Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: A meta-analysis

INTERNET-BASED COGNITIVE BEHAVIOUR THERAPY FOR

SUBTHRESHOLD DEPRESSION

IN PEOPLE OVER 50 YEARS OLD

Viola Spek

© Viola Spek, 2007

ISBN/EAN: 978-90-5335-135-2

Printed by Ridderprint Offsetdrukkerij B.V., Ridderkerk

INTERNET-BASED COGNITIVE BEHAVIOUR THERAPY FOR

SUBTHRESHOLD DEPRESSION IN PEOPLE OVER 50 YEARS OLD

Proefschrift

ter verkrijging van de graad van doctor

aan de Universiteit van Tilburg,

op gezag van de rector magnificus,

prof. dr. F.A. van der Duyn Schouten,

in het openbaar te verdedigen ten overstaan van een

door het college voor promoties aangewezen commissie

in de aula van de Universiteit

op vrijdag 30 november 2007 om 16:15 uur

door Viola Rosalinde Mirjam Spek

geboren op 30 december 1976 te Roosendaal.

Promotores: Prof. dr. V.J.M. Pop

Prof. dr. W.J.M.J. Cuijpers

Copromotor: Dr. I. Nyklíček

Promotiecommissie: Prof. dr. G. Andersson

Prof. dr. A.T.F. Beekman

Prof. dr. J.K.L. Denollet

Prof. dr. G.L. van Heck

Dr. H.F.E. Smit

Prof. dr. M.J.M. van Son

To all those who participated in this study

CONTENTS

Voorwoord 9

Chapter 1 General introduction 11

Chapter 2 Internet-based cognitive behaviour therapy for symptoms

of depression and anxiety: A meta-analysis

21

Chapter 3 Internet administration of the Edinburgh Depression Scale 41

Chapter 4 Internet-based cognitive behavioural therapy for

subthreshold depression in people over 50 years old:

A randomized controlled clinical trial

53

Chapter 5 One-year follow-up results of a randomized controlled

clinical trial on internet-based cognitive behavioural therapy

for subthreshold depression in people over 50 years

75

Chapter 6 Predictors of outcome of group and internet-based cognitive

behaviour therapy

91

Chapter 7 General discussion 111

Summary 119

Samenvatting 121

Curriculum Vitae 123

9

VOORWOORD

Aan het begin van dit proefschrift zou ik graag de mensen bedanken, die hebben

bijgedragen aan het onderzoek.

Als eerste bedank ik mijn promotoren en co-promotor. Victor, het was geweldig

om samen te werken met iemand die zo enthousiast en gedreven is als jij. Zeker in het

laatste jaar, waarin ik op de UvT een kamer met je deelde, heeft jouw enthousiasme ervoor

gezorgd dat ik met extra veel plezier mijn proefschrift heb afgerond. Pim, ondanks de grote

afstand tussen onze beide werkplekken, was je toch nauw betrokken bij dit project. Jouw

expertise op het gebied van onderzoek naar internet interventies was onmisbaar. Daarnaast

legde je de lat qua methodologie altijd hoog, dit heeft me gestimuleerd om me in allerlei

statistische technieken te verdiepen, iets wat ook nog eens erg interessant bleek te zijn!

Ivan, jouw werkplek was zo dichtbij, dat jij degene was bij wie ik altijd binnen kon lopen

voor vragen. Samen hebben we heel wat grote en kleine knopen doorgehakt.

Een groot deel van dit onderzoek is uitgevoerd bij het Diagnostisch Centrum

Eindhoven. Voor deze mogelijkheid wil ik Jules Keyzer hartelijk bedanken. De faciliteiten

van het DCE waren onmisbaar voor de uitvoering van het onderzoek.

De internet interventie, die is onderzocht in dit proefschrift, is ontwikkeld door het

Trimbos-instituut. De twee makers van de interventie, Heleen Riper en Jeannet Kramer, wil

ik bedanken voor hun enorme inspanningen om de interventie zo snel mogelijk gereed te

hebben voor het onderzoek.

Ik ben veel dank verschuldigd aan Peter van Nierop van GGD Eindhoven vanwege

zijn geweldige hulp bij het werven van deelnemers voor de studie.

Mijn kamergenote bij het DCE, Colette Wijnands, was een stabiele factor tijdens

de uitvoering van de trial. In de hectiek van het werven en includeren van deelnemers,

waren jouw rust en relativeringsvermogen een enorme steun voor me.

Ook Ton Heinen heeft in die tijd een belangrijke rol gespeeld. Heel erg bedankt

voor je hulp, Ton.

Niels Smits bedank ik, omdat hij me wegwijs heeft gemaakt in de wereld van

Multiple Imputatie.

Graag wil ik ook een aantal vrienden bedanken voor de bijdrage die ze hebben

geleverd aan mijn onderzoeksproject. Tamara, bedankt voor je steun en je ‘wijze raad’ over

mijn project en voor de gezellige etentjes in de meest onwaarschijnlijke eetcafés. Lisanne,

wat een goed idee van je om aan het begin van je onderzoek bij mij langs te komen! Jouw

10

vragen geven mij altijd nieuwe ideeën voor mijn eigen onderzoek. Anton, wat geweldig dat

we eerst allebei min of meer tegelijk onze scripties schreven en dat we daarna ook nog

allebei AIO werden bij de UvT. Jouw nuchtere kijk op het leven en je humor hebben altijd

een gunstige uitwerking op mijn humeur.

Mijn paranimfen Angélique en Eva wil ik eveneens bedanken voor de rol die ze

allebei hebben gespeeld bij mijn promotie onderzoek. Met jullie allebei heb ik liters thee

gedronken en urenlange gesprekken gevoerd, over de meest uiteenlopende onderwerpen,

maar ook erg veel over onze onderzoeken. Jullie hebben telkens weer mijn enthousiasme

voor psychologie en voor de wetenschap aangewakkerd. Ik ben erg blij dat jullie achter me

staan tijdens de verdediging.

Mijn andere vrienden en (schoon)familie wil ik bedanken voor de voor de

welkome afleiding van het onderzoek die ze boden en hun belangstelling in de voortgang

van het project. In het bijzonder noem ik mijn klimvrienden, vanwege de gezellige

klimweekendjes, barbecues en gedenkwaardige avonden bij Kandinsky.

Mijn collega’s van FSW wil ik bedanken voor hun gezelligheid en de goede

werksfeer. Tijdens mijn AIO tijd maakte ik, met mijn afwijkende onderzoeksonderwerp,

niet echt deel uit van een bepaalde onderzoeksgroep, maar dat was geen probleem, ik

voelde me toch erg welkom bij jullie.

Zonder de juiste vooropleiding kun je niet promoveren. Ik wil mijn ouders

bedanken voor het feit dat ze me altijd gestimuleerd hebben om te leren en te studeren.

Inderdaad, het studeren heeft zijn vruchten afgeworpen: ik doe al jaren werk wat ik

geweldig vind.

Joost, jij vindt het onzin als ik jou noem in dit voorwoord, maar je was onmisbaar.

Jij zorgt voor de balans in mijn leven. Dat doe je door me te stimuleren op sportief gebied

en door altijd weer met de meest geweldige voorstellen voor vakanties en weekendjes weg

te komen. Samen hebben we de mooiste en ook vaak de zwaarste, maar altijd de meest

speciale toeren, routes, trektochten, boulders en puinbakken gedaan. Dat is ‘ze magic life’,

zoals de Bleausards het bedoelen!

CHAPTER 1

GENERAL INTRODUCTION

12

INTRODUCTION

Depression is a major health problem. In people over 50 years of age, the prevalence of

major depression is 1-3%, and the prevalence of subthreshold depression in this population

is 8-16% (Beekman et al. 1995; Cole & Dendukuri, 2003). Depression is characterised by

two core symptoms: depressed mood and lack of interest, persisting for at least two weeks.

Additional symptoms, causing further functional impairment, consist of the following: lack

of energy, sleep disturbance, lack of concentration, lack or increase of appetite, apathetic or

agitated behaviour, negative feelings about oneself, thoughts about death and suicide. At

least one core symptom and four additional symptoms must be present to meet the DSM-IV

criteria for a diagnosis for major depression (APA, 1994).

Patients with subthreshold depression have symptoms of depression, but not

enough to meet the DSM-IV criteria for major depression. Subthreshold depression has

considerable effects on well-being and psychosocial functioning (Beekman et al. 1995,

2002; Rapaport & Judd, 1998; Lewinsohn et al. 2000). In fact, persons suffering from

subthreshold depression are rather similar to those with a diagnosis of major depression

with regard to their psychosocial functioning (Gotlib et al. 1995). Furthermore, persons

suffering from subthreshold depression experience almost the same degree of impairment

of health status, functional status, and disability as those diagnosed with major depression

(Wagner et al. 2000).

An association has been shown between depressive symptomathology and

developing a major depressive episode (Cuijpers & Smit, 2004). Up to 27% of elderly

persons suffering from subthreshold depression will develop a major depressive episode

within three years (Beekman et al. 2002). Depression in later life is characterized by an

unfavourable prognosis, reduced quality of life, and excess mortality (Cole et al. 1999; Smit

et al. 2006).

The annual per capita excess costs of major depression are €2278. The per capita

costs of subthreshold depression are about two thirds of those of major depression (Cuijpers

et al. 2007).

For the above-mentioned reasons, the treatment of subthreshold depression is very

important. Due to its high prevalence and the fact that probably less than 20% of people

with depression are detected and treated (Cole & Dendukuri, 2003), new approaches are

needed to treat subthreshold depression and to prevent major depressive episodes. It is

imperative that these methods can reach large populations and those persons who would not

https://www.researchgate.net/publication/276960158_The_Natural_History_of_Late-Life_Depression?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=


https://www.researchgate.net/publication/6835623_Predicting_the_onset_of_major_depressive_disorder_and_dysthymia_in_older_adults_with_subthreshold_depression_A_community_based_study?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=


https://www.researchgate.net/publication/6502121_Economic_Costs_of_Minor_Depression_A_Population-based_Study?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=


https://www.researchgate.net/publication/10734314_Risk_Factors_for_Depression_Among_Elderly_Community_Subjects_A_Systematic_Review_and_Meta-Analysis?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=


https://www.researchgate.net/publication/8650720_Subthreshold_depression_as_a_risk_indicator_for_major_depressive_disorder_A_systematic_review_of_prospective_studies?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

13

otherwise seek treatment. Furthermore, treatment should be evidence-based, since it does

not make sense to provide people with treatment for which no support exists with regard to

effectiveness. Currently, the most researched evidence-based treatment is cognitive

behaviour therapy (Ebmeier et al., 2006). This type of therapy is based on the ideas of

Beck. Later, Lewinsohn adapted Beck’s cognitive therapy to his own ideas, and developed

the Coping With Depression course. Since adaptations of the Coping With Depression

course are being examined in this study, this treatment and its underlying theories are

summarized below.

Cognitive therapy for depression

The foundation of Beck’s cognitive theory of depression is a stress-diathesis model:

persons may be vulnerable to depression because they have dysfunctional beliefs. These

beliefs may remain latent for years, prior to and between depressive episodes, but they can

become primed by environmental stressors. Dysfunctional beliefs are usually those about

being helpless or unlovable, and are incorporated in schemas that are used to interpret

experiences. When the schemas are primed, any situation remotely related to self-worth or

social acceptation is interpreted as proof of being helpless or unlovable (Beck, 1991). This

eventually leads to depression. In order to alleviate this depression, the dysfunctional

beliefs have to be challenged, dismissed, and replaced by other, more constructive,

interpretations of experiences. This is the main aim of cognitive therapy.

Lewinsohn’s theory of depression

According to the social learning theory, emotional disorders are learned responses that

influence and are influenced by a person’s interaction with the environment (Lewinsohn et

al. 1985). With regard to depression, it is hypothesised that a prolonged reduction in

positive reinforcement triggers the occurrence of depression. Positive reinforcements are

person-environment interactions with positive outcomes: outcomes that make the person

feel good. People with depression are assumed not to behave in ways that lead to positive

reinforcement. Because of the lack of positive reinforcement, persons with depression find

it difficult to maintain or initiate behaviour and they become more passive. The lack of

reinforcement is also assumed to cause the dysphoric mood. A second hypothesis is that a

high rate of punishing experiences can cause depression. Punishment is defined as person-

environment interactions with aversive consequences (Lewinsohn et al. 1985).

14

The main reasons why a person may experience low rates of positive

reinforcement or high rates of punishment are as follows: (1) the person’s environment

provides few positive reinforcements or may have many punishing aspects (2) the person

may lack the skills to obtain the available positive reinforcements or may lack the skills to

cope effectively with punishment.

The aim of treatment is (1) to increase the quantity and quality of positively

reinforcing interactions between the depressed person and the environment, and (2) to

decrease the quantity and the quality of punishing interactions (Lewinsohn et al. 1985).

Lewinsohn’s Coping With Depression course

Based on this theory about depression, Lewinsohn developed a group treatment for

depression: the Coping With Depression (CWD) course. This course addresses the

behaviour and thinking patterns that are problematic for depressed people. These include a

reduction in pleasant activities, problems in social interactions, depressive thoughts and

anxiety. In order to change these problematic behaviours and thinking patterns, the CWD

course uses evidence-based intervention strategies, such as Beck’s cognitive therapy, social

skills training, increasing pleasant activities, and relaxation (Lewinsohn et al. 1985). The

course also incorporates the common and critical components of all the recent cognitive

behavioural treatments (Zeiss et al. 1979):

1. The CWD course begins with an elaborate, well-planned rationale which

convinces participants that they can control their own behaviour, and thus their

depression.

2. The CWD course provides training in skills that participants can use to feel more

effective in the handling of their daily lives.

3. The CWD course emphasizes the independent use of these skills outside the

therapy context.

4. The CWD course encourages the participants to attribute their improvement in

mood to their own increased skills and not to the therapist’s skill.

Since the CWD course is provided in group-form, it is an efficient treatment approach in

the sense that ten persons can be treated at the same time.

15

Internet-based cognitive behaviour therapy

A potentially even more efficient approach than group treatment is internet-based

treatment. Internet-based cognitive behaviour therapy has advantages over traditional

cognitive behaviour therapy for both clients and health care. The low-threshold

accessibility of the internet makes it very suitable for offering and receiving help for

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 at all or minimal therapist contact via e-mail or

telephone, to the amount of involvement as seen in classic individual therapy. Thus,

internet-based treatment may reduce the therapist time while maintaining efficacy (Wright

et al. 2005).

Aims of the thesis

The main aim of this study was to validate a newly developed internet-based treatment by

comparing it to the Coping With Depression course, and to a waiting list control condition.

The Coping With Depression course (Lewinsohn et al. 1985) was adapted to the

Dutch situation by Cuijpers (2000). It has been shown to be effective (Cuijpers 1998,

Allart-van Dam et al. 2003, Haringsma et al. 2005, Allart-Van Dam et al. 2006) and has

been used for over ten years by mental health institutions in The Netherlands. There is a

special version for persons aged over 50 years, which consists of ten weekly group

sessions. The CWD course can be seen as a gold standard to which we compared the newly

developed internet-based intervention.

The internet-based cognitive behaviour therapy intervention was developed by the

Trimbos institute, the Netherlands Institute of Mental Health and Addiction. It is a self-help

intervention consisting of eight modules including text, exercises, videos, and figures. The

internet-based intervention covers the same subjects as the group course, since it was based

on the Coping with Depression Course. It was studied purely as a self-help intervention,

and no professional support was offered alongside the intervention.

This is the first study in which a face-to-face treatment for depressive symptoms is

compared to internet-based treatment for depressive symptoms. As stated above, the

content of both treatments is the same; however, presentation of the content is very

https://www.researchgate.net/publication/8918265_Computer-Aided_CBT_Self-Help_for_Anxiety_and_Depressive_Disorders_Experience_of_a_London_Clinic_and_Future_Directions?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/222146647_A_psychoeducational_approach_to_the_treatment_of_depression_A_meta-_analysis_of_Lewinsohn's_'Coping_With_Depression'_course?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

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different. Therefore, this provides an excellent opportunity to investigate the importance of

the presentation of cognitive behaviour therapy.

In order to investigate the differences between these two treatments, we also

studied predictors of treatment outcome. If treatment outcome for the two interventions is

predicted by different participant characteristics, it is likely that this difference would be

related to the differences between the two types of cognitive behaviour therapy. A major

motivation for studying the differences between these two treatments is that the results

might provide us with information regarding what kind of treatment is optimal for which

client.

Outline of the thesis

The main research questions addressed in this thesis were the following:

• What knowledge is there about the effectiveness of internet-based treatment for

depression and anxiety?

• Is internet-based screening for depression possible?

• Is the effectiveness of internet-based treatment comparable to the gold standard of

Lewinsohn’s evidence-based Coping With Depression course?

• What is the effectiveness of internet-based treatment compared to a waiting-list

condition?

• Is it possible to detect any long term effects for internet-based treatment?

• Are there any differences between group treatment and internet-based treatment?

• Which personality characteristics are predictors for treatment outcome for internet-

based treatment and group treatment?

• Do different personality characteristics predict treatment outcome of the two types of

treatment?

The general outline of the thesis is as follows: Chapter 2 presents a meta-analysis on the

efficacy of internet-based treatment in general. The psychometric aspects of internet-based

screening for depression are discussed in Chapter 3. The study of the short term efficacy of

internet-based treatment compared to group treatment and a waiting-list can be found in

Chapter 4. The long term efficacy of internet-based treatment is discussed in Chapter 5.

Chapter 6 addresses predictors of treatment outcome. Finally, in Chapter 7, a general

discussion of the research conducted for this thesis can be found.

17

REFERENCES

Allart-Van Dam, E., Hosman, C.M.H., Hoogduin, C.A.L., Schaap, C.P.D.R. (2003).

The Coping With Depression Course: Short-term outcomes and mediating effects of a

randomized controlled trial in the treatment of subclinical depression. Behavior

Therapy 34, 381-396.


Prevention of depression in subclinically depressed adults: Follow-up effects on the

‘Coping with Depression’ course. Journal of Affective Disorders 97, 219-228.

American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental

Disorders, Fourth Edition. Washington, DC: American Psychiatric Association.

Beekman, A.T.F., Deeg, D.J.H., Van Tilburg, T., Smit, J.H., Hooijer, C., Van Tilburg,

W. (1995). Major and minor depression in later life: a study of prevalence and risk

factors. Journal of Affective Disorders 36, 65-75.

Beekman, A.T.F., Geerlings, S.W., Deeg, D.J.H., Smit, J.H., Schoevers, R.S., De Beurs,

E., Braam, A.W., Pennix, B.W.J.H., Van Tilburg, W. (2002) The natural history of

late-life depression. Archives of General Psychiatry 59, 605-611.

Beck, A.T. (1991). Cognitive therapy: A 30-year retrospective. American Psychologist 46,

368-375.

Cole, M.G., Bellavance, F., Mansour, A. (1999). Prognosis of depression in elderly

community and primary care populations: A systematic review and meta-analysis.

American Journal of Psychiatry 156, 1182-1189.

Cole, M.G., Dendukuri, N. (2003). Risk factors for depression among elderly community

subjects: a systematic review and meta-analysis. American Journal of Psychiatry 160,

1147-1156.

Cuijpers, P. (1998). A psychoeducational approach to the treatment of depression: a meta-

analysis of Lewinsohn’s ‘Coping with depression’ course. Behavior Therapy 29, 521-

533.

Cuijpers, P. (2000). In de put, uit de put: Zelf depressiviteit overwinnen 55+. Utrecht:

Trimbos-instituut. [Dutch translation and adaptation to Lewinsohn’s Coping With

18

Depression Course, original authors: Lewinsohn, P.M., Antonuccio, D.O.,

Breckenridge, J.S., Teri, L.]

Cuijpers, P., Smit, F. (2004). Subthreshold depression as a risk indicator for major

depressive disorder: a systematic review of prospective studies. Acta Psychiatrica

Scandinavica 109, 325-331.

Cuijpers, P., Smit, F., Oostenbrink, J., de Graaf, R., ten Have, M., Beekman, A.

(2007). Economic costs of minor depression: A population-based study. Acta

Psychiatrica Scandandinavica 115, 229-236.

Ebmeier, K.P., Donaghey, C., Steele, J.D. (1996). Recent development and current

controversies in depression. The Lancet 367, 153-167.

Gega, L., Marks, I., Mataix-Cols, D. (2004). Computer-aided CBT self-help for anxiety

and depressive disorders: Experience of a London clinic and future directions.

JCLP/In Session 60, 147-157.

Gotlib, I.H., Lewinsohn, P.M., Seeley, J.R. (1995). Symptoms versus a diagnosis of

depression: differences in psychosocial functioning. Journal of Consulting and

Clinical Psychology 63, 90-100.

Haringsma, R., Engels, G.I., Cuijpers, P., Spinhoven, P. (2005). Effectiveness of the

Coping With Depression (CWD) course for older aduls provided by the community-

based mental health care system in the Netherlands: a randomized controlled trial.

International Psychogeriatrics 17, 1-19.

Lewinsohn, P.M., Solomon, A., Seeley, J.R., Zeiss, A.M. (2000). Clinical implications of

“subthreshold” depressive symptoms. Journal of Abnormal Psychology 109, 345-351.

Lewinsohn, P.M., Steinmetz, J.L., Antonuccio, D., Teri, L. (1985). Group therapy for

depression: The Coping With Depression course. International Journal of Mental

Health 13, 8-33.

Rapaport, M.H., Judd, L.L. (1998). Minor depressive disorder and subsyndromal

depressive symptoms: functional impairment and response to treatment. Journal of

Affective Disorders 48, 227-232.

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Smit, F., Ederveen, A., Cuijpers, P., Deeg, D., Beekman, A. (2006). Opportunities for

cost-effective prevention of late-life depression: An epidemiological approach.

Archives of General Psychiatry 63, 290-296.

Wagner, H.R., Burns, B.J., Broadhead, W.E., Yarnall, K.S.H., Sigmon, A., Gaynes,

B.N. (2000). Minor depression in family practice: Functional morbidity, co-

morbidity, service utilisation and outcomes. Psychological Medicine 30, 1377-1390.

Wright, J.H., Wright, A.S., Albano, A.M., Basco, M.R., Goldsmith, L.J., Raffield, T. &

Otto, M.W. (2005). Computer-assisted cognitive therapy for depression: Maintaining

efficacy while reducing therapist time. American Journal of Psychiatry 162, 1158-

1164.

Zeiss, A.M., Lewinsohn, P.M., Munoz, R.F. (1979). Nonspecific improvement effects in

depression using interpersonal, cognitive, and pleasant events focused treatments.

Journal of Consulting and Clinical Psychology 47, 427-439.

CHAPTER 2

INTERNET-BASED COGNITIVE BEHAVIOUR THERAPY FOR SYMPTOMS OF

DEPRESSION AND ANXIETY: A META-ANALYSIS*

* Viola Spek, Pim Cuijpers, Ivan Nyklíček, Heleen Riper, Jules Keyzer, Victor Pop (2007).

Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: A

meta-analysis. Psychological Medicine 37, 319-328.

22

ABSTRACT

Background: We studied to what extent internet-based cognitive behaviour therapy

programs for symptoms of depression and anxiety are effective.

Methods: A meta-analysis of twelve randomised controlled trials.

Results: The effects of internet-based cognitive behaviour therapy were compared to

control conditions in thirteen contrast groups, with a total number of 2334 participants. A

meta-analysis on treatment contrasts resulted in a moderate to large mean effect size (FEA:

d = 0.40; 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 (FEA and MEA: d = 0.22) 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 had a large

mean effect size (FEA and MEA: d = 1.00), while interventions without therapist support

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.

23

INTRODUCTION

Cognitive behaviour therapy 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 in 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. Cognitive behaviour therapy is very suitable

for adaptation to a computer format. It is a structured treatment approach with the aim to

develop new behaviour and cognition.

Internet-based cognitive behaviour therapy has advantages over traditional

cognitive behaviour therapy for both clients and health care. The anonymity and

accessibility of the internet make it very suitable for offering and receiving help with




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 efficacy (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 randomised studies examining the

effects of internet interventions on mood and anxiety disorders has grown rapidly. This

study aimed to integrate the results of these studies in a meta-analysis of randomised

controlled trails examining the effects of internet-based cognitive behavioural programs

with or without minimal therapist assistance, for mood and anxiety disorders.


24

METHODS

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

cognitive behaviour therapy with control groups such as waiting-lists, treatment as usual,

and placebos. Studies that compared internet-based cognitive behaviour therapy with active

treatments were excluded.

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 cognitive behaviour therapy 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 standardized 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 will be 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 important advantage, but there is also a

disadvantage. 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

25

participants 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.

Types of outcome measures

As we were interested in the effects of internet-based cognitive behaviour therapy on

symptoms of depression and anxiety, we only used those instruments that explicitly

measure depression or anxiety. The following types of outcome measures are included: (1)

self-rating scales measuring symptoms of depression or anxiety; and (2) clinician rated

scales. Other outcome measures, measuring intermediate outcomes, such as cognition, were

not included. All outcome measures included, except two used in one study (Klein 2001),

are validated instruments.

Search strategy for identification of studies

Studies were retrieved through systematic literature searches in the databases of PubMed

(1990-February 2006), PsychINFO (1990-February 2006), and 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

excluded, 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 corresponding authors of all included papers to obtain information about

any other published or unpublished studies they were aware of.

26

Study selection

The retrieved papers were independently assessed on inclusion criteria by two of the

authors (HR and VS) to guarantee an error free inclusion procedure. When the two

disagreed 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)

foreknowledge of treatment assignment is prevented; (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 treatment 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 experimental group (Me) and dividing

the result by the pooled standard deviations of the experimental and control group (SDec).

An effect size of 0.5 thus indicates that the mean of the experimental 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 depression 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 experimental

condition was compared to a control condition. In these cases, the number of subjects in the

27

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 (FEM) and the random effects model

(REM) to 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 heterogeneity, 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 analyses

(FEA) and the mixed effects analyses (MEA), as implemented in the Comprehensive Meta-

analysis software. In the fixed effects analyses, the fixed effects model is used to calculate

the effect sizes for each subgroup of studies, and also for the difference between the

subgroups. In the mixed effects analyses, the random effects model is used to calculate the

effect size for each subgroup, while the fixed effects model 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 twelve trials with 2334 subjects were included. Five studies

focused 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

disorders, one on social phobia, and one on subclinical 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

subjects participating in the depression trials included was 1982. In none of the studies

were subjects required to meet diagnostic criteria for a depressive disorder. In only one of

28

the five treatment studies (Andersson et al. 2005) therapists 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 participants of 178. 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 randomised to either an internet-based cognitive

behaviour therapy for social phobia or to a waiting-list (Andersson et al. in press). 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 therapy intervention for people at risk of developing

anxiety disorders. Eighty-three participants with elevated anxiety sensitivity were

randomised to either an intervention group or to a waiting-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 randomised. Selected characteristics of the

included studies are summarized in Table 1.

Tab

le 1

Sel

ecte

d ch

arac

teri

stic

s of

the

stud

ies

Fir

st

auth

or

Yea

r

Rec

ruit

men

t;

Mai

n in

clus

ion

crit

erio

n

Inte

rven

tion

: N

umbe

r of

m

odul

es;

The

rapi

st

invo

lvem

ent

N

Mea

sure

s A

naly

ses

Con

trol

gr

oup

TA

U

allo

wed

F

ollo

w

up

Att

riti

on

rate

P

ost-

trea

tmen

t co

mpa

riso

n

Aim

E

ffec

t si

ze

Cla

rke

2002

C

R &

Clin

ical

Pa

tien

ts;

No

7;

Non

e 29

9 C

ES-

D

ITT

T

AU

Y

es, i

n bo

th

grou

ps

4, 8

, 16,

32

wee

ks

34%

In

terv

enti

on

vs. C

TR

T

0.

0

Cla

rke

2005

C

R &

C

linic

al

Pati

ents

; N

o

7;

Non

e 25

5 C

ES-

D

ITT

T

AU

Y

es, i

n al

l gr

oups

5,

10,

16

wee

ks

34%

In

terv

+

post

card

re

min

ders

vs.

In

terv

+ p

hone

re

min

ders

vs.

T

AU

T

0.3

(mai

l)

0.2

(pho

ne)

Chr

iste

nsen

20

04

CR

; C

ut-o

ff o

n K

PDS

5;

Non

e 52

5 C

ES-

D

ITT

A

tten

tion

pl

aceb

o N

o 6

wee

ks

17%

In

terv

enti

on

vs. P

sych

o ed

ucat

ion

vs.

Plac

ebo

T

0.4

And

erss

on

2005

C

R;

Cut

-off

on

CID

I-S

F

5;

Mon

itor

ing

&

Feed

back

117

BD

I, M

AD

RS

ITT

Pa

rtic

ipat

ion

in o

nlin

e di

scus

sion

gr

oup

Yes

, sta

ble

med

icat

ion

allo

wed

Post

-tr

eatm

ent

& 6

m

onth

s

27%

In

terv

enti

on +

pa

rtic

ipat

ion

in

onlin

e di

scus

sion

gr

oup

vs.

Part

icip

atio

n in

on

line

disc

ussi

on

grou

p

T

0.9

Patt

en

2003

C

R;

No

4;

Non

e 78

6 C

ES-

D

Unc

lear

Ps

ycho

ed

ucat

ion

Unc

lear

Po

st-

trea

tmen

t &

3

mon

ths

3%

Inte

rven

tion

vs

. Psy

cho

educ

atio

n

P 0.

0

Tab

le 1

(co

ntin

ued)

Sel

ecte

d ch

arac

teri

stic

s of

the

stud

ies

Fir

st

auth

or

Yea

r

Rec

ruit

men

t;

Mai

n in

clus

ion

crit

erio

n

Inte

rven

tion

: N

umbe

r of

m

odul

es;

The

rapi

st

invo

lvem

ent

N

Mea

sure

s A

naly

ses

Con

trol

gr

oup

TA

U

allo

wed

F

ollo

w u

p A

ttri

tion

ra

te

Pos

t-tr

eatm

ent

com

pari

sons

Aim

E

ffec

t si

ze

Kle

in

2001

C

R;

Pani

c di

sord

er

Unc

lear

; N

one

22

PAR

F,

DR

F C

O

Self

-m

onit

orin

g U

ncle

ar

Post

-tr

eatm

ent

4%

Inte

rven

tion

+

self

m

onit

orin

g vs

. se

lf

mon

itor

ing

T

0.4

Kle

in

2006

C

R;

Pani

c di

sord

er

6;

Mon

itor

ing

&

Feed

back

55

Clin

icia

n ra

ting

PD

& A

P, n

o.

of P

A,

PDSS

, D

ASS

ITT

T

hera

pist

as

sist

ed

CB

T m

anua

l an

d in

form

atio

n on

ly

No

Post

-tr

eatm

ent&

3

mon

ths

16%

In

terv

enti

on

vs.

info

rmat

ion

T

1.5

Car

lbri

ng

2001

C

R;

Pani

c di

sord

er

6;

Mon

itor

ing

&

Feed

back

41

BSQ

, MI,

BA

I IT

T

Wai

ting-

list

Yes

, if

stab

le a

nd

if n

ot C

BT

Post

-tr

eatm

ent

12%

In

terv

enti

on

vs. W

aitin

g-lis

t

T

1.0

Car

lbri

ng

2006

C

R;

Pani

c di

sord

er

10;

Mon

itor

ing

&

Feed

back

+

wee

kly

shor

t ph

one

calls

60

BSQ

, MI,

BA

I IT

T

Wai

ting-

list

Yes

, if

stab

le a

nd

if n

ot C

BT

Post

-tr

eatm

ent&

9

mon

ths

5%

Inte

rven

tion

vs

. Wai

ting-

list

T

1.1

And

erss

on

2006

C

R;

Soci

al p

hobi

a 9;

M

onit

orin

g &

Fe

edba

ck +

6

hour

s of

gro

up

sess

ions

64

BA

I, SP

SQ,

LS

AS-

SR,

SPS

ITT

W

aitin

g-lis

t Y

es, b

ut

only

sta

ble

med

icat

ion

Post

-tr

eatm

ent&

1

year

3%

Inte

rven

tion

vs

. Wai

ting-

list

T

0.8

Hir

ai

2005

C

R;

Cut

-off

on

DSM

-IV

cr

iter

ia f

or

PTSS

8;

Non

e 27

ST

AI-

S,

IESR

, SR

Q-F

CO

W

aitin

g-lis

t Y

es

Post

-tr

eatm

ent

18%

In

terv

enti

on

vs. W

aitin

g-lis

t

T

0.8

Ken

ardy

20

03

CR

; C

ut-o

ff o

n A

SI

6;

Non

e 83

B

SQ

CO

W

aitin

g-lis

t N

o Po

st-

trea

tmen

t 10

%

Inte

rven

tion

vs

. Wai

ting-

list

P 0.

3

31

Note (Table 1): AP = Agoraphobia; ASI = Anxiety Sensitivity Index; BSQ = Body Sensations Questionnaire; CO = Completers Only; CR = community recruitment; CTR= control group; DASS = Depression Anxiety Stress Scales; DRF = Daily Record Form; IESR = Impact of Event Scale Revised; ITT = intention to treat; KPDS = Kessler psychological distress scale; LSAS-SR = Liebowitz Social Anxiety Scale self-report version; MI = Mobility Inventory; P = Prevention; PA = Panic Attack; PARF = Panic Attack Record Form; PDSS = Panic Disorder Severity Scale; PTSS = Post Traumatic Stress Disorder; SPS = Social Phobia Scale; SPSQ = Social Phobia Screening Questionnaire; SRQ-F = Stressful Responses Questionnaire-Frequency; STAI-S = State Trait Anxiety Inventory-State Scale; T = Treatment; TAU = treatment as usual

Figu

re 1

Stu

dy n

ame

Sta

tist

ics

for

each

stu

dyS

td d

iff i

n m

eans

and

95%

CI

Std

diff

S

tand

ard

Low

er

Upp

er

in m

eans

erro

rV

aria

nce

lim

itli

mit

Z-V

alue

p-V

alue

And

erss

on 2

005

0,88

00,

230

0,05

30,

430

1,33

03,

833

0,00

0A

nder

sson

, in

pre

ss0,

769

0,25

90,

067

0,26

11,

276

2,96

70,

003

Car

lbri

ng 2

001

0,99

10,

327

0,10

70,

350

1,63

23,

032

0,00

2C

arlb

ring

, in

pre

ss1,

142

0,27

80,

078

0,59

61,

687

4,10

00,

000

Chr

iste

nsen

200

4 0,

365

0,10

60,

011

0,15

70,

574

3,43

70,

001

Cla

rke

2002

0,00

00,

116

0,01

3-0

,227

0,22

70,

000

1,00

0C

lark

e 20

05 M

ail

0,31

00,

184

0,03

4-0

,050

0,67

01,

690

0,09

1C

lark

e 20

05 P

hone

0,

247

0,18

10,

033

-0,1

080,

601

1,36

40,

173

Hir

ai 2

005

0,81

20,

401

0,16

10,

026

1,59

72,

026

0,04

3

Ken

ardy

200

3 0,

293

0,23

40,

055

-0,1

660,

751

1,25

10,

211

Kle

in 2

001

0,40

00,

422

0,17

8-0

,426

1,22

60,

949

0,34

3K

lein

in

pres

s1,

516

0,37

30,

139

0,78

52,

248

4,06

30,

000

Pat

ten

2003

0,

000

0,07

20,

005

-0,1

410,

141

0,00

01,

000

0,51

00,

115

0,01

30,

284

0,73

64,

429

0,00

0 -4,0

0-2

,00

0,00

2,00

4,00

Fav

ours

AF

avou

rs B

Met

a A

nal

ysis

Met

a A

nal

ysis

33

Methodological quality of included studies

The quality of the included studies was reasonable to good. Foreknowledge of treatment

assignment 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 treatment assignment (Patten 2003), and

in another paper 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 (Figure 1, Table 2), resulting

in a mean effect size of d = 0.24 (95% CI: 0.16~0.33), while the random effects model

resulted in a mean effect size of d = 0.51 (95% CI: 0.28~0.74). The hypothesis of

homogeneity was rejected, because a significant Q-value was found (Q = 58.65, I² =

79.5%). We examined possible sources of heterogeneity through post hoc subgroup

analyses. A subgroup analysis based on the aim of the intervention (prevention or

treatment) still 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 Figure 2, 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 fixed effects analysis and 0.32 (95% CI: 0.08~0.57) according

to the mixed effects analysis. The Q-value was 13.37 and the I² was 70.1%, indicating

considerable heterogeneity. However, further analyses showed that one study (Andersson

2005) could be regarded as an outlier. Analyses without this study showed a mean effect

size of 0.22 for both the fixed effects analysis and the mixed effects analysis (95% CI:

0.09~0.35 and 0.03~0.41 respectively) and moderate, non significant heterogeneity (Q =

5.75, I² = 47.8%).

34

Figure 2. Flow chart of post-hoc analyses

All contrasts (n =13) FEM d = 0.24REM d = 0.51Q = 58.65***

Treatment studies (n = 11)FEA d = 0.40MEA d = 0.60Q = 39.77***

Depression (n = 5)FEA d = 0.27MEA d = 0.32Q = 13.37***

(1 contrast with support; 4 contrasts without support)

Anxiety (n = 6)FEA d = 0.96MEA d = 0.96

Q = 5.10(4 contrasts with support;

2 contrasts without support)

Support (n = 5)FEA d = 1.00MEA d = 1.00

Q = 3.24(1 contrast depr symptoms;

4 contrasts anxiety)

Without support (n = 6)FEA d = 0.24MEA d = 0.26

Q = 8.02(4 contrasts depr symptoms;

2 contrasts anxiety)

Depression without outlier(n = 4)

FEA d = 0.22MEA d = 0.22

Q = 5.75(4 contrasts without support)

For anxiety studies (n = 6), both the fixed and the mixed effects analyses 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 also was the only

depression treatment with therapist support, we conducted other subgroup analyses based

on therapist support (Figure 2). 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 fixed effects analysis and 0.26 (95% CI: 0.08~0.44) in the mixed

effects analysis, which is small. Interventions with support had a large pooled mean effect

size: 1.00 (95% CI 0.75~1.24), both in the fixed effects and in the mixed effects analyses

and no heterogeneity (I2 was 0).

35

Table 2 Meta-analyses of studies examining the effects of internet-based psychological treatment of mood and anxiety disorders

Ncomp d 95% CI Q I2 (%) Difference between subgroups

All contrasts 13 FEM 0.24 0.16~0.33 58.65 *** 79.5% REM 0.51 0.28~0.74 Type of intervention Treatment studies 11 FEA 0.40 0.29~0.51 39.77 *** 74.9% *** MEA 0.60 0.35~0.86 Prevention studies 2 FEA 0.03 -0.11~0.71 1.43 30.2% MEA 0.06 -0.17~0.30

Disorder Depression 5 FEA 0.27 0.15~0.40 13.37 70.1% *** MEA 0.32 0.08~0.57 Depression without outlier¹ 4 FEA 0.22 0.09~0.35 5.75 47.8% MEA 0.22 0.03~0.41 Anxiety 6 FEA 0.96 0.69~1.22 5.10 2.0% MEA 0.96 0.69~1.22 Support No support 6 FEA 0.24 0.11~0.37 8.02 37.6% *** MEA 0.26 0.08~0.44 Support 5 FEA 1.00 0.75~1.24 3.24 0% MEA 1.00 0.75~1.24

¹ outlier is study of Andersson et al. (2005) *** significant at p<0.05 Abbreviations: Ncomp: number of comparisons; 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

DISCUSSION

When looking at all studies in this meta-analysis of internet-based cognitive behaviour

therapy for symptoms of depression and anxiety, we found a moderate overall mean effect

size and significant heterogeneity. Subsequently, when looking at prevention 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 treatment. The analyses on treatment for

depression showed a small mean effect size with significant heterogeneity, which was

mainly to be 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

36

analyses, treatment with support showed a large mean effect size and no 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 cognitive behaviour therapy. 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 whether support is added or not. However, because of the substantial differences

in design of the studies that were included (differences in symptoms, differences in

treatment), future studies are needed to support this hypothesis.

This meta-analysis has several limitations. Because internet-based cognitive

behaviour therapy is a rather 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 rather 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 studies on

depression all had large numbers of subjects; 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 eleven studies included

was the presence or absence of a disorder established. Three studies had a cut-off score on a

questionnaire as the main inclusion criterion. Three studies did not have such inclusion

criteria at all.

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 effectiveness of internet-based cognitive behaviour therapy. 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 receive treatment.

37

REFERENCES

Andersson, G., Bergström, J., Carlbring, P. & Lindefors, N. (2004). The use of the

internet in the treatment of anxiety disorders. Current Opinion in Psychiatry 18, 1-5.

Andersson, G., Bergström, J., Holländare, F., Carlbring, P., Kaldo, V. & Ekselius, L.

(2005). Internet-based self-help for depression: randomised controlled trial. British

Journal of Psychiatry 187, 456-461.

Andersson, G., Carlbring, P., Holmström, A., Sparthan, E., Furmark, T., Nilsson-

Ihrfelt, E., Buhrman, M., & Ekselius, L. (2006). Internet-based self-help with

therapist feedback and in-vivo group exposure for social phobia: a randomised

controlled trial. Journal of Consulting and Clinical Psychology 74, 677-686.

Carlbring, P., Westling, B.E., Ljungstrand, P., Ekselius, L. & Andersson, G. (2001).

Treatment of panic disorder via the Internet: A randomised trial of a self-help

program. Behaviour Therapy 32, 751-764.

Carlbring, P., Nilsson-Ihrfelt, E., Waara, J., Kollenstam, C., Buhrman, M., Kaldo, V.,

Söderberg, M., Ekselius, L. & Andersson, G. (2005). Treatment of panic disorder:

live therapy vs. self-help via the Internet. Behaviour Research and Therapy 43, 1321-

1333.

Carlbring, P., Bohman, S., Brunt, S., Buhrman, M., Westling, B.E., Ekselius, L. &

Andersson, G. (2006). Remote treatment of panic disorder: A randomised trial of

Internet-based cognitive behavioural therapy supplemented with telephone calls.


Christensen, H., Griffiths, K.M. & Jorm, A.F. (2004). Delivering interventions for

depression by using the Internet: randomised controlled trial. British Medical Journal

328, 265-267.

Clarke, G., Reid, E., Eubanks, D., O’Connor, E., DeBarr, L., Kelleher, C., Lynch, F.

& Nunley, S. (2002). Overcoming depression on the Internet (ODIN): a randomised

controlled trial of an Internet depression skills intervention program. Journal of

Medical Internet Research 4, e14

Clarke, G., Eubanks, D., Reid, E., Kelleher, C., O’Connor, E., DeBarr, L., Lynch, F.,

Nunley, S. & Gullion, C. (2005). Overcoming depression on the Internet (ODIN)

https://www.researchgate.net/publication/7142242_The_use_of_the_Internet_in_the_treatment_of_anxiety_disorders?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/7142242_The_use_of_the_Internet_in_the_treatment_of_anxiety_disorders?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

38

(2): A randomised trial of a self-help depression skills program with reminders.

Journal of Medical Internet Research 7, e16.

Gega, L., Marks, I., & Mataix-Cols, D. (2004) Computer-aided CBT self-help for anxiety


JCLP/In Session, 60, 147-157.

Higgins, J.P.T. & Green S. (2005). Cochrane Handbook for Systematic Reviews of

Interventions 4.2.5 [updated May 2005]. In The Cochrane Library, Issue 3. John

Wiley: Chichester.

Higgins, J.P.T., Thompson, S.G., Deeks, J.J. & Altman, D.G. (2003). Measuring

inconsistency in meta-analyses. British Medical Journal 327, 557-560.

Hirai, M. & Clum, G.A. (2005) An Internet-based self-change program for traumatic

event related fear, distress, and maladaptive coping. Journal of Traumatic Stress, 18,

6, 631-636.

Hollon, S.D., Stewart, M.O. & Strunk, D. (2006). Enduring effects for cognitive

behaviour therapy in the treatment of depression and anxiety. Annual Review of

Psychology 57, 285-315.

Kenardy, J., McCafferty, K. & Rosa, V. (2003). Internet-delivered indicated prevention

for anxiety disorders: a randomised controlled trial. Behavioural and Cognitive

Psychotherapy 31, 279-289.

Klein, B. & Richards, J.C. (2001). A brief Internet-based treatment for panic disorder.

Behavioural and Cognitive Psychotherapy 29, 113-117.

Klein, B., Richards, J.C. & Austin, D.W. (2006). Efficacy of internet therapy for panic

disorder. Journal of Behaviour Therapy and Experimental Psychiatry 37, 213-238.

Lamerichs, J. (2003). Discourse of support: exploring online discussions on depression.

Dissertations Wageningen University.

Lipsey, M.W. & Wilson, D.B. (2001). Practical meta-analysis. Applied social research

methods series volume 49. Sage Publications: Thousand Oaks.

39

Marks, I.M., Mataix-Cols, D., Kenwright, M., Cameron, R., Hirsch, S., & Gega, L.

(2003) Pragmatic evaluation of computer-aided self-help for anxiety and depression.

British Journal of Psychiatry 183, 57-65.

Patten, S.B. (2003). Prevention of depressive symptoms through the use of distance

technologies. Psychiatric Services 54, 396-398.

Proudfoot, J., Goldberg, D., Mann, A., Everitt, B., Marks, I., & Gray, J.A. (2003)

Computerized, interactive, multimedia cognitive-behavioural program for anxiety and

depression in general practice. Psychological Medicine 33, 217-227.

Ritterband, L.M., Gonder-Frederick, L.A., Cox, D.J., Clifton, A.D., West, R.W. &

Borowits, S.M. (2003). Internet interventions: In review, in use, and into the future.

Professional Psychology: Research and Practice 34, 527-534.

Tate, D.F. & Zabinski, M.F. (2004). Computer and Internet applications for psychological

treatment: Update for clinicians. JCLP/In Session 60, 209-220.


Otto, M.W. (2005) Computer-assisted cognitive therapy for depression: Maintaining


1164.

40

Appendix 1 Flow chart of study selection

Read abstracts & references: Pubmed (26 hits) Psychinfo (126 hits) Earlier reviews Reference lists Corresponding authors

Included studies (n = 12)

Reviewed papers (n = 28)

No randomized controlled trial (n = 5)

No internet-based treatment (n = 3)

No cognitive behaviour therapy (n = 2)

No self-help (n = 3)

No symptoms of mood or anxiety disorders (n = 2)

Active control condition (n = 3)

CHAPTER 3

INTERNET ADMINISTRATION OF THE EDINBURGH DEPRESSION SCALE*

* Viola Spek, Ivan Nyklíček, Pim Cuijpers, Victor Pop (in press). Internet administration of

the Edinburgh Depression Scale. Journal of Affective Disorders. Published online 8 August

2007.

42

ABSTRACT

Background: Internet-based screening for depression is becoming increasingly important.

The aim of this study is to validate the Edinburgh Depression Scale (EDS) for internet

administration.

Methods: In 407 participants (64% females; 36% males) with subthreshold depression

(mean age = 55 years; S.D. = 4.9) positive predictive values for a syndromal CIDI

diagnosis of clinical depression were calculated and compared with those from paper and

pencil validation studies.

At one-year follow up, internal consistency and convergent validity of the internet-based

EDS were determined in 177 participants by Cronbach’s alpha and correlations with the

internet-administered BDI and SCL-90 subscales depression and anxiety.

Results: Positive predictive values ranged between 29% and 33% at cut-off scores 12 to

14. Cronbach’s alpha for the internet-administered EDS was 0.87. The EDS correlated

significantly with the internet-administered BDI (r = .75; p < .001) and two internet-

administered subscales of the SCL-90: Depression (r = .77; p < .001) and Anxiety (r = .72;

p < .001). A major limitation of the study is that it was conducted without the use of a

control group of healthy subjects.

Conclusions: The psychometric properties of the internet-administered EDS are

comparable to those of the paper-and-pencil EDS.

43

INTRODUCTION

With the increasing popularity of internet-based treatments (Marks et al. 2007), internet-

based screening for depression has also increased in importance. As it is clear, even in the

most ideal situation, that not all people with depression can be treated within the present

capacity of face-to-face interventions (Andrews et al. 2004), internet-based self-help may

provide a partial solution to this problem. Internet-based self-help has many advantages

over traditional therapies for both clients and health care. The low-threshold accessibility of

the internet makes it very suitable for offering and receiving help for 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. Furthermore, internet-based self-help

has the advantage that it can be offered anonymously, thereby lowering the threshold for

starting treatment even more. However, clients must be provided with guidance to help

them find the intervention most appropriate for them. Internet-based questionnaires can

play an important role in this process. In order to be able to provide people with valid

advice, it is imperative to be knowledgeable about the psychometric properties of internet-

administered questionnaires. With it’s high reliability, the concise ten-item Edinburgh

Depression Scale could well be an effective internet-administered screening device for

depression, although the good psychometric properties of the paper-and-pencil version of a

questionnaire do not guarantee the good psychometric properties of its internet-

administered version (Buchanan, 2003).

Therefore, the aim of this study is to validate the Edinburgh Depression Scale for

internet use.

METHODS

Participants and procedure

Participants with subthreshold depression were recruited as part of a large randomized,

controlled trial which compared internet-based cognitive behaviour therapy, group

cognitive behaviour therapy and a waiting-list control group (Spek et al. 2007). Potential

participants were informed about the study by means of advertisements in free regional

newspapers, and by personal letters from the City of Eindhoven Municipal Health Care

Service. These letters and advertisements provided information about the study and the

details of the study’s homepage, which contained general information about depression and


https://www.researchgate.net/publication/7476096_Internet-based_Questionnaire_Assessment_Appropriate_Use_in_Clinical_Contexts?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/8533750_Utilising_survey_data_to_inform_public_policy_Comparison_of_the_cost-effectiveness_of_treatment_of_ten_mental_disorders?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

44

the study, as well as an application form which included the Edinburgh Depression Scale

(EDS; Cox et al. 1987; Cox et al. 1996; Matthey et al. 2001).

Participants who scored above the cut-off score of 12 on the internet-based EDS (n

= 699, screening data) were invited for an in-person structured clinical interview for

depression (Composite International Diagnostic Interview; WHO, 1997). The participants

were unaware of what cut-off score was being used to select who would be invited for an

interview. During the interview, participants were informed about the study and its

conditions, demographic data were collected, and a structured interview was conducted to

assess the DSM-IV criteria of depression. At the end of the clinical interview, participants

considered eligible (those without a diagnosis of major depression, defined as subthreshold

depression) were asked to participate in an intervention study, described in greater detail

elsewhere (Spek et al. 2007). One year after the start of treatment, another assessment was

made, which included the internet version of EDS and BDI (Figure 1).

45

Figure 1. Flow chart of participants

EDS ≥ 12n = 699

Meeting inclusion criterian = 606

Cronbach’s Alpha internet-administered EDS Correlation with internet-administered BDICorrelation with internet-administered SCL-90 Subscales Depression and Anxiety

Negative CIDI (subthreshold depression)

n = 301

Filled in EDSn = 930

Intervention study

Participated at CIDI interviewn = 407

Assessment of positive predictive values of internet-administered EDS ≥ 12 on DSM-IVcriteria for depression (range EDS = 12-29)

Provided 1-year follow-up datan = 177

Comparison with psychometric aspects of EDS in previous paper-and-pencil studies

The study protocol was approved by the ethics committee of the Maxima Medisch

Centrum Eindhoven (a regional hospital in Eindhoven, the Netherlands); this committee is

certified by the Central Committee on Research involving Human Subjects in The

Netherlands.

Measures

The Edinburgh Depression Scale (EDS)

The EDS is a ten-item self-report scale (total scale range 0 – 30) which assesses the

common symptoms of depression. It was originally designed to assess post partum

depression and was known as the Edinburgh Postnatal Depression Scale (EPDS; Cox et al.

1987). The EPDS was later validated in The Netherlands (Pop et al. 1992), in other age

46

strata (Murray et al. 1990; Cox et al. 1996; Becht et al. 2001; Nyklíček et al. 2004) and in

male subjects (Matthey et al. 2001) and renamed the EDS. Internal consistency (Cronbach’s

alpha) has been shown to be at least .80 (Cox et al. 1987; Matthey et al. 2001). The EDS

was found to correlate .64 with the Beck Depression Inventory (Pop et al. 1992). With a

clinical diagnosis of major depression as the criterion, the sensitivity, specificity, and

positive predictive value (PPV) are good: 81-88%, 80-96%, and 21%-43%, respectively, at

cut-off point 12 (Murray et al. 1990; Cox et al. 1996; Becht et al. 2001; Nyklíček et al.

2004). In the internet-based version of the EDS, all ten items were presented on the same

website. In order to be able to send the answers to the study database, the participants had

to complete all the items; no items could be left out.

Composite International Diagnostic Interview (CIDI)

The World Health Organization CIDI (World Health Organization, 1997) is a fully

structured interview developed to identify DSM-IV and ICD-10 symptoms, and to report

whether the diagnostic criteria are met. Reliability of the CIDI for mood disorders is good:

the test-retest kappa coefficient is .71 and the interrater kappa coefficient is .95 (Wittchen,

1994).

Beck Depression Inventory – second edition (BDI-II)

The BDI (Beck et al. 1996) is the most frequently used self report measure for depressive

symptoms and contains 21 items. The BDI was developed to assess the intensity of

depressive symptoms. Internal consistency is high: in the Dutch manual, Cronbach’s alphas

of 0.92 and 0.93 are reported (Van der Does, 2002). The internet-administered BDI was

found to correlate 0.94 with the paper-and-pencil BDI (Carlbring et al. 2007).

Symptom Checklist-90 (SCL-90)

The SCL-90 (Derogatis et al. 1973, Derogatis & Cleary, 1977) assesses psychopathology

indicators. Only the depression and anxiety subscales of this checklist were used. The

reliability and validity of these subscales are good (Arindell & Ettema, 1986).

https://www.researchgate.net/publication/285829236_Confirmation_of_dimensional_structure_of_SCL-90_-_study_in_construct-validation?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/18484700_SCL-90_an_outpatient_psychiatric_rating_scale--preliminary_report?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/241860592_SCL-90_Handleiding_bij_een_multidimensionele_psychopathologie-indicator?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/223546587_Internet_vs_Paper_and_Pencil_Administration_of_Questionnaires_Commonly_Used_in_PanicAgoraphobia_research?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

47

Analyses

Statistical analyses were preformed using SPSS 14.0. The positive predictive values

(percentages of high scorers on the EDS who received a diagnosis of depression according

to the CIDI) were calculated on the screening data. In order to determine the internal

consistency of the internet-administered EDS, Cronbach’s alpha was calculated with the

one-year follow-up data. As the screening data only contained EDS scores equal or above

12, these were not suitable for reliability measures due to the restriction of range (all scores

≥ 12). One year after the start of treatment, there was a far greater variety in scores; the

natural range of scores was covered and therefore it was possible to calculate Cronbach’s

alpha reliably. Moreover, the correlations between the internet-administered EDS and the

internet-administered BDI and between the internet-administered EDS and the internet-

administered SCL-90 subscales Depression and Anxiety were also calculated.

RESULTS

A total of 407 participants completed a clinical interview, including the CIDI. The mean

screening internet EDS score was 17.58 (S.D. = 3.89). All interviewees scored ≥ 12 (range

12-29). In the 117 participants with a positive CIDI (diagnosis of major depression) the

mean EDS score was 20.18 (S.D. = 3.55); in the 295 participants with a negative CIDI (no

diagnosis of major depression, defined as subthreshold depression) the mean EDS score

was 16.48 (S.D. = 3.45). Positive predictive values for different cut-off scores can be found

in Table 1, and varied between 29 and 33% according to different cut-off scores. These

were compared with different PPVs of paper-and-pencil EDS (Table 1). One-year follow-

up measures were completed by 177 participants. Completers did not differ from non-

completers with regard to age, gender, having a partner, educational level, employment

status, assigned condition, EDS scores at screening, and BDI baseline scores (data not

shown). With regard to the EDS one year after the start of treatment (n = 177; mean = 8.91;

S.D. = 5.34; range 0-28) we found a Cronbach’s alpha of .87. The internet-administered

EDS correlated significantly with the internet-administered BDI (r = .75; p < .001).

Furthermore, the internet-administered EDS also correlated significantly with the internet-

administered subscales of the SCL-90: depression (r = .77; p < .001), and anxiety (r = .72;

p < .001) at follow-up.

48

Table 1 Positive predictive values of internet-administered EDS at different cut-off points

compared to those found in paper-and-pencil studies (Murray et al. 1990; Cox et al. 1996;

Becht et al. 2001; Nyklíček et al. 2004)

EDS score PPV internet-administered EDS PPV paper and pencil EDS 12 29% 21 – 43% 13 31% 24 – 50% 14 33% 28 – 58%

DISCUSSION

In this study, the validity of the internet-administered Edinburgh Depression Scale was

assessed in two samples. The positive predictive values were comparable to those found in

previous paper-and-pencil studies (Murray et al. 1990; Cox et al. 1996; Becht et al. 2001;

Nyklíček et al. 2004; Table 1). We found that the internet-administered EDS has good

internal consistency: comparable to that of the paper-and-pencil EDS. We found a high

correlation of the internet-administered EDS with the internet-administered BDI, which has

been validated for internet administration in an earlier study (Carlbring et al. 2007). Our

correlation is similar to the correlation of paper-and-pencil EDS and BDI (Pop et al. 1992).

Furthermore, we found high correlations with SCL-90 subscales depression and anxiety.

These results are comparable to those from a study of the paper-and-pencil EDS and the

paper-and-pencil SCL-90 (Pop et al. 1992).

This study has several limitations. Firstly, since we only interviewed participants

with a score of 12 or more on the EDS, we were unable to calculate sensitivity and

specificity of an internet-administered EDS. Secondly, the study was conducted without the

use of a control group of healthy subjects. Furthermore, all participants in this study were

over 50 years of age. Therefore, it may not be possible to generalise our results with regard

to the general population. Finally, we did not obtain our own paper-and-pencil data.

However, in an early study, a correlation of .98 was found for paper-and-pencil and

computerized EDS scores (Glaze & Cox, 1991). This suggests that data from paper-and-

pencil administration and computerized administration are identical.

Despite its limitations, the current study shows that the internet-administered EDS

has good psychometric properties, which suggests that it can be used in practice.


49

REFERENCES

Andrews, G., Issakidis, C., Sanderson, K., Corry, J., Lapsley, H. (2004). Utilising

survey data to inform public policy: comparison of the cost-effectiveness of treatment

of ten mental disorders. British Journal of Psychiatry 184, 526-533.

Arindell, W.A., Ettema, J.H.M. (1986). Symptom Checklist SCL-90. Handleiding bij een

multidimensionele psychopathologie indicator. Swets & Zeitlinger: Lisse. [Dutch

Manual of the Symptom Checklist-90].

Becht, M.C., Van Erp, C.F., Teeuwisse, T.M., Van Heck, G.L., Van Son, M.J., Pop,

V.J. (2001). Measuring depression in women around menopausal age: towards a

validation of the Edinburgh Depression Scale. Journal of Affective Disorders 63, 209-

213.

Beck, A.T., Steer R.A., Brown, G.K. (1996). Beck Depression Inventory manual (2nd ed.)

San Antonio, TX: Psychological Corporation.

Buchanan, T. (2003). Internet-based questionnaire assessment: Appropriate use in clinical

contexts. Cognitive Behaviour Therapy 32, 100-109.

Carlbring, P., Brunt, S., Bohman, S., Austin, D., Richards, J., Öst, L., Andersson, G.

(2007). Internet vs. paper and pencil administration of questionnaires commonly used

in panic/agoraphobia research. Computers in Human Behavior 23, 1421-1434.

Cox, J.L., Holden, J.M., Sagovsky, R. (1987). Detection of postnatal depression:

Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of

Psychiatry 150, 782-786.

Cox, J.L., Chapman, G., Murray, D., Jones, P. (1996). Validation of the Edinburgh

Postnatal Depression Scale (EPDS) in non-postnatal women. Journal of Affective

Disorders 39, 185-189.

Derogatis, L.R., Cleary, P.A. (1977). Confirmation of the dimensional structure of the

SCL-90: A study in construct validation. Journal of Clinical Psychology 33, 981-989.

Derogatis, L.R., Lipman, R.S., Covi, L. (1973). SCL-90: An outpatient psychiatric rating

scale – Preliminary report. Psychopharmacology 9, 13-28.







https://www.researchgate.net/publication/14349869_Validation_of_the_Postnatal_Depression_Scale_EPDS_in_non-postnatal_women?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=












50




Glaze, R., Cox, J.L. (1991). Validation of a computerized version of the 10-item (self-

rating) Edinburgh Postnatal Depression scale. Journal of Affective Disorders 22, 73-

77.

Marks, I.M., Cavanagh, K., Gega, L. (2007). Hands-on Help: Computer-aided

psychotherapy. Psychology Press: Hove, East Sussex.

Matthey, S., Barnett, B., Kavanagh, D.J., Howie, P. (2001). Validation of the Edinburgh

Postnatal Depression Scale for men, and comparison of item endorsement with their

partners. Journal of Affective Disorders 64, 175-184.

Murray, L., Carothers, A.D. (1990). The validation of the Edinburgh Post-natal

Depression Scale on a community sample. British Journal of Psychiatry 157, 288-

290.

Nyklíček, I., Scherders, M.J., Pop, V.J. (2004). Multiple assessments of depressive

symptoms as an index of depression in population-based samples. Psychiatry

Research 128, 111-116.

Pop, V.J., Komproe, I.H., Van Son, M.J. (1992). Characteristics of the Edinburgh

Depression Scale in the Netherlands. Journal of Affective Disorders 26, 105-110.

Spek, V., Nyklíček, I., Smits, N., Cuijpers, P., Riper, H., Keyzer, J. Pop, V. (2007).

Internet-based cognitive behavioural therapy for subthreshold depression in people

over 50 years old: A randomized controlled trial. Psychological Medicine Published

online by Cambridge University Press 30 Apr 2007.

Van der Does, A.J.W. (2002). BDI-II-NL Handleiding: De Nederlandse versie van de

Beck Depression Inventory-second edition. Ipskamp, Enschede. [Dutch BDI-II

Manual, original authors: Beck, A.T., Steer, R.A., Brown, G.K.].

Wittchen, H.U. (1994). Reliability and validity studies of the WHO-Composite

International Diagnostic Interview (CIDI): A critical review. Journal of Psychiatric

Research 28, 57-84.

51

World Health Organization (1997). Composite International Diagnostic Interview,

version 2.1. WHO, Geneva.

CHAPTER 4

INTERNET-BASED COGNITIVE BEHAVIOURAL THERAPY FOR

SUBTHRESHOLD DEPRESSION IN PEOPLE OVER 50 YEARS OLD:

A RANDOMIZED CONTOLLED CLINICAL TRIAL*

* Viola Spek, Ivan Nyklíček, Niels Smits, Pim Cuijpers, Heleen Riper, Jules Keyzer, Victor

Pop (in press). Internet-based cognitive behavioural therapy for subthreshold depression in

people over 50 years old: A randomized controlled clinical trial. Psychological Medicine.

Published online by Cambridge University Press 30 Apr 2007.

54

ABSTRACT

Background: Subthreshold depression is a highly prevalent condition and a risk factor for

developing a major depressive episode. Internet-based cognitive behaviour therapy may be

a promising approach for the treatment of subthreshold depression. The current study had

two aims: (1) to determine whether an internet-based cognitive behaviour therapy

intervention and a group cognitive behaviour therapy intervention are more effective than a

waiting-list control group (2) to determine whether the effect of the internet-based cognitive

behaviour therapy differs from the group cognitive behaviour therapy intervention.

Methods: A total of 191 women and 110 men (mean age = 55 years, SD = 4.6) with

subthreshold depression were randomized into internet-based treatment, group cognitive

behaviour therapy (Lewinsohn’s Coping With Depression Course), or a waiting-list control

condition. The main outcome measure was treatment response after ten weeks, defined as

the difference in pre and post-treatment scores on the Beck Depression Inventory. Missing

data, a major limitation of this study, were imputed using the Multiple Imputation

procedure Data Augmentation.

Results: In the waiting-list control group, we found a pre to post improvement effect size

of 0.45, which was 0.65 in the group cognitive behaviour therapy condition and 1.00 within

the internet-based treatment condition. Helmert contrasts showed a significant difference

between the waiting-list condition and the two treatment conditions (p = 0.04) and no

significant difference between both treatment conditions (p = 0.62).

Conclusions: An internet-based intervention may be at least as effective as a commonly

used group cognitive behaviour therapy intervention for subthreshold depression in people

over 50 years.

55

INTRODUCTION

In people over 50 years of age, the prevalence of major depression is 1% to 3%; the

prevalence of subthreshold depression in this population is 8% to 16% (Cole & Dendukuri,

2003). Patients with subthreshold depression have symptoms of depression, but not enough

to meet DSM-IV criteria for major depression (Cuijpers & Smit, 2004). Subthreshold

depression has considerable effects on well-being and psychosocial functioning (Beekman

et al. 1995; Rapaport & Judd, 1998). In fact, people with subclinical depression are quite

similar to those with a diagnosis of major depression with regard to their psychosocial

functioning (Gotlib et al. 1995). Furthermore, people with subthreshold depression

experience nearly the same degree of impairment in health status, functional status, and

disability as those being diagnosed with major depression (Wagner et al. 2000).

An association between depressive symptomathology and developing a major

depressive episode has been shown (Cuijpers & Smit, 2004). Up to 27% of elderly with

subthreshold depression develop a major depressive episode within three years (Beekman

et al. 2002). Late-life depression is characterized by an unfavourable prognosis, reduced

quality of life, and excess mortality (Cole et al. 1999; Smit et al. 2006). Therefore,

treatment of subthreshold depression is very important.

Given its high prevalence and the fact that probably less than 20% of people with

depression are detected and treated (Cole & Dendukuri, 2003), new approaches are needed

to treat subthreshold depression and to prevent major depressive episodes. It is important

that these methods can reach large populations and people who otherwise would not seek

treatment.

Internet-based cognitive behaviour therapy has advantages over traditional

cognitive behaviour therapy for both clients and health care. The low-threshold

accessibility of the internet makes it suitable for offering and receiving help for




treatment, clients are guided by programmes 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,

internet-based treatment may reduce the therapist time while maintaining efficacy (Wright

et al. 2005).










56

A recent meta-analysis showed that this kind of treatment programs may be

effective (Spek et al. 2007). However, more research is needed, especially studies that,

within one design, include a control group, an intervention group with a proven effective

therapy and an internet-based therapy. Moreover, more data are needed concerning internet-

based treatment in older adults, as this has not yet been studied.

The current study evaluated an internet-based intervention for subthreshold

depression in people over fifty years of age. Two hypotheses were tested. First, we wanted

to determine whether internet-based cognitive behaviour therapy and group cognitive

behaviour therapy were more effective than a waiting-list condition. Second, we tested

whether the two interventions differed regarding their effectiveness.

METHODS

Participants

Participants were recruited by advertisements in free regional newspapers, and by personal

letters sent by the Municipal Health Care Service of the city of Eindhoven. The letters (n =

15697) were sent in cohorts to all inhabitants of Eindhoven, born between 1955 and 1949.

In each mailing round, inhabitants of Eindhoven who were born in the same year received

letters. The letters and advertisements provided information about the study and the address

of the study homepage. The study homepage contained general information about

depression, information about the study, and an application form including the screening

instrument, the Edinburgh Depression Scale (EDS; Cox et al. 1987; Cox et al. 1996;

Matthey et al. 2001). In all communications it was made clear that only people who had

both depressive symptoms and internet access were eligible for the study.

Participants who scored above the cut-off score of 12 on the EDS (n = 699) were

invited for an in-person structured clinical interview for depression (Composite

International Diagnostic Interview, World Health Organization, 1997). To be included in

the study, participants had to meet the following criteria: an EDS-score of 12 or more, but

no compliance with the DSM-IV diagnostic criteria of depression, signed informed consent,

age between 50 to 75 years, access to the internet and the ability to use the internet.

Exclusion criteria were suffering from any other psychiatric disorder in immediate need of

treatment and suicidal ideation.

57

Of the 606 people who attended the interview, 301 (49.7%) were included in the

study. The most important reasons for exclusion were DSM-IV diagnoses for depression (n

= 125, 41.0% of the exclusions; these people were referred to their general practitioner with

a request for treatment), psychiatric disorders in immediate need of treatment (n = 79,

25.9%), bipolar disorder (n = 7, 2.3%), and insufficient computer skills (self-report, n = 18,

5.9%). The remaining exclusions (10.8%) were based on other, less common reasons, such

as relocating to another geographical area, serious physical illness, and busy work

schedules. Several people were excluded on more than one criterion. Forty-three people

(14.1%) decided that they did not want to participate in the study (Figure 1).

Figure 1 Flow chart of inclusions

Invited for clinical interviewEDS ≥ 12n = 699

Present at interviewn = 606

Not included in studyn = 305

Diagnosed with major depressive episode n = 125Other psychiatric disorders n = 79Insufficient computer skills n = 18Bipolar disorder n = 7Other reasons for exclusion n = 33Total excluded n = 262

Did not want to participate n = 43

Randomizedn = 301

Completed EDSn = 930

Letters sentn = 15694

Internet interventionn = 102

Group interventionn = 99

Waiting listn = 100

EDS < 12n = 231

Did not show up at interviewn = 93

35 did notcompletepost-test

15 did notstart intervention20 withdrew


22 did notstart intervention21 withdrew


67 completedpost-test



58

The study protocol was approved by the Maxima Medisch Centrum (local

hospital) ethics committee, which is certified by the Central Committee on Research

involving Human Subjects in the Netherlands.

Measures


The EDS is a 10-item self-report scale assessing the common symptoms of depression. It

was originally designed to assess post partum depression and was called the Edinburgh

Postnatal Depression Scale (EPDS; Cox et al. 1987). The EPDS has later been validated in

other age strata (Murray & Carothers, 1990; Cox et al. 1996; Becht et al. 2001; Nyklíček et

al. 2004) and in men (Matthey et al. 2001) and renamed into EDS. Internal consistency

(Cronbach’s alpha) has been shown to be at least .80 (Cox et al. 1987; Matthey et al. 2001).

The EDS was found to correlate .64 with the Beck Depression Inventory (Pop et al. 1992).

With a clinical diagnosis of major depression as the criterion, the sensitivity is 84%, the

specificity is 92%, and positive predictive value (PPV) is 46% at cut-off point 12/13 (total

scale ranges from 0 to 30) in a sample of middle-aged Dutch participants (Nyklíček et al.

2004, Becht et al. 2001). Because of its conciseness this scale was used as the screening

instrument.


The 21-item BDI (Beck et al. 1961) is the most frequently used self report measure for

depressive symptoms. The BDI was developed to assess the intensity of depressive

symptoms. Internal consistency is high, in the Dutch manual, Cronbach’s alphas of 0.92

and 0.93 are reported (Van der Does 2002). Cut off scores, based on extensive validation

studies in The Netherlands, are the following: scores of 0 to 13 indicate minimal symptoms,

scores of 14 to 19 reflect light symptoms, scores of 20 to 28 are interpreted as moderate

symptoms, and scores of 29 to 63 indicate serious symptoms (Dutch BDI manual, Van der

Does 2002). The BDI was used as the primary outcome measure.


The World Health Organization Composite International Diagnostic Interview (CIDI;

World Health Organization 1997) is a fully structured interview developed to map DSM-IV

and ICD-10 symptoms, and to report whether the diagnostic criteria are met. Reliability of

59

the CIDI for mood disorders is good: the test-retest kappa coefficient is .71 and the

interrater kappa coefficient is .95 (Wittchen, 1994). The CIDI is available in three different

versions: referring to the previous four weeks (one month prevalence), to the previous 12

months (one year prevalence), and to an episode earlier in life (life time prevalence). The

12-month version was used in the interview to assess subthreshold depression.

Procedure

Participants with an EDS score of 12 or more were invited for a face-to-face clinical

interview at a centre for diagnosis in Primary Care (Diagnostisch Centrum Eindhoven).

During this interview, participants were informed about the study and the study conditions,

demographic data were collected, and a structured interview was conducted to assess the

DSM-IV criteria of depression. At the end of the clinical interview, eligible participants

were randomized. For this purpose a random allocation sequence was generated. The

randomization list was kept in an administrative office that was not related to the study.

After the inclusion of a participant in the study, the interviewer made a telephone call to the

‘randomization office’ to inquire to which condition the participant was randomized. On the

randomization list, the time and date of randomization were noted.

After the interview, and after randomization, the participants were asked to fill in

the BDI at home. After completion of this questionnaire, the treatment started. Ten weeks

after the start of the treatment or after ten weeks on the waiting-list, participants were asked

to complete the post-treatment BDI. All questionnaires were completed at home and sent to

the study site.

Interventions

The group cognitive behaviour therapy protocol was the Coping with Depression Course

(Lewinsohn et al. 1992), adapted to the Dutch situation by Cuijpers (2000). This is a highly

structured cognitive behavioural treatment for depression. The course consists of ten

weekly group sessions on psycho-education, cognitive restructuring, behaviour change, and

relapse prevention. It has been used for over ten years by mental health institutions in The

Netherlands and has been shown to be effective (Cuijpers, 1998; Allart-van Dam et al.

2003; Haringsma et al. 2005; Allart-Van Dam et al. 2007). The treatment sessions were led

by psychologists and trained social workers. There were always two group leaders, of

which at least one was a psychologist. Groups consisted of no more than ten participants.


60

The sessions took place at the centre for diagnosis in Primary Care where the participants

had been interviewed before their inclusion in the study.


Trimbos institute, the Netherlands Institute of Mental Health and Addiction. It is a self-help

intervention of eight modules with text, exercises, videos, and figures. The internet-based

intervention covers the same subjects as the group course, as it was based on the Coping

with Depression Course. The internet-based treatment was studied as a self-help

intervention, no professional support was offered to the participants of this study. The

participants accessed the intervention from their home computers via the internet. The

amount of time advised for completion of the course was 8 weeks, one session per week.

Participants on the waiting-list did not receive treatment immediately, but were

invited to participate in the intervention of their choice after the end of the trial.

Analyses

The target sample size of 300 participants was calculated to yield 78% power to detect a

small effect (Cohen’s f = .10). The study was a priori powered to detect a small effect

because we wanted to test if there was a difference between the two interventions. The

calculation was based on an ANOVA with an alpha of .05 (Cohen, 1988).

Preliminary analyses included checks for normality and the computation of

descriptive statistics. All variables were distributed acceptably close to normal. ANOVAs,

T-tests and χ²-tests were used to compare the following groups on baseline characteristics:

(a) participants randomized to the interventions and the waiting-list (b) people who

completed all questionnaires versus people who did not, and (c) people who completed

treatment versus those who did not.

Analyses regarding the main hypotheses were performed according to the

intention-to-treat approach on imputed data. Missing data were imputed using the Multiple

Imputation procedure Data Augmentation with the Norm library from the statistical

package R (R Development Core Team 2005) written by Schafer (1998), because Data

Augmentation is currently the most sophisticated method available to create Multiple

Imputations (MI) (Allison 2001). The data file was imputed five times resulting in five new

data files on which all of the analyses were performed. The five sets of outcomes were then

pooled using so-called Multiple Imputation inference to come to a single set of results. This

pooling makes use of both the variance of the outcomes within a data file and between data

61

files. For a more extensive description of MI, see, Schafer (1999). All randomized

participants were included in the analyses, regardless of how many treatment modules or

sessions they had completed. The effects of the interventions were tested by means of

Helmert contrasts. These contrasts explicitly allow for testing hypotheses concerning

differences among conditions, as opposed to ANOVA, which is an omnibus test that needs

post-hoc tests to see where the differences lie.

We calculated improvement effect sizes (dimpr) by dividing the absolute difference

between the post-treatment average score (Mpost) and the pre-treatment average score (Mpre)

by the pre-treatment standard deviation (SDpre). An effect size of 0.5 thus indicates that the

post-treatment average score is half a standard deviation larger than the pre-treatment

average score.

For between group effect sizes, we calculated effect sizes by subtracting the effect

size of the experimental group from the effect size 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 (Cohen 1988).

To assess clinically significant change, we used the definition of Jacobson et al.

(1984); they defined clinically significant change as the extent to which therapy moves

someone outside the range of the dysfunctional population or within the functional

population. As we did not include any people with a clinical diagnosis of major depression

in the study, we decided to use a cut-off score as an indication of functional status

(Haringsma et al. 2005). People with a BDI score ≥ 20 have moderate to serious symptoms

of depression and were considered to be dysfunctional (Dutch BDI manual, Van der Does

2001). People scoring below 20 on the BDI have minimal to light symptoms and were

considered to be in the functional population. Clinically significant change was a change

from a baseline BDI score of ≥ 20 to a post-treatment BDI score of < 20. This was assessed

using the McNemar test.

62

RESULTS

Post-treatment measures were completed by 67 of 102 participants in the internet group, 56

of 99 participants in the group course condition and by 58 of 100 participants on the

waiting-list. Intention to treat analyses were done on imputed data of all 301 participants,

regardless of the amount of treatment received.

There were no differences between the three conditions regarding age (F(2, 298) =

.79, p > .10), gender (χ²(2) = 1.63, p > .10), having a partner (χ²(2) = 2.62, p > .10),

educational level (χ²(4) = 8.21, p > .05), employment status (χ²(6) = 6.39, p > .10), and

completion of post-treatment measures (χ²(2) = 2.53, p > .10), EDS scores at screening

(F(2, 298) = .61, p > .10), or BDI baseline scores (F(2, 245) = .25, p > .10) (Table 1).

Those who did not complete post-treatment measures did not differ from people

who did complete post-treatment measures regarding age (t(299) = -1.03, p > .10), gender

(χ²(1) = 2.52, p > .10), having a partner (χ²(1) = 0.07, p > .10), educational level (χ²(2) =

0.67, p > .10), employment status (χ²(3) = .68, p > .10), assigned condition (χ²(2) = 2.53, p

> .10), EDS scores at screening (t(299) = -.326, p > .10) and BDI baseline scores (t(246) =

-1.926, p = .06).

Those who did not complete treatment did not differ from people who did

complete treatment regarding age (t(125) = 0.35, p > .10), gender (χ²(1) = 2.03, p > .10),

having a partner (χ²(1) = .01, p > .10), educational level (χ²(2) = 3.57, p > .10), employment

status (χ²(3) = 1.33, p > .10), EDS scores at screening (t(125) = -.37 p > .10) or BDI

baseline scores (t(123) = 0.18, p > .10). However, those who did not complete treatment

were more often assigned to the internet course (χ²(1) = 27.96, p < .01) than those who did

complete treatment. Completion of treatment was measured by self-report. Participant

characteristics are shown in Table 1.

64

Table 2 Means (standard deviations) for depressive symptoms according to the BDI

Pre-treatment Post-treatment Internet-based intervention n = 102 19.17 (7.21) 11.97 (8.05) Group intervention n = 99 17.89 (9.95) 11.43 (9.41) Waiting-list n = 100 18.13 (8.10) 14.46 (10.42)

For improvement within the waiting-list control group, we found a moderate

improvement effect size of 0.45. The group cognitive behaviour therapy condition had a

large improvement effect size: 0.65, while an even larger improvement effect size of 1.00

was found within the internet-based treatment condition.

When comparing the two treatments with the waiting-list group, we found an

effect size of 0.20 for group treatment and 0.55 for the internet-based treatment.

In both treatment groups, a significant proportion of participants achieved

clinically significant change in functional status from moderate to serious symptoms at

baseline to minimal to light symptoms at post-treatment (McNemar, p < .01). In the group

cognitive behaviour therapy condition, 28 out of 42 people who scored BDI ≥ 20 at

baseline achieved a clinically significant change (14 out of 42 people who scored BDI ≥ 20

at baseline remained ≥ 20 at post-treatment, 55 scored below 20 at baseline and remained

below 20, and 2 participants worsened from below 20 at baseline to ≥ 20 at post-treatment).

In the internet-based treatment condition 30 out of 45 people who scored BDI ≥ 20 at

baseline achieved clinically significant change (15 out of 45 people who scored BDI ≥ 20 at

baseline remained ≥ 20 at post-treatment, 48 scored below 20 at baseline and remained

below 20, and 8 participants worsened from below 20 at baseline to ≥ 20 at post-treatment).

In the waiting-list group, there was no significant proportion of participants who showed a

significant change in status (McNemar, p = .103). Only 17 out of 39 participants who

scored BDI ≥ 20 at baseline achieved clinically significant change. (22 out of 39 people

who scored BDI ≥ 20 at baseline remained ≥ 20 at post-treatment, 52 scored below 20 at

baseline and remained below 20, and 8 participants worsened from below 20 at baseline to

≥ 20 at post-treatment).

65

DISCUSSION

In this study, both internet-based cognitive behaviour therapy and group cognitive

behaviour therapy were significantly more effective than a waiting-list in people over 50

years of age with subthreshold depression. Furthermore, the effect of internet-based

treatment did not significantly differ from that of standard group cognitive behaviour

therapy. For the internet-based cognitive behaviour therapy, we found a moderate effect

size of 0.55, compared to the waiting-list condition. When looking at clinically significant

change, we found that in both treatments a significant proportion of participants had made

the change from moderate to serious symptoms at baseline to minimal to light symptoms at

post-treatment.

In this study we were faced with a large amount of missing data. This is a common

problem in trials on internet-based treatment for symptoms of depression, as shown in a

recent meta-analysis (Spek et al. 2007). Two studies on internet-based treatment for

depression with very similar designs also obtained post-treatment data of 66% of

participants (Clarke et al. 2002; Clarke et al. 2005). A study with a follow-up period of six

weeks obtained post-treatment data of 83% of participants (Christensen et al. 2005), and a

study regarding an intervention that included therapist assistance obtained post-treatment

data of 73% of participants (Andersson et al. 2005). There is only one study on minimal

contact internet-based interventions for depression in which an extremely low dropout rate

has been observed (3%), but the way that dropout was defined in this study is not clearly

mentioned (Patten, 2003).

As we were examining a self-help intervention, we were very careful with the

amount of attention individual participants received. Therefore, we were reluctant to

contact participants personally when they were late completing assessments. We sent the

first three reminders via email. This was not always effective, as some people did not

access their email account regularly. If, after three emails, the assessment was still not

completed, we telephoned participants once. After this reminder, we did not contact

participants anymore.

Participants who did not provide post-treatment data did not differ from those who

did. However, for BDI baseline scores (t(246) = -1.926, p = .06) we found a trend for

higher scores in people who did not provide post-treatment data. This might indicate that

people with more serious symptoms did not complete treatment, but, at least for the

https://www.researchgate.net/publication/7744906_Overcoming_Depression_on_the_Internet_ODIN_2_A_Randomized_Trial_of_a_Self-Help_Depression_Skills_Program_With_Reminders?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

66

internet-based treatment, we cannot be certain that all people who did not provide post-

treatment data also did not complete treatment.

We dealt with missing values through the application of Data Augmentation,

multiply imputing the unobserved values. The assumption of this method is that the

probability of a participant having missing values may depend on observed values (such as

covariates and pre-treatment measures) but not on missing ones (i.e., the values of the post-

treatment measures had they been recorded). By contrast, other methods that are often used,

such as last observation carried forward and complete case analysis are based on stronger

and more unrealistic assumptions; namely that the probability of dropout does not depend

on anything, dropout is purely random. As the missing post-treatment measures are

unobserved, it is impossible to test by what mechanism dropout occurred; only assumptions

can be made. Multiple Imputation (MI) has weaker assumptions concerning the missing

data than other methods such as complete case analysis. Consequently, MI provides better

outcomes than the alternative methods. Moreover, MI methods have been said to reduce

missing data bias even when their assumptions are not strictly valid. Therefore, we assume

that the imputed values of the post-treatment measures and subsequent analyses are sound.

When looking at the rate of completion of the courses, it becomes clear that the

internet-based treatment is less often completed. When started, the group cognitive

behaviour therapy is usually completed: dropout from the study among participants

randomized into the group course was due to participants not being willing or able to start

with group cognitive behaviour therapy within the desired time period. The completion rate

for the internet-based intervention, however, was only 50%. We believe that social

interaction might be a reason for this difference in completion. If starting a group treatment

of ten sessions, it is common to finish it. Group treatment involves social support and social

control. Participants get to know each other and the course leaders. It does not seem

appropriate to end treatment once one feels better. However, in internet-based self-help, it

is much easier to end or postpone treatment when an effect is noticed or when the

symptoms become less urgent. As this kind of treatment is exclusively the participant’s

responsibility, there are no expectations of others regarding continuation of the treatment. It

has been found, that, in internet-based treatment, regular telephone calls from a therapist

enhance participants’ completion of internet-based treatment (Kenwright et al. 2005). This

supports our hypothesis regarding the role of social support and social control.

67

The effect size we found for internet-based cognitive behaviour therapy roughly

corresponds with effect sizes found in a recent meta-analysis (Spek et al. 2007). For

internet-based treatment without therapist support for symptoms of depression, we found

effect sizes (compared to inactive control groups) from 0 to 0.4 (Clarke et al. 2002; Patten,

2003; Christensen et al. 2004; Clarke et al. 2005).

The effect size for the control condition in our study also roughly corresponds with

effect sizes found in other studies on internet-based treatment for symptoms of depression:

we found effect sizes ranging from 0.35 to 0.70 (Andersson et al. 2005, Clarke et al. 2005,

Clarke et al. 2002).

Apart from the above mentioned dropout, this study has several limitations. As the

post-treatment assessment was directly after treatment, we can not draw any conclusions

about long term effects. Another limitation is the fact that participants could only be

included in the study if they had computer skills and access to internet. The participants of

this study were more highly educated than the general population in this age group

(Statistics Netherlands, 2006). Therefore, it is uncertain whether the results of this study

can be generalized to people with lower educations. Furthermore, all participants were self-

referred. A recent study (Mataix-Cols et al. 2006) showed that self-referred patients are

more likely to benefit from computerized cognitive behaviour therapy than patients referred

by mental health professionals. This implies that the results might not be generalizable to

populations with other sources of referral. Finally, as our participants suffered from

subthreshold depression, we can not draw any conclusions about the effects both treatments

might have on major depressive episodes.

More research on internet-based cognitive behaviour therapy is needed, especially

research into the predictors of improvement after treatment, in order to be able to tailor

effective interventions to specific subgroups of clients.

Despite these limitations, our findings suggest that people over 50 can benefit at

least as much from internet-based treatment for subthreshold depression as from the

commonly used Coping With Depression course. As this internet-based intervention is a

self-help intervention, there is less therapist time involved; therefore, this may be a very

efficient approach in treating subthreshold depression and in preventing major depressive

episodes. Furthermore, in this study many participants reported not seeking help through

the regular health care system because they were very concerned about being stigmatized.



68

This suggests that internet-based interventions for depression might reach patients who

otherwise would not seek help.

69

REFERENCES




Therapy 34, 381-396.




Allison, P.D. (2001). Missing data. Sage: Thousand Oaks, CA







213.

Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbauch, J. (1961). An inventory for

measuring depression. Archives of General Psychiatry 4, 561-571.




Beekman, A.T.F., Geerlings, S.W., Deeg, D.J.H., Smit, J.H., Schoevers, R.S., De Beurs,

E., Braam, A.W., Pennix, B.W.J.H., Van Tilburg, W. (2002) The natural history of

late-life depression. Archives of General Psychiatry 59, 605-611.



328, 265-267.




https://www.researchgate.net/publication/12086411_Measuring_depression_in_women_around_menopausal_age_towards_a_validation_of_the_Edinburgh_Depression_Scale?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=




70

Clarke, G., Reid, E., Eubanks, D., O’Connor, E., DeBarr, L., Kelleher, C., Lynch, F.

& Nunley, S. (2002). Overcoming depression on the Internet (ODIN): a randomised

controlled trial of an Internet depression skills intervention program. Journal of

Medical Internet Research 4, e14





Cohen, J. (1988). Statistical power analysis for the behavioural sciences. Second edition.

Lawrence Erlbaum Associates: Hillsdale, New Jersey

Cole, M.G., Bellavance, F., Mansour, A. (1999). Prognosis of depression in elderly

community and primary care populations: A systematic review and meta-analysis.




1147-1156.









533.





71














Jacobson, N.S., Follette, W.C., Revenstorf, D. (1984). Psychotherapy outcome research:

Methods for reporting variability and evaluating clinical significance. Behavior

Therapy 15, 336-352.

Kenwright, M., Marks, I., Graham, C., Franses, A., Mataix-Cols, D. (2005). Brief

scheduled phone support from a clinician to enhance computer-aided self-help for

obsessive-compulsive disorder: Randomized controlled trial. Journal of Clinical

Psychology, 61, 1499-1508.

Lewinsohn, P.M., Munoz, R.F., Youngren, M.A., Zeiss, A.M. (1992). Control your

depression. New York (NY): Fireside.




Mataix-Cols, D., Cameron, R., Gega, L., Kenwright, M., Marks, I.M. (2006). Effect of

referral source on outcome with cognitive-behaviour therapy self-help.

Comprehensive Psychiatry 47, 241-245.

72



290.



Research 128, 111-116.

Patten, S.B. (2003). Prevention of depressive symptoms through the use of distance

technologies. Psychiatric Services 54, 396-398.



R Development Core Team (2005). R: A language and environment for statistical

computing. Vienna, Austria.




Schafer, J.L. (1998). Norm: Multiple imputation of incomplete multivariate data under a

normal model. [Software for S-PLUS 4.0 for Windows, available from

http://www.stat.psu.edu/~jls/misoftwa.html].

Schafer, J.L. (1999). Multiple imputation: A primer. Statistical Methods in Medical

Research. 8: 3-15.

Smit, F., Ederveen, A., Cuijpers, P., Deeg, D., Beekman, A. (2006). Opportunities for

cost-effective prevention of late-life depression: An epidemiological approach.

Archives of General Psychiatry 63, 290-296.

Spek, V., Cuijpers, P., Nyklíček, I., Riper, H., Keyzer, J., Pop, V. (2007). Internet-based

cognitive behaviour therapy for symptoms of depression and anxiety: A meta-

analysis. Psychological Medicine 37, 319-328.

Statistics Netherlands (2006). Education level in Dutch population increasing.

(http://www.cbs.nl/en-

73

gb/menu/themas/onderwijs/publicaties/artikelen/archief/2005/2005-1745-wm.htm).

Accessed 04 April 2007.



Manual, original authors: Beck, A.T., Steer, R.A., Brown, G.K.]






Research 28, 57-84.


Otto, M.W. (2005). Computer-assisted cognitive therapy for depression: Maintaining


1164.


version 2.1. WHO, Geneva

CHAPTER 5

ONE-YEAR FOLLOW-UP RESULTS OF A RANDOMIZED CONTROLLED

CLINICAL TRIAL ON INTERNET-BASED COGNITIVE BEHAVIOURAL

THERAPY FOR SUBTHRESHOLD DEPRESSION IN PEOPLE OVER

50 YEARS*

* Viola Spek, Pim Cuijpers, Ivan Nyklíček, Niels Smits, Heleen Riper, Jules Keyzer, Victor

Pop (submitted). One-year follow-up results of a randomized controlled clinical trial on

internet-based cognitive behavioral therapy for subthreshold depression in people over 50

years.

76

ABSTRACT

Background: Internet-based cognitive behaviour therapy is a new promising approach to

the treatment of depressive symptoms. The current study had two aims: (1) to determine

whether, after one year, an internet-based cognitive behaviour therapy intervention was

more effective than a waiting-list control group; and (2) to determine whether the effect of

internet-based cognitive behaviour therapy differs from the effect of group cognitive

behaviour therapy, one year after the start of treatment.

Methods: A total of 191 women and 110 men (mean age = 55 years, SD = 4.6) with

subthreshold depression were randomized into internet-based treatment, group cognitive

behaviour therapy (Lewinsohn’s Coping With Depression Course), or a waiting-list control

condition. The main outcome measure was treatment response after one year, defined as the

difference in pre-treatment and follow-up scores on the Beck Depression Inventory.

Missing data were imputed using the Multiple Imputation procedure Data Augmentation.

Analyses were performed using Multiple Imputation-inference.

Results: In the waiting-list control group, we found a pre-treatment to follow-up

improvement effect size of 0.69, which was 0.62 in the group cognitive behaviour therapy

condition and 1.22 within the internet-based treatment condition. Simple contrasts showed

a significant difference between the waiting-list condition and internet-based treatment (p =

0.03), there was no difference between both treatment conditions (p = .08).

Conclusions: People over fifty with sub-threshold depression can still benefit from

internet-based cognitive behaviour therapy one year after the start of treatment.

77

INTRODUCTION

Subthreshold depression is a highly prevalent and serious condition. In people over 50

years of age, the prevalence is 8% to 16% (Cole & Dendukuri, 2003). People with

subthreshold depression have symptoms of depression, but not as many to meet DSM-IV

criteria for major depression (Cuijpers & Smit, 2004). Subthreshold depression has

considerable effects on well-being and psychosocial functioning (Beekman et al. 1995;

Rapaport & Judd, 1998): people with subthreshold depression are quite similar to those

with a diagnosis of major depression with regard to their psychosocial functioning (Gotlib

et al. 1995) and they experience nearly the same degree of impairment in health status,

functional status, and disability as those being diagnosed with major depression (Wagner et

al. 1995). Furthermore, people with subthreshold depression have an increased risk of

developing depression (Cuijpers & Smit, 2004; Cuijpers et al. 2006). Treatment of

subthreshold depression is very important. In a recent meta-analytic review, it was found

that psychological treatments can have significant effects on subthreshold depression, and

moreover, that these treatments may prevent the onset of major depression (Cuijpers et al.

2007). A new promising approach to treatment of depressive symptoms is internet-based

cognitive behaviour therapy. The short term effectiveness of this type of treatment for

symptoms of depression has been proven in several studies (Christensen et al. 2004;

Andersson et al. 2005; Clarke et al. 2005). However, there hardly has been any research on

long term effects. The longest follow-up period so far has been six months (Andersson et

al. 2005).

This study evaluated the effects of internet-based treatment after one year. We

studied an internet-based cognitive behaviour therapy intervention for subthreshold

depression in people over fifty years old. The intervention was proven to be effective at

post-treatment (Spek et al. 2007a). In the current study, two hypotheses were tested. First,

we wanted to determine whether internet-based cognitive behaviour therapy was more

effective than a waiting-list condition after one year. Second, we tested whether the

internet-based cognitive behaviour therapy differed from group cognitive behaviour therapy

regarding the effectiveness after one year.








78

METHODS

Participants

Participants were recruited by advertisements in free regional newspapers, and by personal

letters sent by the Municipal Health Care Service of the city of Eindhoven. The letters (n =

15697) were sent in cohorts to all inhabitants of Eindhoven, born between 1955 and 1949.

In each mailing round, inhabitants of Eindhoven who were born in the same year received

letters. The letters and advertisements provided information about the study and the address

of the study homepage. The study homepage contained general information about

depression, information about the study, and an application form including the screening

instrument, the Edinburgh Depression Scale (EDS; Cox et al. 1987; Cox et al. 1996;

Matthey et al. 2001). In all communications it was made clear that only people who had

both depressive symptoms and internet access were eligible for the study.


invited for an in-person structured clinical interview for depression, the World Health

Organization Composite International Diagnostic Interview (WHO CIDI; World Health

Organization, 1997). To be included in the study, participants had to meet the following

criteria: an EDS-score of 12 or more, but no DSM-IV diagnosis of depression, signed

informed consent, age between 50 to 75 years, access to the internet and the ability to use

the internet. Exclusion criteria were suffering from any other psychiatric disorder in

immediate need of treatment (which was assessed by means of an anamnesis during the

interview) and suicidal ideation.

Of the 606 people who attended the interview, 301 (49.7%) were included in the

study. The most important reasons for exclusion were DSM-IV diagnoses for depression (n

= 125, 41.0% of the exclusions), psychiatric disorders in immediate need of treatment (n =

79, 25.9%), bipolar disorder (n = 7, 2.3%), and insufficient computer skills (self-report, n =

18 people, 5.9%) mainly. The remaining exclusions (10.8%) were based on other, less

common reasons, such as relocating to another geographical area, serious physical illness,

and busy work schedules. Several people were excluded on more than one criterion. Forty-

three people (14.1%) decided that they did not wish to participate in the study.

79

Measures




Postnatal Depression Scale (EPDS; Cox et al. 1987). The EPDS was later validated in other

age strata (Cox et al. 1996; Murray & Carothers, 1990; Becht et al. 2001; Nyklíček et al.

2004) and in men (Matthey et al. 2001) and renamed the EDS. Internal consistency

(Cronbach’s alpha) has been shown to be at least .80 (Cox et al. 1987; Matthey et al. 2001).

The EDS was found to correlate .64 with the Beck Depression Inventory (Pop et al. 1992).

With a clinical diagnosis of major depression as the criterion, the sensitivity is 84%, the

specificity is 92%, and positive predictive value (PPV) is 46% at cut-off point 12/13 (total

scale ranges from 0 to 30) in a sample of middle-aged Dutch participants (Becht et al. 2001;

Nyklíček et al. 2004). Because of its conciseness, this scale was used as the screening

instrument.


The 21-item BDI (Beck et al. 1961) is the most frequently used self report measure for

depressive symptoms. The BDI was developed to assess the intensity of depressive

symptoms. Internal consistency is high, in the Dutch manual Cronbach’s alphas of 0.92 and

0.93 are reported (Van der Does, 2002). The BDI was used as the primary outcome

measure.

WHO CIDI

The WHO CIDI (World Health Organization, 1997) is a fully structured interview

developed to map DSM-IV and ICD-10 symptoms, and to report whether the diagnostic

criteria are met. Reliability of the CIDI for mood disorders is good: the test-retest kappa

coefficient is .71 and the interrater kappa coefficient = .95 (Wittchen, 1994). In this study,

only the depression module of the CIDI was used.

Procedure

Participants with an EDS score of 12 or more were invited for a face-to-face clinical

interview at a centre for diagnosis in primary care (Diagnostisch Centrum Eindhoven). In

this interview, participants were informed about the study and the study conditions,

80

demographic data were collected, and a structured interview was conducted to assess the

DSM-IV criteria of depression. At the end of the clinical interview, eligible participants

were randomized. For this purpose a random allocation sequence was generated. The

randomization list was kept in an administrative office that was not related to the study.

After the inclusion of a participant in the study, the interviewer made a telephone call to the

‘randomization office’ to inquire to which condition the participant was randomized. On the

randomization list, the time and date of randomization were noted.

After the interview, and after randomization, the participants were asked to fill in

the BDI at home. After completion of this questionnaire, the treatment started. Ten weeks

after the start of the treatment or after ten weeks on the waiting-list, participants were asked

to complete the post-treatment BDI. One year after the interview, participants were asked to

complete another BDI. All questionnaires were completed at home and sent to the study

site.

The study protocol was approved by the Maxima Medisch Centrum (local

hospital) ethics committee, which is certified by the Central Committee on Research

involving Human Subjects in the Netherlands.

Interventions

The group cognitive behaviour therapy protocol was the Coping with Depression Course

(Lewinsohn et al. 1992), adapted to the Dutch situation by Cuijpers (2000). This is a highly

structured cognitive behavioural treatment for depression. The course consists of ten

weekly group sessions on psycho-education, cognitive restructuring, behaviour change, and

relapse prevention. It has been used for over ten years by mental health institutions in The

Netherlands and has been shown to be effective (Cuijpers, 1998; Allart-Van Dam et al.

2003, 2007; Haringsma et al. 2005). The group cognitive behaviour therapy sessions were

led by psychologists and trained social workers. There were always two group leaders, of

which at least one was a psychologist. Groups consisted of no more than ten participants.

The sessions took place at the centre for diagnosis in primary care where the participants

had been interviewed before their inclusion in the study.


Trimbos institute, The Netherlands Institute of Mental Health and Addiction. It is a self-

help intervention of eight modules with text, exercises, videos, and figures. The internet-

based intervention covers the same subjects as the group course, as it was based on the


81

Coping with Depression Course. The internet-based treatment was studied as a self-help

intervention, and no professional support was offered to the participants of this study. The

participants accessed the intervention from their home computers via the internet. The

amount of time advised for completion of the course was 8 weeks, one session per week.

The intervention was found to be effective at post-treatment (Spek et al. 2007a).

Participants on the waiting-list did not receive treatment immediately but were invited to

participate in the intervention of their choice after the end of the trial, which was one year

after the interview.

Analyses

The target sample size of 300 participants was calculated to yield 78% power to detect a

small effect (Cohen’s f = .10). The study was a priori powered to detect a small effect,

because we wanted to test whether there was a difference between the two interventions.

The calculation was based on an ANOVA with an alpha of .05 (Cohen, 1988).

Preliminary analyses included checks for normality and the computation of

descriptive statistics. All variables were distributed acceptably close to normal. ANOVAs, t

tests and χ² tests were used to compare the following groups on baseline characteristics: (1)

participants randomized to the interventions and the waiting-list, and (2) people who

completed all questionnaires vs. people who did not.

Analyses regarding the main hypotheses were performed according to the intention

to treat approach. Missing data were imputed using the Multiple Imputation procedure Data

Augmentation in Norm (Schafer, 1999a) because Data Augmentation is currently the most

sophisticated method available to create Multiple Imputations (MI) (Allison, 2001). The

data file was imputed five times resulting in five new data files on which all of the analyses

were performed. The five sets of outcomes were then pooled using so-called Multiple

Imputation inference to come to a single set of results. This pooling makes use of both the

variance of the outcomes within a data file and between data files. For a more extensive

description of MI, see, Schafer (1999b). All randomized participants were included in the

analyses. The effects of the interventions were tested by means of contrasts. These contrasts

explicitly allow for testing hypotheses concerning differences among conditions, as

opposed to ANOVA, which is an omnibus test that needs post-hoc tests to see where the

differences lie.

82

We calculated improvement effect sizes (dimpr) by dividing the absolute difference

between the post-treatment average score (Mpost) and the pre-treatment average score (Mpre)

by the pre-treatment standard deviation (SDpre). An effect size of 0.5 thus indicates that the

post-treatment average score is half a standard deviation (of the pre-score) larger than the

pre-treatment average score.

For between group effect sizes, we calculated effect sizes by subtracting the effect

size of the experimental group from the effect size 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 (Cohen, 1988).

Table 1 Characteristics of participants

Complete sample

(n = 301)

Internet treatment (n = 102)

Group treatment (n = 99)

Waiting-list

(n = 100) EDS score, screening 16.32 (3.41) 16.39 (3.10) 16.54 (3.99) 16.02 (3.08)BDI score, baseline 18.45 (8.17) 19.07 (7.04) 17.99 (9.39) 18.31 (7.88)Age 55 (4.6) 55 (4.9) 54 (3.9) 55 (5.0) Percentage women 63.5% 67.6% 63.6% 59.0% Partner Single Divorced Widowed

78% 8%

10% 4%

82% 6%

10% 2%

79% 7%

10% 4%

73% 10% 11% 6%

Educational level Low 16% Mid 46% High 38%

Low 13% Mid 43% High 44%



Employed Unemployed Retired Homemaker

57% 23% 9%

11%

62% 16% 10% 12%

56% 24% 6%

14%

54% 27% 10% 9%

RESULTS

Follow-up measures were completed by 58 of 102 participants (57%) in the internet group,

66 of 99 participants (67%) in the group course condition and by 66 of 100 participants

(66%) on the waiting-list. Intention to treat analyses were done on imputed data of all 301

participants.

83

There were no differences between the three conditions regarding age, gender,

having a partner, educational level, employment status, completion of post-treatment

measures, EDS scores at screening, or BDI baseline scores (Table 1).

Those who did not complete follow-up measures did not differ from participants

who did complete follow up measures regarding age, gender, having a partner, educational

level, employment status, assigned condition, EDS scores at screening, and BDI baseline

scores. Participant characteristics are shown in Table 1.

For the intention to treat analyses, we fitted contrasts to the imputed data to test

hypotheses about the differences between conditions. The first contrast tested the

hypothesis whether internet-based treatment differed from the waiting-list control group.

The second contrast tested whether the internet-based treatment and the group CBT were

different from another. The first fitted contrast showed a significant difference between the

internet-based treatment and the waiting list condition (p = 0.03). We found no difference

in effects of internet-based cognitive behaviour therapy and group cognitive behaviour

therapy (p = 0.08). For means and standard deviations of all conditions, see Table 2.

Table 2 Means (standard deviations) for depressive symptoms according to the BDI

Pre-treatment Follow-up Internet-based intervention n = 102 19.07 (7.04) 10.45 (8.05) Group intervention n = 99 17.99 (9.39) 12.14 (8.76) Waiting-list n = 100 18.31 (7.88) 12.88 (10.10)

For improvement within the waiting-list control group, we found a large

improvement effect size of 0.69. The group cognitive behaviour therapy condition also had

a large improvement effect size: 0.62, while an even larger improvement effect size of 1.22

was found within the internet-based treatment condition. When comparing the group

treatment with the waiting-list group, we found an effect size of -0.07. For internet-based

treatment compared with the waiting-list, we found an effect size of 0.53.

84

DISCUSSION

In this study, one year after the start of treatment, internet-based cognitive behaviour

therapy was significantly more effective than a waiting-list condition in people over 50

years of age with subthreshold depression. We found a non-significant difference in effects

of internet-based cognitive behaviour therapy and group cognitive behaviour therapy:

internet-based treatment was more effective than group treatment. One year after the start

of treatment, we found a moderate effect size of 0.53 for the internet-based cognitive

behaviour therapy compared to the waiting-list condition.

The improvement effect size of 1.22 we found for internet-based cognitive

behaviour therapy roughly corresponds with a six months improvement effect size (1.03)

found in a study of Andersson et al (2005).

For group cognitive behaviour therapy we found an improvement effect size of

0.62, which does not correspond with the one year improvement effect size of the same

intervention found in an earlier study: 0.78 (Allart-Van Dam et al. 2007). A reason for this

might be that our participants preferred to be treated with an internet-based intervention. In

recruitment materials for the study, we had to mention that people without access to the

internet were not eligible for inclusion in the study. As there was considerable media

attention to internet-based treatment at that time, many people guessed that the aim of the

study was to evaluate internet-based treatment and many participants told us during the

interview that they hoped to be randomized to the internet-based condition of the study.

In this study we were faced with a large amount of missing data: 37% of our

participants did not provide follow-up data. This is a common problem even in short term

trials on internet-based treatment for symptoms of depression, as shown in a recent meta-

analysis (Spek et al. 2007b).

We dealt with missing values through the application of Data Augmentation,

multiply imputing the unobserved values. The assumption of this method is that the

probability of a participant having missing values may depend on observed values (such as

covariates and pre-treatment measures) but not on missing ones (i.e., the values of the post-

treatment measures had they been recorded). In contrast, other methods that are often used,

such as last observation carried forward and complete case analysis, are based on stronger

and more unrealistic assumptions; namely that the probability of dropout does not depend

on anything, dropout is purely random (Molenberghs et al. 2004). As the missing post-

treatment measures are unobserved, it is impossible to test by what mechanism dropout

85

occurred; one can only make assumptions. Multiple Imputation (MI) has weaker

assumptions concerning the missing data than other methods such as complete case

analysis. Consequently, MI provides better outcomes than the alternative methods.

Moreover, MI methods have been said to reduce missing data bias even when their

assumptions are not strictly valid. Therefore, we assume that the imputed values of the

post-treatment measures and subsequent analyses are sound.

Apart from the above mentioned dropout, this study has several limitations.

Participants could only be included in the study if they had computer skills and access to

internet. The participants of this study were more highly educated than the general

population in this age group (Statistics Netherlands 2007). Therefore, it is uncertain

whether the results of this study can be generalized to the general population. Second, as

our participants suffered from subthreshold depression, we can not draw any conclusions

about the long term effects of internet-based cognitive behaviour therapy on major

depressive episodes.

Despite these limitations, our findings suggest that people over fifty with sub-

threshold depression can continue to benefit from internet-based cognitive behaviour

therapy one year after the start of treatment.

86

REFERENCES




Therapy 34, 381-396.











213.

Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbauch, J. (1961). An inventory for







328, 265-267.









https://www.researchgate.net/publication/294638382_The_prevalence_and_risk_factors_associated_with_major_and_minor_depression_in_later_life?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=



87

Cohen, J. (1988). Statistical power analysis for the behavioural sciences. Second edition.

Lawrence Erlbaum Associates: Hillsdale, New Jersey



1147-1156.









533.








Cuijpers, P., Beekman, A., Smit, F., Deeg, D. (2006). Predicting the onset of major

depressive disorder and dysthymia in older adults with subthreshold depression: A

community based study. International Journal of Geriatric Psychiatry 21, 811-818.

Cuijpers, P., van Straten, A., Smit, F. (2007). Psychological treatments of subthreshold

depression: a meta-analytic review. Acta Psychiatrica Scandinavica 155, 434-441.




88










Molenberghs, G., Thijs, H., Jansen, I., Beunckens, C., Kenward, M.G., Mallinckrodt,

C., Carroll, R.J. (2004). Analyzing incomplete longitudinal clinical trial data.

Biostatistics 5, 445-464.



290.



Research 128, 111-116.



R Development Core Team (2005). R: A language and environment for statistical

computing. Vienna, Austria.




Schafer, J.L. (1999a). Norm: Multiple imputation of incomplete multivariate data under a

normal model. [Software for S-PLUS 4.0 for Windows, available from

http://www.stat.psu.edu/~jls/misoftwa.html].

89

Schafer, J.L. (1999b). Multiple imputation: A primer. Statistical Methods in Medical

Research. 8, 3-15.

Spek, V., Nyklíček, I., Smits, N., Cuijpers, P., Riper, H., Keyzer, J. Pop, V. (2007a).

Internet-based cognitive behavioral therapy for subthreshold depression in people

over 50 years old: A randomized controlled trial. Psychological Medicine. Published


Spek, V., Cuijpers, I., Nyklíček, I., Riper, H., Keyzer, J., Pop, V. (2007b). Internet-

based cognitive behaviour therapy for symptoms of depression and anxiety: A meta-

analysis. Psychological Medicine 37, 319-328.

Statistics Netherlands (CBS). (2006) Education level in Dutch population increasing.

(http://www.cbs.nl/en-

gb/menu/themas/onderwijs/publicaties/artikelen/archief/2005/2005-1745- wm.htm).

Accessed 5 April 2007.









Research 28, 57-84.



CHAPTER 6

PREDICTORS OF OUTCOME OF GROUP AND INTERNET-BASED

COGNITIVE BEHAVIOUR THERAPY*

* Viola Spek, Ivan Nyklíček, Pim Cuijpers, Victor Pop (in press). Predictors of outcome of

group and internet-based cognitive behaviour therapy Journal of Affective Disorders.

Published online 31 May 2007.

92

ABSTRACT

Background: Little is known about which participant characteristics determine the

effectiveness of various types of cognitive behaviour therapy for subthreshold depression.

The aim of this study was to investigate which characteristics predict treatment outcome of

group and internet-based interventions for subthreshold depression, with a special focus on

(i) the five main personality factors, and (ii) their different predictive power in the different

types of treatment.

Methods: A total of 85 women and 45 men (mean age = 55 years, S.D. = 4.4) were

randomly assigned to a group treatment and an internet-based treatment. The outcome

measure was the difference between pre-treatment and post-treatment BDI scores. Analyses

of Covariance were conducted to examine which participant characteristics could predict

outcome for the two different types of treatment.

Results: Higher baseline BDI scores (F(1,111) = 52.88, p < .01), female gender (F(1,111)

= 6.45, p = .01), and lower neuroticism scores (F(1,111) = 7.24, p = .01) predicted better

outcome after both treatments. In the group intervention, participants with higher altruism

scores improved significantly more after treatment (F(1,111) = 3.94, p = .05) compared to

the internet-based condition.

Conclusions: Outcomes of different types of cognitive behaviour therapy for subthreshold

depression are partly predicted by different participant characteristics. Neuroticism was

associated with worse outcomes in both types of treatment, while altruism seems to be

exclusively related to more favourable outcomes in the group treatment.

93

INTRODUCTION

Depression is a major health problem. Yet, despite its high prevalence, probably fewer than

20% of people with depression are detected and treated (Cole & Dendukuri, 2003). People

with subthreshold depression represent an important group, but they generally do not

receive treatment. Despite having symptoms of depression, they do not meet DSM-IV

criteria for major depression (Cuijpers & Smit, 2004). People with subthreshold depression

have an increased risk of developing depression (Cuijpers & Smit, 2004; Cuijpers et al.

2006) and, more importantly, subthreshold depression has serious effects on well-being and

psychosocial functioning (Rapaport & Judd, 1998). In fact, in their psychosocial

functioning, people with subthreshold depression are quite similar to those diagnosed with

major depression (Gotlib et al. 1995). They experience nearly the same degree of

impairment as those diagnosed with major depression in terms of health, functioning, and

disability (Wagner et al. 2000). Furthermore the costs of subthreshold depression are

comparable, although lower, to the costs of major depression; about two thirds of the per

capita costs of major depression (Cuijpers et al. 2007).

Cognitive behaviour therapy has been proven to be effective in treating

subthreshold depression, (Willemse et al. 2004), and there are currently many different

forms: e.g., individual, group, and internet-based cognitive behaviour therapy. However,

little is known about which participant characteristics determine the effectiveness of the

various forms of cognitive behaviour therapy for subthreshold depression; even less is

known about the relatively recent internet-based therapy.

For traditional individual and group cognitive behaviour therapy, pre-treatment

severity, previous episodes of depression, and marital status have been shown to be

important predictors of treatment outcome (Hoberman et al. 1988; Neimeyer & Weiss,

1990; Jarrett et al. 1991; Thase et al. 1994; Elkin et al. 1995; Hamilton et al. 2002,

Andersson et al. 2004).

Although gender differences in treatment outcome have rarely been found

(Hoberman et al. 1988; Neimeyer & Weiss, 1990; Jarrett et al. 1991; Thase et al. 1994),

men attended significantly fewer individual cognitive behaviour therapy sessions than

women (Thase et al. 1994). Since internet-based self-help and group cognitive behaviour

therapy have very different adherence rates (Spek et al. 2007), it seems important to control

for gender, as there might be differences in participation and, consequently, in treatment

outcome.







94

Similarly, although there is little evidence to suggest that educational level is a

predictor of response to cognitive behaviour therapy in general (Hoberman et al. 1988;

Neimeyer & Weiss, 1990; Jarrett et al. 1991), it might be a predictor for treatment outcome

for internet-based self-help, since study skills and experience with computers could well

affect this condition.

Little is known about the value of the “Big Five” personality characteristics (Costa

& McCrae, 1992) in predicting treatment outcomes of cognitive behaviour therapy for

subthreshold depression and major depression. In a recent review of thirteen, mostly

antidepressant, treatment outcome studies of major depression, high neuroticism scores

were shown to be associated with worse outcome (Mulder, 2002). Extraversion has also

been associated with treatment outcome for major depression: Zuckerman et al. (1980)

found that higher pre-treatment extraversion scores predicted better social adjustment at one

year follow-up. In a study on personality traits in a large sample of outpatients with mood

and anxiety disorder exhibiting differing patterns of comorbidity, it was found that

neuroticism, extraversion and agreeableness differed considerably in subjects with one

disorder compared with subjects with more disorders (Cuijpers et al. 2005a). The other two

personality factors, openness and conscientiousness, do not appear to have a predictive

value for cognitive behaviour therapy outcome; however, this might be different for

internet-based cognitive behaviour therapy.

The aim of this study was to investigate which participant characteristics predict

treatment outcome for group and internet-based interventions of subthreshold depression

with a special focus on (i) the five main personality factors, and (ii) their different

predictive power in the different types of treatment.

We expected that personality factors would predict treatment outcome. We

hypothesized that, because of the different form of the treatments, different predictors

would be relevant for the two interventions.

METHODS

Participants

Participants born between 1930 and 1955 were recruited by advertisements in free regional

newspapers. Furthermore, with the help of the Municipal Health Care Service of the city of

Eindhoven, we sent personal letters to invite people to participate in the study. The letters

https://www.researchgate.net/publication/11492321_Personality_Pathology_and_Treatment_Outcome_in_Major_Depression_A_Review?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/7992911_Personality_traits_of_patients_with_mood_and_anxiety_disorders?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/247408295_Professional_Manual_of_the_Revised_NEO_Personality_Inventory_and_NEO_Five-Factor_Inventory?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=


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(n = 15,697) were sent in cohorts to all residents of Eindhoven born between 1949 and

1955. Only this younger subgroup was invited by letter, because they were more likely to

be eligible for inclusion in the study. We knew from data from Statistics Netherlands that

younger people had access to the internet more often than older ones. The letters and

advertisements provided information about the study and the web address of the study

homepage. The study homepage contained general information about depression,

information about the study, and an application form including the screening instrument

(Edinburgh Depression Scale; EDS; Cox et al. 1987; Cox et al. 1996; Matthey et al. 2001).

In all communications, it was made clear that only people who had both depressive

symptoms and internet access were eligible.


invited for a structured face-to-face clinical interview for depression (Composite

International Diagnostic Interview, World Health Organization, 1997). To be included in

the study, participants had to meet the following criteria: they had to have an EDS-score of

12 or more, but not enough symptoms to meet the DSM-IV criteria of major depression,

they had to sign an informed consent release, they had to have access to the internet, and be

able to use the internet. Participants were excluded if they were suffering from another

recently diagnosed comorbid psychiatric disorder, or had suicidal ideation as assessed by

the CIDI.

The 201 participants that were included in the study were randomly divided into

two groups, one with an internet-based treatment (n = 102), the other with group treatment

(n = 99). A total of 71 participants did not provide post-treatment data, which left us with

67 participants for the internet-based condition and 63 participants for group treatment.

Analyses were only conducted on the complete cases (Figure 1).

The study protocol was approved by the ethics committee of the Maxima Medisch

Centrum Eindhoven (a local hospital in Eindhoven, The Netherlands); this committee is

certified by the Central Committee on Research involving Human Subjects in the

Netherlands.

96

Measures




Postnatal Depression Scale (EPDS; Cox et al. 1987). The EPDS has later been validated for

other age strata (Murray & Carothers, 1990; Cox et al. 1996; Becht et al. 2001), and in men

(Matthey et al. 2001; Nyklíček et al. 2004) and was renamed the EDS. The total scale

ranges from 0 to 30. For clinical diagnosis of major depression, the sensitivity was found to

be 88%, the specificity 88%, and the positive predictive value 41% at a cut-off point of 12

in a sample of Dutch participants (Nyklíček et al. 2004, Becht et al. 2001). Due to its

reliability and conciseness, the EDS was used as the screening device.

Figure 1 Flow chart of participants

Invited for clinical interviewEDS ≥ 12n = 699

Present at interviewn = 606

Not included in studyn = 305

Diagnosed with depression n = 125Other psychiatric disorders n = 79Insufficient computer skills n = 18Bipolar disorder n = 7Other reasons for exclusion n = 33Total excluded n = 262

Did not wish to participate n = 43

Randomizedn = 301

Filled in EDSn = 930

Letters sentn = 15694

Internet interventionn = 102

Group interventionn = 99

Waiting listn = 100

EDS < 12n = 231

Did not show up at interviewn = 93

Analyzedn = 67

Did not provide post-treatment data

n = 36 (36%)

Did not provide post-treatment data

n = 35 (34%)

Analyzedn = 63

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The World Health Organization Composite International Diagnostic Interview (CIDI;

World Health Organization, 1997) is a fully structured interview developed to identify

DSM-IV and ICD-10 symptoms, and to report whether the diagnostic criteria are met. Only

the depression section of the CIDI was administered during the interview, taking 30 to 45

minutes to complete.


The BDI (Beck et al. 1961) is the most frequently used self report measure for depressive

symptoms. It contains 21 items. The BDI was developed to assess the intensity of

depressive symptoms. Internal consistency is high: the Dutch manual reports Cronbach’s

alphas of 0.92 and 0.93 (Van der Does, 2002). The BDI scores were used as the primary

outcome measure.

NEO-Five Factor Inventory

The NEO-FFI (Costa & McCrae, 1992) is a 60-item questionnaire assessing personality

with subscales for each of the “Big Five” personality factors: neuroticism, extraversion,

openness to experience, altruism, and conscientiousness. The reliability and validity of the

Dutch version of the NEO-FFI are sufficient (Hoekstra et al. 2003). The internal

consistencies of the subscales of the Dutch NEO-FFI are comparable to those of the

American NEO-FFI and range between α = .68 and α = .86, while indices of validity range

between r = .50 and r = .84, reflecting correlations with questionnaires measuring similar

personality characteristics (Hoekstra et al. 2003).

Procedure

Participants with an EDS score of 12 or more were invited for face-to-face clinical

interviews at a diagnostic primary care facility (Diagnostisch Centrum Eindhoven). During

this interview, demographic data were collected, the NEO-FFI was administered, and a

structured interview was conducted to assess the DSM-IV criteria of depression. The

interviews usually lasted around 60 minutes. At the end of the interview, eligible

participants were randomly assigned to the two conditions, using a random allocation

sequence. The randomization list was kept in an administrative office that was not related to

the study. After a participant had been included in the study, the interviewer called the


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“randomization office” to inquire which condition the participant had been assigned to. The

time and date of randomization were noted on the list, and the participant was immediately

informed to which condition he or she had been assigned.

After the interview, and after being assigned to a group, the participants were

asked to complete the pre-treatment BDI online. Internet-based participants started

treatment within one week. Group treatment started two to five weeks after the interview.

Participants in group treatment completed the pre-treatment BDI one week before the start

of treatment. Ten weeks after the start of the treatment, participants were asked to complete

the post-treatment assessment. The post-treatment assessment was conducted online; no

face-to-face interviews were conducted.

Interventions

The group cognitive behaviour therapy used the Coping with Depression Course protocol

(Lewinsohn et al. 1992), adapted to the Dutch situation by Cuijpers (2000). A highly

structured cognitive behavioral treatment for depression, the course consists of psycho-

education, cognitive restructuring, behaviour change, and relapse prevention provided

during ten weekly meetings. It has been used for over ten years by mental health

institutions in The Netherlands and has been shown to be effective (Cuijpers, 1998; Allart-

van Dam et al. 2003; Cuijpers et al. 2005b; Haringsma et al. 2005; Allart-Van Dam et al.

2006). The group sessions were led by psychologists and trained social workers. All group

leaders had previously led a Coping With Depression course, as their department had been

using this intervention of over five years. Of the group leaders, 41% were men, and 59%

were women. The sessions took place at the Diagnostic Centre for Primary Care in

Eindhoven, where the participants had been interviewed prior to their inclusion in the

study. Groups were no larger than ten participants.

The internet-based cognitive behaviour therapy intervention is a self-help

intervention of eight modules with text, exercises, videos, and figures. The internet-based

intervention was based on the Coping with Depression Course and covered the same topics

as the group course. The internet course has been shown to be effective (Spek et al. 2007).

The internet-based treatment was provided as a self-help intervention, and no professional

support was offered to the participants. However, participants could call/email the Center in

case of any problems, regarding the intervention, or regarding their depressive symptoms.


https://www.researchgate.net/publication/7739214_Outcome_of_cognitive_behaviour_therapy_for_minor_depression_in_routine_practice?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

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This possibility was only used for IT difficulties. No participants contacted the Center with

worsening symptoms.

The participants accessed the intervention from their personal computers via the

internet. The recommended time for completion of the course was eight weeks, one session

per week.

Analyses

All analyses were conducted using SPSS 14.0. ANOVAs, MANOVAs, and χ²-tests were

used to determine whether people who dropped out after the interview but before

completing the baseline BDI differed from people who did complete the baseline

assessment. The same techniques were also used to determine whether people who did not

complete post-treatment measurements differed from those who did provide post-treatment

data.

Treatment outcome was defined as the difference in pre-treatment BDI scores and

those taken post-treatment. The independent variable was type of treatment. To examine

which participant characteristics could influence treatment outcome for the two treatments,

we conducted Analyses of Covariance using the General Linear Model procedure. This

technique permitted us to test for all relevant effects, including the different effects of the

predictors for the two treatments. In addition, this procedure enhanced the power to detect

effects.

First, we conducted an ANCOVA with the following covariates: previous

depressive episode(s), gender, educational level, having a partner, and BDI pre-treatment

score. BDI pre-treatment score was included as a covariate, to gain insight into the relative

degree of change. Educational level was expressed in years of education, divided into high

education (≥ 15 years) and lower education (< 15 years).

A second ANCOVA model was constructed using the results of the first one,

combining the significant covariates from the first model with the personality factors from

the NEO-FFI. Both ANCOVAs were only conducted on complete cases.

RESULTS

Preliminary analysis checked for normality and computed descriptive statistics. All

variables were found to be distributed acceptably close to normal.

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Participants who dropped out after randomization, but before the start of the

intervention, did not differ from participants who started treatment on most characteristics:

type of treatment (group CBT vs. internet-based CBT), gender, age, having a partner,

employment status, previous depressive episodes, EDS screening scores, and NEO-FFI

personality characteristics. However, there were significantly more dropouts among

participants with lower education: 34% of participants with a lower educational level

dropped out, versus 13% of participants with medium education and 18% of those with

higher education (χ²(2) = 7.62, p = .02) .

No differences were found between participants who dropped out after completing

baseline and participants who provided post-treatment data on the following characteristics:

type of treatment, age, educational level, having a partner, employment status, previous

depressive episodes, EDS screening scores, baseline BDI scores, and NEO-FFI personality

characteristics. However, women dropped out of treatment significantly more often than

men: 31% of women versus 15% of men (χ²(1) = 4.81, p = .04).

Table 1. Pre-treatment characteristics of 130 participants: means, (standard deviations) percentages

Internet-based treatment (n = 67)

Group treatment (n = 63)

EDS score at screening 16.39 (3.10) 16.54 (3.99) BDI score at baseline 18.55 (6.90) 18.28 (9.37) BDI score at post-treatment 10.93 (7.49) 12.12 (9.33) Age 55 (4.9) 54 (3.9) Percentage women 67.6% 63.6% With partner 82.4% 78.8% Educational level* Low 13%

Mid 43% High 44%


Previous depressive episodes 78% 78% Neuroticism Extraversion Openness Altruism Conscientiousness

3.34 (0.44) 2.95 (0.50) 3.27 (0.50) 3.55 (0.42) 3.46 (0.48)

3.40 (0.54) 2.77 (0.49) 3.14 (0.57) 3.71 (0.41) 3.45 (0.60)

Educational level: low ≤ 9 years; mid = 10 to 14 years; high ≥ 15 years *significantly different: (χ²(2) = 7.62, p = .02)

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No differences were found between participants in the group treatment and

participants in the internet-based treatment on the following characteristics: gender, age,

having a partner, employment status, previous depressive episodes, EDS screening scores,

baseline BDI scores, and NEO-FFI personality characteristics. However, participants in the

internet-based condition were more highly educated: 29% in the group intervention versus

44% in the internet course (χ²(2) = 6.22, p = .05) (Table 1).

Using the first ANCOVA to predict changes in BDI scores from baseline to post-

treatment, showed the following main effects: higher BDI baseline scores were related to

more improvement after treatment (F(1,116) = 48.86, p < .01), women improved more after

treatment than men (F(1,116) = 4.74, p = .03), and participants with a higher education

level improved more after treatment (F(1,116) = 5.14, p = .03). No significant interaction

effects were associated with type of treatment. (Table 2a)

Table 2a First Analysis of Covariance, dependent variable: difference in BDI scores from pre-treatment to post-treatment, N = 130

Source F p Partial eta²

Observed power

Main effects Type of treatment 0.38 .537 .003 .094 BDI baseline score 48.86 .000 .296 1.000 Female gender 4.74 .031 .039 .579 Highest educational level 5.14 .025 .042 .614 Previous depressive episodes 0.00 .994 .000 .050 With partner 0.17 .681 .001 .069 Interaction effects Type of treatment * BDI baseline score 1.76 .188 .015 .260 Type of treatment * Female gender 0.35 .556 .003 .090 Type of treatment * High educational level 0.05 .819 .000 .056 Type of treatment * Previous depressive episodes

0.09 .763 .001 .060

Type of treatment * With partner 0.81 .369 .007 .145

The second ANCOVA, in which the five personality factors were added to the

model containing the significant background characteristics, showed significant main

effects for baseline BDI scores (F(1,111) = 52.88, p < .01) and gender (F(1,111) = 6.45, p =

.01), but also revealed a main affect for neuroticism (F(1,111) = 7.24, p = .01). Higher

scores for neuroticism were associated with poorer outcome. A significant interaction effect

was also found between type of treatment and altruism (F(1,111) = 3.94, p = .05), showing

that in the group intervention participants with higher altruism scores improved

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significantly more after treatment than those in the internet-based condition. No effects

were found for the other personality characteristics, extraversion, conscientiousness, and

openness, neither as main effects nor in interaction with condition (Table 2b).

Table 2b Second Analysis of Covariance, dependent variable: difference in BDI scores from pre-treatment to post-treatment, N = 130

Source F p Partial eta²

Observed power

Main effects Type of treatment 3.89 .051 .034 .498 BDI baseline score 52.88 .000 .323 1.000 Female gender 6.45 .012 .055 .712 Highest educational level 2.60 .110 .023 .359 Neuroticism 7.24 .008 .061 .761 Extraversion 0.31 .578 .003 .086 Openness 0.02 .895 .000 .052 Altruism 0.74 .393 .007 .136 Conscientiousness 1.39 .241 .012 .215 Interaction effects Type of treatment * Neuroticism 0.92 .341 .008 .158 Type of treatment * Extraversion 0.37 .545 .003 .092 Type of treatment * Openness 0.24 .628 .002 .077 Type of treatment * Altruism 3.94 .050 .034 .503 Type of treatment * Conscientiousness 0.06 .811 .001 .056

DISCUSSION

This study investigated the influence of the five main personality factors on treatment

outcome of cognitive behavioral therapy interventions for older adults with subthreshold

depression (having symptoms of depression, but not enough to meet DSM-IV criteria for

major depression). We found a negative association between outcome of cognitive

behaviour therapy for subthreshold depression and neuroticism, and a positive association

between group cognitive therapy outcome and altruism. Educational level was a significant

predictor for treatment outcome: a higher educational level was associated with better

treatment outcome. Women were also found to improve more after treatment than men.

Higher BDI baseline scores predicted greater improvement after treatment, even after

controlling for baseline scores (this is necessary because improvement is limited by the

baseline score: if the baseline score is not very high, there is not as much room for

improvement as there would be with a high baseline score). We found a main effect close to

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significance (p = .051) for type of treatment: internet-based treatment was more effective

than group treatment. This is consistent with our earlier findings of different, but not

significantly different, effect sizes for both types of treatment (Spek et al. 2007). We also

found that participants with lower education dropped out more often prior to the start of

treatment. During treatment, we found that men dropped out less often than women.

Participants with higher levels of neuroticism showed less improvement after

treatment. This is in line with previous investigations showing that neuroticism is a

predictor for poorer treatment outcome (Mulder, 2002). However, this study is the first to

show that neuroticism is also a predictor of poorer treatment outcome of cognitive

behaviour therapy for older adults with subthreshold depression.

Participants with more altruism benefited more from group treatment, though not

from internet-based treatment. We hypothesize that participants who get along well with

others and who generally view others as trustworthy will do better in group treatment than

people without these characteristics. They may have felt more at ease in the group and

experienced more support from the group members than their less altruistic counterparts.

As this is the first study to investigate the five personality factors associated with outcome

of group or internet-based cognitive behaviour therapy, there are no other studies to

compare our findings to.

Regarding basic socio-demographic characteristics, we found that high educational

level was associated with better treatment outcome. This differs from earlier studies, where

no significant results were found for educational level as a predictor for depression

treatment outcome (Hoberman et al. 1988; Neimeyer & Weiss, 1990; Jarrett et al. 1991).

The participants in our study were more highly educated than the general population, with

37% of our participants having 15 years or more of education. This is comparable the study

of Hoberman et al. (1988), in which 33% of participants had at least some college

education; however, they did not find educational level to be significantly associated with

treatment outcome. A major difference between the two studies is the age of the

participants; in Hoberman’s study the mean age was 38 years, in our study the mean age

was 55. More research is needed to investigate the influence of educational level on

treatment outcome.

Women improved more after treatment than men. This finding disagrees with the

majority of earlier research (Hoberman et al. 1988; Jarrett et al. 1991; Thase et al. 1994)


104

that found no differences between women and men in treatment outcomes. Possibly this

difference in findings is also related to the higher age of our participants.

Also unlike earlier studies (Hoberman et al. 1988; Andersson et al. 2004), we did

not find a relation between previous depressive episodes and treatment outcome. However,

our results are similar to those of Bockting et al. (2006). Participants in both Bockting et

al.’s study and ours met the criteria for subthreshold depression, while the participants in

Andersson et al. (2004) and Hoberman et al. (1988) were diagnosed with major depression.

Marital status did not predict treatment outcome. This differs from the findings of

earlier studies (Hoberman et al. 1988; Jarrett et al. 1991). However, in these studies, half of

the participants had partners (Hoberman 55%, n = 40; Jarrett 56%, n = 37) whereas in our

study as many as 81% had partners. This might have affected the power in our study.

In our study, participants with lower education dropped out prior to intervention

more often than did people with higher education. This is in accordance with earlier

findings (Last et al. 1985). The higher dropout rate among people with a lower level of

education might also be related to computer skills, as people over 50 with lower education

are often less familiar with computers and the internet (Statistics Netherlands, 2005). Only

half of the participants were assigned to the internet course, but the study questionnaires for

both conditions were internet-based: this might have been an obstacle for people with little

computer experience, even if they were assigned to the group treatment condition.

Another important finding was that men dropped out less frequently during both

treatments. This was strongest for internet-based treatment, where only 7% of men dropped

out, whereas in the group course 23% of men dropped out. Dropout rates for women were

much higher: 29% during group treatment and 31% during internet-based treatment. The

reason for this is unclear, as we do not have data on most of the people who dropped out.

Gender differences in dropout have not been reported before (Simons et al. 1984; Arnow et

al. 2007).

This study has several limitations. First, Axis-II disorders were not examined;

personality traits were assessed, rather than personality disorders. Therefore, it is unclear if

potential personality disorders may have biased the importance of personality traits.

Second, dropout in this study was quite high, resulting in a rather small sample size, which

may have made it impossible to detect other predictors. Third, because most participants in

our study were between 50 and 56 years old, generalizability to older populations may be

limited. Finally, the participants of this study were somewhat more highly educated than

https://www.researchgate.net/publication/6929180_Dropouts_versus_completers_among_chronically_depressed_outpatients?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=


105

the general population in this age group (Statistics Netherlands, 2005a). Therefore, it is

uncertain whether the results of this study can be generalized to the whole population.

This study indicates that outcomes of different types of cognitive behaviour

therapy for subthreshold depression are partly predicted by different participant

characteristics. Of the personality factors studied, neuroticism was associated with worse

outcome with both types of therapy, while altruism seems to be associated with more a

favourable outcome in group treatment but not in internet-based treatment. If these results

can be replicated, we should be able to make more educated decisions about treatment

allocation of clients.

106

REFERENCES



randomized controlled trial in the treatment of subclinical depression. Behaviour

Therapy 34, 381-396.




Andersson, G., Bergström, Hollandäre, Ekselius, L., Carlbring, P. (2004). Delivering

cognitive behavioural therapy for mild to moderate depression via the internet:

Predicting outcome at 6-month follow-up. Verhaltenstherapie 14, 185-189.

Arnow, B.A., Blasey, C., Manber, R., Constantino, M.J., Markowitz, J.C., Klein, D.N.,

Thase, M., Kocsis, J.H., Rush, A.J. (2007). Dropouts versus completers among

chronically depressed outpatients. Journal of Affective Disorders 97, 197-202.




213.

Beck, A.T., Ward, C.H., Meldelson, M., Mock, J., Erbauch, J. (1961). An inventory for


Bockting, C.L.H., Spinhoven, P., Koeter, M.W.J., Wouters, L.F., Visser, I. Schene,

A.H. (2006). Differential predictors of response to preventive cognitive behaviour

therapy in recurrent depression: A 2-year prospective study. Psychotherapy and

Psychosomatics 75, 229-236.



1147-1156.

Costa, P.T., Jr., McCrae, R.R. (1992). Revised NEO Personality Inventory (NEO-PI-R)

and the Five Factor Inventory (NEO-FFI): Professional manual. Odessa, Florida:

Psychological Assessment Resources Inc.

https://www.researchgate.net/publication/6997939_Differential_Predictors_of_Response_to_Preventive_Cognitive_Therapy_in_Recurrent_Depression_A_2-Year_Prospective_Study?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=










https://www.researchgate.net/publication/9879450_An_Inventory_For_Measuring_Depression?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

https://www.researchgate.net/publication/9879450_An_Inventory_For_Measuring_Depression?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=




https://www.researchgate.net/publication/224881353_Delivering_Cognitive_Behavioural_Therapy_for_Mild_to_Moderate_Depression_via_the_Internet_Predicting_Outcome_at_6-Month_Follow-Up?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=



107








analysis of Lewinsohn’s ‘Coping with depression’ course. Behaviour Therapy 29,

521-533.


Trimbos-instituut. (Dutch translation and adaptation to Lewinsohn’s Coping With


Breckenridge, J.S., Teri, L.)




Cuijpers, P., van Straten, A., Donker, M. (2005a). Personality traits of patients with

mood and anxiety disorders. Psychiatry Research 133, 229-237.

Cuijpers, P., Smit, F., Voordouw, I., Kramer, J. (2005b). Outcome of cognitive

behaviour therapy for minor depression in routine practice. Psychology and

Psychotherapy: Theory, Research and Practice 78, 179-188.

Cuijpers, P., Beekman, A., Smit, F., Deeg, D. (2006). Predicting the onset of major

depressive disorder and dysthymia in older adults with subthreshold depression: A

community based study. International Journal of Geriatric Psychiatry 21, 811-818.

Cuijpers, P., Smit, F., Oostenbrink, J., de Graaf, R., ten Have, M., Beekman, A.

(2007). Economic costs of minor depression: A population-based study. Acta

Psychiatrica Scandinavica 115, 229-236.

Elkin, I., Gibbons, R.D., Shea, M.T., Sotsky, S.M., Watkins, J.T., Pilkonis, P.A.,

Hedeker, D. (1995). Initial severity and differential treatment outcome in the












https://www.researchgate.net/publication/292795543_Detection_of_postnatal_depression_development_of_the_10-item_Edimburgh_Postnatal_Depression_Scale?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=









108

National Institute of Mental Health Treatment of Depression Collaborative Research

Program. Journal of Consulting and Clinical Psychology 63, 841-847.




Hamilton, K.E., Dobson, K. (2002). Cognitive therapy for depression: Pretreatment

patient predictors of outcome. Clinical Psychology Review 22, 875-893.


Coping With Depression (CWD) course for older adults provided by the community-



Hoberman, H.M., Lewinsohn, P.M., Tilson, M. (1988). Group treatment of depression:

Individual predictors of outcome. Journal of Consulting and Clinical Psychology 56,

393-398.

Hoekstra, H.A., Ormel, J., De Fruyt, F. (2003). NEO-PI-R / NEO-FFI Big Five

Persoonlijkheidsvragenlijst Handleiding. Swets & Zeitlinger, Lisse (Dutch manual of

NEO-FFI)

Jarrett, R.B., Eaves, G.G., Grannemann, B.D., Rush, A.J. (1991). Clinical, cognitive,

and demographic predictors of response to cognitive behaviour therapy for

depression: a preliminary report. Psychiatry Research 37, 245-260.

Last, C.G., Thase, M.E., Hersen, M., Bellack, A.S., Himmelhoch, J.M. (1985). Patterns

of attrition for psychosocial and pharmacologic treatments of depression. Journal of

Clinical Psychiatry 46, 361-386.






109

Mulder, R.T. (2002). Personality pathology and treatment outcome in major depression: A

review. American Journal of Psychiatry 159, 359-371.


Depression Scale on a community sample. Br. J. Psychiatry 157, 288-290.

Neimeyer, R.A., Weiss, M.E. (1990). Cognitive and symptomatic predictors of outcome of

group therapies for depression. Journal of Cognitive Psychotherapy 4, 23-32.



Research 128, 111-116.






Simons, A.D., Levine, J.L., Lustman, P.J., Murphy, G.E. (1984). Patient attrition in a

comparative outcome study of depression. Journal of Affective Disorders 6, 163-173.

Spek, V., Nyklíček, I., Smits, N., Cuijpers, P., Riper, H., Keyzer, J. Pop, V. (2007).

Internet-based cognitive behavioural therapy for subthreshold depression in people

over 50 years old: A randomized controlled trial. Psychological Medicine. Published


Statistics Netherlands (2005a). Education level in Dutch population increasing.

(http://www.cbs.nl/en-GB/menu/themas/mens-

maatschappij/onderwijs/publicaties/artikelen/archief/2005/2005-1745-

wm.htm?languageswitch=on) Accessed December 14, 2006.

Statistics Netherlands (2005b). Thirty percent of the adult population does not use

internet. (http://www.cbs.nl/nl-nl/menu/themas/bedrijfsleven/innovatie-ict-

investeringen/publicaties/artikelen/archief/2005/2005-1719-wm1.htm) Accessed

November 9, 2006.

110

Thase, M.E., Reynolds, C.F., Frank, E., Simons, A.D., McGeary, J., Fasiczka, A.L.,

Garamoni, G.G., Jennings, J.R., Kupfer, D.J. (1994). Do depressed men and

women respond similarly to cognitive behaviour therapy? American Journal of








Willemse, R.G.W.M., Smit. F., Cuijpers, P., Tiemens, B.G. (2004). Minimal contact

psychotherapy for sub-threshold depression in primary care. British Journal of




Zuckerman, D.M., Prusoff, B.A., Weissman, M.M., Padian, N.S. (1980). Personality as

a predictor of psychotherapy and pharmacotherapy outcome of depressed outpatients.

Journal of Consulting and Clinical Psychology 48, 730-735.

CHAPTER 7

GENERAL DISCUSSION

112

DISCUSSION

In the present thesis, a study concerning internet-based treatment in a large sample of

persons with subthreshold depression was described. At screening, at baseline, directly after

treatment (10 weeks after baseline) and one year after baseline, participants completed a

number of questionnaires with regard to their depressive symptomatology.

Before the inclusion of participants began, a meta-analysis regarding the effects of

internet-based treatment for symptoms of depression and anxiety was conducted. We found

that the effectiveness of internet-based treatments varied considerably in the literature.

Some interventions had no measurable effects; others had evident effects (effect sizes

ranged from 0.0 to 1.1). Internet-based interventions with therapist support seemed to be

more effective than internet-based interventions without therapist support. However, there

was a large overlap between the kind of symptoms the treatments were aimed at and the

amount of support provided. Most interventions for depressive symptoms were of the self-

help type, whereas most interventions for anxiety included therapist support. Therefore, we

can only speculate that it is the amount of support that differentiates between greater and

smaller effects (Chapter 2).

In order to gain more insight into the applicability of internet-based screening for

depression, we studied the psychometric properties of the online administered Edinburgh

Depression Scale (EDS). We chose the EDS as a screening instrument because of the

reliability and conciseness of the paper-and-pencil version. We found that the internet-

administered EDS had appropriate reliability and validity. Moreover, it had good positive

predictive values: about one third of the respondents with an internet-administered EDS

score equal to or over 12 was clinically depressed. The psychometric characteristics of the

internet-administered EDS were comparable to those of the paper-and-pencil EDS;

therefore, it is concluded that the EDS is suitable for use as an online screening instrument

(for a more extensive description of this subject, see Chapter 3).

We conducted a large randomized controlled trial (N = 301) to study the

effectiveness of a newly developed internet-based intervention. At post-treatment (ten

weeks after the start of the intervention), our findings suggest that this intervention was

effective: persons of over 50 years of age can benefit at least as much from this internet-

based intervention for subthreshold depression as from Lewinsohn’s Coping With

Depression course. Furthermore, we found that the internet-based intervention was

substantially more effective than a waiting-list condition (compared to the waiting-list we

113

found a moderate effect size of 0.55; Chapter 4). The strength of this effect was somewhat

surprising in the light of our meta-analysis, in which we found that internet-based treatment

without therapist support was not very effective. We believe it may be due to the

characteristics of Lewinsohn’s Coping With Depression course, on which our internet

course was based, that such a relatively large effect size was found. Although most internet-

based interventions are closely protected and difficult to obtain access to for researchers,

we were able to look in more detail into one other internet-based self-help programme. This

had similar qualities to our intervention and proved to be one of the few self-help

interventions in the meta-analysis which had a relatively large effect size.

This is the first study in which the long-term effects of internet-based treatment

were compared to a control condition. One year after the start of treatment, the internet-

based cognitive behaviour therapy was significantly more effective than a waiting-list

condition in persons over 50 years of age with subthreshold depression. Once again, we

found a moderate effect size for the internet-based cognitive behaviour therapy compared to

the waiting-list condition. This suggests that persons over 50 years of age with subthreshold

depression can still benefit from internet-based cognitive behaviour therapy one year after

the start of treatment (Chapter 5). This finding is important, because it is often suggested

that symptoms of depression dissipate with time. Our findings suggest that persons with

subthreshold depression do benefit from (internet-based) cognitive behaviour therapy, even

if the effect of treatment is compared to a no treatment control condition in which the

depressive symptoms also diminish over time.

We did not find any differences in effectiveness between internet-based treatment

and group treatment (Chapter 4 and 5) at the 95% significance level. However, at the 90%

significance level we did find differences at the one-year follow-up, indicating that internet-

based treatment could even be slightly more effective than group treatment (p = 0.08). One

reason for this difference could be that our participants preferred to be treated with an

internet-based intervention. During recruitment, we mentioned that people without access

to the internet were not eligible for inclusion in the study. As there was considerable media

attention at that time surrounding internet-based treatments, many people guessed that the

aim of the study was to evaluate internet-based treatment and many participants told us

during the interview that they hoped to be randomized to the internet-based condition of the

study. Although the participants were randomized to the three different conditions, a

selection bias of the group as a whole might have occurred.

114

When examining personality characteristics as predictors of post-treatment

outcome, we found a negative association between the outcome of both types of cognitive

behaviour therapy and neuroticism, indicating that, after treatment, participants with higher

neuroticism scores improve less on depressive symptoms. This is in line with previous

studies showing that neuroticism is a predictor of poorer treatment outcome (Mulder,

2002). However, this study is the first to show that neuroticism is also a predictor of poorer

treatment outcome of cognitive behaviour therapy in older adults with subthreshold

depression.

In order to further investigate whether there were any differences between the two

treatments, we studied the interaction between type of treatment and personality

characteristics. We found a significant interaction effect for type of treatment and altruism.

Participants who scored higher on altruism benefited more from group treatment, but not

from internet-based treatment. We hypothesize that participants who get along well with

others, and who generally view others as trustworthy will do better in group treatment than

persons without these characteristics (Chapter 6).

Clinical implications

The internet-based intervention studied in this thesis has proved to be effective and can be

implemented in practice, at least in persons over 50 years of age.

Implementation in practice could be performed stepwise. Firstly, it may be wise to

start implementing the programme via mental health institutes. These could offer the

programme to clients with subtreshold depression and possibly also to those with major

depression. Secondly, it may be useful to implement the programme in primary care. GPs

who diagnose depression without suicidal ideation could advise their patients to follow the

internet course.

Although this study evaluated an intervention aimed at older adults, there is

evidence that similar internet-based interventions can be effective in younger patients with

symptoms of depression (Christensen et al. 2004, Andersson et al. 2005). Therefore, it

might be relevant to implement internet-based treatment to a wider range of age groups.

Limitations of the study

This study has several limitations. Firstly, the percentage of drop outs was considerable;

this is a common problem in trials of internet-based treatment for symptoms of depression



115

(see Chapter 2, Table 1). We dealt with missing values by applying the Data Augmentation

procedure to multiply impute the missing values. This is currently the most sophisticated

method available to create Multiple Imputations (MI) (Allison 2001), and it provides

outcomes that are more in line with the real situation than alternative methods for handling

missing data. Therefore, we can assume that the imputed values and subsequent analyses

are sound.

Since this was the first trial regarding internet-based treatment in older adults in

The Netherlands, we only included participants with subthreshold depression. As a

consequence, the effect of this internet-based intervention on major depression remains to

be examined. After finding such good results for subthreshold depression, we are

encouraged to feel that this intervention may also be effective in persons with major

depression. Furthermore, since the Coping With Depression course, on which the internet

intervention was based, has also been used as bibliotherapy in older adults with depression,

and was proved to be effective (Scogin et al. 1989), there is no reason to suggest that this

would not be true for the internet-based version.

Finally, the study was designed to evaluate internet-based treatment for persons of

over 50 years of age (of whom 88% were between 50-60 years), which limits the ability to

generalise the findings.

Future research

In the traditional doctor-centred view of health care, it seems less desirable to ‘put clients

off’ with internet-based self-help. However, there is a large number of persons who refuse

any mental health care that is currently on offer. In the view of client-centred health care, it

is the client who defines the conditions under which treatment will be accepted. Internet-

based treatment might offer the kind of help clients are willing to accept, on their

conditions. Clients who are treated on the internet can avoid the stigma incurred by seeing a

therapist (Gega et al. 2004). On the internet, they are more anonymous compared to face-to-

face treatment. Furthermore, they can obtain treatment at any time and place, work at their

own pace, and review the material as often as desired.

An important task for researchers studying internet-based treatment is to

investigate the possibility of providing internet-based treatment to patients with other

mental health problems, such as severe depression and anxiety disorders. Since internet-

based treatment is a new alternative, we should investigate if this group would be willing to


https://www.researchgate.net/publication/283799528_Comparative_efficacy_of_cognitive_and_behavioral_bibliotherapy_for_mildly_and_moderately_depressed_older_adults?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=

116

accept this type of treatment, and if so, we should aim to develop the best possible internet-

based interventions for such persons. Moreover, in countries where people live long

distances from treatment facilities, internet-based interventions could increase the

willingness to accept treatment.

Cost-effectiveness of internet-based interventions is something else that should be

examined thoroughly. Knowledge of cost-effectiveness of internet-based treatment could be

used to convince public policy makers of the usefulness of internet-based treatment. Cost-

effectiveness studies could provide knowledge that would help in the decision over to what

extent internet-based treatment could be used in the prevention of mental health problems.

Depending on cost-effectiveness, a whole range of increasingly extensive treatments could

be designed: (i) for universal prevention, short psycho-educational programmes directed at

the entire population; (ii) for selective prevention, somewhat more extensive programs may

be appropriate; and (iii) for indicated prevention, interventions such as the one that was

studied in this thesis: aimed at resolving symptoms already present in very high risk groups.

117

REFERENCES






depression by using the Internet: randomised controlled trial. British Medical

Journal, 328, 265-267.




Mulder, R.T. (2002). Personality pathology and treatment outcome in major depression: A

review. American Journal of Psychiatry 159, 359-371.

Scogin, F., Jamison, C., Davis, N. (1989). Comparative efficacy of cognitive and

behavioral bibliotherapy for mildly and moderately depressed older adults. Journal of

Consulting and Clinical Psychology 57, 403-407.



https://www.researchgate.net/publication/7960132_Delivering_interventions_for_depression_by_using_the_internet_Randomised_controlled_trial?el=1_x_8&enrichId=rgreq-be01c4941ba01f58afe963906508f6c7-XXX&enrichSource=Y292ZXJQYWdlOzY2ODYyMTg7QVM6MTAyNjE3NjEwOTgxMzgwQDE0MDE0NzczNTMzNzA=








119

SUMMARY

Depression is a major health problem. In persons over 50 years of age, the prevalence of

major depression is 1-3%, and the prevalence of subthreshold depression in this population

is 8-16% (Beekman et al. 1995; Cole & Dendukuri, 2003). Persons with subthreshold

depression have symptoms of depression, but enough to meet DSM-IV criteria for major

depression. Subthreshold depression has considerable effects on well-being and

psychosocial functioning (Beekman et al. 1995, 2002; Rapaport & Judd, 1998; Lewinsohn

et al. 2000). In fact, persons with subthreshold depression are quite similar to those with a

diagnosis of major depression with regard to their psychosocial functioning (Gotlib et al.

1995). Furthermore, persons with subthreshold depression experience nearly the same

degree of impairment in health status, functional status, and disability compared to those

diagnosed with major depression (Wagner et al. 2000). Treatment of subthreshold

depression is very important. Given its high prevalence and the fact that probably less than

20% of persons with depression are detected and treated, new approaches are needed to

treat subthreshold depression and to prevent major depressive episodes. Internet-based

treatment may partly help to solve this problem. The main aim of this study was to validate

a newly developed internet-based treatment, comparing it, by means of a randomized

controlled trial, to an evidence based group treatment and to a waiting list control condition.

Before the randomized controlled trial was begun, a meta-analysis on the effects of

internet-based treatment for symptoms of depression and anxiety was conducted. We found

that the effectiveness of internet-based treatments varied considerably, some interventions

were very effective; other interventions had no measurable effects. It appears that the

amount of professional support provided with the interventions could play an important part

in differentiating between effective and less effective interventions.

In order to gain more insight into the applicability of internet-based screening for

depression, we studied the psychometric properties of the online-administered Edinburgh

Depression Scale (EDS). We found that these were comparable to the psychometric

characteristics of the paper-and-pencil version of the EDS.

In a large randomized controlled trial, we investigated the efficacy of a new

internet-based self-help intervention by comparing it to participants receiving evidence-

based group treatment and to a waiting-list. A total of 301 participants were randomized to

these three conditions. Depressive symptoms were measured before the start of treatment

and directly after treatment by means of the BDI. The improvement scores of the



120

participants in the treatment groups were compared to those of the waiting-list. Internet-

based treatment was significantly more effective compared to the waiting-list. Moreover,

internet-based treatment was as effective as evidence-based group treatment.

One year after the start of treatment, we measured depressive symptoms once

more. Once again, the internet-based cognitive behaviour therapy was significantly more

effective than the waiting-list condition. We did not find any significant differences in

effectiveness between group treatment and internet-based treatment.

Personality characteristics were studied as predictors of treatment outcome. We

found that participants with higher neuroticism scores had worse treatment outcomes.

Furthermore, participants with higher altruism scores benefited more from group treatment

but not from internet-based treatment. Participants with higher altruism scores may have

felt more at ease in the group, and have experienced more support from other group

members than their less altruistic counterparts.

Having carried out this extensive study on internet-based treatment, we have come

to the following conclusions: The new internet-based treatment for persons over 50 years of

age with subthreshold depression is effective, even without the support from a therapist,

and it can be implemented in mental health care practice. Internet-based screening for

depression using the Edinburgh Depression Scale is works very well. Internet-based

treatment in general seems to be a very promising approach to treatment, especially when

taking into consideration the discrepancy between the prevalence of mental health problems

and the current capacity to offer face-to-face treatment.

121

SAMENVATTING

Depressie is een groot gezondheidsprobleem. De prevalentie van depressie bij 50-plussers

is 1% tot 3%; depressieve klachten komen vaker voor, bij 8 tot 16% van de 50-plussers

(Beekman et al. 1995; Cole & Dendukuri, 2003). Mensen met depressieve klachten hebben

symptomen van depressie, maar niet zoveel dat de DSM-IV diagnose depressie gesteld kan

worden. Het hebben van depressieve klachten heeft behoorlijke effecten op het

welbevinden en het psychosociaal functioneren (Beekman et al. 1995, 2002; Rapaport &

Judd, 1998; Lewinsohn et al. 2000). Mensen met depressieve klachten lijken in hun

psychosociaal functioneren zelfs erg op mensen met een depressie (Gotlib et al. 1995).

Daarnaast ervaren mensen met depressieve klachten bijna dezelfde beperkingen op het

gebied van hun gezondheidsstatus, functionele status en lichamelijke handicaps als mensen

met een depressie (Wagner et al. 2000). Daarom is de behandeling van depressieve klachten

erg belangrijk. De hoge prevalentie van depressie en het feit dat waarschijnlijk minder dan

20% van de mensen met een depressie behandeld wordt, vraagt om een nieuwe aanpak met

betrekking tot de behandeling van depressieve klachten en de preventie van depressie.

Internet interventies zouden hierbij uitkomst kunnen bieden. Het belangrijkste doel van dit

onderzoeksproject was het valideren van een nieuw ontwikkelde internet interventie, door

deze in een gerandomiseerd, gecontroleerd onderzoek te vergelijken met een bewezen

effectieve groepscursus en met een wachtlijst conditie.

Voor aanvang van het onderzoek naar de nieuwe cursus, is er eerst een meta-

analyse uitgevoerd op de effecten van internet interventies voor symptomen van depressie

en angst. Hieruit bleek, dat de effecten van internet interventies behoorlijk uiteen kunnen

lopen: sommige interventies waren erg effectief, maar van andere interventies was geen

effect aan te tonen. Het leek erop, dat de hoeveelheid professionele ondersteuning die bij de

interventies werd gegeven, het verschil maakte tussen effectieve en minder effectieve

interventies.

Om meer inzicht te krijgen in de toepasbaarheid van screening voor depressie via

het internet, hebben we de psychometrische eigenschappen van de via internet afgenomen

Edinburgh Depression Scale bestudeerd. Deze bleken vergelijkbaar te zijn met de

psychometische eigenschappen van de op papier afgenomen Edinburgh Depression Scale.

In een grote gerandomiseerde, gecontroleerde studie onderzochten we de

effectiviteit van een nieuwe internet interventie door deze te vergelijken met een bewezen

effectieve groepscursus en met een wachtlijst conditie. In totaal werden 301 deelnemers



122

gerandomiseerd over de drie groepen. Depressieve symptomen werden vlak voor aanvang

van de behandeling en direct na de behandeling gemeten met behulp van de Beck

Depression Inventory. De verbetering in depressieve symptomen van de deelnemers aan de

internet interventie werd vergeleken met die van de deelnemers aan de groepscursus en die

van de deelnemers op de wachtlijst. De internet interventie was significant effectiever dan

de wachtlijst conditie; de internet interventie bleek even effectief als de groepscursus.

Een jaar na aanvang van de behandeling werden opnieuw de depressieve

symptomen van de deelnemers gemeten. Ook op dit meetmoment was de internet

interventie significant effectiever dan de wachtlijst conditie. We vonden geen verschil in

effectiviteit tussen de groepscursus en de internet interventie.

Er werd ook onderzocht of persoonlijkheidskenmerken invloed hadden op de

uitkomsten na behandeling. Het bleek, dat deelnemers die hoger scoorden op neurotisisme,

na de behandeling minder verbetering in depressieve symptomen lieten zien. Daarnaast

vonden we, dat deelnemers met hogere scores op altruïsme, betere resultaten hadden na de

groepscursus, maar niet na de internet interventie. Mogelijk voelden deelnemers met hogere

altruïsme scores zich beter op hun gemak in de groep en ervoeren ze meer sociale steun dan

de minder altruïstische deelnemers.

Naar aanleiding van deze resultaten, kunnen we de volgende conclusies trekken.

De nieuw ontwikkelde internet interventie voor 50-plussers met depressieve klachten is

effectief, zelfs zonder professionele ondersteuning, en kan worden geïmplementeerd in de

praktijk. Screening voor depressie via het internet is heel goed mogelijk. Over het algemeen

genomen, lijken internet interventies een veelbelovende aanpak, vooral gezien de

discrepantie tussen de prevalentie van psychische klachten en de huidige

behandelcapaciteit.

123

CURRICULUM VITAE

Viola Spek was born December 30th, 1976 in Roosendaal, The Netherlands. She completed

her pre-university education at the Gertrudis Lyceum in Roosendaal in 1996. From 1996 to

1997, she studied Social Work at Hogeschool Rotterdam en Omstreken. In 1997, she

started her psychology studies at Tilburg University. After finishing her studies in 2003,

she started her PhD research. She has published papers in Psychological Medicine and the

Journal of Affective Disorders and has presented her work at international conferences in

Stockholm and Barcelona. In 2007, she won the Faculty of Social and Behavioural

Sciences’ PhD Article prize. Currently, she is working as a post-doctoral researcher at

Tilburg University.

Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: A meta-analysis

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