Behavioral activation treatments of depression: A meta-analysis
Pim Cuijpers⁎, Annemieke van Straten, Lisanne Warmerdam
Department of Clinical Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
Received 4 July 2006; received in revised form 28 October 2006; accepted 9 November 2006
Activity scheduling is a behavioral treatment of depression in which patients learn to monitor their mood and daily activities,
and how to increase the number of pleasant activities and to increase positive interactions with their environment. We conducted a
meta-analysis of randomized effect studies of activity scheduling. Sixteen studies with 780 subjects were included. The pooled
effect size indicating the difference between intervention and control conditions at post-test was 0.87 (95% CI: 0.60~1.15). This is a
large effect. Heterogeneity was low in all analyses. The comparisons with other psychological treatments at post-test resulted in a
non-significant pooled effect size of 0.13 in favor of activity scheduling. In ten studies activity scheduling was compared to
cognitive therapy, and the pooled effect size indicating the difference between these two types of treatment was 0.02. The changes
from post-test to follow-up for activity scheduling were non-significant, indicating that the benefits of the treatments were retained
at follow-up. The differences between activity scheduling and cognitive therapy at follow-up were also non-significant. Activity
scheduling is an attractive treatment for depression, not only because it is relatively uncomplicated, time-efficient and does not
require complex skills from patients or therapist, but also because this meta-analysis found clear indications that it is effective.
© 2006 Elsevier Ltd. All rights reserved.
Keywords: Activity scheduling; Depression; Meta-analysis; Cognitive behavior therapy
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Identification and selection of studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2. Quality assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3. Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.Description of studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.Effects of activity scheduling at post-test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3. Comparison to other treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.Effects at follow-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Clinical Psychology Review 27 (2007) 318–326
⁎Corresponding author. Tel.: +31 20 598 8757; fax: +31 20 598 8758.
E-mail address: P.Cuijpers@psy.vu.nl (P. Cuijpers).
0272-7358/$ - see front matter © 2006 Elsevier Ltd. All rights reserved.
It is already more than three decades ago that research showed that there is a significant relationship between mood
and the number of pleasant activities engaged in (Lewinsohn & Graf, 1973; Lewinsohn & Libet, 1972; Gallagher,
1981); that depressed individuals find fewer activities pleasant, engage in pleasant activities less frequently, and obtain
therefore less positive reinforcement than other individuals (MacPhillamy & Lewinsohn, 1974).
Based on these premises, Lewinsohn, Biglan and Zeiss (1976) developed a behavioral treatment of depression, in
the patients learn how to develop a plan to increase number of pleasant activities and to increase positive interactions
with their environment. In this approach, specific attention is paid to social skills and interactions with other people.
In the seventies and eighties, several trials were conducted in which the effects of this approach were examined
(Barrera, 1979; McNamara & Horan, 1986; Zeiss, Lewinsohn & Munoz, 1979). Most of these studies found promising
results. Other cognitive behavioral treatments developed in that period integrated activity scheduling into their manual
as one of the basic elements. The most important examples of these treatments include cognitive therapy as developed
by Beck, Rush, Shaw and Emery (1979), and the “Coping with Depression” course, as developed by Lewinsohn,
Antonuccio, Steinmetz and Teri (1984). Both treatment modalities have been examined in dozens of studies, and there
is no doubt that these treatments are effective. However, it is not clear what the contribution of activity scheduling is to
Since that period, activity scheduling has not received much attention by researchers, except for an important
dismantling study (Jacobson et al., 1996), and the use of activity scheduling in specific patient groups in which other
psychological treatments are less feasible. In the dismantling study by Jacobson and colleagues in the nineties the core
elements of cognitive behavioral treatment were compared to each other and to the full package (Jacobson et al., 1996).
Although this study indicated that activity scheduling is an effective treatment for depression on its own, this has not
prompted researchers to further examine the effects of this approach.
Other more recent approaches have used activity scheduling in difficult target populations, in which more complex
approaches are not feasible. For example, it has been used with success in dementia patients, after the training of their
caregivers in the principles of activity scheduling (Teri, Logsdon, Uomoto & McCurry, 1997). Because activity
scheduling is a relatively uncomplicated and time-efficient method for treating depression, it has also been used
recently with success in a pilot project with psychiatric inpatients (Hopko, Lejuez, Lepage, Hopko & McNeil, 2003).
Although activity scheduling seems to be an interesting treatment approach for depression, and recent studies show
that it is a useful method for specific target populations, no formal meta-analysis of activity scheduling has been
conducted until now (NICE, 2004). We decided, therefore, to conduct a meta-analysis to examine the effects of activity
scheduling on depression, on the relative effects of activity scheduling compared to other treatments, and on the longer
2.1. Identification and selection of studies
Studies were traced by means of several methods. First, we used a large database of 777 papers on the psychological
treatment of depression in general. This database was developed through a comprehensive literature search (from 1966
to March 2005) in which we examined 5178 abstracts in Pubmed (1224 abstracts), Psycinfo (1336), Embase (1118) and
the Cochrane Central Register of Controlled Trials (1500). We identified these abstracts by combining terms indicative
of psychological treatment (psychotherapy, psychological treatment, cognitive therapy, behavior therapy, interpersonal
therapy, reminiscence, life review) and depression (both MeSH-terms and text words). For this database, we also
collected the primary studies from 22 meta-analyses of psychological treatment of depression (Cuijpers & Dekker,
2005). For the current study, we examined the abstracts of these 777 studies, and selected the ones which focused on
activity scheduling. In addition, we examined references of an earlier review on activity scheduling (NICE, 2004), and
we reviewed reference lists of retrieved papers.
We included studies in which (–) effects of activity scheduling (–) on adults (–) with a depressive disorder or an
elevated level of depressive symptomatology, (–) were compared to a control condition or another (psychological or
pharmacological) treatment (–) in a randomized controlled trial. No language restrictions were applied.
319 P. Cuijpers et al. / Clinical Psychology Review 27 (2007) 318–326
Selected characteristics of controlled and comparative studies on activity scheduling
1st authorC Population RecrDefinition of depressionConditionsNNse
Barrera (1979)US AdultsCom MMPI criteria1. Activity
108 GRP Pre, post, 1, 7 mnMMPI-D, BDI
8 Comas-Diaz (1981)US Low SES
Com Depressed (not further
nr GRPPre, post, 5 week BDI; HRSD
2. Activity scheduling
2. Supportive therapy
14Gallagher (1981) USElderlyClin NR10 GRPPreMMPI-d,
Pre, Post, 1½, 3,
6, 12 month
USElderly ComMDD (SADS/RDC)+
16 IND HRSD
2. Activity scheduling
3. Psychodynamic ther
2. Nondirective therapy
3. Act sched+CT
2. Activity scheduling
3. CT+act schedul
4. High demand control
and McNeil (2003)
Jacobson et al. (1996)
Clin MDD4–5 IND Pre, post
57 USAdultsCom/clin MDD (SCID/DSM-III-R)+
20 IND Pre, post, 6,
12, 24 mn
McNamara and Horan
US Young adults
ClinBDI[18+HRSD[208–10IND Pre, post, 2 mnBDI, HRSD
Padfield (1976)USLow SES
Com Depr disorder Grinker Int.NrIND Pre, postZung
8 Shaw (1977) USYoung adults
8 GRPPre, post, 1 mn BDI, HRSD
2. Activity scheduling
3. Nondirective control
P. Cuijpers et al. / Clinical Psychology Review 27 (2007) 318–326
Taylor and Marshall
Au Young adults
Com BDI[13+D-scale[301. CT
2. Activity scheduling
3. CT+Act schedul
2. Problem solving
4. WL (6-weeks)
1. Act Sched+AD
6 IND Pre, post, 5 weekBDI, D-30
Teri et al. (1997) USDementia
ClinMDD or minD (RDC/DSM)9 INDPre HRSD
Thompson and Gallagher
US Elderly NrMDD; HRSD[14;
Post, 1½, 3, 6,
12, 24 mn
and Breckenridge (1987)
US Elderly ComMDD (RDC); BDI[17;
Wilson (1982) Au Adults
7 IND Pre, post, 6 mn BDI,
scale 2. Relaxation+AD
3. AD+min contact
4. Act Sched+PLAC
6. Min contact+PLAC
8 Wilson, Goldin and
8 IND Pre, post, 5 mnHRSD, BDI
2. Activity scheduling
11Zeiss, Lewinsohn and
12 INDPre, post, 1 mnMMPI-D
2. Activ Scheduling
3. Social skills training
Abbreviations: C: country; Recr: recruitment; N: number of subjects; Nse: number of sessions; Frm: format; Com: community recruitment; WL: waiting list; GRP: group treatment; IND: individual
treatment; mn: months; wk: weeks; CT: cognitive therapy; nr: not reported; clin: clinical sample; MDD: major depressive disorder; Act sched: activity scheduling; Au: Australia; minD: minor
P. Cuijpers et al. / Clinical Psychology Review 27 (2007) 318–326
We considered an intervention to be activity scheduling when the registration of pleasant activities and the increase
of positive interactions between a person and his or her environment were the core elements of the treatment. Social
skills training could be a part of the intervention. Although this intervention has been developed by Lewinsohn et al.
(1976), we also included studies that used the principles of this intervention, but did not refer directly to the work of
2.2. Quality assessment
The methodological quality of the studies was assessed using four basic criteria (Higgins & Green, 2005): allocation
to conditions was done by an independent (third) party; adequacy of random allocation concealment to respondents;
blinding of assessors of outcomes; and completeness of follow-up data.
We calculated effect sizes (d) by subtracting (at post-test) the average score of the control group (Me) from the
average score of the experimental group (Mc) and dividing the result by the pooled standard deviations of the
experimental and control groups (SDec; Hedges & Olkin,1985; Cooper & Hedges, 1994). 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 .56 to 1.2 can be assumed to be large, while effect sizes of .33 to .55 are moderate, and effect sizes of 0 to
.32 are small (Lipsey & Wilson, 1993).
In calculations of effect sizes we only used those instruments that explicitly measure depression (Table 1). If more
than one depression measure was used, the mean of the effect sizes was calculated, so that each study (or contrast
group) only had one effect size.
To calculate pooled mean effect sizes, we used the computer program Comprehensive Meta-analysis (version
2.2.021), developed for support in meta-analysis. As we expected considerable heterogeneity, we decided to calculate
mean effect sizes both with the random effects model and the fixed effects model. However, because heterogeneity was
low in all analyses, and the differences between the results based on the fixed and those based on the random effects
were very small, we present only the results of the analyses based on the fixed effects model.
As indicator of homogeneity, we calculated Cochran's heterogeneity statistic Q. With this statistic the null
hypothesis is tested that effect sizes from each of the studies were similar enough that a common population effect size
could be calculated (Cochran, 1954). Cochran's Q was calculated using the following formula:
where diis the effect size index for the ith study, withe weight for that particular effect size and dwis the weighted
average effect size from all studies. The significance of Q is determined by a chi-squared distribution with k−1 degrees
of freedom where k is the number of studies included in the analysis.
We also calculated the I2-statistic, which is an indicator of heterogeneity in percentages (I2=100%×(Q−df )/Q,
where Q is Cochran's heterogeneity statistic and df the degrees of freedom). 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).
We examined whether the effect sizes of specific subgroups differed from each other, with the subgroup analyses as
implemented in Comprehensive Meta-analysis version 2.2.021.
3.1. Description of studies
Sixteen studies, with a total of 780 subjects (241 in the Activity scheduling conditions, 367 in other intervention
conditions, and 172 in the control conditions) met the inclusion criteria and were included in the current study. Selected
characteristics of the included studies are described in Table 1.
322 P. Cuijpers et al. / Clinical Psychology Review 27 (2007) 318–326
In 10 studies, subjects were recruited from the community, while in four studies subjects were recruited from clinical
settings (in the two other studies, a mixed recruitment method was used or the recruitment method was not reported.
Five studies were aimed at older adults, and the remaining eleven studies focused on younger adults. Two studies
focused on women with lower socio-economic status, three others focused on younger adults (mostly students), one on
psychiatric inpatients, and one other on dementia patients. In five studies, the participating subjects had to meet
diagnostic criteria for a major depression (in one study subjects with minor depression were also included). The
remaining eleven studies included subjects who scored high on a self-rating depression, or used another definition of
depression. In four studies the activity scheduling intervention was delivered in group format, in the other twelve
studies in individual format. Number of sessions varied between four and twenty. In ten studies, activity scheduling was
compared to a control condition; seven of the ten used a waiting list control group, while three used a psychological
placebo intervention. Activity scheduling was compared to other treatments in fourteen studies, with eighteen
comparisons between activity scheduling and another psychological treatment. Activity scheduling was compared to
cognitive therapy in ten studies. In fourteen of the sixteen studies the BDI was used as an outcome measure. Thirteen
studies were conducted in the United States, and three in Australia.
independent party. Concealment of random allocation to respondents was not possible or not reported in any of the
studies. However, ten of the sixteen studies reported blinding of assessors of outcomes. Drop-out numbers ranged from
3.2. Effects of activity scheduling at post-test
We could compare effects of activity scheduling to control conditions at post-test in ten studies (Table 2), totaling
239 subjects (106 in the Activity scheduling conditions, and 133 in the control conditions). The mean effect size was
0.87 (95% CI: 0.60~1.15). Heterogeneity was low (Q=11.37; n.s.; I2=20.87%). The effect sizes and 95% confidence
intervals of the individual contrast groups are plotted in Fig. 1. Because heterogeneity was low, we did not conduct
3.3. Comparison to other treatments
Activity scheduling could be compared directly to other psychological treatments in 18 contrast groups (in 14
studies). The pooled effect size indicating the difference between activity scheduling and other psychological
treatments was 0.13, in favor of activity scheduling. This difference was not significant (95% CI: −0.05~0.30), and
heterogeneity (indicated by I2) was zero.
Meta-analyses of studies examining the effects of activity scheduling on depression compared to control conditions at post-test: Overall results and
AS vs. control
Comparison to other treatments
AS vs. all other treatments
AS vs. CT
AS vs. CT+AS
CT vs. CT+AS
Effects at follow-up
Post-test to 1–3 months follow-up
Post-test to 4–6 months follow-up
Post-test to 7–12 months follow-up
Comparison to CT at follow-up
At 1–3 months follow-up
At 4–6 months follow-up
100.87 0.60~1.1511.37 n.s. 20.87
aA positive d indicates that the first treatment is more effective than the second.
bNone of the Q-statistics was significant (pb0.05).
323P. Cuijpers et al. / Clinical Psychology Review 27 (2007) 318–326
In ten studies activity scheduling was compared to cognitive therapy. The pooled effectsize indicating the difference
between these two types of treatment was 0.02 (95% CI: −0.21~0.25), with heterogeneity (I2) again being zero. In three
studies, activity scheduling was compared to the combination of cognitive therapy and activity scheduling. The
resulting pooled effect size indicating the difference between the two was −0.01 (not significant; fixed effects model;
95% CI: −0.34~0.33), with low heterogeneity (I2=26.41). In the same three studies, the effects of cognitive therapy
could be compared to the combination of cognitive therapy and activity scheduling. The resulting pooled effect size
was 0.16 (not significant; fixed effects model; 95% CI: −0.19~0.52), with low heterogeneity (I2=30.84).
Activity scheduling was compared to antidepressant medication in only one study (Wilson, 1982; effect size 0.26 in
favor of activity scheduling).
3.4. Effects at follow-up
The effects of activity scheduling compared to a control condition at follow-up could be calculated in only two
studies (because most studies used a waiting list control condition). The effect sizes ranged from 0.88 (at two months
follow-up), to 0.54 (at six months follow-up), suggesting some support for the effectiveness of activity scheduling at
the longer term.
We could calculate the effect size indicating the change between post-test and follow-up in nine studies. In five
studies, the follow-up period was between one and three months. The resulting pooled effect size was 0.18 (95% CI:
−0.25~0.31), indicating a small, but non-significant improvement from post-test to follow-up. The change between
post-test and four to six months follow-up could also be calculated in five studies, and resulted in a pooled effect size of
0.03 (95% CI: −0.25~0.31). The change from post-test to seven to twelve months follow-up was presented in only two
studies (d=0.53; 95% CI: −0.11~1.17).
The effects of activity scheduling at follow-up could be compared to the effects of cognitive therapy at follow-up
in seven studies. The four comparisons at one to three months follow-up resulted in a pooled effect size of 0.02 (95%
CI: −0.47~0.51), while the four comparisons at four to six months follow-up had a pooled effect size of −0.13
(−0.46~0.20), indicating non-significant differences between cognitive therapy and activity scheduling at follow-up.
Only one study compared cognitive therapy to activity scheduling at one-year follow-up (d=0.30).
We found clear indications that activity scheduling is effective in the treatment of depression in adults. The overall
effect size of 0.87 is large (Lipsey & Wilson, 1993), and comparable to effect sizes found for other psychological
Fig. 1. Standardized effect sizes of activity scheduling for depression compared to control conditions at post-test.
324 P. Cuijpers et al. / Clinical Psychology Review 27 (2007) 318–326
treatments and treatment with antidepressants (Churchill et al., 2001; Gloaguen, Cottrauxa, Cucherata & Blackburn,
1998). The low heterogeneity in our meta-analyses indicated that the studies, target populations and interventions are
probably highly comparable to each other.
Several of the studies on activity scheduling have compared it to cognitive therapy, and these comparisons indicated
that activity scheduling and cognitive therapy are equally effective. This is true directly after the interventions at post-
test, but also at follow-up periods up to six months.
These results should be considered with caution, however, because of the limitations of our study. First, the number
of studies was small. Second, several studies were included with very small sample sizes. Third, the quality of the
included studies was not optimal. Fourth, we found only one study in which activity scheduling was compared to
antidepressant medication. Fifth, most studies used samples that were recruited from the community, and few studies
used clinical populations. And sixth, in most studies waiting list control conditions were used, which typically result in
larger effect sizes than care-as-usual or pill placebo control conditions.
Despite these limitations, effects of activity scheduling seem to be convincing, particularly because heterogeneity
was low and all studies pointed in the same direction. Considering this, it is remarkable that activity scheduling has
received so little attention from researchers in the past years. Although the study by Jacobson and colleagues in the
nineties (Jacobson et al., 1996) pointed in a similar direction, and has received much attentionfrom researchers, this has
not resulted in more efficacy and effectiveness studies on activity scheduling.
One of the attractive aspects of activity scheduling is that it is a relatively simple intervention, easy to understand for
depressed patients and does not require difficult or complex skills from patients and therapists (Lejuez, Hopko &
Hopko, 2001). This makes this intervention especially interesting for ‘difficult’ target populations. Studies by Teri et al.
(1997) in depressed dementia patients and the study by Hopko and colleagues in depressed psychiatric inpatients
(Hopko et al., 2003) show that this may be a useful approach. More research using this intervention in difficult
populations is certainly needed.
Our meta-analysis found indications that activity scheduling is as effective as cognitive therapy and as other
psychological treatments. This can be seen as support for the hypothesis that all psychological treatments are equally
effective and that the effects of a treatment are not achieved by the techniques of the treatment itself, but by common
factors, such as an intensive relationship between a patient and a therapist, the expectation of a patient to be cured, the
‘ritual’ of the therapy, and the presentation of a clear ‘rationale’ with which the problems of the patient can be explained
(Lambert & Barley,2001). The discussion about these common factors and equal effectsof all psychological treatments
has been going on for three decades now (Luborsky, Singer & Luborsky, 1975), and has resulted in support both for the
common factors and the therapy-specific techniques.
Accordingly, it may be possible that effects of activity scheduling are realized completely, or to a certain degree by
these common factors. If this is the case, the advantages of activity scheduling are still large: a simple, easy to
understand treatment, which does not require complex skills from therapists, which on the other hand does meet all
requirements for common factors to be active. From this perspective, it would certainly be useful to examine whether
activity scheduling can be applied in difficult populations which cannot be treated with complex interventions.
Furthermore, it is also useful from a patient's perspective to examine whether activity scheduling could replace more
complex psychological interventions, which require greater intellectual capabilities from patients.
Several questions concerning activity scheduling remain unanswered. And future research should use clinical
samples, larger numbers of subjects, care-as-usual control conditions, and should focus on the relative efficacy of
activity scheduling compared to antidepressant medication. The research conducted in the past three decades,
however, shows that activity scheduling may be an effective, and interesting alternative to other treatments of
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