Behavioral Activation Treatments for Depression in Adults: A Meta-analysis and Review

Behavioral Activation Treatments for Depression in

Adults: A Meta-analysis and Review

Trevor Mazzucchelli, Robert Kane, and Clare Rees, School of Psychology, Curtin University of

Technology

Behavioral activation (BA) treatments for depression

require patients to increase overt behavior to bring

them in contact with reinforcing environmental contin-

gencies. This meta-analysis sought to identify all ran-

domized controlled studies of BA, determine the effect

of this approach, and examine the differential effective-

ness of variants. Thirty-four studies with 2,055 partici-

pants reporting symptoms of depression were included.

The pooled effect size indicating the difference between

BA and control conditions at posttest was 0.78. For par-

ticipants who satisfied the criteria for major depressive

disorder, the overall effect size of 0.74 remained large

and significant. No differences in effectiveness between

BA and cognitive therapy were found. BA may be con-

sidered a well-established and advantageous alternative

to other treatments of depression.

Key words: behavioral activation, cognitive therapy,

depression, meta-analysis, psychotherapy. [Clin Psychol

Sci Prac 16: 383–411, 2009]

Major depressive disorder (MDD) is associated with

significant distress, impairment of functioning and an

increased risk of suicide (Hirschfeld et al., 1997). Com-

munity surveys report that 4–10% of the general popula-

tion experience an episode of MDD in any given year

(Andrews, Henderson, & Hall, 2001; Kessler et al.,

2003), and MDD is expected to impose the second larg-

est burden of ill health worldwide by 2020 (Murray &

Lopez, 1996). While several evidence-based treatments

for MDD exist—including antidepressant medication,

cognitive, and interpersonal therapy—studies have

shown that many people with depression receive inade-

quate treatment or no treatment at all (Hirschfeld et al.,

1997). The search for more effective and cost-effective

treatments continues (Segal, Williams, & Teasdale,

2002).

Behavioral activation (BA) treatments evolved out of

the reinforcement explanation of depression, which pro-

poses that the behavior of depression is the result of a loss

or lack of response-contingent positive reinforcement

(Lewinsohn, 1974). Under such reinforcement condi-

tions, repertoires of behavior are insufficiently rewarded

and behavior deteriorates in frequency, intensity, and

quality. In support of this proposal was the finding that

there is a significant relationship between mood and par-

ticipation in pleasant activities (Lewinsohn & Graf, 1973;

Lewinsohn & Libet, 1972). Individuals with depression

find fewer activities pleasant and engage in pleasant

activities less frequently, and therefore obtain less posi-

tive reinforcement than other individuals (MacPhillamy

& Lewinsohn, 1974).

Based on this theory, Lewinsohn, Sullivan, and

Grosscap (1980) developed a behavioral treatment of dep-

ression in which patients monitor their mood and daily

activities in order to see the connection between them.

Subsequently, each patient is taught how to decrease

the frequency and subjective aversiveness of unpleasant

events inhisorher life, and to increasepleasantones.

The self-control theory of depression by Rehm

(1977) elaborated the traditional behavioral model by

Address correspondence to Trevor Mazzucchelli, Curtin

University of Technology, School of Psychology, GPO Box

U1987, Perth, WA 6849, Australia. E-mail: trevorm@

iinet.net.au.

� 2009 American Psychological Association. Published by Wiley Periodicals, Inc., on behalf of the American Psychological Association.All rights reserved. For permissions, please email: [email protected] 383

formulating it from the viewpoint of the model by

Kanfer (1970) of self-control. From this perspective,

self-control skills are seen as important for ensuring that

an individual obtains external reinforcement by either

persisting in or changing goal-directed behavior in the

face of setbacks. Individuals with depression selectively

attend to immediate negative consequences of their

behavior to the exclusion of delayed positive conse-

quences or regardless of actual contingencies. These

individuals may also set very high standards for them-

selves and consequently fail to achieve goals and self-

reinforce at a very low rate and self-punish at a very

high rate.

Teaching patients self-management skills to help

progress toward personally important goals and partici-

pate more in behaviors that are reinforcing became a

central component of a treatment program based on

Rehm’s self-control theory. Participants are required to

keep a daily log of their activities and mood in order

to see the association between the two. Participants are

also taught to define goals in positive ways (e.g., mak-

ing better friends with women in my neighborhood)

and to break those goals down into realistic, attainable,

sub-goal activities (e.g., phone a friend to chat).

Finally, participants are taught a system to self-evaluate

their behavior and to self-administer rewards (Fuchs &

Rehm, 1977; Rehm & Kornblith, 1979).

Trials of these approaches found mostly promising

results (e.g., Barrera, 1977, 1979; Fuchs & Rehm,

1977; Kornblith, Rehm, O’Hara, & Lamparski, 1983;

McNamara & Horan, 1986; Rehm, Kornblith,

O’Hara, Lamparski, Romano, & Volkin, 1981; Zeiss,

Lewinsohn, & Munoz, 1979). Multicomponent treat-

ments for depression that combined these BA tech-

niques with cognitive approaches were also developed

during this period. In their original Cognitive Therapy of

Depression treatment manual, Beck, Rush, Shaw, and

Emery (1979) devoted an entire chapter to behavioral

techniques, including activity scheduling, self-monitoring,

graded task assignment, and role-playing. Lewinsohn,

Antonuccio, Steinmetz, and Teri (1984) developed a

psychoeducational course called coping with depression,

which included elements relating to increasing pleasant

activities, social skills training, relaxation training,

and cognitive restructuring. Cognitive restructuring

was added as another approach to increase availability

of perceived reinforcement and decrease perceived

punishment. Similarly, Rehm’s self-control program

was expanded and revised in a series of therapy out-

come studies, with an increased emphasis on covert

reinforcement involving positive self-statements as con-

tingent rewards following difficult positive activities or

sub-goal activities (Rehm, 1984; Reynolds & Coats,

1986; Rokke & Rehm, 2001; Stark, Reynolds, &

Kaslow, 1987). These treatments have been demon-

strated to be effective, but only recently has evidence

emerged indicating that it may be the behavioral

components that largely contribute to these effects.

In an elegant treatment-dismantling study of cogni-

tive therapy (CT) for depression, Jacobson and col-

leagues found that the behavioral component of CT

was equally effective alone or in combination with the

cognitive components (Gortner, Gollan, Dobson, &

Jacobson, 1998; Jacobson et al., 1996). On the basis of

this result, an expanded version of this behavioral

intervention was developed (Jacobson, Martell, &

Dimidjian, 2001; Martell, Addis, & Jacobson, 2001).

This model draws from the work of Ferster (1973),

emphasizing the role of avoidance in depression and

contextualism (Jacobson, 1994). Avoidance behavior

(e.g., of interpersonal situations, occupational or daily-

life demands, and distressing thoughts or feelings) is

viewed as a coping strategy to avoid the short-term

distress that is often associated with pursuing potentially

mood-enhancing reinforcers, at the longer-term cost of

reducing opportunities to contact potentially mood-

enhancing environmental reinforcers and by creating or

exacerbating new problems secondary to the decreasing

activity. Increased activation and engagement is

presented as a strategy to break this cycle.

The initial treatment objective of Jacobson and col-

leagues’ BA approach is to increase patients’ awareness

of avoidance patterns by teaching a functional analytic

model for understanding their behavior. Once these

patterns are recognized, the principal objective

becomes one of helping the patients to identify and

reengage with activities and contexts that are reinforc-

ing and consistent with their long-term goals. Many of

the same behaviorally focused activation strategies used

in CT are used, including self-monitoring, structuring,

and scheduling daily activities, rating the degree of

pleasure and accomplishment experienced during

CLINICAL PSYCHOLOGY: SCIENCE AND PRACTICE • V16 N4, DECEMBER 2009 384

engagement in specific daily activities, exploring alter-

native behaviors related to achieving goals, and using

role-playing to address specific behavioral deficits. In

addition, this protocol includes the establishment or

maintenance of routines, and behavioral strategies for

targeting rumination, including an emphasis on the

function of ruminative thinking and on moving atten-

tion away from the content of ruminative thoughts

toward direct, immediate experience.

Jacobson and colleagues’ contextual BA approach

has been found to be comparable with antidepressant

medication (paroxetine) with respect to the reduction

in acute distress regardless of the level of initial severity

and superiority to CT among more severely depressed

patients. Further, BA demonstrated an advantage over

paroxetine by having a significantly lower attrition rate

(Dimidjian et al., 2006).

In an independent research program, Lejuez,

Hopko, and Hopko (2001, 2002) and Lejuez, Hopko,

LePage, Hopko, and McNeil (2001) developed Brief

Behavioral Activation Treatment for Depression

(BATD). This treatment is based on behavioral match-

ing theory (Hernstein, 1970; McDowell, 1982). It sug-

gests that depression occurs when environmental

change causes reinforcers for depressed behavior to

become more accessible and immediate relative to rein-

forcers for healthy behavior. This results in a directly

proportional change in the time and effort devoted to

exhibiting depressed behaviors relative to nondepressed

behaviors. Based on this model, the BATD model

attempts to create an environment that supports healthy

behavior by seeking the agreement from family and

friends to notice and respond positively to healthy

behavior and reduce reinforcement (such as sympathy

and opportunities to escape from responsibilities) in

response to depressed behavior. The emphasis then

shifts to identifying goals in major life areas such as

relationships, education, employment, hobbies, and

recreational activities. Activities related to these goals

are developed and put on activity hierarchies that

patients progressively move through. Patients are taught

to reward themselves for achieving weekly goals by

scheduling enjoyable activities that they can engage in

if they complete their activity goals. The BATD proto-

col has been reported to be successful in a number of

mostly small open trials (e.g., Hopko, Bell, Armento,

Hunt, & Lujuez, 2005; Hopko, Lejuez, & Hopko, 2004;

Hopko, Lejuez, LePage, Hopko, & McNeil, 2003;

Hopko, Sanchez, Hopko, Dvir, & Lejuez, 2003;

Lejuez et al., 2001).

The significance of the BA approach is that it

may be simpler to deliver and thus represent a more

parsimonious treatment option (Jacobson et al., 1996).

If similar health outcomes could be achieved with

simpler interventions, or a lesser dose of psychotherapy,

there is potential for increasing the efficiency of

services and the reach of effective interventions. The

present review suggests that there are at least four

interventions that satisfy the definition of BA by

Hopko, Lejuez, Ruggiero, and Eifert (2003), namely

‘‘a therapeutic process that emphasizes structured

attempts at engendering increases in overt behavior

that is likely to bring the patient into contact with

reinforcing environmental contingencies and produce

corresponding improvements in thoughts, mood, and

overall quality of life’’ (p. 700). However, even these

interventions differ in terms of their complexity.

Jacobson and colleagues’ protocol includes a significant

emphasis on assisting patients with functional analytic

interpretations of behavior. It also includes many strate-

gies not incorporated within other BA interventions,

such as mental rehearsal, periodic distraction, mindful-

ness training, and skill-training procedures. It remains

to be seen whether this omnibus style intervention is

superior to other simpler versions of BA, and which

treatment strategies account for the greatest outcome

variance.

A recent meta-analysis by Cuijpers, van Straten, and

Warmerdam (2007) included 16 studies involving BA

and concluded that pleasant activity scheduling is

slightly superior to other psychological treatments and

equal to CT at posttest and follow-up. A subsequent

meta-analysis by Ekers, Richards, and Gilbody (2008)

included 17 studies and concluded that behavior thera-

pies were superior to controls, brief psychotherapy,

supportive therapy, and equal to cognitive behavioral

therapy. The present study replicates and significantly

extends these meta-analyses by including the results

from 34 studies. Moreover, in addition to examining

the effects of BA relative to other therapeutic

approaches, the present study examines the differential

effectiveness of variants of BA and hence goes partway

BEHAVIORAL ACTIVATION TREATMENTS FOR DEPRESSION • MAZZUCCHELLI ET AL. 385

to exploring whether more complex versions of BA

add anything to more parsimonious versions of the

approach. Unlike previous studies, the present study

also considers whether the strong effect sizes obtained

with participants reporting elevated symptoms of

depression are still shown with participants who satisfy

the criteria for MDD. Finally, the present meta-analysis

is complemented with a focused evidence review using

the criteria developed by the Task Force within

Division 12 (Society of Clinical Psychology) of the

APA to identify well-established and probably effica-

cious BA treatments for depression (Chambless et al.,

1998; Task Force on Promotion and Dissemination of

Psychological Procedures, 1995).

METHOD

Identification and Selection of Studies

A computer search (using PsycINFO and MEDLINE

databases) was conducted to find articles, chapters, and

dissertations published between January 1970 and

September 2008 that included the terms activity schedul-

ing, behavioral activation or behavioural activation, pleasant

events, or pleasant activities. Reference lists of all articles

were searched for additional articles. Studies were

included in the meta-analysis if effects of a BA interven-

tion on typically developing (i.e., without an intellectual

disability) adults with a depressive disorder or an

elevated level of depressive symptomatology were

compared with a control condition or another psycho-

logical or active pharmacological treatment in a random-

ized controlled trial. No language restrictions were

applied, and unpublished dissertations, where available,

were included so as to describe the universe of studies.

Over 580 articles, chapters, and theses were reviewed.

Studies were excluded for a variety of reasons,

including the absence of a control group, use of a child

sample, participants had an intellectual disability, insuf-

ficient information to extract effect sizes, or because

the BA approach was combined with other treatment

components such as the modification of dysfunctional

thoughts or pharmacotherapy ⁄ placebo (Hollon &

DeRubeis, 1981).

Behavioral Activation

The BA approach is rooted in the behavioral tradition

established by Ferster (1973) and Lewinsohn (1974). A

treatment was considered to be BA when it primarily

involved strategies to prompt participants to engage

with, or act on, the environment so as to increase posi-

tive reinforcement and undermine punishment. Social

skills training could be a part of the intervention if the

purpose of this training was explicitly framed within

the goals of the BA approach. Four variants of the BA

approach were identified.

Pleasant activities. These interventions credited Lew-

insohn and mostly involved monitoring and scheduling

pleasant activities. Social skills training was sometimes

also part of the intervention protocol.

Self-control. These interventions credited Rehm and

included such elements as monitoring activities and

mood, goal setting, self-evaluation of performance,

and self-administering rewards. Only versions of this

approach that had a behavioral focus were included.

Contextual. These interventions either credited Jacobson

and colleagues (Jacobson et al., 2001; Martell et al.,

2001) or included the behavioral component of CT

that was evaluated by Jacobson and colleagues (Gortner

et al., 1998; Jacobson et al., 1996). Activity scheduling,

self-monitoring, graded task assignment, and role-playing

were part of this intervention as well as functional analy-

sis, mental rehearsal, and mindfulness in newer versions.

Behavioral activation treatment for depression. These inter-

ventions credited Lejuez, Hopko, and Hopko (2001)

or Lejuez, Hopko, LePage, et al. (2001). They con-

sisted of contracting to change immediate environ-

mental contingencies, goal setting and graduated task

assignment, monitoring, and self-administering rewards.

Comparators

The effects of BA were examined relative to a range of

comparison conditions. These are listed below.

Control conditions. Waiting list or a range of nontreat-

ment options (minimal contact, pharmacological pla-

cebo) delivered to the patient in the absence of any

active treatment. In most cases this condition continued

for the duration of, but not beyond, the active inter-

vention conditions.

Cognitive behavioral therapy ⁄ cognitive therapy (CBT ⁄ CT).

Interventions that identified, questioned, and modified


cognitive responses to situations and their emotional

consequences. We included any intervention that

explicitly aimed to change thinking—whether automatic

thoughts or self-statements. Behavioral strategies were

sometimes also part of the intervention protocol.

Other. This included all other active treatment condi-

tions including psychotherapy approaches that focused

on developing insight and subsequent character devel-

opment through interpersonal relationships with the

therapist, including brief psychodynamic therapy

(Bellak & Small, 1965; Bernard & Klein, 1977;

Horowitz & Kaltreider, 1979). It also included

supportive counseling (Rogers, 1961), problem solving

(D’Zurilla, 1986), assertiveness training, education

about the intervention, monitoring and increasing

placebo activities, and treatment as usual.

Quality Assessment

The methodological quality of each study was assessed

using nine criteria based on a range of important

methodological features of psychotherapy research

(Chambless & Hollon, 1998). These included adequacy

of sample size to allow a stable estimate of effect

size, confidence in sample description, confidence in

outcome assessment tools, use of treatment manuals,

adequacy of therapist training and monitoring, extent

that investigator allegiance is balanced, equivalence of

comparison groups at pretest, completeness of data set

such that observations did not systematically exclude

participants who refused treatment or dropped out, and

checks for therapist or site effects. Studies were

allocated a numerical rating from 0 to 17 according to

the extent that these criteria were met. No studies

were excluded on the basis of methodological quality.

Disagreements regarding study quality were dealt with

through discussion.

Meta-analysis

Standardized mean difference effect sizes (ESsm) were

calculated with the following formula: ESsm = (Mc )Mt) ⁄ SDp, in which Mt is the mean of the treatment

group on a specific outcome, Mc is the mean of the

comparison group, and SDp is the pooled standard

deviation of the two groups. If mean values or standard

deviations were not provided, effect sizes were calcu-

lated from the t or F ratio, or from the significance

level when t or F were not reported. When only

diagnostic status data were encountered, the arcsine

transform method was used to adjust for the dichoto-

mization. When necessary, effect sizes were also calcu-

lated from chi-squared data. Calculations of effect sizes

relied on methods described by Lipsey and Wilson

(2001) using Effect Size Determination Program

(Wilson, 2004).

In calculations of effect sizes for depression, only

those instruments that explicitly measured symptoms of

depression were used. If more than one depression

measure was used, the mean of the effect sizes was cal-

culated, so that each study (or comparison group) only

had one effect size. Where studies included two com-

parisons under the same category (i.e., CT and CBT)

or presented results using subcategories (e.g., high ⁄ low

depression severity), these comparisons were combined,

taking into account the relative proportions of partici-

pants in the different conditions.

Hedges’s (1981) correction for small sample bias

was applied to all effect sizes. The resultant Hedges’s gs

were then combined using the following formula: Mg =P

wigi ⁄P

wi, where wi is the weight for each study and

gi is the effect size for each study. Comprehensive meta-

analysis (version 2.2.046; Borenstein, Hedges, Higgins,

& Rothstein, 2007) was used for these calculations.

Although a widely used convention for interpreting

effect size values was established by Cohen (1988)

based on his informal observations of behavioral

research, the present study used the distribution of

mean effect sizes for 302 meta-analyses of psychologi-

cal, behavioral, and educational interventions as a

benchmark (Lipsey & Wilson, 1993). Effect sizes of

0.67 or greater are assumed to be large, while effect

sizes of 0.31–0.66 are medium, and effect sizes of

0.00–0.30 are small (Lipsey & Wilson, 2001).

As an indicator of homogeneity, Cochran’s hetero-

geneity statistic Q was calculated. This statistic tests the

null hypothesis that effect sizes from each of the studies

are similar enough that a common population effect

size can be estimated (Cochran, 1954). Cochran’s Q

was calculated using the following formula:

Q =P

wi(gi ) gw)2, where gi is the individual effect

size for i = 1 to k (the number of effect sizes), gw is the

weighted mean effect size over the k effect sizes, and


wi is the individual weight for gi. The significance of Q

is evaluated against a chi-squared distribution with

k ) 1 degrees of freedom.

The I2 statistic—an indicator of heterogeneity in

percentages (I2 = 100% · (Q ) d.f.) ⁄ Q, where Q is

Cochran’s heterogeneity statistic and d.f. the degrees of

freedom)—was also calculated. A value of 0% indicates

no observed heterogeneity, and larger values show

increasing heterogeneity, with 25% as low, 50% as

moderate, and 75% as high heterogeneity (Higgins,

Thompson, Deeks, & Altman, 2003).

It was assumed that each effect size estimated the

corresponding population effect with random error

stemming only from chance factors associated with the

study’s subject-level sampling error. Consequently, in

the first instance, mean effect sizes were calculated with

the fixed effects model. If the fixed effects assumptions

were rejected on the basis of a significant Q, however,

a random effects model was adopted. As one of the

goals of the present study was to investigate the differ-

ential effectiveness of variants of BA, subgroup analysis

of variants was routinely conducted. When the fixed

effects assumptions were rejected because of systematic

variability beyond subject-level sampling error, linear

regression was also used to investigate the relationship

between study characteristics and effect size (Lipsey &

Wilson, 2001).

Caution is needed in interpreting meta-analytical

findings because of the potential for selection and other

biases that may be introduced in the process of locat-

ing, selecting, and combining studies (Egger, Davey-

Smith, Schneider, & Minder, 1997). Such bias was

examined using a funnel graph, a plot of sample size

versus effect size estimate (Light & Pillemer, 1984). If

no bias is present, this plot should be shaped like an

inverted funnel, with a broad spread of points for the

less precise smaller studies at the bottom and decreasing

spread as the sample size increases. Asymmetry was

tested using the Egger-weighted regression test where

the intercept is 0 if no bias is present (Egger et al.,

1997). If asymmetry was found to be significant, the

trim and fill method of Duval and Tweedie (2000) was

used to estimate the number of missing studies that

might exist and the effect that these studies might have

had on its outcome. Finally, the fail-safe N,1 an estimate

of the number of unpublished studies reporting null

results needed to reduce the calculated effect to the

point of nonsignificance, was calculated. If the fail-safe

N is greater than or equal to the critical number of stud-

ies,2 an estimate of the number of nonsignificant studies

filed away, it was assumed that the significance of the

observed effects is unchallengeable. A level of a = .05

was used for all statistical tests.

The American Psychological Association’s (APA’s)

Division 12 Task Force on Promotion and Dissemina-

tion of Psychological Procedures developed criteria for

well-established and probably efficacious treatments

(Chambless & Hollon, 1998; Chambless et al., 1996;

Task Force, 1995). Well-established treatments must be

shown to be superior or equivalent to an already

established treatment, in experiments with adequate

sample sizes (25–30 per condition) and conducted by

different investigators. Alternatively, they must have

demonstrated efficacy in a large series of single case

designs. Further, experiments must be conducted with

the use of treatment manuals and well-specified sam-

ples. Designation as probably efficacious requires two

studies showing the treatment to be more effective

than a waiting list control group, or to an already

established treatment (but conducted by the same

investigator), or a small series of single case designs.

RESULTS

Description of Studies

Thirty-four studies, with a total of 2,055 participants,

met the inclusion criteria and were included in the

current study. Data on these studies were sourced from

30 published papers, one published book chapter, and

eight unpublished theses. Published and unpublished

studies did not differ from each other in terms of their

quality (t[32] = .12, p = .90). Thirty-eight of these

works were in English, one in Spanish. Selected char-

acteristics of the included studies are described in

Table 1. (Note: throughout the following sections,

numbers within square brackets refer to the study

numbers listed in Table 1.) Three studies included in

the meta-analysis of Cuijpers et al. (2007) were

excluded. The study by Teri, Logsdon, Uomoto, and

McCurry (1997) was excluded because the sample of

patients with Alzheimer’s disease was mostly passive in

scheduling activities and it was caregivers who rated

patients’ mood. The study by Zeiss et al. (1979) was


Tab

le1.

Sele

cted

char

acte

rist

ics

of

contr

olle

dan

dco

mpar

ativ

est

udie

son

beh

avio

ralac

tiva

tion

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[1]

Bar

low

(1986)

US

Old

erad

ults

(M=

77)

Senio

rci

tize

nap

artm

ent

build

ings

Age

‡65

(exc

luded

ifac

tive

lysu

icid

al,

moder

atel

yor

seve

rely

dem

ente

d,

or

rece

ivin

gtr

eatm

ent

for

dep

ress

ion);

Hig

hdep

ress

edsu

bgro

up

(CES

-D>

15)

1.

Self-c

ontr

ol

[sel

f-co

ntr

ol]�

10

�6

6G

roup

6(9

0m

in)

�9

Pre

,post

CES

D9

(mod)

2.

Wai

ting

list

(6w

eeks

)�

5�

6N

AN

A6

(90

min

)�

9

[2]

Bar

rera

(1977,

1979)a

US

Adults

(M=

36)

Com

munity

MM

PI-

D>

80T;

GFC

CFa

ctor

I>

1.0

;G

FCC

Fact

or

I-II

I,V

>.7

0

1.

Act

ivity

sched

ulin

g,

imm

edia

tetr

eatm

ent

[ple

asan

tac

tivi

ties

]

10

60

4G

roup

8(6

0m

in)

10

Pre

,post

,1-,

2-,

8-

month

FU

MM

PI-

D,

BD

I11

(mod)

2.

Self-

monitoring,

del

ayed

trea

tmen

t

10

40

4Tel

e-phone

4(N

R)

10

3.

Wai

ting

list

(4w

eeks

),del

ayed

trea

tmen

t

11

27

NA

NA

NA

18

[3]

Bes

yner

(1979)

US

Adults

(M=

42)

Com

munity

Self-r

eport

eddep

ress

ion

of

atle

ast

2-w

eek

dura

tion;

BD

I‡

13

1.

Cognitiv

eth

erap

y10

40

4G

roup

4(1

20

min

)0

Pre

,post

,1-m

onth

FU

BD

I9

(mod)

2.

Beh

avio

rth

erap

y[p

leas

ant

activi

ties

]

14

�27

4G

roup

4(1

20

min

)21

3.

Nonsp

ecifi

cth

erap

y10

�33

4G

roup

4(1

20

min

)10

4.

Wai

ting

list

(4w

eeks

)16

�18

NA

NA

4(1

20

min

)31

[4]

Cat

a-nes

eet

al.

(1979)

US

Young

adults

(M=

NR

)

Univ

ersi

tyBD

I‡

9;

SRD

S‡

40

1.

Ove

rtre

war

d(p

leas

ant

activi

tyco

ntingen

ton

blu

em

ood)

[ple

asan

tac

tivi

ties

]

26

�27

2G

roup

2(3

0m

in)

12

Pre

,post

,2-w

eek

FU

BD

I,SR

DS

5(low

)


Tab

le1.

(Conti

nued

)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat

bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

2.

Cove

rtre

war

d25

�27

2G

roup

2(3

0m

in)

12

3.

Ove

rtpunis

hm

ent

25

�27

2G

roup

2(3

0m

in)

24

4.

Cove

rtpunis

hm

ent

21

�27

2G

roup

2(3

0m

in)

10

5.

Soci

alin

fluen

ce26

�27

2G

roup

2(3

0m

in)

27

6.

Wai

ting

list

(4w

eeks

)32

�27

NA

NA

NA

34

[5]

Com

as-

Dia

z(1

981)

US

Low

-SES

Puer

toR

ican

wom

en(M

=38)

Com

munity

Dep

ress

ed;

BD

I;H

RSD

(not

furt

her

spec

ified

)

1.

Cognitiv

eth

erap

y8

04

Gro

up

5(9

0m

in)

0Pre

,post

,5-w

eek

FU

BD

I,H

RSD

9(m

od)

2.

Act

ivity

sched

ulin

g[p

leas

ant

activi

ties

]

80

4G

roup

5(9

0m

in)

0

3.

Wai

ting

list

(4w

eeks

)10

0N

AN

AN

A0

[6]

Culle

n(2

002);

Culle

net

al.

(2006)

US

Adults

(M=

38)

Com

munity

DSM

-IV

criter

iafo

rm

ajor

dep

ress

ion

(SC

ID-I

);BD

I-II

‡20;

RH

RSD

‡14

1.

Beh

avio

ral

activa

tion

[conte

xtual

]

13

69

10

Indiv

idual

10

(NR

)31

Pre

,post

BD

I-II

12

(hig

h)

2.

Wai

ting

list,

del

ayed

trea

tmen

t(6

wee

ks)

12

67

6In

div

idual

3(N

R)

33

[7]

Dim

idj-

ian

etal

.(2

006);

Dobso

net

al.

(2008)

US

Adults

(M=

40)

Com

munity

DSM

-IV

criter

iafo

rm

ajor

dep

ress

ion;

BD

I-II

‡20;

HR

SD‡

14

1.

Beh

avio

ral

activa

tion

and

cognitiv

eth

erap

y

45

�34

16

Indiv

idual

Max

24

(50 min

)

13

Pre

,m

id,

post

,12-,

24-m

onth

FU

BD

I-II

,H

RSD

15

(hig

h)

2.

Beh

avio

ral

activa

tion

[conte

xtual

]

43

�34

16

Indiv

idual

Max

24

(50 min

)

16

3.

Anti-

dep

ress

ant

med

icat

ion

100

�34

16

Indiv

idual

8(3

0m

in)

44

4.

Pla

cebo

med

icat

ion

53

�34

8In

div

idual

4(3

0m

in)

22

Pre

,m

idBD

I-II

,H

RSD


Tab

le1.

(Conti

nued

)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[8]

Eman

u-

els-

Zuurv

-ee

nan

dEm

mel

-ka

mp

(1996)

ND

Adults

who

wer

em

arried

or

cohab

itat

-in

g(M

=38)

Com

munity

DSM

-III

-Rcr

iter

iafo

runip

ola

rdep

ress

ion;

BD

I>

14;

spouse

will

ing

toco

oper

ate;

mar

ital

dis

tres

s

1.

Beh

avio

ral

ther

apy

incl

udin

gso

cial

skill

s[p

leas

ant

activi

ties

]

18

�48

8In

div

idual

8(6

0m

in)

12

Pre

,m

id,

post

BD

I10

(mod)

2.

Beh

avio

ral

mar

ital

ther

apy

18

�48

8C

ouple

8(6

0m

in)

43

Pre

,m

id,

post

BD

I

[9]

Fuch

san

dR

ehm

(1977)

US

Adults

(M=

29,

range

=18–4

8)

Com

munity

MM

PI:

F£

80,

L£

60,

D‡

70,

D>

Hy,

and

D>

Pt

and

Dam

ong

the

2hig

hes

tel

evat

ions

1.

Self-c

ontr

ol

[sel

f-co

ntr

ol]

12

06

Gro

up

6(1

20

min

)33

Pre

,post

,6-w

eek

FU

BD

I,M

MPI-

D8

(mod)

2.

Nonsp

ecifi

cth

erap

y12

06

Gro

up

6(1

20

min

)16

3.

Wai

ting

list

(6w

eeks

)12

06

NA

NA

16

[10]

Gal

la-

gher

(1981)

US

Old

erad

ults

(M=

68)

Clin

ical

sam

ple

Clin

ical

inte

rvie

w;

MM

PI-

D>

2SD

above

mea

n;

no

org

anic

impai

rmen

t

1.

Beh

avio

ral

ther

apy

[ple

asan

tac

tivi

ties

]

14

50

5G

roup

10

(90

min

)�

28

Pre

,post

,5-w

eek

FU

MM

PI-

D,

BD

I,SR

SD

9(m

od)

2.

Support

ive

14

50

5G

roup

10

(90

min

)�

28

[11]

Gal

la-

gher

and

Thom

pso

n(1

982)

US

Old

erad

ults

(M=

68)

Com

munity

RD

Cdia

gnosi

sof

curr

ent

defi

nite

epis

ode

of

nonpsy

chotic

MD

D,

BD

I‡

17,

HR

SD‡

14

1.

Beh

avio

ral

ther

apy

[ple

asan

tac

tivi

ties

]

15

40

12

Indiv

idual

16

(90

min

)33

Pre

,post

,1

1 2-,

3-,

6-,

12-m

onth

FU

BD

I,H

RSD

,SR

SD

9(m

od)

2.

Cognitiv

eth

erap

y11

10

12

Indiv

idual

16

(90

min

)9

3.

Brief

rela

tional

⁄in

sight

psy

cho-

ther

apy

12

20

12

Indiv

idual

16

(90

min

)16


Tab

le1.

(Conti

nued

)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat

bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[12]

Gal

la-

gher

-Thom

pso

net

al.

(2000);

Love

ttan

dG

alla

gher

(1988)

US

Adults

(M=

60;

range

=31–8

1)

Com

munity

Pro

vidin

gca

reto

anad

ult

60+

year

sw

ith

phys

ical

or

men

taldis

abili

ty.

20%

found

tohav

edefi

nite

maj

or

dep

ress

ion;

16%

pro

bab

lem

ajor

dep

ress

ion;

11%

min

or

dep

ress

ion;

16%

subsy

ndro

mal

dep

ress

ion

1.

Life

satisf

action

[ple

asan

tac

tivi

ties

]

78

�17

10

Gro

up

10

(120

min

)28

Pre

,post

RD

Cusi

ng

SAD

Sin

terv

iew

dat

a

10

(mod)

2.

Pro

ble

mso

lvin

g77

�17

10

Gro

up

10

(120

min

)23

3.

Min

imal

conta

ctco

ntr

ol

(10

wee

k)

58

�17

NA

NA

NA

21

[13]

Gra

f(1

977)

US

Young

adults

(M=

19)

Univ

ersi

tyBD

I>

12

1.

Incr

ease

dm

ood-r

elat

edac

tivi

ties

[ple

asan

tac

tivi

ties

]

30

�43

2In

div

idual

1(6

0m

in)

30

Pre

,post

BD

I10

(mod)

2.

Incr

ease

dco

ntr

ol

activi

ties

30

�43

2In

div

idual

1(6

0m

in)

10

3.

Act

ivity

monitoring

conditio

n

30

�43

2In

div

idual

1(6

0m

in)

27

[14]

Ham

-m

enan

dG

lass

(1975),

exper

imen

t1

US

Young

adults

(M=

NR

)

Univ

ersi

tyM

oder

ate

dep

ress

ion

(bas

edon

scre

enin

gw

ith

D30,

FCC

,&

PFS

)

1.

Incr

ease

activi

ties

[ple

asan

tac

tivi

ties

]

10

NR

2N

R1

(NR

)N

RA

vera

ge

score

from

dai

lyre

cord

ings

ove

r2

wee

ks

DA

CL,

EDSc

ale

5(low

)

2.

Expec

tancy

contr

ol

10

NR

2N

R1

(NR

)N

R

3.

Self-

monitor

contr

ol

10

NR

2N

R1

(NR

)N

R

4.

No

trea

tmen

tco

ntr

ol

(2w

eeks

)

10

NR

NA

NR

1(N

R)

NR

[15]

Ham

-m

enan

dG

lass

(1975),

exper

imen

t2

US

Young

adults

(M=

NR

)

Univ

ersi

tyD

epre

ssio

n(b

ased

on

scre

enin

gw

ith

D30

&BD

I)

1.

Incr

ease

activi

ties

[ple

asan

tac

tivi

ties

]

4or

5N

R1

NR

1(N

R)

NR

Ave

rage

score

from

dai

lyre

cord

ings

ove

r1

wee

k

DA

CL,

EDSc

ale

5(low

)


Tab

le1.

(Conti

nued)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

2.

Self-

monitor

5or

4N

R1

NR

1(N

R)

NR

[16]

Hopko

,Le

Juez

,Le

Pag

e,et

al.

(2003)

US

Psy

chia

tric

inpat

ients

(M=

30)

Clin

ical

sam

ple

Princi

pal

dia

gnosi

sof

MD

D

1.

Beh

avio

ral

activa

tion

trea

tmen

tfo

rdep

ress

ion

[BA

TD

]

10

�64

2In

div

idual

6(2

0m

in)

0Pre

,post

BD

I9

(mod)

2.

Support

ive

psy

choth

erap

y15

�64

2In

div

idual

6(2

0m

in)

0

[17]

Jaco

bso

net

al.

(1996);

Gort

ner

etal

.(1

998)

US

Adults

(M=

38)

Clin

ical

⁄co

mm

unity

DSM

-III

-Rcr

iter

iafo

rm

ajor

dep

ress

ion;

BD

I‡

20;

HR

SD‡

14

1.

Beh

avio

ral

activa

tion

[conte

xtual

]

57

28

NR

Indiv

idual

20

(NR

)�

8Pre

,post

,6-,

12-,

18-,

24-

month

FU

BD

I,H

RSD

15

(hig

h)

2.

Auto

mat

icth

oughts

(beh

avio

ral

activa

tion

and

modifi

cation

of

dys

funct

ional

thoughts

)

44

23

NR

Indiv

idual

20

(NR

)�

8

3.

Cognitiv

eth

erap

y(b

ehav

iora

lac

tiva

tion,

auto

mat

icth

oughts

and

core

schem

a)

50

24

NR

Indiv

idual

20

(NR

)�

8

[18]

Korn

blit

het

al.

(1983)

US

Adults

(M=

38)

Com

munity

BD

I‡

20;

SAD

S-R

DC

-m

ajor

affe

ctiv

edis

ord

er

1.

Com

pre

hen

-sive

self-

contr

ol

[sel

f-co

ntr

ol]

16

012

Gro

up

12

(90

min

)31

Pre

,post

,3-m

onth

FU

BD

I,M

MPI-

D,

HR

SD,

RTID

S

13

(hig

h)

2.

Self-m

onitor-

ing

plu

sse

lf-

eval

uat

ion

12

012

Gro

up

12

(90

min

)0

3.

Princi

ple

s-only

15

012

Gro

up

12

(90

min

)27

4.

Psy

choth

er-

apy

60

12

Gro

up

12

(90

min

)17

[19]

Mal

donad

oLo

pez

(1982)

SPA

dults

(M=

NR

)C

om

munity

outp

atie

nt

Psy

chia

tris

tdia

gnosi

sre

active

dep

ress

ive

dis

ord

er

1.

Cognitiv

ere

stru

cturing

8N

R10

Indiv

idual

10

(60

min

)0

Pre

,post

,FU

BD

I,H

RSD

,SR

DS

7(m

od)

2.

Beh

avio

ral

asse

rtiv

etr

ainin

g[p

leas

ant

activi

ties

]

8N

R10

Indiv

idual

10

(60

min

)0

3.

Phar

mac

o-

logic

alco

ntr

ol

8N

R10

Indiv

idual

NA

0


Tab

le1.

(Conti

nued

)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat

bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[20]

McN

amar

aan

dH

ora

n(1

986)

US

Young

adults

(M=

23;

range

=19–3

1)

Univ

ersi

tycl

inic

Dep

ress

ive

epis

ode,

BD

I‡

18,

HR

SD‡

20

1.

Cognitiv

eth

erap

y�

12

�27

8In

div

idual

8(5

0m

in)

�20

Pre

,post

,2-m

onth

FU

BD

I10

(mod)

2.

Beh

avio

rth

erap

y[p

leas

ant

activi

ties

]

�12

�27

8In

div

idual

8(5

0m

in)

�20

3.

Com

bin

edth

erap

y�

12

�27

8In

div

idual

8(5

0m

in)

�20

4.

Hig

h-

dem

and

contr

ol

(clie

nt

cente

red

ther

apy)

�12

�27

8In

div

idual

8(5

0m

in)

�20

[21]

Pac

e(1

978)

Study

1

AU

Young

adults

(M=

NR

)

Univ

ersi

tySe

lf-r

efer

red

ifth

eybel

ieve

dth

eyhad

difficu

lty

contr

oll

ing

their

moods;

SRSD

>32;

Inte

rvie

w:

no

evid

ence

of

psy

chosi

sor

oth

erm

ajor

pro

ble

ms.

SRSD

M=

71,

seve

re–e

xtre

me

1.N

om

onito

ring,

no

activi

tyin

stru

ctio

ns

20

30

20

Indiv

idual

1(1

20

min

)35

Concu

rren

t(1

0·

bi-

wee

kly)

BD

I,SR

DS

8(m

od)

2.N

om

onito

ring,

activi

tyin

stru

ctio

ns

20

30

20

Indiv

idual

1(1

20

min

)35

3.

Monitoring,

no

activi

tyin

stru

ctio

ns

20

30

20

Indiv

idual

1(1

20

min

)35

4.

Monitoring

and

activi

tyin

stru

ctio

ns

[ple

asan

tac

tivi

ties

]

20

30

20

Indiv

idual

1(1

20

min

)35

[22]

Pad

-fiel

d(1

976)b

US

Low

-SES

wom

en(r

ange

=21–5

6)

Com

munity

Moder

ate

dep

ress

ion

(SR

SD,

GFC

C)

1.

Counse

ling

12

33

12

Indiv

idual

12

(NR

)0

Pre

,post

SRD

S8

(mod)

2.

Beh

avio

ral

[ple

asan

tac

tivi

ties

]

12

17

12

Indiv

idual

12

(NR

)0

[23]

Reh

met

al.

(1979)

US

Adults

(ran

ge

=21–6

0)

Com

munity

MM

PI:

F£

80T,

L£

60T,

D‡

70T,

D>

Hy,

D>

Pt

and

Dam

ong

the

hig

hes

tel

evat

ions

1.

Self-c

ontr

ol

[sel

f-co

ntr

ol]

14

06

Gro

up

6(N

R)

0Pre

,post

BD

I,M

MPI-

D8

(mod)

2.

Ass

ertion

skill

s13

06

Gro

up

6(N

R)

23

[24]

Reh

met

al.

(1987)

US

Adults

(M=

39)

Com

munity

BD

I‡

20,

MM

PI-

D‡

70T,

SAD

S-R

DC

1.

Self-c

ontr

ol

com

bin

edbeh

avio

ral-

and

cognitiv

e-ta

rget

�46

010

Gro

up

10

(90

min

)�

26

Pre

,post

,6-m

onth

FU

BD

I,M

MPI-

D,

HR

SD,

RTID

S

12

(hig

h)

2.

Self-c

ontr

ol

beh

avio

ral-

targ

et[s

elf-

contr

ol]

�46

010

Gro

up

10

(90

min

)�

24

3.

Self-c

ontr

ol

cognitiv

e-ta

rget

�46

010

Gro

up

10

(90

min

)�

24


Tab

le1.

(Conti

nued)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat

bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[25]

Reh

met

al.

(1981)

US

Adults

(M=

39;

range

=20–5

8)

Com

munity

MM

PI-

D‡

70T,

MM

PI-

Pt

£M

MPI-

D,

MM

PI-

Hy

£M

MPI-

D;

SAD

S-R

DC

maj

or

dep

ress

ive

dis

ord

er

1.

Self-m

onitoring

[sel

f-co

ntr

ol]

12

07

Gro

up

7(9

0m

in)

25

Pre

,post

BD

I,M

MPI-

D8

(mod)

2.

Self-m

onitoring

and

self-e

valu

a-tion

[sel

f-co

ntr

ol]

11

07

Gro

up

7(9

0m

in)

0

3.

Self-m

onitoring

and

self-r

ewar

d[s

elf-

contr

ol]

12

07

Gro

up

7(9

0m

in)

0

4.

Self-c

ontr

ol

[sel

f-co

ntr

ol]

12

07

Gro

up

7(9

0m

in)

25

5.

Wai

ting

list

(7w

eeks

)16

07

NA

7(9

0m

in)

6

[26]

Rokk

eet

al.

(1999)

US

Old

erad

ults

(M=

66,

range

=60–8

6)

Com

munity

HR

SD‡

10,

HR

SD‡

10,

GD

S‡

11;

eith

ernot

curr

ently

on

med

icat

ion

for

dep

ress

ion

or

hav

eta

ken

the

sam

edose

of

med

icat

ion

for

am

inim

um

of

3m

onth

san

dst

illm

eeting

the

above

criter

ia,

not

recu

rren

tly

rece

ivin

gan

yoth

erpsy

choth

erap

y

1.W

aiting

list

(10

wee

ks)

29

69

NA

NA

NA

21

Pre

,post

,3-m

onth

FU,

12-

month

FU

BD

I,G

DS,

HR

SD

9(m

od)

2.

Self-

man

agem

ent

trai

nin

g:

choic

e—co

gnitiv

e

7�

50

10

Indiv

idual

10

(60

min

)14

3.

Self-

man

agem

ent

trai

nin

g:

choic

e—beh

avio

ral

[sel

f-co

ntr

ol]

8�

40

10

Indiv

idual

10

(60

min

)25

4.

Self-

man

agem

ent

trai

nin

g:

no

choic

e—co

gnitiv

e

11

�50

10

Indiv

idual

10

(60

min

)73

5.

Self-

man

agem

ent

trai

nin

g:

no

choic

e—beh

avio

ral

[sel

f-co

ntr

ol]

9�

40

10

Indiv

idual

10

(60

min

)78


Tab

le1.

(Conti

nued)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat

bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[27]

Shaw

(1977)

CA

Young

adults

(ran

ge

=18–2

6)

Univ

ersi

tyBD

I‡

18,

HR

SD‡

20,

VA

S‡

40

1.

Cognitiv

em

odifi

cation

838

4G

roup

8(1

20

min

)0

Pre

,m

id,

post

,1-

month

FU

BD

I,H

RSD

8(m

od)

2.

Beh

avio

rm

odifi

cation

[ple

asan

tac

tivi

ties

]

825

4G

roup

8 (120

min

)0

3.

Nondirec

tive

838

4G

roup

8 (120

min

)0

4.

Wai

ting

list

(4w

eeks

)8

25

NA

NA

NA

0

[28]

Skin

-ner

(1984)

US

Adults

(M=

34,

range

=20–6

1)

Com

munity

BD

I>

12

1.

Cognitiv

e�

10

28

4In

div

idual

5(6

0m

in)

�30

Pre

,post

BD

I6

(mod)

2.

Beh

avio

r[p

leas

ant

activi

ties

]

�10

38

4In

div

idual

5(6

0m

in)

�20

3.

Self-

assi

gned

cognitiv

e

�10

12

4In

div

idual

5(6

0m

in)

�20

4.

Self-

assi

gned

beh

avio

ral

�10

50

4In

div

idual

5(6

0m

in)

�20

5.

Self-

monitoring

contr

ol

�10

33

4In

div

idual

5(6

0m

in)

�10

[29]

Tay

lor

and

Mar

-sh

all(1

977)

CA

Young

adults

(M=

22,

range

=18–2

6)

Univ

ersi

tySe

lf-r

eport

eddep

ress

ion

‡2w

eeks

;BD

I‡

13;

D30

‡70T;

no

med

icat

ion

or

oth

ertr

eatm

ent

1.

Cognitiv

e7

28

4In

div

idual

6(4

0m

in)

NR

Pre

,post

,5-w

eek

FU

BD

I,D

30

6(m

od)

2.

Beh

avio

ral

[ple

asan

tac

tivi

ties

]

728

4In

div

idual

6(4

0m

in)

NR

3.

Cognitiv

ean

dbeh

avio

ral

728

4In

div

idual

6(4

0m

in)

NR

4.

Wai

ting

list

(4w

eeks

)7

28

NA

NA

NA

0

[30]

Thom

pso

net

al.

(1987);

c

Thom

pso

nan

dG

alla

-gher

(1984);

b

Gas

ton

etal

.(1

988)

US

Old

erad

ults

(M=

67)

Com

munity

MD

D(R

DC

);BD

I‡

17;

HR

SD‡

14

1.

Beh

avio

ral

[ple

asan

tac

tivi

ties

]

�29

�32

12–1

6In

div

idual

16–2

0(N

R)

�14

Pre

,6-

wee

km

id,

post

BD

I,H

RSD

,G

DS;

BSI

-D

11

(mod)

2.

Cognitiv

e�

37

�41

12–1

6In

div

idual

16–2

0(N

R)

�27

3.

Brief

psy

chody-

nam

ic

�28

�33

12–1

6In

div

idual

16–2

0(N

R)

�14

4.

Wai

tlis

t(6

wee

ks)

20

�21

NA

NA

NA

5


Tab

le1.

(Conti

nued

)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[31]

van

den

Hout

etal

.(1

995)

ND

Adults

(M=

34,

range

=20

–59)

Clin

ical

SCID

-I,

maj

or

dep

ress

ion

and

⁄dys

thym

ia.

Excl

uded

ifbip

ola

rm

ood

dis

ord

er;

psy

chotic

dis

ord

er,

alco

hol

or

dru

gdep

enden

ce;

anxi

ety

dis

ord

er,

PTSD

.SR

DS

‡50

1.

Self-c

ontr

ol

ther

apy

and

stan

dar

dtr

eatm

ent

[sel

f-co

ntr

ol]

15

38

12

Gro

up

12

(90

min

)�

10

Pre

,post

,13-w

eek

FU

SRD

S,V

RO

P-

SOM

8(m

od)

2.

Stan

dar

dtr

eatm

ent

(str

uct

ure

dgro

up

ther

apy;

crea

tive

ther

apy

and

phys

ical

exer

cise

;so

cial

skill

str

ainin

gan

docc

upat

ional

ther

apy)

14

42

12

Gro

up

NR

�10

[32]

Wag

-ner

etal

.(2

007)

US

Adults

(M=

34)

Hosp

ital

PTSD

Chec

klis

t‡3

5;

CA

PS-

IV&

SCID

-I:

PTSD

1.

BA

for

phys

ical

lyin

jure

dtr

aum

asu

rviv

ors

[Jac

obso

n’s

BA

]

475

8In

div

idual

4–6

(60–

90

min

)0

Pre

,post

CES

D9

(mod)

2.

TA

U—

com

munity

refe

rral

40

8N

AN

A0

[33]

Wei

n-

ber

g(1

978)

US

Young

adults

(M=

NR

)

Univ

ersi

tyBD

I‡

81.

Incr

ease

dposi

tive

rein

-fo

rcem

ent

[ple

asan

tac

tivi

ties

]

10

�12

4G

roup

4(6

0m

in)

10

Pre

,post

,2-m

onth

FU

BD

I,M

AA

CL-

D5

(low

)

2.

Syst

emat

icra

tional

rest

ruct

uring

10

�12

4G

roup

4(6

0m

in)

0

3.

Emotional

awar

enes

str

ainin

g

10

�12

4G

roup

4(6

0m

in)

20

4.

Wai

ting

list

(4w

eeks

)9

�12

NA

NA

NA

0


Tab

le1.

(Conti

nued)

Study

Countr

y

Age

gro

up

and

age

(yea

rs)

Rec

ruit

men

tIn

clusi

on

crit

eria

Condit

ion

Cel

lsi

zeat bas

elin

e

Mal

epar

tici

-pan

ts(%

)

Length

of

inte

r-ve

nti

on

(wee

ks)

Form

ator

mode

Num

ber

of

sess

ions

(ses

sion

length

)

Att

riti

on

at post

test

(%)

Mea

sure

-m

ents

Mea

-su

res

of

dep

res-

sion

Qual

ity

of

rese

arch

des

ign

(low

=0–

17

=hig

h)

[34]

Wils

on

etal

.(1

983)

AU

Adults

(M=

40;

ran-

ge

=z2

0–

58)

Com

munity

BD

I‡

17;

no

pre

vious

concu

rren

ttr

eatm

ent

with

maj

or

tran

quili

zers

or

lithiu

m;

abse

nce

of

oth

erm

ajor

phys

ical

or

psy

chia

tric

dis

ord

ers;

self-r

eport

eddura

tion

of

dep

ress

ion

of

atle

ast

3m

onth

s;ab

sence

of

suic

idal

inte

ntion

or

idea

tion

1.

Beh

avio

rth

erap

y[p

leas

ant

activi

ties

]

925

8In

div

idual

8(6

0m

in)

11

Pre

,m

id,

post

,5-

month

FU

BD

I,H

RSD

5(low

)

2.

Cognitiv

eth

erap

y11

12

8In

div

idual

8(6

0m

in)

27

3.

Wai

ting

list

(8w

eeks

)

922

NA

NA

NA

0

Note

:A

U=

Aust

ralia

;BD

I=

Bec

kD

epre

ssio

nIn

vento

ry;

BD

I-II

=Bec

kD

epre

ssio

nIn

vento

rySe

cond

Editio

n;

BSI

-D=

Brief

Sym

pto

mIn

vento

ryD

epre

ssio

nSc

ale;

CA

=C

anad

a;C

APS-

IV=

Clin

i-ci

an-A

dm

inis

tere

dPTSD

Scal

efo

rD

SM-I

V;

CES

D=

Cen

tre

for

Epid

emio

logic

alSt

udie

sD

epre

ssio

nSc

ale;

D30

=D

30

Dep

ress

ion

Scal

e;D

AC

L=

Dep

ress

ion

Adje

ctiv

eC

hec

klis

t;ED

Scal

e=

Elat

ion-

Dep

ress

ion

Scal

e;FC

C=

Feel

ings

and

Conce

rns

Chec

klis

t;FU

=fo

llow

-up;

GD

S=

Ger

iatr

icD

epre

ssio

nSc

ale;

GFC

C=

Grinke

rFe

elin

gs

and

Conce

rns

Chec

klis

t;H

RSD

=H

amilt

on

Rat

ing

Scal

efo

rD

epre

ssio

n;

MA

AC

L-D

=M

ultip

leA

ffec

tA

dje

ctiv

eC

hec

klis

tD

epre

ssio

nSc

ale;

MD

D=

Maj

or

Dep

ress

ive

Dis

ord

er;

MM

PI-

D=

Min

nes

ota

Multip

has

icPer

sonal

ity

Inve

nto

ryD

epre

ssio

nSc

ale;

NA

=not

applic

able

;N

D=

The

Net

her

lands;

NR

=N

ot

report

or;

PFS

=Per

sonal

Feel

ings

Scal

es;

PTSD

=post

trau

mat

icst

ress

dis

ord

er;

RD

C=

rese

arch

dia

gnost

iccr

iter

ia;

RH

RSD

=R

evis

edH

amilt

on

Rat

ing

Scal

efo

rD

epre

ssio

n;

RTID

S=

Ras

kin

Thre

e-It

emD

epre

ssio

nSc

ale;

SAD

S=

Sched

ule

for

Aff

ective

Dis

ord

ers

and

Schiz

ophre

nia

;SC

ID-I

=St

ruct

ure

dC

linic

alIn

terv

iew

for

DSM

Axi

sI

Dis

ord

ers;

SP=

Spai

n;

SRD

S=

Zung

Self-R

atin

gD

epre

ssio

nSc

ale;

US

=U

nited

Stat

esof

Am

eric

a;V

AS

=V

isual

Anal

ogue

Scal

e;V

RO

PSO

M=

Dutc

hve

rsio

nof

the

DA

CL.

aD

ata

wer

eobta

ined

from

the

raw

dat

apre

sente

din

Bar

rera

(1977)

rath

erth

anin

Bar

rera

(1979)

bec

ause

the

latt

erpap

erdoes

not

pro

vide

the

resu

lts

of

com

par

isons

untilone-

month

follo

w-u

pw

hen

the

two

conditio

ns

com

par

edhav

eboth

rece

ived

the

beh

avio

ral

activa

tion

inte

rven

tion.

bPad

fiel

d(1

976)

was

excl

uded

from

the

met

a-an

alys

isof

Eker

set

al.

(2008)

on

the

gro

unds

that

ther

ew

asin

suffi

cien

tre

port

eddat

a.c T

he

met

a-an

alys

isof

Cuijp

ers

etal

.(2

007)

incl

udes

Thom

pso

nan

dG

alla

gher

(1984)

and

Thom

pso

net

al.

(1987)

astw

ose

par

ate

studie

s;how

ever

,fo

rth

ism

eta-

anal

ysis

they

wer

etr

eate

das

asi

ngle

study.

The

latt

erst

udy

was

judged

tobe

anex

tensi

on

of

the

form

erby

recr

uitin

gm

ore

par

tici

pan

ts.


excluded because of insufficient information to extract

effect sizes. The study by Wilson (1982) was excluded

because of a potential interaction between placebo and

other interventions, making it impossible to calculate

the effect for the BA intervention alone (see Hollon &

DeRubeis, 1981). Moreover, whereas Cuijpers and

colleagues included Thompson and Gallagher (1984)

and Thompson, Gallagher, and Breckenridge (1987) as

two separate studies, we treated them as a single study.

The latter study was judged to be an extension of the

former by recruiting more participants. Six studies in-

cluded in the meta-analysis of Ekers et al. (2008) were

excluded. The studies by Cole (1984), McKendree-

Smith (2000), McLean and Hakstian (1979), and

Scogin, Jamison, and Gochneaur (1989) were all

excluded because the behavioral interventions were

confounded with cognitive components. The studies

by Maldonado Lopez (1984) and Wilson (1982) were

excluded because of a potential pharmacological ⁄placebo interaction with other interventions, rendering

it impossible to calculate the effect for the BA inter-

ventions. One paper excluded by the meta-analysis of

Ekers et al. (2008) was included in this study, namely

Padfield (1976).

Behavioral Activation Versus Control Conditions

Sixteen studies with a total of 453 participants contrib-

uted data to this analysis [1–6, 9, 12, 21, 25–27, 29,

31, 33–34]. Participants were taken from adult com-

munity sources consisting of volunteers [1–6, 9, 12, 25,

26, 31, 34] and students [4, 21, 27, 29, 33]. Two stud-

ies [1, 26] used older adults. Control conditions con-

sisted of waiting list [1–6, 9, 12, 21, 25–27, 29, 33, 34]

and treatment as usual [31]. Ten interventions were

based on pleasant activities [2–5, 12, 21, 27, 29, 33,

34], five on behavioral self-control [1, 9, 25, 26, 31],

and one on contextual BA [6].

The effect of BA interventions against control con-

ditions was large, with a pooled effect size of 0.78 for

the fixed effects model, demonstrating a highly signifi-

cant difference favoring BA (Table 2). Heterogeneity

was low to moderate, but significant (p = .04). Con-

sequently, results for the random effects model were

also calculated and yielded a mean effect size of 0.87

(95% CI: 0.60–1.14, p < .01). Both estimates sug-

gested a large and significant difference in favor of

BA. There was some evidence of selection bias for

this outcome (Egger’s regression intercept = 2.33;

95% CI: 0.50–4.16, p = .01) and a funnel graph

showed some asymmetry with smaller studies tending

to show more pronounced beneficial effects in favor

of BA. Duval and Tweedie’s trim and fill method for

correcting bias estimated that four studies were miss-

ing, adjusting for which yielded a medium random

effects point estimate of 0.65 (95% CI: 0.34–0.96;

Q = 48.78). The fail-safe N resulted in a figure of

Table 2. Effects of behavioral activation on measures of depression compared with control conditions and other interventions at posttest for participantsreporting elevated symptoms of depression

Ncmp Nprtcpnts Hedges’s g 95% CI Q I 2

Comparison with controlAll forms of BA 16 453 0.78*** 0.58 to 0.97 26.33* 43.04Pleasant activities 10 292 0.76*** 0.52 to 1.00 18.01* 50.03Self-control 5 147 0.74*** 0.39 to 1.09 5.39 25.80Contextual 1 14 1.81** 0.62 to 3.01 0.00 0.00BATD 0 – – – – –

Comparison with CBT ⁄ CTAll forms of BA 15 536 )0.01 )0.17 to 0.16 13.99 0.00Pleasant activities 11 243 )0.15 )0.40 to 0.10 10.21 2.08Self-control 2 85 )0.02 )0.38 to 0.34 0.61 0.00Contextual 2 208 0.17 )0.10 to 0.45 0.20 0.00BATD 0 – – – – –

Comparison with other interventionsAll forms of BA 17 533 0.33*** 0.16 to 0.49 29.78* 46.26Pleasant activities 12 419 0.30** 0.11 to 0.49 13.14 16.26Self-control 3 81 0.36 )0.09 to 0.82 15.74*** 87.29Contextual 0 – – – – –BATD 1 25 0.69 )0.11 to 1.49 0.00 0.00

Note: – = no data; Ncmp = number of comparisons; Nprtcpnts = number of participants. *p < .05; **p < .01; ***p < .001.


286 studies, which exceeded the critical number of 85

studies.

Subgroup analyses indicated that the pleasant activi-

ties, self-control, and contextual variants of BA all pro-

duced large and significant effect sizes that did not

differ significantly from each other (p = .23). However,

only one study included in these analyses tested con-

textual BA, which makes comparisons between it and

other forms of BA unreliable. The effect sizes and 95%

confidence intervals of these comparisons are listed in

Table 2. Additionally, subgroup analysis found that the

effects obtained for patients with elevated depressive

symptoms did not differ significantly from those meet-

ing the criteria for MDD (p = .83).

To account for heterogeneity, either linear regres-

sion or Pearson’s product moment correlation coeffi-

cients were used to investigate the influence of

participant, intervention, and methodological character-

istics. A number of variables were tested but failed to

significantly account for variance in effect size, includ-

ing the following: type of BA (R2 = .14, p = .38), type

of control (R2 = .07, p = .34), severity of self-reported

depression at pretest (R2 = .07, p = .64), level of self-

reported activity at pretest (R2 = .08, p = .78), extent

of differences between groups on outcome measures at

pretest (R2 = .05, p = .70), population age (R2 = .14,

p = .36), mode of intervention (i.e., group or individ-

ual; r2 = .04, p = .47), number of sessions (r2 = .03,

p = .55), length of intervention (r2 = .08, p = .30),

density of sessions (R2 = .16, p = .33) and quality of

study (R2 = .19, p = .26).

Behavioral activation interventions often try to

increase participants’ engagement in pleasant activities;

it is therefore of interest to consider the impact of these

interventions on measures of activity. Posttest compari-

sons between BA and control conditions on measures

of activity (typically the Pleasant Events Schedule;

MacPhillamy & Lewinsohn, 1982) were possible in 11

studies [1–3, 9, 12, 21, 25, 26, 31, 33, 34], totaling

300 participants. These comparisons yielded a moder-

ately large and significant mean effect size of 0.54 (95%

CI: 0.31–0.77, p < .01) by the fixed effects model.

Heterogeneity was small and nonsignificant (Q = 6.67;

p = .76; I2 = 0.00%). A funnel graph showed no evi-

dence of asymmetry, providing little indication of selec-

tion bias (Egger’s test = )0.17; 95% CI: )2.20 to 1.86,

p = .85). The fail-safe N resulted in a figure of 44 stud-

ies, which does not exceed the critical number of 65

studies. A medium positive relationship was found

between mean effect size for activity and mean effect

size for mood (r = .44, p = .17).

Comparison With Other Treatments

Behavioral activation could be compared directly with

other psychological treatments in 23 studies (Table 2).

In 15 studies BA was compared with CBT ⁄ CT [3, 5,

7, 11, 17, 19, 20, 24, 26–30, 33, 34]. The negligible

pooled effect size of )0.01 between these two types

of treatment was nonsignificant (p = .91). Heteroge-

neity was small and nonsignificant (Q = 13.99;

p = .45; I2 = 0.00%). Although not reaching signifi-

cance, there was some evidence of selection bias

(Egger’s regression intercept = )1.22; 95% CI: )2.59

to 0.16, p = .08) and a funnel graph showed some

asymmetry with smaller studies tending to show more

pronounced beneficial effects in favor of CBT ⁄ CT.

However, Duval and Tweedie’s trim and fill method

for correcting bias estimated that zero studies were

missing. Subgroup analyses indicated that the pleasant

activities variant of BA yielded a small effect in favor

of CBT ⁄ CT, the self-control variant yielded a negligi-

ble effect in favor of CBT ⁄ CT, and the contextual

variant yielded a small effect in favor of BA. The

effect sizes of the different variants of BA were not

found to differ significantly from each other (p = .23).

Subgroup analysis also found that the effects obtained

for patients with elevated depressive symptoms did

not differ significantly from those meeting the criteria

for MDD (p = .44).

Three of the studies included in the BA versus

CBT ⁄ CT comparison [5, 7, 17] used a CBT ⁄ CT inter-

vention that clearly included a BA component. When

these studies were removed to permit a purer compari-

son between interventions with a behavioral focus ver-

sus interventions with a cognitive focus, the pooled

effect size of )0.09 (95% CI: )0.30 to 0.12, p = .38)

in favor of CBT ⁄ CT was small and nonsignificant.

Ten studies comparing BA with CBT ⁄ CT reported

data from both self-report depression measures and

clinician- or interviewer-administered instruments [5,

7, 11, 17, 19, 24, 26, 27, 30, 34]. Both forms of data

yielded negligible pooled effect sizes in favor of BA


(0.02 and 0.01, respectively). These effect sizes did not

differ significantly from each other (p = .97).

In 17 studies BA was compared with psychotherapy

or other interventions [9–16, 18, 20–23, 28, 30, 32,

33]. The significant pooled effect size of 0.33 indicated

a medium difference between BA and other treatments,

favoring BA. There was no evidence of selection bias

for this outcome (Egger’s regression intercept = )0.30;

95% CI: )2.57 to 1.96, p = .78). The fail-safe N was

37, which does not exceed the critical number of 95

studies. Heterogeneity was low to moderate, but signif-

icant (p = .02); consequently results for the random

effects model were also calculated and yielded a med-

ium mean effect size of 0.31 (95% CI: 0.06–0.55,

p = .01). Subgroup analyses indicated that the pleasant

activities and self-control variants of BA resulted in

medium effect sizes, and BATD produced a large effect

size in the one study in which it was applied. The

effect sizes of the different variants of BA were not

found to differ significantly from each other (p = .82).

Subgroup analysis also found that there was no signifi-

cant difference between effects obtained for patients

with elevated depressive symptoms versus those meet-

ing the criteria for MDD (p = .15). A large and signifi-

cant positive relationship was found between mean

effect size for activity and mean effect size for depres-

sive symptoms (r = .68, p = .04).

To account for heterogeneity, either linear regres-

sion or Pearson’s product moment correlation coeffi-

cients were used to investigate the influence of

participant, intervention, and methodological character-

istics. Although study quality and differences between

groups on outcome measures at pretest approached sig-

nificance, none of the variables significantly accounted

for variance in effect size. Variables tested included

the following: type of BA (R2 = .16, p = .50), type of

control (R2 = .22, p = .53), severity of self-reported

depression at pretest (R2 = .03, p = .81), level of self-

reported activity at pretest (R2 = .50, p = .12), extent

of differences between groups on outcome measures at

pretest (R2 = .33, p = .06), population age (R2 = .09,

p = .50), mode of intervention (i.e., group or individ-

ual; r2 = .10, p = .24), number of sessions (r2 < .01,

p = .84), length of intervention (r2 = .00, p = .99),

density of sessions (R2 = .03, p = .83), therapist experi-

ence (r2 = .00, p = .94), and quality of study (R2 = .34,

p = .05).

Effects at Follow-Up

Behavioral activation could be compared directly with

a control condition at one- to three-month follow-up

in five studies [2–4, 31, 33]. The effect of BA inter-

ventions against control conditions was large with a

pooled effect size of 0.78 for the fixed effects model,

demonstrating a highly significant difference favoring

BA (Table 3). There was no evidence of selection bias

for this outcome (Egger’s regression intercept = )2.60;

95% CI: )18.73 to 13.54, p = .64). The fail-safe N of

15 studies exceeded the critical number of 35 studies.

Heterogeneity was high and significant; consequently

Table 3. Effects of behavioral activation on measures of depression at follow-up for participants reporting elevated symptoms of depression


Comparison with control1–3 month FU 5 123 0.78*** 0.42 to 1.15 17.40** 77.014–6 month FU 0 – – – – –7–12 month FU 2 58 0.08 )0.43 to 0.60 3.65 72.6313–24 month FU 0 – – – – –

Comparison with CBT ⁄ CT1–3 month FU 8 163 )0.04 )0.35 to 0.27 16.83* 58.414–6 month FU 4 219 0.05 )0.22 to 0.31 0.90 0.007–12 month FU 4 170 )0.10 )0.40 to 0.21 1.54 0.0013–24 month FU 2 153 0.00 )0.33 to 0.34 0.59 0.00

Comparison with other interventions1–3 month FU 6 137 0.23 )0.10 to 0.56 11.85* 57.804–6 month FU 1 19 0.71 )0.18 to 1.60 0.00 0.007–12 month FU 1 18 0.78 )0.13 to 1.70 0.00 0.0013–24 month FU 0 – – – – –



results for the random effects model were also calcu-

lated and yielded a mean effect size of 0.71 (95% CI:

)0.08 to 1.50, p = .08) Both estimates suggested a

large effect size in favor of BA, although the point esti-

mate derived from the random effects model was not

significant.

Behavioral activation could only be compared with

control conditions at seven- to 12-month follow-up in

two studies [2, 7]. During this period, the effect of BA

interventions against control conditions was small and

nonsignificant, with a pooled effect size of 0.08 for the

fixed effects model in favor of BA (Table 3). Heteroge-

neity was moderate to high but not significant

(p = .06).

Behavioral activation could be compared directly

with CBT ⁄ CT at one- to three-month follow-up in

eight studies [3, 5, 10, 19, 20, 26, 29, 33], at four- to

six-month follow-up in four studies [11, 17, 24, 34], at

seven- to 12-month follow-up in four studies [7, 11,

17, 26], and at 13- to 24-month follow-up in two

studies [7, 17]. At each comparison point, the effect

was small and nonsignificant with a pooled effect size

ranging from )0.10 (in favor of CBT ⁄ CT) to 0.05 (in

favor of BA; Table 3). There was no evidence of selec-

tion bias for any of these outcomes and heterogeneity

was low and nonsignificant for each comparison except

for the one- to three-month follow-up where signifi-

cant moderate to high heterogeneity was observed.

Results for the random effects model were also calcu-

lated for this time point and yielded a negligible mean

effect size of )0.02 (95% CI: )0.50 to 0.45, p = .92)

in favor of CBT ⁄ CT.


with psychotherapy or other treatments at one- to

three-month follow-up in six studies [9–11, 18, 20,

33]. The pooled effect size of 0.23 indicated a small

nonsignificant difference between BA and these other

treatments in favor of BA. Heterogeneity was moderate

to high and significant (p = .04). Consequently, results

for the random effects model were also calculated and

yielded the medium nonsignificant mean effect size of

0.32 (95% CI: )0.20 to 0.84, p = .23) in favor of BA.

There was some evidence of selection bias for this out-

come (Egger’s regression intercept = 10.27; 95% CI:

5.80–14.75, p < .01) and a funnel graph showed some

asymmetry with smaller studies tending to show bene-

ficial effects in favor of BA. Duval and Tweedie’s trim

and fill method for correcting bias estimated that two

studies were missing, adjusting for which yielded a

negligible random effects size estimate of 0.03 in favor

of BA (95% CI: )0.49 to 0.56; Q = 21.60).

Behavioral activation could only be compared with

psychotherapy in one study at both four- to six-month

and seven- to 12-month follow-up [11]. At both points

the effect sizes of 0.71 and 0.78 suggested a large, but

nonsignificant, difference in favor of BA (Table 3).

Patients With Major Depressive Disorder

Of particular interest were the effects of BA on patients

meeting the diagnostic criteria for MDD. The effects of

BA on measures of depression at posttest could be com-

pared with control conditions in three studies [6, 25,

31]. A large and significant mean effect size of 0.74 was

obtained in favor of BA (Table 4). Although nonsignifi-

cant, there was some evidence of selection bias for this

outcome (Egger’s regression intercept = 4.15; 95% CI:

)6.46 to 14.76, p = .13) and a funnel graph showed

some asymmetry with a smaller study showing more

pronounced beneficial effects in favor of BA. Neverthe-

less, Duval and Tweedie’s trim and fill method for cor-

recting bias estimated that zero studies were missing.

The fail-safe N of eight studies exceeded the critical

number of 25 studies. Heterogeneity was low to moder-

ate and nonsignificant. Although only based on three

studies, subgroup analyses indicated that the self-control

and contextual variants of BA both produced significant

effect sizes that were medium and large in magnitude,

respectively. The differences between variants did not

reach statistical significance (p = .06).

The effects of BA on measures of depression at

posttest could be compared with CT in six studies [7,

11, 17, 19, 24, 30]. A negligible and nonsignificant

mean effect size of 0.04 was obtained in favor of BA.

Although not reaching significance, there was some

evidence of publication bias for this outcome (Egger’s

regression intercept = )1.55; 95% CI: )4.00 to 0.90,

p = .15) and a funnel graph showed some asymmetry

with smaller studies tending to show more pronounced

beneficial effects in favor of CT. However, Duval and

Tweedie’s trim and fill method for correcting bias esti-

mated that zero studies were missing. Heterogeneity

was very low and nonsignificant. Subgroup analyses


indicated that the pleasant activities, self-control, and

contextual variants of BA produced small and non-

significant effect sizes, and that they did not differ

significantly from each other (p = .36).

The effects of BA on measures of depression at

posttest could be compared with other interventions in

four studies [11, 16, 18, 30]. A small nonsignificant

mean effect size of 0.11 was obtained in favor of BA.

There was no evidence of selection bias for this out-

come (Egger’s regression intercept = 3.06; 95% CI:

)15.94 to 22.06, p = .56). Heterogeneity was low to

moderate and nonsignificant. Subgroup analyses sug-

gested some variability in outcome with the self-con-

trol variant yielding a medium effect size in favor of

psychotherapy and other interventions, the pleasant

activities variant yielding a small effect size in favor of

BA, and the BATD variant yielding a large effect size

in favor of BA. The difference between variants did

not reach statistical significance (p = .06).

Effects at Follow-Up for Patients With Major Depressive

Disorder

For patients diagnosed with MDD, BA could be com-

pared with control conditions in only one study at

both one- to three-month [31] and seven- to 12-

month [7] follow-up. At both points the effect sizes of

0.47 and 0.31 suggested a medium, but nonsignificant,

difference in favor of BA (Table 5).


with CT at one- to three-month follow-up in two

small studies [11, 19]. These yielded an effect size of

)0.20, suggesting a small, but nonsignificant, difference

in favor of CBT ⁄ CT. Heterogeneity was very low and

nonsignificant. At four- to six-month follow-up, com-

parisons were possible in three studies [11, 17, 24].

These yielded a negligible and nonsignificant effect size

of 0.03 in favor of BA. There was no evidence of

selection bias for this outcome (Egger’s regression

intercept = )1.31; 95% CI: )8.52 to 5.90, p = .26).

Heterogeneity was very low and nonsignificant. At

seven- to 12-month follow-up, comparisons were pos-

sible in three studies [7, 11, 17]. These yielded a negli-

gible and nonsignificant effect size of )0.06 in favor of

CBT ⁄ CT. There was no evidence of publication bias

for this outcome (Egger’s regression intercept = 1.73;

95% CI: )16.16 to 16.64, p = .43). Heterogeneity was

very low and nonsignificant. Two studies permitted a

comparison at 13- to 24-month follow-up [7, 17]. The

effect of 0.00 suggested no difference in effect between

BA and CBT ⁄ CT.


with psychotherapy and other treatments at one- to

three-month follow-up in two studies [11, 18]. The

pooled effect size of )0.22 indicated a small nonsignifi-

cant difference in favor of psychotherapy and other

interventions. Heterogeneity was high and significant

Table 4. Effects of behavioral activation on measures of depression compared with control conditions for patients diagnosed with major depressivedisorder


Comparison with controlAll forms of BA 3 96 0.74** 0.31 to 1.17 3.60 44.38Pleasant activities – – – – – –Self-control 2 82 0.58* 0.12 to 1.04 0.05 0.00Contextual 1 14 1.81** 0.62 to 3.01 0.00 0.00BATD – – – – – –

Comparison with CBT ⁄ CTAll forms of BA 6 400 0.04 )0.16 to 0.23 3.61 0.00Pleasant activities 3 88 )0.14 )0.55 to 0.27 1.37 0.00Self-control 1 104 )0.09 )0.50 to 0.31 0.00 0.00Contextual 2 208 0.17 )0.10 to 0.45 0.20 0.00BATD – – – – – –

Comparison with other interventionsAll forms of BA 4 133 0.11 )0.23 to 0.45 5.64 46.78Pleasant activities 2 69 0.23 )0.23 to 0.69 0.04 0.00Self-control 1 39 )0.48 )1.11 to 0.16 0.00 0.00Contextual – – – – – –BATD 1 25 0.69 )0.11 to 1.50 0.00 0.00



(p = .04). Consequently, results for the random effects

model were also calculated and yielded the small non-

significant mean effect size of )0.10 (95% CI: )1.18 to

0.98, p = .86) in favor of psychotherapy and other

interventions.

Behavioral activation could be compared with psy-

chotherapy and other conditions in only one study at

both four- to six-month [11] and seven- to 12-month

[11] follow-up. At both points, the effect sizes of 0.71

and 0.78 suggested a large, but nonsignificant, differ-

ence in favor of BA (Table 5).

Empirically Validated Criteria

Six studies satisfied the randomization, sample size, and

treatment manual standards required by the American

Psychological Association’s Division 12 Task Force on

Promotion and Dissemination of Psychological Proce-

dures [4, 7, 12, 13, 17, 24, 30] (Chambless et al., 1998;

Task Force on Promotion and Dissemination of Psy-

chological Procedures, 1995). Four used a sample of

patients who met the criteria for MDD [7, 17, 24, 30],

one recruited carers of older adults with physical or

mental disability (a sizable proportion of whom were

subsequently determined to have a depressive disorder)

[12], and one study used university students who

reported elevated symptoms of depression [13]. Three

were based on pleasant activities [12, 13, 30], two on

contextual BA [7, 17], and one on behavioral self-con-

trol [24]. Four studies showed BA to be equivalent to

an already established treatment [7, 17, 30] or

psychological placebo [13]. One study found BA to be

superior to a waiting list control group [12] (Table 6).

Based on these outcomes, the contextual variant of BA

satisfies the probably efficacious designation for the treat-

ment of MDD and only misses out on the well-estab-

lished designation because both studies were completed

by the same investigating team. When these results

are taken with the outcomes of Thompson et al.

(1987), the BA approach satisfies the well-established

designation.

DISCUSSION

These results provide clear indication that BA interven-

tions are effective in the treatment of depression in

adults. For individuals with elevated scores on self-

report depression measures, the overall effect size of

0.78 in favor of BA over control conditions is large,

and comparable with the effect size found by previous

meta-analyses (Cuijpers et al., 2007; Ekers et al., 2008).

For patients meeting the diagnostic criteria for MDD

the overall effect size of 0.74 remained large and signif-

icant. Comparisons of BA with CT or cognitive and

behavior therapy indicated that these treatments are

equally effective. There is also evidence that BA inter-

ventions have equivalent holding power to CBT ⁄ CT

interventions for up to 24 months.

An interesting finding of the present research was

that although more recent versions of the BA

approach, such as Jacobson and colleagues’ contextual

BA, yielded greater intervention effects compared with

Table 5. Effects of behavioral activation on measures of depression at follow-up for patients diagnosed with major depressive disorder


Comparison with control1–3 month FU 1 29 0.47 )0.25 to 1.19 0.00 0.004–6 month FU 0 – – – – –7–12 month FU 1 48 0.31 )0.25 to 0.88 0.00 0.0013–24 month FU 0 – – – – –

Comparison with CBT ⁄ CT1–3 month FU 2 36 )0.20 )0.83 to 0.42 0.52 0.004–6 month FU 3 203 0.03 )0.25 to 0.31 0.72 0.007–12 month FU 3 159 )0.06 )0.38 to 0.26 0.89 0.0013–24 month FU 2 153 0.00 )0.33 to 0.34 0.59 0.00

Comparison with other interventions1–3 month FU 2 59 )0.22 )0.73 to 0.30 4.12* 75.734–6 month FU 1 19 0.71 )0.18 to 1.60 0.00 0.007–12 month FU 1 18 0.78 )0.13 to 1.70 0.00 0.0013–24 month FU 0 – – – – –



earlier variants, all variants produced effects of similar

magnitude and differences between them were not

statistically significant. Nevertheless, a focused evidence

review indicated that Jacobson and colleagues’ contex-

tual BA has the strongest evidence base and satisfies the

APA’s Division 12 Task Force’s probably efficacious

designation for the treatment of MDD. It might further

be argued that the collective evidence suggests that the

BA approach in general satisfies the Task Force’s

well-established designation.

There is a significant gap between the demand for

psychological therapy services and the available supply

(Bebbington et al., 2000; Lovell & Richards, 2000).

One proposal to overcome this problem is to increase

efficiency of provision through the adoption of least

intrusive and least costly interventions within stepped

care models (Marks et al., 2003; National Collaborat-

ing Centre for Mental Health, 2004; Scogin, Hanson,

& Welsh, 2003). BA interventions are comparatively

simple and easy to understand for depressed patients,

and do not require difficult or complex skills from

patients and therapists (Lejuez, Hopko, & Hopko,

2001). They are therefore a good candidate as a sim-

ple first-line treatment and have the potential for

deriving the greatest benefit from available therapeutic

resources. BA interventions have been effectively

delivered in a variety of different formats, including

group therapy, brief individual therapy, and longer-

term individual therapy. The approach would also

appear to be suitable for self-help applications in the

form of bibliotherapy (e.g., Addis & Martell, 2004;

Hopko & Lejuez, 2007) or computer-based interven-

tions that would involve no therapist input beyond an

initial assessment. These applications would further

improve the accessibility of effective treatments for

depression and, in combination with other modes of

delivery, might represent an efficient method of deliv-

ering psychological services for MDD.

The simplicity of BA interventions also makes them

suitable for a broad range of populations. The present

meta-analysis included studies with samples of patients

who were severely depressed and the depressed elderly,

indicating the approach may be suitable for both. The

BA approach has also been successfully applied to chil-

dren and adolescents (e.g., Reynolds & Coats, 1986;

Stark et al., 1987), and depressed dementia patients

(e.g., Teri et al., 1997). Similar interventions involving

the presentation of favorite stimuli, or scheduling lei-

sure activities, have also been piloted with individuals

with severe or profound intellectual and multiple dis-

abilities to increase indices of happiness (e.g., Green &

Reid, 1996; Lancioni et al., 2007; Logan et al., 2002).

Although the evidence base on BA offers much to

appreciate, some limitations warrant attention. First,

contextual BA satisfies the criteria for being a probably

efficacious treatment for MDD on the basis of two

studies, and only misses out on the well-established

designation because both studies were completed by

Table 6. Outcomes of behavioral activation studies satisfying APA Division 12 Task Force criteria for methodological rigor

Study BA variant Client sample Outcome at posttest Outcome at FU

Dimidjian et al. (2006);Dobson et al. (2008)

Contextual Adults with MDD High severity: BA = ADM > CTLow severity: BA = ADM = CT

pCT = pBA = cADM(1-year FU);pCT = pBA > pcADM(2-year FU)

Gallagher-Thompsonet al. (2000)

Pleasant activities Carers of older adults withphysical or mental disability

LS > PS = WL –

Graf (1977) Pleasant activities University students, BDI > 12 MRA > CA&AM –Jacobson et al. (1996);Gortner et al. (1998)

Contextual Adults with MDD BA = AT = CT BA = AT = CT(2-year FU)

Rehm et al. (1987) Self-control Adults with MDD SCC = SCB = SCB&C SCC = SCB = SCB&C

(6-month FU)Thompson et al. (1987) Pleasant activities Older adults with MDD BA = CT = BP > WL –

Note: ADM = antidepressant medication; AT = automatic thoughts; BA = behavioral activation; BDI = Beck Depression Inventory; BP = brief psychody-namic; CA&AM = increase control activities and activity monitoring; cADM = continued antidepressant medication; CT = cognitive therapy; FU = follow-up; LS = life satisfaction; MDD = major depressive disorder; MRA = increase mood-related activities; pcADM = prior continued antidepressant medica-tion; pBA = prior behavioral activation; pCT = prior cognitive therapy; PS = problem solving; WL = waiting list; SCB = self-control—behavioral target;SCB&C = self-control—combined behavioral and cognitive target; SCC = self-control—cognitive target.


the same investigating team. Another high-quality

study completed by a different team is required. Such a

study may well provide a definitive indication regard-

ing this variant of BA.

Second, the present study did not find that the dif-

ferent variants of BA produce significantly different

outcomes; however, the failure to determine this might

be due to insufficient spread of studies across different

BA conditions. Many of the comparisons between the

different variants of BA included only one or two stud-

ies for one or more of the comparison groups, making

such comparisons unreliable. The more recent contex-

tual BA and BATD had the smallest number of ran-

domized controlled trials devoted to them. Small open

trials suggest that BATD has great promise, but only

one trial of BATD met the inclusion criteria of the

present meta-analysis. A research priority should be to

subject BATD to a high-quality efficacy trial. More-

over, while having a comparatively greater research

base, the number of quality trials of the behavioral self-

control variant of BA is still limited and only one trial

satisfied the quality standards required by the APA’s

Division 12 Task Force on Promotion and Dissemina-

tion of Psychological Procedures. It is of clinical

importance to determine whether more complex ver-

sions of BA offer the additional benefit to warrant their

use. Consequently, more high-quality research trials

investigating the variants of BA are needed.

A related limitation is that some of the variants,

including Jacobson and colleagues’ BA, are omnibus in

style—comprising a variety of intervention procedures

but without a clear picture of which procedures really

matter. If it is true that all variants of the approach pro-

duce effects of similar magnitude, this would suggest

that some of the treatment components incorporated in

some variants are unnecessary for good outcomes.

Interest in BA approaches was renewed by a compo-

nent analysis of CT (Jacobson et al., 1996), but the

present evidence indicates that further dismantling

research is needed. BA interventions are already attrac-

tive because of their apparent simplicity; however,

making treatment programs leaner and more efficient

could further enhance their attractiveness to practition-

ers, render the procedures more teachable, and increase

treatment viability, and thus perhaps reach even more

patients who need their benefits.

More research on the change processes that account

for observed outcomes in treatment are needed. At

present, we know much more about what outcomes

our treatments produce than about what actually causes

the outcomes. Early treatment models identified partic-

ipation in pleasant activities, but more recently it has

been suggested that understanding and countering

avoidance may be a critical element in treatment

(Dimidjian et al., 2006; Lewinsohn & Graf, 1973). We

need more studies in which proposed mediators are

identified a priori and carefully measured on a repeated

basis. Recently, three measures which tap hypothesized

processes of change in BA have been developed, the

Cognitive-Behavioral Avoidance Scale (Ottenbreit &

Dobson, 2004), the Environmental Reward Observa-

tion Scale (Armento & Hopko, 2007), and the Behav-

ioral Activation for Depression Scale (Kanter, Mulick,

Busch, Berlin, & Martell, 2007). The validity of the

use of daily diaries has also been demonstrated (Hopko,

Armento, et al., 2003). It is hoped that the use of these

approaches may lead to an increased understanding of

the processes underlying therapeutic improvement.

Such an understanding will improve prospects for

understanding and addressing impediments in treat-

ment, training practitioners by teaching them what

change processes they need to effect rather than simply

what techniques to use, and identifying principles that

can be used in refining interventions.

It may also be noted that, with the exception of

comparisons with CBT ⁄ CT interventions, few studies

have examined whether BA has lasting effects beyond

three months. Further research should seek to clarify

whether the effects achieved by BA are maintained

over time. The present review included more studies

than previous reviews due to the inclusion of recent

and unpublished data. The decision to include unpub-

lished data was made to avoid any systematic bias in

the size of identified effects from only including pub-

lished data. No systematic difference in the quality of

studies was observed between published and unpub-

lished studies, but nevertheless interventions and the

quality of included trials varied considerably across

studies. Perhaps as a consequence, more heterogeneity

was obtained for some comparisons than those reported

by Cuijpers et al. (2007). When necessary we

attempted to account for this by the use of random


effects modeling and linear regression of participant,

intervention, and methodological characteristics. Also,

in two of the comparisons there was evidence of signif-

icant selection bias favoring BA. Adjustments for these

biases reduced the magnitude of effect size estimates

from large to medium in one case and from small to

negligible in the other. Caution should be exercised in

interpreting these comparisons.

The research conducted in the past three decades

shows that BA may be considered a well-established treat-

ment for depression that has advantages over alternative

treatments. More research on the variants of BA is

needed to determine whether simpler variants of the

approach are as effective as more complex versions.

Future research should use clinical samples, larger

numbers of participants, longer follow-up to confirm

sustainability of treatment effects, and investigate

specific processes of change.

NOTES

1. k0 = k[MESk ⁄ MESc ) 1], where k0 is the number

of effect sizes with a value of zero needed to reduce

the mean effect size to MESc, k is the number of

studies in the mean effect size, MESk is the

weighted mean effect size, and MESc is the criterion

effect size level (Rosenthal, 1979).

2. kc = 5k + 10, where kc is the critical number of

studies and k is the number of studies in the mean

effect size (Rosenthal, 1979).

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Received November 22, 2008; revised February 16, 2009;

accepted March 31, 2009.


Behavioral Activation Treatments for Depression in Adults: A Meta-analysis and Review

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