhor, &Becker-Blease, 2007) on several grounds. First, the lack of face-toface visibility—which prevents the transmission and detection of aclient’s nonverbal communication cues, on the one hand, and theuse of a therapist’s body language, on the other—created massive
resistance. This opposition based on the lack of nonverbal communication was considered an essential component of therapeutic relationships. Second, ethical issues—relating to secrecy and confidentiality,
identity of patients and therapists, impersonations, handling of
emergency situations, and more—became a central problem withthe application of computer-mediated, distance therapy. Third,
contemporary laws and regulations did not always cover various
situations created by online therapy, such as local licensing requirements, legal jurisdiction, professional insurance of negligence, andmore, resulting in often unresolved legal issues. Fourth, practicaland technical concerns led to arguments related to the training of
online therapists, the dependency on electricity and on complicated,fragile technologies, as well as to worries about the digital divide,and more. All of these criticisms, though they still exist, have beenanswered to a great degree as the field developed, numerous professionals have connected, literally and figuratively, to this new channelof therapy, advanced technologies emerged, ethical codes weredeveloped, training courses and workshops began to be offered, andso on (Chester & Glass, 2006; Grohol, 2004). Not least, many clients
seemed to like this innovative therapeutic option (King et al., 2006).
In the attempt to respond to the questions and critiques posed by
the opponents of online therapy, quite a few therapeutic process
studies were conducted that generally tried to examine the special
characteristics of the therapeutic dynamics created in distant,invisible, interpersonal circumstances. The findings of thoseinvestigations—obviously concentrating in etherapy of different
forms—often showed that therapy processes online are, in many
ways, similar to the traditional form of therapy, though they possess
some unique features as well, which were identified. Cook and Doyle(2002), for example, found that clients of e-mail- or chat-based
therapy rated therapeutic working alliance similar and even superior
to that of face-to-face therapy. In an analogue study, Mallen, Day,and Green (2003), however, found higher ratings of disclosure,112

JOURNAL OF TECHNOLOGY IN HUMAN SERVICES

closeness, and satisfaction with the face-to-face therapy experience
than with that online, though no difference in emotional understanding was detected between the two interaction modes. Escoffery,
McCormick, and Bateman (2004) reported on the process development of goal-setting, consciousness growing, and satisfaction ofclients using web-based therapy for smoking cessation. Lewis,
Coursol, and Herting (2004) studied client and counselor experiencesin videoconferencing-based counseling in a qualitative analysis of acase study. They were able to qualify interesting themes in counselor’s
and client’s experiences, and noted the client’s positive feelings. In ananalogue study, Rochlen, Land, and Wong (2004) found that men
with high emotionality preferred online counseling over face-to-facecounseling more than did men with low or restricted emotionality.
Bickmore, Gruber, and Picard (2005) showed that bond and working
alliance may be achieved even when working with an automated software agent. In an observational analogue study, Rees and Stone(2005) saw that clinicians rated working alliance in videoconferencingbased therapy lower than in traditional, face-to-face sessions. Young(2005) investigated the attitudes of clients treated through online chatgroups; although convenience and anonymity were cited as favorable
factors, privacy and security concerns were listed against its use.
Barak and Bloch (2006) found that session-impact factors in chat-based
therapy were related to the perceived helpfulness of sessions. Leibert,
Archer, Munson, and York (2006) studied clients’ working alliancein, and satisfaction with, e-mail- and chat-based counseling; both wererated inferior to face-to-face experiences. Reynolds, Stiles, and Grohol(2006) found session-impact factors and therapeutic alliance in e-mailbased therapy to be similar to face-to-face therapy for both therapists
and clients. According to Ritterband et al. (2006), the use of audio, graphics, and interactivity in website-based treatment of encopresis withchildren contributed to their elevated knowledge, motivation, andreadiness to change. Thus, generally speaking, these studies show that
counseling and psychotherapy relationships can effectively take place
under the special circumstances enabled by the Internet as far as major
therapeutic processes are concerned.
The main questions consistently asked throughout these studies
and through numerous other publications have been whether therapy
practiced online was effective, whether therapy could be conductedeffectively (i.e., achieve its therapeutic goals) through the Internet,
whether it was as effective as traditional therapy, and how various

Barak et al.

113

methods and variables associated with online therapy affected its
effectiveness. Although quite a few individual studies on treating avariety of psychological problems have been conducted to date andnumerous case studies have been published and presented, a comprehensive review and examination of these questions are still lacking.
Several attempts were made to provide an inclusive review on theeffectiveness of online psychological interventions. These attempts,
however, offered a rather limited view of the question at hand, mainly
because of their partial inclusion of the published research (e.g.,
Anthony, 2006; Ritterband et al., 2003; Tate & Zabinski, 2004;
Ybarra, Eaton, & Bickman, 2005); stenographic, encyclopedic-stylesummary (Barak, 2004); emphasis on history and development rather
than effectiveness (e.g., Skinner & Zack, 2004); concentration on a
specific problem area, such as anxiety (Andersson, Bergström,
Carlbring, & Lindefors, 2005), depression (Andersson, 2006), panic
disorder (Carlbring & Andersson, 2006), smoking cessation (Etter,
2006; Walters, Wright, & Shegog, 2006), weight loss (Weinstein,2006), health-related problems (Strecher, 2007), or problem drinking(Walters, Miller, & Chiauzzi, 2005); concentration on web-based
interventions only (Andersson, 2006; Griffiths & Christensen, 2006;
Pull, 2006); focus on video-based therapy (Simpson, 2003); or theirmixing together therapy and support (Mallen, Vogel, Rochlen, &Day, 2005). Although the general conclusion of these reviews, as well
as several others, was highly supportive of Internet therapy, it seems that
reliance on these resources is insufficient because their surveys of theliterature are in effect incomplete or narrow. In addition, none of thesereviews made an attempt to examine interactions of relevant moderators(e.g., age of clients, therapeutic approach) with therapy outcome.
It should be noted that several books focusing on etherapy ande-counseling (e.g., Derrig-Palumbo & Zeine, 2005; Hsiung, 2002; Kraus,
Zack, & Stricker, 2004; Tyler & Sabella, 2003) also provided a generaland partial review of research, as well as numerous case examples, but
did not provide a thorough and comprehensive view of the area.
Three meta-analytic reviews that are relevant in part to our current
research questions were conducted. Wantland, Portillo, Holzemer,
Slaughter, and McGhee (2004) conducted a meta-analysis of 22
web-based versus non-web-based psychological interventions intended
to educate and create behavioral change in people with chronic
illness. They found a large variability in effect size (ES), ranging
from –0.01 to þ 0.75, which averaged out to a moderate mean ES.

114

JOURNAL OF TECHNOLOGY IN HUMAN SERVICES

In another meta-analytic review, Spek et al. (2007) examined 12 studies that tested the effectiveness of web-based cognitive-behavioral
therapy (CBT) for depression and anxiety. They found a small-tomoderate ES for the treatment of depression and a large ES for thetreatment of anxiety. Provision of therapist support (provided online)
moderated these findings, as therapist support resulted in large effects and no such support resulted in small effects. These two metaanalyses referred only to web-based interventions in specific problem
areas. Hirai and Clum (2006b) conducted a meta-analysis of the effectiveness of various self-help venues in helping people with anxiety
problems, including computer and Internet self-help interventions
among other methods (e.g., printed materials, videotapes). They
found that computer- and Internet-based self-help interventions
appeared, for the most part, to yield equally effective treatment outcomes as the other self-help interventions.
The aim of the current meta-analytic study was to provide fuller,
more comprehensive answers to questions relating to the effectivenessof online psychological interventions. Our research was meant tocover a broad data set that referred to a variety of online technological methods, intervention settings, psychological approaches,
problem areas, and other features that exist in the provision of
psychotherapy through the Internet. The purpose was to examine
the effectiveness of online interventions in general, and specificallyin quantitative empirical studies and in the impact of moderatorsthat interact with therapy outcome.

METHOD
Data Collection
We searched and collected all published studies relevant toour meta-analysis. The studies we used met these criteria: (1) they were
published in a refereed journal in English at any time until March 2006
(inclusive); (2) they empirically studied the effectiveness of psychological treatments conducted through online channel(s) of communication (that is, Internet-delivered therapy); (3) the intervention was
based on the actual implementation of a psychological intervention (rather than just the provision of online support or an online
assessment); (4) the study contained more than five participantsBarak et al.

115

receiving online treatment; (5) treatment effectiveness was based on
at least pre-post quantitative comparisons; (6) effectiveness of treatment was based on at least one actual outcome measure.
The search for studies was conducted by using the PsycINFO andMEDLINE databases as well as the Google Scholar and Scopus
online scientific search engines. In addition, we checked the bibliographies of numerous articles to detect possible missing items. Except
for a single article, all of the studies included in our research had
been published up to and in March 2006; in the single exception,
the study was made public by being posted on a publisher’s website(it came out in print shortly thereafter).
In total, we collected 69 articles that met the inclusion criteria.
Additional 47 articles that examined the effectiveness of online therapy were rejected because they had either insufficient data enabling
the calculation of ES, used qualitative and descriptive approaches(usually through case studies), were based on mere literature reviews,
or lacked major details in regard to the nature of the therapy or the
research design employed. In two cases, in which specific information
was missing (i.e., unclear treatment for a comparison group, missing
information about participants), the authors of the studies were
contacted to complete the information. Furthermore, we found thatthe results reported in 5 of the 69 usable articles had been duplicatedand published in other articles in this set; therefore, the information
gathered from these studies were included only once in the analysis.
The final data set, therefore, contained 64 articles. Most of them
reported a single study; several articles, however, described two or
more (up to four) studies, each of which differed by gender of patients(e.g., Christensen, Griffiths, Korten, Brittliffe, & Groves, 2004),
intervention method (e.g., Carlbring, Ekselius, & Andersson, 2003),
or another factor. In sum, the 64 articles reported on 92 independentstudies (based on different patients) of various online interventions
that were aimed at treating patients who suffered from a psychological problem or distress. This collection, then, contains the final data
set for our meta-analysis. (The first author may be contacted for a listof keywords used in searching for articles and for a list of articles
rejected from the data set used for the meta-analysis.)
In all, the 92 studies examined 11,922 participants, 9,764 of whom
received some form of psychological intervention online. The number of patients in each study ranged from 6 to 2,341 (mean ¼ 106;
median ¼ 28). Table 1 presents a summary of the basic characteristics

116

Website andforum

Eatingdisorders(2) 22parents(1) 102
kids

24

Tinnitus

Andersson,
Strömgren,Ström, &Lyttkens(2002)
Bruning Brown,
Winzelberg,
Abascal, &Taylor(2004)(2) 1736

2. Web-based andtelephonesupport
Website ande-mailsupport

Recurrent
headacheAndersson,
Lundström,& Ström(2003)

Website and
forum

N

(1) 13

Depression

Andersson
et al.
(2005)

Online Channel& Format

1. Website ande-mailProblem Area

Author(s)

PE

CBT

CBT

CBT

Therapeutic
Approach

RCT, compared
to WL CG

RCT, comparisonof 2 TGs

RCT, compared
to WL CG

Research
DesignAdolescents
TG vs. noand parents
treatment
comparison
group

AdultsAdultsAdultsAge & Typeof Patients

Findings

Reduction in tinnitus
annoyance andimproved
psychologicalmeasures
Improvement in all
SR of eating
measures of TG
disorderscompared torelated scales,
followingknowledge test,
treatment but
and parents’
not at 1-year
attitudes
follow-up.
Parents attitudes’
positively
changedSR of tinnitus
annoyance,
anxiety, anddepression

TG improved in allmeasures while
no change in CG.
Changemaintained in
6-monthfollow-up
Headache diary
No change inand SR ofheadaches instress, anxiety,both TGs;and depression
improvement in
SR outcomes inboth TGs

Outcome
Measures
SR of depression,anxiety, andquality of life

TABLE 1. Articles Included in the Meta-analysis.57 (.51).08 (.34).32 (.49).18 (.67).23 (.77).67 (.43)

ES1 (CI 2)

117

Website ande-mailWebsite ande-mailSocial phobia

Panic disorder

Carlbring,
Furmark,,
Steczko
Ekselius, &Andersson(2006)
Carlbring et al.
(2005)

Body
Website anddissatisfaction
forum

Celio et al.
(2000)

Website ande-mailPanic disorder

Carlbring et al.
(2001)

Website ande-mailPanic disorder

Carlbring et al.
(2003)

Website andtelephonesupportChronic backpain

Buhrman et al.
(2004)

24

20

25

PE

CBT

CBT

(2) Applied
relaxation
CBT(2) 1126(1) CBTCBT(1) 11

22

FemaleadultsAdultsAdultsAdultsAdultsAdults

RCT, compared
to FTF
treatment andto WL CG

RCT, TG
compared
to WL CG

RCT, compared
to FTF
treatment

RCT, compared
to WL CG

RCT, compared
to applied
relaxation
treatment

RCT, compared
to WL CGSR of social
TG improved in all
anxiety, generalmeasures atanxiety,
posttreatment, anddepression, andmaintained or
quality of life
further improved at
6-month follow-up
SR ofBoth treatment
agoraphobia,
groups improved
anxiety,in all measures.
depression, andEffects maintained
quality of lifeat
1-year follow- up
SR of panic
Positive effects in
attacks and
TG in all outcome
anxietymeasures while nochange in CG
SR of body and
Improvement pre- toeating concerns
posttreatment in all
and attitudes
measures. Results
maintained andcontinued to
improve at
6-month follow-up(Continued ).27 (.73).84 (.61).81 (.55).98 (.54)

TG improved in.04 (.55)
some measures,
attitudes, andability to decrease
pain but not inothers. Some
effects maintained
at 3-monthfollow-up
SR of
TG improved in most
.44 (.83)
agoraphobia,
measures, but wasanxiety,a little inferior todepression, andthe improvement
quality of life;of the CG
diary of attacks.71 (.83)

SR of levelof pain,depression,and anxiety

118

Depression andanxiety

Depression andanxiety

Christensen,
Griffiths, &Korten(2002)

Christensen
et al.
(2004)

WebsiteWebsiteWebsiteDepression

Christensen,
Griffiths, &Jorm(2004)

Online Channel& Format

WebsiteProblem Area

Chiauzzi, Green, Binge drinking
Lord, Thum, &Goldstein(2005)

Author(s)(1) CBT M(2) CBT F(3) PE M(4) PE F(1) PE(2) CBTCBT (1) M(2) F
CBT (1) F(2) M(2) 63(3) 50(4) 60(1) 165(2) 182(1) 30(2) 48(1) 102(2) 36

Therapeutic
Approach(1) 42

N

AdultsAdultsAdultsAdultsAge & Typeof Patients

Compared 2 TGs:
experimental
trial vs. use ofopen site

Pre-post andfollow-up
comparisons

RCT, 2 TGs vs.
WL CG

RCT, TG vs.
WL CG (CG
instructed toread online
articles onconsequencesof heavy
drinking)

Research
DesignFindings

SR of depression
and anxiety

SR of depression
and anxiety

SR of depression
and knowledgeof treatment

Reduction ofdepression andanxiety of bothgroupsReduction ofdepression andanxiety at eachmeasurement
point

Both treatments
effective in
reducing
depression
symptoms

SR of attitudes
Reduction of drinking
toward drinking,and improved
and drinking
attitudes; effects
behaviors
continued andimproved in
3-month follow-up

Outcome
Measures

TABLE 1. Articles Included in the Meta-analysis (continued).84 (.46).45 (.39)
1.03 (.27).44 (.21).30 (.51).48 (.35).82 (.39).51 (.36).66 (.21).61 (.43)

ES1 (CI 2)

119

Depression

Clarke et al.
(2002)

Heart transplant- Website andrelatedforum
psychologicalsymptoms(2) 17(1) 20(2) 2739

2. Video chatWebsite ande-mailDew et al.
(2004)(1) 26

1. Audio chat

Chronic
headache

12

Chat

144

PE

CBT

CBT

Unspecified(2) Telephone
remindersPE(2) 80WebsitePE and (1)Postcard
reminders

Website and phone (1) 75
or postcard
reminders

Devineni &Blanchard(2005)

Cohen & Kerr
Precounseling(1998)
anxiety
Day & Schneider Various(2002)

Depression

Clarke et al.
(2005)(2) caregivers

Adult (1)patients

AdultsAdultsAdultsAdultsAdults

RCT, TG vs. CG

RCT, TG vs.
WL CG

TGs vs. FTF
vs. WL CG

TG vs. FTF

SR of headache
symptoms,
depression,andanxiety
SR of depression,anxiety, andsocialfunctioning ofpatients, SR of
hostility ofcaregivers

SR of complaints
and functioning

SR of anxiety

RCT, TG
SR of depression
compared
to no-treatment
CG

TG (of 2 versions)effective over
CG. Results
maintained in
2-month follow-up
TG improved in allmeasures while
CG did notchange. Greater
compliance of TG
than CG withmedicalprocedures

No difference
betweenTG and CG inchanging over
the research
periodAnxiety decreased
in both groups
Audio, video, andFTF weresimilarly effective(Continued ).86 (.58).53 (.57)

1.05 (.54).36 (.43).91 (.53).86 (.80).04 (.23)

RCT, TG
SR of depression
Reduction in.38 (.31)
comparedand utilization ofdepression over
to no-treatment
health services
therapy period inCGcomparison with
CG. No differencein use of healthservices.53 (.31)

120

Smoking
cessation

Etter(2005)

Website ande-mail andchat room

Overweight

Harvey-Berino
et al.
(2002)(1) Information(2) CBT
Unspecified(2) 12130

CBT group
therapy(1) 136

20

Chat room andforum

Website12

AdultsAdultsFemaleadultsAdults(2) Emphasis
on nicotine
replacement
CBT

Age & Typeof Patients(2) 1896

Therapeutic
Approach
PE (1)
AdultsEmphasis on
healthriskN(1) 2341

Website andforum ande-mailWebsiteOnline Channel& Format

Griffiths,
Depression
Christensen,
Jorm, Evans,& Groves(2004)

Farvolden,
Panic disorder
Denisoff,andSelby, Bagby,
agoraphobia& Rudy(2005)
Gollings &Body image andPaxtondisordered(2006)
eatingProblem Area

Author(s)RCT, TG
compared to
2 FTF
maintenancesupport

RCT, TGs vs.
CG

RCT, TG vs.
FTF
treatment

Pre-post TG

RCT, comparisonof 2 treatments

Research
DesignDecrease in panic
attacks for eachconsecutive stage

Both treatments
produceddecrease insmoking, withadvantage of
1st over 2nd
intervention

Findings

Body weight andadherence totreatment

FTF meetings formaintenance of
weight loss
treatment more
effective than
online intervention

SR of body image, Both groups
eating disorders,
improved
depression,in all measuresanxiety, andwith no mode
self-esteem
difference
SR of depression, Intervention caused
stigma, thoughts
slightly reduced
stigmatic attitudesof depression

SR of panic
attacks

SR of smoking
abstinence

Outcome
Measures

TABLE 1. Articles Included in the Meta-analysis (continued).09 (.24).45 (.51).16 (.23).78 (.62).60 (.80).49 (.04).58 (.06)

ES1 (CI 2)

121

36

Anxiety disorders Website

Anxiety disorders Website

Kenardy,
McCafferty,& Rosa(2003)
Kenardy,
McCafferty,& Rosa(2006)

Kenwright,
Phobia or panic
Marks, Gega,
disorder& Mataix(2004)

36

Loneliness (forpeople withdisabilities)

Hopps, Pépin,& Boisvert(2003)

Website andtelephonesupportChat rooms,
in groups of
2–3 patients

WebsiteTraumatic
event

10

10

13

129

Website andchatStress77

Website ande-mail andchat room

Overweight andobese eating

Harvey-Berino,
Pintauro,
Buzzell, &Gold(2004)
Hasson,Anderberg,
Theorell, &Arnetz(2005)
Hirai & Clum(2006a)

CBT

CBT

CBT

CBT

CBT

CBT

Unspecified

AdultsAdultsAdultsAdultsAdultsAdultsAdultsPre-post
evaluationof TG

RCT, compared
to WL CG

RCT, TG
compared
to WL CG

RCT, TG
compared
to WL CG

RCT, TG
compared
to WL CG

RCT, TG
compared
to 2 FTF
maintenancesupport
RCT, TG
compared
to informationonly CG(Continued )Body weight,
Online intervention.07 (.31)
adherence towas as effective as
treatment, and2 FTF support inenergy expense
maintaining
treatment effects
PhysiologicalTG improved.29 (.23)
markers andover CG in
self-report ofmost outcomestressmeasuresmanagement
SR of anxiety,
TG improved in all.62 (.75)
depression,
measuresand impact ofrelative to CG
event
SR of loneliness
TG improved in all
1.18 (.90)
and acceptancemeasures andof disability
exceeded CG.
Results
maintained in
4-monthfollow-up
Some improvement.61 (.43)
SR of anxiety,
was found,depression,and related
unrelated tocognitions
treatment
SR of anxiety,
Positive effects of.62 (.45)
fear,
treatment indepression,
most measuresand related
over CG,
cognitions
maintainedin 6-monthfollow-up
SR of fear,
Patients improved
1.09 (.88)
depression,in all outcome
and social
measures.
adjustment
Effects
maintained at
1-month follow-up

122

Problem Area

Panic disorder

Panic disorder

Hazardous
healthbehaviors

Hazardous
drinking

PosttraumaticstressAuthor(s)Klein & Richards(2001)

Klein,
Richards, &Austin(2006)

Kypri &
McAnally(2005)

Kypri et al.
(2004)

Lange et al.
(2003)

69

CBT

PE

CBT

42
Site-based
assessment andprescriptive
feedback

WebsiteTherapeutic
Approach
CBT

PE

19

11

N

Site-based60
assessment andprescriptive
feedback

Website andWebsiteOnline Channel& Format

AdultsAdultsAdultsAdultsAdultsAge & Typeof Patients

Outcome
MeasuresFindings

ES1 (CI 2)

Incidents of panic
TG improved in.26 (.83)
attacks, SR of
most outcome
anxiety,measures while
depression,
no changes in CG
self-efficacy,
and body
vigilance
RCT, TG
SR of panic
Effective changes to 1.13 (.64)
compared CGs
attacks, anxiety,posttherapy andof information
depression,
3-month follow-up
and written
body vigilance,of both TG &manualand related
written manual TC
cognitions
on most outcome
measures, but not
information TG
RCT, TG
SR of behaviors
TG found effective.30 (.36)
comparedin changing
to assessment
behaviors similar
only andto assessment but
minimal contact
better thanCGs
minimal contact.35 (.43)
SR of behaviors
Reductions in
RCT, TG
hazardous
compared
behaviors in 6
to written
weeks and 6
material CG
months following
treatment
compared to CG.92 (.42)
RCT, TG
SR of behaviors
Positive effects ofcompared toand relevant
TG on all outcome
WL CG
emotionsmeasures relative
to CG; thesemaintained at
6-month follow-up

RCT, TG
compared withNT CG

Research
DesignTABLE 1. Articles Included in the Meta-analysis (continued)

123

PosttraumaticstressSmoking
cessation

Jet lag

Depression in
Parkinson’s
diseasepatients

Binge drinking

Depression

Lange et al.
(2000)~oz,
Lenert, Mun
Perez, &Bansod(2004)

Lieberman(2003)

Lieberman et al.
(2005)

Moor et al.
(2005)

O’Kearney,
Gibson,Christensen,& Griffiths(2006)

WebsiteE-mailChat room andforum for (1)homogeneousor (2)
heterogeneous(2) 74

2. Website
and e-mailWebsiteCBTPE(2) 1453

20

PE and general
supportPrescriptive
PE

PE

CBT

(1) 1720(1) 7020

1. Website

WebsiteAdolescents

AdultsAdultsAdultsAdultsAdults

RCT, TG
compared with
WL CG

Effective treatment
evident in pre-postcomparisons,similar in bothgroups
SR of depressive
Some positive
symptoms,
moderate effects
attribution style,were found; onlyand self-esteemchange in selfand related
esteem sustained
cognitionsin 16-weekfollow-up
TG compared
SR of drinking
with CG
behavior
receiving writtenmaterials

Negative correlation
betweencompliance withintervention andseverity ofsymptoms
Reduction in
depression inhomogeneous
but not inheterogeneousgroupsTrauma symptoms, Positive effects ofanxiety, andthe treatment indepression
all outcome
measures,sustained 6-weekfollow-up
Percentage ofHigher smokingsmoke
quitting rates
quitting
when e-mailmessages sent
to site users

Relationship
SR of jet-lagbetween
relatedprescriptive
symptoms
treatment andsymptomreliefPre-post
SR ofcomparisons ofdepression
2 TGs
and qualityof lifeComparisons ofseveral time
points between
TGs

Pre-post
comparisonsof TG(Continued ).19 (.68).52 (.74).17 (.38)

.01 (.67)

1.06 (.65).40 (.32).29 (.33).86 (.62)

124

Website ande-mailWebsiteDepression

Panic disorder

Panic disorder

Encopresis

Patten(2003)

Richards &Alvarenga(2002)

Richards
et al.
(2006)

Ritterband
et al.
(2003)

WebsiteWebsiteAnxiety andWebsite and forum
mood related
to breast
cancerOwen, Klapow,
Roth,Shuster, &Bellis
(2005)

Online Channel& Format

Problem Area

Author(s)(2) CBT andstressmanagement(3) information
PE andbehavioral(2) 11(3) 9
12(1) CBT

CBT

PE

CBT

Therapeutic
Approach(1) 12

9

406

26

N

Children

AdultsAdultsAdultsFemaleadultsAge & Typeof Patients
SR of healthrelated
quality of life,
psychologicaland physicalwell-being
SR of depression

Outcome
Measures

Weak effects oftreatment frompre- to postintervention and to
12-week follow-up

Findings.19 (.54)

ES1 (CI 2)

Relevant
RCT, TG
comparedknowledge,
with comparison
encopresis
group thatincidents, andreceived routine
toilet habits
medical care

Effective change inmost outcome
measures relative
to pretreatment
and to CG

.10 (.92).65 (.80)

RCT, TGNo effects of the.04 (.14)
compared withintervention in
information-only
either group
CG
Pre- to 3-month
SR of panic and
Improvement in.61 (1.00)
following
anxiety severity,some of thetreatment
body vigilance,outcomecomparisonsand body
measuressensations
RCT, 2 TGs
SR of panic
Positive.78 (.80)
compared withseverity,
improvement
information-only
depression,in all measuresCG
anxiety,were found equallyagoraphobic
in both TGs
cognitions,compared to CG;
body vigilance,these effects
and quality
sustained atof life
3-month follow-up
1.07 (.83)

RCT, TG
compared withWL CG

Research
DesignTABLE 1. Articles Included in the Meta-analysis (continued)

125Website ande-mailWebsite ande-mailPhobic andpanic
disordersSmoking
cessation

Schneider
et al.
(2005)

Strecher
et al.
(2005)

Ström,Recurrent
Pattersson, &headacheAndersson(2000)

WebsiteWeightWebsitemanagement

Rothert et al.
(2006)

E-mailEatingdisordersRobinson &Serfaty(2001)

20(2) 418(1) 446(2) Nontailored
CBT
CBT(2) CBT, selfmanagement
without
exposure(1) Tailored
AdultsCBT(2) 15AdultsAdults(2) PE(1) CBT, with
exposure(2) 429(1) 33

AdultsAdults(1) Tailoredbehavior

CBT or eclectic(1) 438

19

RCT, TG
comparedwith WL CG

Compared 2
TGs at 6 and
12 weeks
followingintervention

RCT, compared
2 TGs

RCT, comparedTG toinformationonly CG

Pre- toposttreatmentcomparison.37 (.58)(Continued ).41 (.59)

Over 20 percent
1.68 (.13)
abstinence on
28-day continuous
rates in bothgroups, withadvantage totailoredintervention
1.43 (.14)

Reduced body.19 (.13)
weight in TG
more than CG,
at both 3- and6-month follow-up
assessment.08 (.13)
Both TGs equally
1.31 (.50)
improved at
posttreatment on
most measures,
but TG(1)sustained better
outcome at
4-week follow-up
1.28 (.74)

Effective change inall measures frompre- toposttreatment

Headache diary
Reduction inand effects
headacheand depression
severityand depression

SR of smoking
abstinence

SR of state ofproblem andevaluation offear by blind
assessor

SR of depression,and symptoms
and severity ofeatingdisorders
Body weight

126

Weight loss

Traumatic
brain injury

Tate, Wing, &Winett(2001)

Wade, Wolfe,
Brown, &Pestian(2005)

2. Website ande-mailWebsite andwebcam(2) 46

2. Website ande-mail
1. Website

(2) 8

(1) 6

(2) 32(1) 30(1) 46N

1. Website

WebsiteWeight loss

87

E-mail and web
page

Ström,Insomnia
Pattersson, &Andersson(2004)
Swartz, Noell,
Smoking
Schroeder, &cessation
Ary(2006)

Tate,
Jackvony, &Wing(2003)

30

Online Channel& Format

Problem Area

Author(s)CBT(2) Behavioral(1) PE

Behavioral

PE

CBT

Therapeutic
Approach(2) Children(1) Parents

AdultsAdultsAdultsAdultsAge & Typeof Patients

Pre- toposttreatmentcomparisons

RCT, 2 TGs
comparedpre- toposttreatment

RCT, 2 TGs
comparedpre- toposttreatment

RCT, TG
compared with
WL CG

RCT, TG
compared withWL CG

Research
DesignChild adjustment,
parent-childconflicts,
familyfunctioning

Body weight
and waist
circumference

Body weight
and waist
circumference

Smokingabstinence

Sleep diary,
depression,and anxiety

Outcome
Measures

TABLE 1. Articles Included in the Meta-analysis (continued)

Small improvement
in mostoutcomemeasures

Both TGs improvedin outcome
measures; 2ndgroup was more
successful

Abstinence rateof TG was higher
than in CG at
3-monthfollow-up
Both TGs
improved in
outcomemeasures;2nd group
was more
successful

Improved sleeping
quality

Findings.44 (.1.13).65 (.98).46 (.48).43 (.49).44 (.41).25 (.41).45 (.28).32 (.44)

ES1 (CI 2)

127

Body image

Weight loss

Smoking
cessation

Winzelberg
et al. (2000)

Womble et al.
(2004)

Woodruff,
Edwards,
Conway, &Elliott(2001)
Zabinski et al.
(2001)

Chat room

Website ande-mail andforum

Website andforum

Website or
website ande-mailEating pathology Website andforum
and bodyimage
dissatisfaction

Weight loss

White et al.
(2004)

27

18

15

24

(4) 28PE

Client-centered

Behavioral(2) PE
adolescents(3) Behavioral
parents(4) Behavioral
adolescents
CBT(2) 28(3) 29(1) PE parents(1) 29FemaleadultsAdolescentsFemaleadultsAdultsAdolescents
andparents

RCT, TG
compared with
WL CG

Pre- toposttreatmentcomparisons

RCT, TG
compared withmanualtreatment CG

RCT, TG
compared with
no-treatment
CG

RCT, 2TGs comparedpre- toposttreatment

SR of body
shape, eating
disorders, andweight andshape
concerns
Weight,biochemical
data, and SRof quality oflife
Abstinence rates,
amountsmoked,and related
attitudes
Body mass index
and SR ofbody image
and eating
disorders

Body massindex, weight,
body fat

Improvement of TG
in mostoutcomemeasures,sustained at
10-week
follow-up

Positive changes
occurred,
maintained at
1-month follow-up

No differences
between groups,
but some
advantage to TG
at 3-monthfollow-up
More changes in
CG than in TG(Continued ).24 (.52).51 (.67).36 (.58).46 (.60).08 (.51).10 (.51)

TGs improved in all .01 (.51)
measures over
6 months, both inchildren andparents, more
successful inbehavioral TG
.07 (.51)

128

Problem Area

StressWebsite3 chatrooms

Online Channel& Format

23

28

N

CBT

PE

Therapeutic
Approach

AdultsFemaleadultsAge & Typeof Patients

RCT, TG
compared with
WL CG

RCT, TG
compared withWL CG

Research
Design

SR of stress,social support,
anxiety, anddepression

Body massindex, eating
disorders,
body image

Outcome
Measures

Improvement of
TG in mostoutcomemeasures,sustained at
10-week
follow-up
Improvement of TG
in most outcome
measures, but
someimprovement in
CG tooFindings.70 (.53).53 (.51)

ES1 (CI 2)

ES ¼ average of effects of outcome measures used in each study.

1

Note: ES ¼ effect size; CBT ¼ cognitive-behavior therapy; PE ¼ psychoeducation; FTF ¼ face-to-face; RCT ¼ randomized controlled trial; SR ¼ self-report; TG ¼ treatment group;
CG ¼ control group; WL ¼ waiting list; NT ¼ no treatment. N ¼ number of participants receiving online treatment in the statistical analyses.

Zetterqvist,
Maanmies,
Ström, &Andersson(2003)

Zabinski, Wilfley, Eating pathology
Calfas,and bodyWinzelberg, &image
Taylordissatisfaction(2004)

Author(s)TABLE 1. Articles Included in the Meta-analysis (continued)

Barak et al.

129

of the studies included in the analysis. The interventions analyzed
were evaluated by a total of 746 measures of effects. Some studies
were evaluated by a single outcome measure (e.g., Cohen & Kerr,
1998), whereas others were assessed by several measures, up to as
many as 21 (Buhrman, Fältenhag, Ström, & Andersson 2004). On
the average, the studies used eight measures to determine theeffectiveness of treatment.

Coding of Moderators
Two coders independently coded various study features as possible
moderators of effects, based on theoretical or methodological considerations. Interrater consensus between the coders revealed a 95 percent agreement. In cases of disagreement or lack of coherence, athird rater was involved to reach a final, agreed-on rating. The threecoders held either masters or doctorate degrees in the behavioral
sciences. As mentioned, when the study provided insufficient datawith respect to a specific moderator, it was coded as absent andnot included in the final analysis. Moderator analyses were performed to examine whether the ES of an intervention or a groupintervention could be explained by moderating variables. In cases
in which a specific moderator resulted in more than two separate
groups, it was designated as a moderating variable if the analysis
revealed significant heterogeneity between two of the groups, eachof which displayed within-group homogeneity (Hedges & Olkin,1985). A minimum of five cases in a category allowed for a meaningful test of homogeneity (Voyer, Voyer, & Bryden, 1995); however, not
all of the coded moderators were used because of the small numberof studies in some of the categories.
To avoid sample-size bias, once Hedge’s g was calculated for agiven variable, the corresponding ES was weighed according to
the number of participants included in the ES calculation. These
calculations were performed by means of the D-STAT programand according to the formulas developed by Hedges and Olkin(1985).
After the mean weighed ES was calculated, tests of homogeneity
were performed to determine whether the ESs could be considered
to share a common population ES. If Q statistics were significant,homogeneity would be rejected for the ESs within the given set andmoderator analyses conducted to identify the sources of systematic

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JOURNAL OF TECHNOLOGY IN HUMAN SERVICES

variations among the ESs. In these calculations, the moderator variables represent the independent variables, while the effectiveness of
the interventions represents the dependent variables. Moderator
analyses were performed as follows: First, the mean effect size andthe value of within-group homogeneity (QW ¼ within) were calculated for each category of the moderator variables. Next, the degreeof homogeneity between the moderator categories (QB ¼ between)
was calculated. A moderator variable is considered to explain thevariance of an effect set if the value of QW is not significant whilethe value of QB is significant.

Calculation of Effect Size
The data collected from the 92 studies investigated were converted
into a uniform, standardized format to enable a quantitative synthesis by means of meta-analytic calculations, using the fixed-effects
method. The studies contained 746 measures of effects of the interventions employed. The intervention ES of each of the 746 measures
was computed through statistical procedures developed by Hedges
and Olkin (1985) and additional mathematical solutions for nonsignificant ES (Rosenthal, 1984).
In several cases, some data were missing. We then calculated the
ES of the effects of individual measures on the available data. Whendata associated with a specific moderator were missing, we either
omitted missing data or included it in the ‘‘other’’ category in presenting the results.
After computing ESs for all measures and the average ES foreach of the studies, we examined as a common procedure in metaanalysis the distribution of effects to detect outliers. Although threeof the 92 mean effects were detected as outliers, we decided not to
exclude these studies for the following reasons: First, the mean
weighted ES did not change when outliers were excluded. Second,
it became clear when analyzing moderating effects that the outliers’
ESs significantly associated with moderator interaction, thus excluding the possibility that these studies could actually damage theexplanatory value of our review. In other words, the outliers actuallycontributed to the homogeneity of variance rather than the
opposite—the main reason for discarding outliers. Thus, our decision
is consistent with statistical requirements and reasoning (Fuller &Hester, 1999; Hedges & Olkin, 1985).

Barak et al.

131

RESULTS
The average weighted ES over all 92 studies, across all dependentmeasures, was 0.53, which is considered to be a medium effect (Cohen,1988). As can be observed in Table 1, average effects varied greatly
from study to study, from a minimum ES of –0.10 (Richards, Klein,& Austin, 2006; treating panic disorder through online information
alone) to a maximum of 1.68 (Strecher, Shiffman, & West, 2005; treating smoking cessation through tailored CBT). The ESs also showed
extreme variations along other variables, especially through the 746
dependent measures: from a low of –2.90 (self-report of pain severityin treating chronic back pains; in Buhrman et al., 2004) to 5.10 (selfreport of ‘‘total phobia’’ in follow-up versus pretreatment in treating
phobic and panic disorders, both with and without exposure in using
CBT; in Schneider, Mataix-Cols, Marks, & Bachofen, 2005). Out of
the 746 effects calculated, 75 were zero or less (10 percent); out of
the 92 mean ESs calculated per study, 5 (5.4 percent) were negative.
We then examined the moderation effects on ES of the various
moderators in searching for meaningful interactions. The following
sections present the moderating effects examined.

Type of Outcome Measure of Effectiveness
Effectiveness of treatments was measured in various ways: clients’
self-report questionnaires, reports of behaviors and activities, assessments and diagnostics by experts and raters, and physiologicalmeasures—all suited to the study and problem in question. Table 2
presents the comparison of ESs by type of dependent measure. The
differences among ES by type of measure were highly significant

TABLE 2. Effect Size by Type of Outcome Measure
Type of Measure

ES

n

N

Evaluation by Expert
Behavior
Self-Report
Physical
Other

0.93
0.61
0.43
0.19
1.54

3
26
62
26
8

140
6272
4518
1892
222

Note: ES ¼ effect size; n ¼ number of effects; N ¼ number of participants. Number ofeffects exceeds 92; some of the studies used more than one type of measure.

132

JOURNAL OF TECHNOLOGY IN HUMAN SERVICES(QB ¼ 226.42; p < .001), varying from 0.93 (evaluation by experts
and raters) to 0.19 (physical; e.g., blood pressure, brain waves).
The ‘‘other’’ category yielded very high average ES, but this seems
to be random, as there is no logical common denominator (e.g., number of visits to a general practitioner before and after treatment,
therapist satisfaction). It seems that psychological treatments conducted online are less successful in producing problem-related changes
that are physical or somatic in nature, such as blood pressure or
weight. Without this type of effectiveness outcome measure, however,
the average ES would apparently have increased significantly andbecome closer to what is considered high ES.

Type of Problem
Patients were treated for a variety of problems and psychologicaldistresses (sometimes associated with medical-considered problems,such as back pains or headaches). We classified most of these
problems into several meaningful categories; however, eight specificproblems (e.g., insomnia) remained in the ‘‘other’’ category. As
may be viewed in Table 3, average ES yielded significant variations
among the problem categories (QB ¼ 197.98; p < .001). While posttraumatic stress disorder (PTSD) (mean ES ¼ 0.88) and panic andanxiety disorders (mean ES ¼ 0.80) were treated most effectively,
weight loss received the least effective treatment (ES ¼ 0.17). Thus,
it looks as if Internet-based interventions are better suited to treat
problems that are more psychological in nature—that is, problems
TABLE 3. Effect Size by Type of Problem
Type of Problem

ES

n

N

PTSD
Panic and Anxiety
Smoking Cessation
Drinking
Body Image
Depression
Physiological
Weight Loss
Other

0.88
0.80
0.62
0.48
0.45
0.32
0.27
0.17
0.55

3
23
8
6
5
16
7
16
8

148
498
5460
351
221
2500
212
1604
1427

Note: ES ¼ effect size; n ¼ number of effects; N ¼ number ofparticipants; PTSD ¼ post-traumatic stress disorderBarak et al.

133

dealing with emotions, thoughts, and behaviors—and less suited totreat problems that are primarily physiological or somatic (althoughthese obviously have psychological components, too). If the latter
categories were removed from the analysis, the average ES would
have exceeded 0.6.
We examined possible confounding effects in that differential effectiveness might have been created by different therapeutic approaches,use of certain type of outcome measures, or other factors. We foundno support for such confounding effects.

Time of Measuring Effectiveness
Most studies measured effectiveness right at the end of the therapy(i.e., posttreatment) or very close to it; a number of studies measured
effectiveness in later follow-up, ranging from four weeks to a year
after the end of therapy. The mean ES for post-therapy measurement,
which involved 85 studies, was 0.52, whereas the mean ES of 33 studies that measured effectiveness at follow-up was 0.59. Despite theoptical, apparent difference in favor of the follow-up effect, it was
not found to be statistically significant (QB ¼ 2.46; p > .05). The lackof difference means that effects of Internet-based interventions last
for a longer time than just to the end of therapy, as should be
expected of effective treatment intervention.

Therapeutic Theoretical Approach
Three main psychotherapeutic approaches characterized thestudies being analyzed: CBT (intervention primarily based on a
combination of changing thought patterns and contents, associatedwith rehearsal of related relevant behaviors), psychoeducational(intervention primarily based on providing information and explanations on a problem area and behaviors and emotions associated with
it and prescribed instructions on how to change), and behavioral(intervention primarily based on modification and shaping of target
behaviors based on learning principles). Table 4 presents the ESs
by approach. Significant differences emerged among the therapy categories (QB ¼ 190.22; p < .001), with the CBT (ES ¼ 0.83) being foundmuch more effective than the other approaches. The behavioral
approach seems to be the least suited for online treatment(ES ¼ 0.23). If behavioral approaches were left out of Internet

134

JOURNAL OF TECHNOLOGY IN HUMAN SERVICESTABLE 4. Effect Size by Type of Theoretical
Approach of Intervention
Type of Intervention

ES

n

N

Cognitive-Behavioral
Psycho-educational
Behavioral
Other

0.83
0.46
0.23
0.65

51
25
14
2

3960
6796
1136
30

Note: ES ¼ effect size; n ¼ number of effects; N ¼ number ofparticipants.

interventions, average ES would have increased, it seems, to a muchhigher level of effectiveness.
Here, too, possible confounding effects were examined to test thatdifferential effectiveness was a result of other factors (e.g., presenting
problem). We found no support for such confounding effects.

Age of Patients
We classified the age of clients into five age groups according to thedata available from the articles (see Table 5). However, quite a few
studies reported only an age range; hence, these data have to bereferred to with caution. The effects of the data used for the analysis
yielded significant differences among age groups (QB ¼ 181.23;
p < .001). An interesting ES pattern emerged, as youth and oldest
adults seem to be less effectively treated (ES ¼ 0.15 and 0.20, respectively), whereas young (19–24) and older (25–39) adults seem to gain
more from Internet-based therapy (ES ¼ 0.48 and 0.62, respectively).

TABLE 5. Effect Size by Age of PatientsAge Group

ES

n

N

18 and under
19–24
25–39
40 and above
Age not reported

0.15
0.48
0.62
0.20
0.63

6
14
27
31
14

287
840
6941
3172
682

Note: ES ¼ effect size; n ¼ number of effects; N ¼ number ofparticipants.

Barak et al.

135Form of Online Intervention: Web-based versus Etherapy
Internet-based therapy can be delivered mainly through a website(i.e., web-based therapy, using a number of intervention methods)
or online communication (i.e., etherapy, through various communication channels and modalities). Although these two forms of delivering therapy are essentially different, they both use the Internet as amajor vehicle for interacting with clients from a distance. Of the 92
studies, the mean ES of 65 studies that examined the effectivenessof web-based therapy was 0.54, which is not significantly different
from the mean ES of 0.46 found for 27 studies that investigatedthe effectiveness of etherapy (QB ¼ 2.49; p > .05). It is important tonote that, generally, web-based and etherapy used similar theoretical
approaches with similar patients (in terms of age and gender), withsimilar presenting problems, and were assessed by similar outcome
measures, hence confounding effects are improbable.

Group versus Individual Therapy
The Internet enables delivering therapy in individual or groupmodes, just as in face-to-face therapy. However, whereas web-based
therapy is conducted individually, etherapy in principle (aside frompossible supplements of group support offered occasionally in thisform of therapy) may be conducted either individually (through various communication channels, such as e-mail and personal chat) or ingroups (through a forum or chat room). Table 6 compares the mean
ES of individual (in web-based therapy and in etherapy) versus group(in etherapy) therapeutic modes. As can be seen in the table, individual therapy—whether delivered through web-based therapy or etherapy—was found to be more effective than group therapy (QB ¼ 7.34;
TABLE 6. Effect Size of Individual versus Group Modesof Therapy
Mode

d

n

N

Individual—website
Individual—etherapyGroup—etherapy

0.53
0.57
0.36

65
9
18

10523
490
909

Note: d ¼ effect size; n ¼ number of effects; N ¼ number of participants.

136

JOURNAL OF TECHNOLOGY IN HUMAN SERVICES

p < .05). The individual therapy mode marked 74 of the studies andyielded a mean ES of 0.54; group therapy, characterizing the remaining 18 studies, yielded a mean of 0.36, a statistically significant difference (QB ¼ 7.12; p < .01). However, since only a small number ofstudies included in the analysis used group therapy, and the numberof their participants was relatively small compared to the other therapy modes, the impact on the overall ES was marginal. Again, it
should be noted that, generally, individual and group interventions
were provided by similar therapeutic approaches to similar patientswith similar problems and were assessed by similar outcome measures, hence the possibility for confounding effects is little.

Web-based Therapy: Interactive versus Static Website
Web-based therapy may be delivered through an interactive
website, where users actively interact with the site according to its
instructions and applications, or through a static website, where users
passively receive information, instructions, and suggestions relating
to their area of concern. Interactive sites are characteristic of CBTand the behavioral psychotherapeutic approaches, in which activating patients cognitively and behaviorally is essential. Static sites are
more typical of psychoeducational or information-only approaches,
which employ didactic and informative techniques. Our analysis
found that of the 65 studies that investigated web-based therapy,
the ES was 0.65 for 51 therapies that used interactive sites, which issignificantly higher than the ES of 0.52 for the 14 interventions thatused static sites (QB ¼ 32.07; p < .001).
It should be noted that problem type were similarly treated by bothtypes of therapies, therefore this factor should not be regarded as apossible confounding variable. However, as mentioned, interactive
sites are more typical of CBT, whereas static sites are more typicalof psychoeducational approaches, thus confounding might bepossible in inferring from the differences reported here.

Web-based Therapy: Open versus Closed Website
Web-based therapy may be delivered through an open-access website, which permits anyone who desires to receive treatment to engage
in it, or through a closed-access (filtered) website, for which patientsare prescreened (according to various criteria) and the site is accessed
only by personal authorization. Among the 65 web-based therapy

Barak et al.

137

studies, the ES for 51 interventions that used closed sites was 0.68,
which is significantly higher than the ES of 0.48 for the 14 interventions that used open sites (QB ¼ 50.40; p < .001). This difference may
be interpreted through several possible explanations, such as: web
therapy does not fit every patient hence prescreening is essential, professional assessment should precede effective web-based therapy,
and=or a closed site creates elevated commitment and motivation
for therapy. Future research should look into these hypotheses.

Etherapy: Synchronicity of Communication
Etherapy can use either the synchronous communication mode—through chat, audio, or webcam—or asynchronous communication,
via e-mail and forum. Of the 27 studies that investigated the effectiveness of etherapy, the mean ES of the 12 studies that studied the use of
the synchronous communication modality for therapy was 0.49,
whereas the mean ES of the 15 studies that investigated asynchronous
therapeutic communication tools was 0.44. This difference was notfound to be statistically significant (QB ¼ 0.20; p > .05).

Etherapy: Type of Modality
The 27 studies that investigated the effectiveness of etherapy examined chat (nine studies), forum (eight studies), e-mail (seven studies),
audio (two studies), and webcam (one study) as means of communication between therapists and clients. Table 7 presents the mean ESof each modality. The differences among the mean ES values were
found to be significant (QB ¼ 55.16; p < .001). It appears that chatand e-mail (both in the 0.50 s) were more effective than forum and
TABLE 7. Effect Size of Etherapy by Communication
Modality (27 Studies)
Communication Modality

ES

n

N

Audio
Chat
WebcamE-mail
Forum

0.91
0.53
0.31
0.51
0.34

1
9
2
7
8

54
231
208
383
523

Note: ES ¼ effect size; n ¼ number of effects; N ¼ number of participants.

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JOURNAL OF TECHNOLOGY IN HUMAN SERVICES

webcam (both in the 0.30 s). As the number of studies for this analysis
is small and the common denominator between webcam and forum,
unlike the other communication modalities, is not obvious, various
speculations may be offered to account for these differences, suchas reduced sense of privacy.
It should be noted that, generally, no systematic difference waspresent between the various modalities in terms of problem type, theoretical approach, or type of clients, hence confounding effects seem tobe improbable. The small number of studies in each modality, however, does not allow further examination of this effect.

Contribution of Online Supplements to Main Treatment Mode
Several Internet-based treatment methods tried online supplements
to accompany the main treatment modality, whether it involved theuse of web-based therapy or any etherapy modality. In some studies,clients were offered the supplemental (and sporadic) use of e-mail(e.g., Carlbring, Westling, Ljungstrand, Ekselius, & Andersson,2001) or a forum (e.g., Dew et al., 2004) in addition to the use of
web-based therapy as the primary therapeutic method. In otherstudies, a complementary website to etherapy was offered that usede-mail (e.g., Moore, Soderquist, & Werch, 2005) or a forum supplement where chat was used as primary therapeutic communication
channel (e.g., Gollings & Paxton, 2006).
In a comparison of studies that used a website as a supplement toother modes of treatment and those that did not, the supplementary
websites were revealed in fact to have the possibility of reducing
the effectiveness of the treatment (mean ES of 0.41 versus 0.54, respectively; QB ¼ 4.26; p < .05). A further analysis showed no differences
between supplementary-site types, open or closed, static or interactive.
Use of e-mail as a supplement to the main treatment modality, too,
was found to be noncontributing, as mean ES of studies that used
this method was 0.53, identical to the studies that did not e-mail asa supplement. Nor did the use of a forum as a supplement contributeto therapy effectiveness: the mean ES of the 15 studies that used thismethod was 0.44, the result of which was not different from the studies that did not use a forum supplement (mean ES ¼ 0.54;
QB ¼ 1.74; p > .05).
Furthermore, the use of an online audio feature as a supplement
did not contribute to therapy; in fact, it actually decreased the value

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of ES. The mean ES of the eight studies that used this feature was
0.32, versus a mean ES of 0.54 for the studies that did not(QB ¼ 7.65; p < .01). The use of chat as a supplement to the main
treatment modality, too, had a diminishing effect: the six studies thatused chat as a complementary channel of communication had a mean
ES of 0.15, as opposed to a mean ES of 0.54 for the rest of the studies(QB ¼ 25.32; p < .001). The use of a webcam as a supplementarychannel revealed that this method hindered effectiveness, as the six
studies that used it had a mean ES of 0.35, compared to a mean
ES of 0.54 for the studies that did not (QB ¼ 3.74; p < .05).
It should be noted that this finding does not necessarily pertain tocausality, as various variables might moderate and be responsible forthis difference. Several explanations may be provided as to why
supplementary features seem not to significantly contribute to theeffectiveness of therapy, such as: multichanneling of communication
with clients may distract their focus and attention, and=or communication channels that reduce level personal sense of anonymity harm
Internet-delivered therapy effectiveness. These (or other) hypotheses
should be tested in future research.

Internet-Based versus Face-to-Face Therapy
Among the 92 studies included in the analysis, there were 14 that
directly compared the Internet-based (n ¼ 940) with the face-to-face(n ¼ 593), traditional treatment of the same problem, with participants being assigned randomly to each treatment mode. While
the average weighted ES of the Internet-based interventions was
0.39, the weighted ES of the face-to-face interventions was 0.34. Thisdifference is not statistically significant (QB ¼ 0.32; p > .05). It
should be mentioned that there was no systematic factor or obvious
reason why these particular studies yielded effects lower than the
average of the rest of the studies; we thus assume there were no
confounding effects involved in this analysis.

DISCUSSION
Overall Effectiveness of Internet-Based Interventions
The meta-analysis performed on 92 studies that investigated theeffectiveness of Internet-based psychological interventions revealed

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that, on the average, such an intervention has an ES of 0.53, or a
medium effect. This average ES was found across different intervention methods and approaches, types of measure of effectiveness,
problem areas, Internet channels and modalities, age of patients,and other variables. Our analysis of the interaction effects of moderators showed that the average ES could have been much higher if, forexample, specific types of outcome measures (e.g., physical andphysiological) were not employed. Actually, our examination showedthat if studies used only the best (i.e., most improved) measures ofeffectiveness, the average weighted ES would have increased to
1.05, which is considered a very high ES. However, despite the poor
effects on some of the outcome measures, despite that some psychological methods were found to be less appropriate for online application than others (e.g., behavioral), and despite that someproblems are apparently less psychologically treatable through theInternet (e.g., weight loss), the average ES that we found, 0.53, isquite impressive. Incidentally, it should be mentioned that—generally
parallel to our findings—some problems (e.g., weight loss) are lesseffectively treated by quite a few traditional, face-to-face therapeutic
approaches too (Hardeman, Griffin, Johnston, Kinmonth, &Wareham, 2000; Shaw, O’Rourke, Del Mar, & Kenardy, 2007), hence
these differences might not have to be attributed to the Internet as
the mode of delivery of the intervention.
Our conclusion concerning the impressive nature of the level of
effectiveness found is based on three foundations. First, on the average, face-to-face psychotherapeutic interventions are not significantly
more effective in producing change in clients. Although quite a few
studies showed that in-person therapy could attain a relatively highES in treating certain problems in a specific population by using specific methods, the average effectiveness—parallel to the average ES of
0.53 found in our meta-analysis—was found to be of medium size,
too. This finding is based on quite a few comprehensive reviews of
the efficacy of psychotherapy, such as the Consumer Report study(see Seligman, 1995) and the comprehensive meta-analyses conductedby Smith and Glass (1977), Wampold and colleauges (1997), andLuborsky and colleagues (1999). Actually, if one summarizes all of
the meta-analytic study results included in Lambert’s and Ogles’(2004) comprehensive review of meta-analyses of the effectivenessof psychotherapy, one would find that the average of a mediumsize effect best represents the results of the numerous studies. The

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conclusion, then, is that Internet-based therapy, on the average, is aseffective, or nearly as efficacious, as face-to-face therapy. In this context, Wampold’s (2001) summation in regard to the effectiveness of
traditional, face-to-face psychological interventions, ‘‘Simply stated,
psychotherapy is remarkably efficacious’’ (p. 71; emphasis in original),
would seem to apply, as well, to Internet-based psychological interventions.
Second, the data collected for our meta-analytic review revealed nodifference in ES between Internet and face-to-face interventions whencompared in the same study. In fact, the average ES of the Internet
intervention in the 14 studies that made such a comparison was only
0.39—for some unknown reason, a lower than average ES of all thestudies included in our meta-analysis—but the average ES of face-toface interventions included in this data set was 0.34. This difference
was not found to be statistically significant, thus supporting thecontention that Internet interventions are as effective as parallel
face-to-face psychological interventions.
Third, despite the common myth (Fenichel et al., 2002) that therapy
cannot or should not be delivered through the Internet—especiallybecause of the lack of visibility and of nonverbal communication cuesand the absence of evidence for its effectiveness (e.g., Clinical Social
Work Federation, 2001)—our findings clearly show that, in most cases,
online therapy can be delivered effectively, by using various Internet
applications and exploiting several online communication options. If
we take into consideration that the use of modern computers and theInternet for therapeutic purposes is a relatively new professional pursuit,and if we add to this that computer and communication technologies
have continuously and significantly developed over the past decade,and if we also pay attention to the fact that, generally, therapy professionals are relative novices to and lack advanced education and training
in this area—the findings of the current meta-analysis are not only
impressive but surprisingly, actually stand high.

Moderating Effects of Theoretical Approaches
The analysis of moderators revealed that several important variables significantly moderate the effectiveness of Internet therapy.
Some of these findings are not surprising and were actually anticipated. For instance, it can reasonably be expected that the type ofoutcome measures is associated with the degree of effectiveness, as142

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this association is quite common in psychotherapy-outcome research(Hill & Lambert, 2004). In this context, it should be noted that expert
ratings were found to be associated the most with effectiveness ofinterventions in the current meta-analysis. However, although‘‘blind’’ raters were used in most studies, it makes sense that anexpert’s evaluation might be the most sensitive (even if in principle
biased) to therapeutic effects—a speculation that directly corresponds
to the well-documented problem of reactivity of outcome measures(Smith, Glass, & Miller, 1980). The obvious, and simple, possibility
that interviewers could become aware of a patient’s method ofintervention and of his or her experiences in the process somewhat
invalidates this type of outcome criterion or, at least, imparts to it
a substantial bias. In this respect, therapist and=or researcher
allegiance (Luborsky et al., 2002) might significantly affect any
examination of effectiveness and influence ratings.
Our findings showed that CBT was more effective than othertherapeutic approaches applied online, while behavioral techniques
were much inferior. This finding—despite its saliency—is far frombeing simple. If we take into account that computer and Internet
technology are advancing rapidly, we must consider that approaches
having less of a text basis might not only become more attractive,
they might even elevate effectiveness. For instance, Dallery and his
associates (Dallery & Glenn, 2005; Dallery, Glenn, & Raiff, 2007;
Glenn & Dallery, 2007) showed that behavioral treatment—using
Skinnerian behavior-modification techniques and advanced remotetechnologies to bring about smoking cessation—can effectively bedelivered through the Internet with exceptional success. (The firststudy cited was excluded from the meta-analysis for too few participants.) Additional examples are studies by Gold, Burke, Pintauro,
Buzzell, and Harvey-Berino (2007) and Polzien, Jakicic, Tate, andOtto (2007), which recently presented highly effective behaviorally
oriented web-therapy technique assisting in reducing clients’ bodyweight—both an approach and a problem area that were relatively
inferior in our meta-analysis. In other words, it might be a questionof time, skill, and method-development before a variety of clinicalapproaches is implemented online with a degree of effectiveness similar to CBT. For instance, Ritterband and colleagues (2006) showedthat adding unique online tools (audio, graphics, interactivity) to previously developed, mainly textual interventions indeed contributed an
added value to therapeutic efficacy.

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Web-Based Therapy, ETherapy, and Additional InternetDelivered Interventions
The number of studies that investigated the effectiveness of webbased therapy (n ¼ 65) included in our review significantly exceeded
those that studied etherapy (n ¼ 27). Apparently, the reason for thisdifference has to do with the relative ease with which research canbe conducted on web-based interventions than on etherapy. In webbased therapy, clients enter a site and follow instructions, including
filling out online questionnaires at various points in time, whereas
etherapy more resembles face-to-face therapy in that a client meetsa therapist for a therapeutic dialogue; hence, in the latter practice,forms and questionnaires are perceived as irrelevant and a nuisance,and contacting patients for questionnaires and other measurements
is usually troublesome and involves ethical and methodological difficulties in addition to practical problems. This difference between thetwo modes of Internet-based interventions, however, is in reversed
direction to that of process research reviewed earlier. It is also interesting to note that the efficacy of etherapy—perhaps because of its
basic, more natural therapeutic nature—has been in the subject ofnumerous nonquantitative studies, including illustrative descriptive
case studies (e.g., Chechele & Stofle, 2003; Luce, Winzelberg,
Zabinski, & Osborne, 2003) and advanced qualitative analyses (e.g.,
Stofle, 2002). These publications could not be included in our quantitatively based meta-analytic review; however, their existence should
not be overlooked, especially as they provide much evidence in support of the application of etherapy in various online communication
modalities and for numerous problem areas. These problem areas
include individual therapy in treating such issues as marital difficulties(Jedlicka & Jennings, 2001), sex problems (Hall, 2004), addictive
behaviors (Stofle, 2002), anxiety and social phobia (Przeworski &Newman, 2004), and eating disorders (Grunwald & Busse, 2003);
and group therapy in treating diverse problems (e.g., Barak &Wander-Schwartz, 2000; Colòn, 1996; Przeworski & Newman, 2004;
Sander, 1999). Perhaps this type of methodology better suits
etherapy-type intervention, especially since experiential-oriented
therapy is commonly applied (Suler, 2008). It seems that with thedevelopment and improved training in this emerging area (Coursol& Lewis, 2004; Mallen, Vogel, & Rochlen, 2005; Trepal, Haberstroh,
Duffey, & Evans, 2007) therapeutic process and outcome will elevate.

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Our findings showed that, on the average, web-based intervention
provides as effective therapy as etherapy. This finding does not meanthat both approaches are as effective in treating similar individuals
and=or similar problems. Web-based therapy is focused on self-help;that is, individual people make use of therapeutic resources—be it
online information, psychoeducation interventions, or a tailored,clinical protocol based on CBT principles—to change their condition.
The essential role and responsibility of a therapist lie in preparing thematerials and providing them online in a way that is attractive,
friendly, and optimally effective. In etherapy, however, a therapist
is actively engaged in therapeutic communication with clients andin exploiting the Internet for a channel of communication of choice(Suler, 2000, 2004, 2008). It is possible, therefore, that clientscharacterized by different preferences, needs, or habits would benefit
differentially from each of these two approaches in interaction withthe problem area. The absence of a meaningful difference in average
effectiveness that we found between the two approaches might reflect
the self-selection of patients and=or therapists; the available data cannot as yet provide answers to these questions. Future research should
focus on these interesting hypotheses.
Very little research has been published on two other uses of theInternet to deliver therapy: Internet component(s) that may complement face-to-face therapy (e.g., use of e-mail in between face-to-facesessions, ask in-person clients to publish posts on a personal blog, useof a website to prepare clients for face-to-face therapy) and Internetoperated software (a programmed robot that simulates a therapist
based on principles of artificial intelligence or prescribed protocols,such as ELIZA). These two category uses, in addition to web-based
therapy and etherapy, create a comprehensive toolkit for therapists
who wish to exploit Internet capabilities in their clinical work. However, only very limited outcome research has been published to dateon the growing use of Internet-assisted therapy as a complementary
therapeutic vehicle, such as using e-mail, blogs, online information,or an online support group in parallel with traditional, face-to-facetherapy (Baily, Yager, & Jensen, 2002; Castelnuovo, Gaggioli,
Mantovani, & Riva, 2003; Suler, 2008; Tate & Zabinski, 2004;
Zuckerman, 2003). Findings published on such therapeutic use havebeen promising, however. For example, Baily and colleagues (2002)
described the use of e-mail as an adjunctive treatment tool for anadolescent with anorexia nervosa and the use of a chat room forBarak et al.

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the enhancement of social life for a patient with social phobia. Likewise, Peterson and Beck (2003) presented a model, and several
illustrative cases, of the use of e-mail as an adjunctive tool in psychotherapy. Golkaramnay, Bauer, Haug, Wolf, and Kordy (2007)
recently presented the use of group therapy, conducted through achat room, following the termination of a patient’s in-person therapy
in order to reduce the risk of relapse. In regard to the category of
more robotic, therapeutic online software (Marks, Cavanagh, &Gega, 2007), empirical outcome research is rare; however, descriptions of such applications exist, such as in helping the treatment ofproblem drinkers (Squires & Hester, 2004) or the more general useof ELIZA (Epstein & Klinkenberg, 2001).

Considerations of Age as a Moderating Factor
The differential effects of age group on Internet-based therapy outcome require special attention. The findings of the meta-analysis
showed that clients’ age made a difference in terms of their ability
to gain from the therapy. Specifically, among four age-group categories employed, the findings showed that the ES of Internet-based
therapy provided to mid-age adults (19–39) was higher than either
to older or younger clients. This finding, however, might be a temporal result of a vanishing factor: that of pervasiveness, acceptance,
and usage skills associated with the Internet. In other words, we
believe that nowadays—after the general penetration of computers
and the Internet into homes, schools, and workplaces—these differences might have disappeared. Actually, recent studies of Internetbased therapy for older adults and children—published after theend of the data collection for the current analysis (March, 2006)—
showed strong therapeutic effects. For instance, using web-based
CBT, Spence, Holmes, March, and Lipp (2006) showed highly effective results on anxious children, while Nelson, Barnard, and Cain(2006) gained similar results on depressive children. Hicks, vonBaeyer, and McGrath (2006) presented highly effective online intervention for children’s recurrent pains. Similarly, at the other endof the age continuum, Brattberg (2006) presented a highly effective, Internet-delivered, psychoeducation intervention in treating thechronic pains of older adults; Hill, Weinert, and Cudney (2006)
showed highly effective web-based intervention of psychologicalsymptoms of chronically ill older women. Lorig, Ritter, Laurent,

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and Plant (2006) presented a highly effective online intervention
program for developing the self-management skills of older adults
suffering from chronic diseases; Marziali and Donahue (2006) very
effectively treated older caregivers (mean age 68) through videoconferencing. Thus, it seems that an age gap interacting with
online-intervention effectiveness is indeed vanishing.
Since cyberspace has become a major social environment for children and adolescents (Fox & Madden, 2006; Hall, 2006; Valkenburg& Peter, 2007), it is not surprising to learn that Internet-based therapeutic and support applications operating online are highly useful foryoungsters (Barak, 2007; Hoffmann, 2006; Mangunkusumo, Brug,
Duisterhout, de Koning, & Raat, 2007), in contrast to what ourreview seemingly found. Likewise, the use nowadays of computers
and the Internet by older people is quickly growing (Carpenter &Buday, 2007); indeed, this age group may gain much mental support
through computer use (Shapira, Barak, & Gal, 2007).

Limitations of the Meta-Analytic Review
Meta-analysis—although becoming a common procedure in
reviewing quantitative empirical findings of various phenomena—is
far from being a flawless procedure. It has been criticized on statistical and methodological grounds in the context of evaluating the effectiveness of psychotherapy (e.g., Wampold, 2001) and other, moregeneral issues (e.g., Field, 2003). One of the main criticisms has to
do with publication bias and ‘‘file-drawer effect,’’ created either by
researchers themselves or by scientific journals, which results in an
overestimation of effects. Although this point might be true, nodoubt it affects face-to-face and Internet-based outcome researchsimilarly; hence, a comparison of the two methods is not erroneous.
In addition, it seems that the results of the moderator analyses that
we conducted add to the validity of our conclusions. We consideredusing the ‘‘fail-safe N’’ (FSN) statistics in order to examine how
many unpublished studies would have been needed to jeopardize
our conclusions; we have avoided this step because of the clear natureof the results, on the one hand, and the problematic assumptions
related to calculating FSN, on the other.
Another issue worth mentioning has to do with the statisticalmethods employed here. Since we used the fixed-effects method forour analysis, different results might be derived from those of theBarak et al.

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random- or mixed-effects methods, throwing into question the accuracy of our results. However, if we compare our results with those ofprevious meta-analyses of Internet interventions—though these are
more limited in scope and content (Hirai & Clum, 2006b; Spek
et al., 2007; Wantland et al., 2004) but employ alternative statisticalmodels and assumptions—we find that the general nature of our findings is highly consistent with those others, thus supporting the validity of our conclusions from this aspect, as well. Future researchshould utilize different statistical models to replicate our analysis.
Also, our data set for performing the meta-analysis included all
eligible articles according to the inclusion criteria determined; qualityof research, however, was not one of them. Using this criterion—
suggested by some meta-analysis experts—might be problematic,
especially as the objective and professional ability to assess qualityof published research is limited and might be erroneous or biased.
After close consideration and actual trials we decided to avoid thisselection criterion and base research quality merely on acceptancefor publication in a peer-reviewed journal. Our approach, however,might have introduced some error variance into the results. Futuremeta-analyses in this area should attempt to evaluate research qualityof studies and either select those meeting a minimal level of thisadditional criterion or, perhaps in using a more informative
approach, using research quality level as an additional moderator
and examine its effects.

SUMMARY AND CONCLUSIONS
The findings presented in this meta-analytic review provide muchsupport for the application of psychotherapeutic interventions
through the Internet, using various approaches, methods, and online
modalities, to treat various problems differentially but effectively;
online therapy is especially effective for treating anxiety andstress—effects that last after therapy ends—and, on the average, isas effective as face-to-face intervention. The effectiveness of interventions can be detected by a variety of outcome measures, but less sowhen using physiological or physical measures. When web-based,
self-help therapy is applied on an interactive website that may beaccessed only by prescreened, authorized patients, it should increase
therapy success. E-mail reminders for patients who use web-based

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therapy are expected to contribute to the success of the therapeuticintervention, too. When etherapy is applied, it seems that textual
are preferred to nontextual modes (e.g., use of audio and webcam),though this should be regarded cautiously due to the small
number of studies reviewed using these features. Furthermore, it
seems that individual Internet therapy is more effective than groupintervention online.
The use of computers and the Internet is rapidly increasing andbecoming a common personal and social phenomenon (Barak &Suler, 2008; Bargh & McKenna, 2004; Haythornthwaite & Hagar,
2004; Madden, 2006). Moreover, the Internet-connected computeris turning into a highly influential social tool (Sassenberg & Jonas,
2007) all while innovative and advanced technology is introduced
frequently and is rapidly changing the culture. Psychotherapy andcounseling should adjust to this changing world and adopt new, innovative tools accordingly to fit into the world of today and tomorrow
so as to better meet clients’ expectations and needs. The current
review shows that this is not only theoretically possible but actuallya developing professional reality.

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Received: 07=15=07
Revised: 09=26=07
Accepted: 10=02=07

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