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Effect of psychotherapy for depression on quality of life:meta-analysis

Published online by Cambridge University Press:  02 January 2018

Spyros Kolovos ,
Annet Kleiboer  and
Pim Cuijpers
Spyros Kolovos*
Affiliation:
Department of Health Sciences, VU University Amsterdam, EMGO Institute for Health and Care Research, Amsterdam, The Netherlands
Annet Kleiboer
Affiliation:
Department of Clinical Psychology, VU University Amsterdam, EMGO Institute for Health and Care Research, VU University, Department of Clinical and Health Psychology, Utrecht University, The Netherlands
Pim Cuijpers
Affiliation:
Department of Clinical Psychology, VU University Amsterdam, EMGO Institute for Health and Care Research, VU University and VU University Medical Centre Amsterdam, The Netherlands, and Leuphana University, Lunebrug, Germany
*
Spyros Kolovos, Department of Health Sciences, EMGOInstitute for Health and Care Research, De Boelelaan 1085, 1081 HVAmsterdam, The Netherlands. Email: s.kolovos@vu.nl
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Abstract

Background

Several meta-analyses have shown that psychotherapy is effective for reducing depressive symptom severity. However, the impact on quality of life (QoL) is as yet unknown.

Aims

To investigate the effectiveness of psychotherapy for depression on global QoL and on the mental health and physical health components of QoL.

Method

We conducted a meta-analysis of 44 randomised clinical trials comparing psychotherapy for adults experiencing clinical depression or elevated depressive symptoms with a control group. We used subgroup analyses to explore the influence of various study characteristics on the effectiveness of treatment.

Results

We detected a small to moderate effect size (Hedges' g = 0.33, 95% CI 0.24–0.42) for global QoL, a moderate effect size for the mental health component (g = 0.42, 95% CI 0.33–0.51) and, after removing an outlier, a small but statistically significant effect size for the physical health component (g = 0.16, 95% CI 0.05–0.27). Multivariate meta-regression analyses showed that the effect size of depressive symptoms was significantly related to the effect size of the mental health component of QoL. The effect size of depressive symptoms was not related to global QoL or the physical health component.

Conclusions

Psychotherapy for depression has a positive impact on the QoL of patients with depression. Improvements in QoL are not fully explained by improvements in depressive symptom severity.

Information

Type
Review Articles
Information
The British Journal of Psychiatry , Volume 209 , Issue 6 , December 2016 , pp. 460 - 468
Copyright
Copyright © Royal College of Psychiatrists, 2016 

Depression is one of the most common mental disorders among adults. Reference Hasin, Goodwin, Stinson and Grant1,Reference Waraich, Goldner, Somers and Hsu2 Major depression in particular ranks currently fourth in disease burden worldwide, and is expected to rank first in high-income countries by 2030. Reference Mathers and Loncar3 In addition, depression is an enormous societal burden due to high healthcare use and reduced work performance. Reference Olesen, Gustavsson, Svensson, Wittchen and Jonsson4–Reference Greenberg, Kessler, Birnbaum, Leong, Lowe and Berglund6 Furthermore, depression is associated with substantial impairments in quality of life. Reference Saarni, Suvisaari, Sintonen, Pirkola, Koskinen and Aromaa7,Reference Ustun, Ayuso-Mateos, Chatterji, Mathers and Murray8 Quality of life (QoL) is a broad concept that comprises a range of life domains of the individual, such as social relationships, physical abilities, mental health functioning, role functioning and engagement in daily activities. Deficits in all these domains have been identified in people experiencing depressive symptoms. Reference Papakostas, Petersen, Mahal, Mischoulon, Nierenberg and Fava9 Several meta-analyses have shown the effectiveness of different psychotherapies in reducing depressive symptoms compared with control conditions. Reference Cuijpers, Geraedts, van Oppen, Andersson, Markowitz and van Straten10,Reference Cuijpers, Smit, Bohlmeijer, Hollon and Andersson11 Even though it is often postulated that improvements in depressive symptoms during treatment coincide with improvements in QoL, evidence to support the effectiveness of depression treatment on QoL is limited. Reference Ezquiaga, Garcia-Lopez, de Dios, Leiva, Bravo and Montejo12 Research indicates that QoL and depressive symptoms are moderately correlated at post-treatment assessment but suggest a weaker relationship in the long term. Reference McKnight and Kashdan13 Additionally, research suggests that people in remission from depression experience persistent deficits in QoL. Reference Ishak, Greenberg, Balayan, Kapitanski, Jeffrey and Fathy14,Reference Zimmerman, Chelminski, McGlinchey and Young15 Therefore, clinical remission, as well as overall well-being, defined exclusively by the absence of depressive symptoms, may be insufficient. To date, no meta-analysis has quantified the effects of psychotherapy for depression on QoL. Therefore, we examined how the effects of psychotherapy for depression compared with control conditions on global QoL and on two specific domains of QoL, namely mental and physical health.

Method

Initially, we searched an existing database (www.evidencebasedpsychotherapies.org) that has previously been used in a series of meta-analyses and contains 1476 randomised controlled trials (RCTs). Reference Cuijpers, van Straten, Warmerdam and Andersson16 This database was developed through a systematic literature search (from 1966 to 1 January 2013) and is periodically updated. Additionally, a systematic literature search was conducted in PubMed, EMBASE, PsycINFO and the Cochrane Central Register of Controlled Trials from 1 January 2013 to 1 January 2015.

Study selection

We included published RCTs in which psychotherapy for depression was compared with a control condition, for participants 18 years old or over, and which reported a measure of QoL at post-treatment assessment. Psychotherapy was defined as an intervention in which the core element was verbal communication between a participant and a therapist, or as a systematic psychological treatment in the form of a website or book which the participant worked through more or less independently but with personal support from a therapist. Reference Cuijpers, van Straten, Warmerdam and Andersson16 The control condition was defined as waiting list, care as usual, placebo or another minimal treatment. Studies were included if participants were diagnosed with a depressive disorder on the basis of a structured clinical interview or if they reported elevated depressive symptoms based on a standardised measurement of depressive symptom severity. A measure of QoL was defined as any patient-reported measure aiming to assess perceived health status, well-being or effective performance in daily life. Reference Higgins and Green17 These measures could provide a global (overall) score, or separate scores for different domains or components. We differentiated between two components, namely mental health and physical health. The mental health component of QoL was defined as personal satisfaction with the current psychological state, whereas the physical health component was defined as the perceived competence for performance and functioning in various everyday activities. Reference Higgins and Green17 Our search was restricted to studies written in English and German. Studies regarding treatment maintenance were excluded. Comorbid psychiatric or medical disorders were not used as an exclusion criterion. Finally, we excluded studies for which we did not have sufficient statistics to perform the meta-analysis.

Quality assessment

The validity of the studies was assessed following the guidelines provided by the Cochrane Collaboration's tool for assessing risk of bias. Reference Higgins, Altman, Gotzsche, Juni, Moher and Oxman18 Risk of bias was examined in four domains: random sequence generation, allocation concealment, blinding (masking) of outcome assessment and intention-to-treat analysis. Two authors (S.K. and A.K.) conducted the assessment. Disagreements between the two reviewers were resolved by discussion until consensus was reached.

Statistical analysis

For the univariate analyses we used the Comprehensive Meta-Analysis (CMA) software package. Reference Borenstein, Hedges, Higgins and Rothstein19 For the multivariate analyses we used the metareg module within Stata, Reference Harbord and Higgins20 because these analyses cannot be performed with CMA. We calculated the effect size following the procedure described by Hedges & Olkin to correct for small sample size bias. Reference Hedges and Olkin21 We estimated the pooled effect sizes using the random effects model to account for heterogeneity among studies. Reference Hedges and Vevea22 Heterogeneity was examined with the I 2 statistic, where a value of 25% determines low heterogeneity, 50% moderate heterogeneity and 75% high heterogeneity. Reference Higgins, Thompson, Deeks and Altman23 We further calculated the 95% confidence intervals around I 2 statistic, Reference Ioannidis, Patsopoulos and Evangelou24 by using the non-central χ2-based approach within the heterogi module for Stata. Reference Orsini, Bottai, Higgins and Buchan25 Finally, publication bias was examined by visual inspection of the funnel plot and by implementing Duval & Tweedie's trim and fill procedure, which is a test of symmetry of the funnel plot. In addition, the method developed by Duval & Tweedie yields an adjusted pooled effect size after accounting for missing studies due to publication bias. Reference Duval and Tweedie26 We conducted a number of subgroup analyses to identify potential moderators of the outcome using the mixed effects model, in which studies within the subgroups are pooled with the random effects model and the tests for significant differences between the subgroups are carried out with the fixed effects model. Reference Borenstein, Hedges, Higgins and Rothstein27 Subgroup analyses were performed when at least three studies were available for each group. Moreover, we conducted sensitivity analyses because a number of studies compared more than one experimental group with the same control condition, and therefore the assumption of independency was violated. In the sensitivity analyses we first included the largest effect size for each study and then the lowest effect size for each study.

We used univariate and multivariate meta-regression analyses to examine the relationship between changes in QoL and depressive symptom severity. The meta-regressions were undertaken using the mixed effects model. Reference Viechtbauer28 For the univariate meta-regressions the effect size of QoL was set as the dependent variable and the effect size of depressive symptom severity as the predictor. For the multivariate meta-regressions a number of potential confounder variables were added simultaneously as predictors alongside the effect size for depressive symptoms.

Power calculation

We presumed that a limited number of studies would have administered QoL measures. Thus, we carried out a power calculation to estimate whether the included studies would provide sufficient statistical power to detect small effect sizes, according to the recommendations of Borenstein et al. Reference Borenstein, Hedges, Higgins and Rothstein27 Although there is no consensus about a clear definition, we defined a small effect size as g = 0.20. Reference Cohen29 We conservatively assumed a high level of between-study variance (τ2), a statistical power of 0.80 and an alpha value of 0.05. The power calculation demonstrated that we would need 20 studies with a mean sample size of 40 participants or 15 studies with 54 participants.

Results

The databases search resulted in 20 461 titles. We retrieved the full text of 1764 studies, from which 44 studies were included in this meta-analysis (Fig. 1).

Fig. 1

Study selection.

Study characteristics

The 44 studies included in total 5264 patients: 2907 in the intervention group and 2357 in the control group (see online Table DS1). More specifically, the meta-analysis of global QoL included 27 studies with 2448 patients, the meta-analysis of the mental health component included 18 studies with 2463 patients and that of the physical health component included 13 studies with 1561 patients. Psychotherapies that could be clustered in the cognitive–behavioural group (i.e. cognitive–behavioural therapy, mindfulness-based cognitive therapy and coping with depression) were provided in 25 studies (56%). Life review was offered in five studies (14%), problem-solving treatment in three studies (8%), acceptance and commitment therapy in three studies (8%) and interpersonal psychotherapy in two studies (5%). Care as usual was the most common control condition and was included in 20 studies (45%). It consisted mainly of psychotherapy, antidepressant medication or combination treatments, but was only superficially described in the published papers. Therefore, we did not have enough information to cluster usual treatments based on their modality. Waiting-list groups were included in 17 studies (39%). Other types of control conditions were included in 7 studies (16%), and consisted of discussion groups, psychoeducation, a 20 min educational video or placebo pill. The mean number of treatment sessions was 10 (median 9, range 1–25). All the quality criteria were met by 24 studies (55%) and at least three out of four criteria were met by 33 studies (75%). Finally, 36 studies (82%) reported a method of handling incomplete outcome data. The characteristics of the participants varied among the studies. Adult patients (both men and women) were included in 27 studies (61%), older adults in 11 studies (25%) and exclusively women in six studies (14%). Eighteen studies (41%) included participants diagnosed with major depressive disorder. Patients with comorbid physical or mental health symptoms were included in 15 studies (34%).

Global QoL

Thirty-one comparisons were included in the meta-analysis of global QoL (Fig. 2). The mean effect size (Hedges' g) was 0.33 (95% CI 0.24–0.42). We detected low between-study heterogeneity (I 2 = 21, 95% CI 0–49). After adjusting for publication bias using the trim and fill procedure the mean effect size was g = 0.30 (95% CI 0.21–0.40), with three imputed studies (Table 1). Moreover, meta-analysis including only the largest effect size of each study resulted in an overall effect size of g = 0.35 (95% CI 0.25–0.45). When only the smallest effect size was included the pooled effect size was g = 0.34 (95% CI 0.24–0.45). We also calculated the effect sizes separately for scores on the Quality of Life Inventory (QOLI; g = 0.32, 95% CI 0.15–0.48) and the EuroQol EQ-5D (g = 0.19, 95% CI 0.07–0.32). Studies including people with major depressive disorder resulted in larger effect sizes (g = 0.49, 95% CI 0.36–0.61) than studies including people without such a diagnosis (g = 0.23, 95% CI 0.13–0.34, P = 0.002). Studies including adults reported larger effect sizes (g = 0.39, 95% CI 0.29–0.49) than studies including older adults (g = 0.15, 95% CI 0.00–0.30, P = 0.009). Other study characteristics were not significantly related to the effect size of global QoL.

Fig. 2

Standardised effect sizes (Hedges' g) of psychotherapy for depression compared with control conditions on global quality of life. CBT, cognitive–behavioural therapy (em, email therapy; sh, guided self-help; st, standard treatment; tai, tailored treatment); PST, problem-solving therapy; SUP, supportive therapy.

Table 1

Global quality of life: effect sizes in meta-analysis of studies comparing psychotherapy with a control group

Number of
comparisons		 Effect size Heterogeneity b
Comparison a g 95% CI I 2 95% CI P
All studies 31 0.33*** 0.24–0.42 21 0–49
    Adjusted values 34 0.30 0.21–0.40
Effect size for depression 31 0.60*** 0.50–0.70 32 0–56
    Adjusted values 36 0.54 0.44–0.64
One effect size per study (highest) 27 0.35*** 0.25–0.45 25 0–53
One effect size per study (lowest) 27 0.34*** 0.24–0.45 28 0–55
QOLI 8 0.32*** 0.15–0.48 0 0–56
EQ-5D 8 0.19** 0.07–0.32 0 0–56
Subgroup analyses
    MDD
        Yes 15 0.49*** 0.36–0.61 0 0–46 0.002
        No 16 0.23*** 0.13–0.34 6 0–48
    Comorbidity
        Yes 10 0.23** 0.08–0.38 0 0–53 0.148
        No 21 0.37*** 0.26–0.49 31 0–59
    Control group
        Care as usual 10 0.18** 0.05–0.32 1 0–53 0.053
        Waiting list 14 0.42*** 0.28–0.56 23 0–59
        Other 7 0.33*** 0.16–0.50 0 0–58
    Intent-to-treat analysis
        Yes 27 0.34*** 0.24–0.43 25 0–53 0.895
        No 4 0.32* 0.03–0.60 5 0–69
    Treatment type
        Individual 10 0.17* 0.01–0.33 0 0–53 0.258
        Group 8 0.35** 0.13–0.57 52 0–77
    Internet-based treatment
        Yes 12 0.41*** 0.28–0.53 3 0–51 0.331
        No 19 0.27*** 0.15–0.40 26 0–57
    Target group
        Adults in general 23 0.39*** 0.29–0.49 11 0–47 0.009
        Older adults 8 0.15 −0.00–0.30 0 0–56
    CBT v. other
        CBT 20 0.36*** 0.25–0.47 21 0–54 0.429
        Other 11 0.28*** 0.13–0.44 18 0–60
    Life review v. other
        Life review 5 0.19 −0.05–0.42 27 0–73 0.171
        Other 26 0.37*** 0.26–0.43 11 0–45

CBT, cognitive–behavioural therapy; MDD, major depressive disorder; QoL, quality of life; QOLI, Quality of Life Inventory.

a. The data presented here are from analysis using the random effects model.

b. Variance between studies as a proportion of the total variance; heterogeneity tested using the I 2 statistic.

* P<0.05,

** P<0.01,

*** P<0.001.

The mean effect size for depressive symptoms was g = 0.60 (95% CI 0.50–0.70) and therefore considerably larger than that for global QoL. Heterogeneity was moderate (I 2 = 32%, 95% CI 0–56). After adjusting for publication bias the mean effect size decreased to g = 0.54 (95% CI 0.44–0.64), with five imputed studies. The univariate meta-regression analysis indicated a significant relationship between the effect size of global QoL and the effect size of depressive symptoms (slope 0.52, 95% CI 0.21–0.84, P = 0.002), suggesting that with each increase in effect size of depressive symptom severity by 1 the effect size for global QoL increased by 0.52. The effect size of global QoL was not significantly associated with the number of treatment sessions (slope 0.00, 95%CI −0.03 to 0.03, P = 0.803). The multivariate meta-regression results showed that none of the predictors included in the model was significantly related to the effect of psychotherapy on global QoL at post-treatment assessment (see Table 4).

Mental health

Twenty-one comparisons were included in the meta-analysis of the effects of psychotherapy for depression on the mental health component of QoL compared with a control condition. The mean effect size was g = 0.42 (95% CI 0.33–0.51). We detected low between-study heterogeneity (I 2 = 3, 95% CI 0–54). After adjustment for publication bias the mean effect size was g = 0.37 (95% CI 0.28–0.47), with five imputed studies (Table 2). Additionally, we performed a meta-analysis including the largest effect size of each study, which resulted in an overall effect size of g = 0.43 (95% CI 0.32–0.53). When the smallest effect size was included the pooled effect size was g = 0.40 (95% CI 0.31–0.50). Finally we conducted a series of subgroup analyses, but none of the included moderators was significantly related to the effect size of the mental health component (Table 2). For this group of studies the mean effect size for depressive symptoms was g = 0.48 (95% CI 0.36–0.60). Between-study heterogeneity was moderate to high (I 2 = 55, 95% CI 17–72). After adjusting for publication bias the effect size decreased (g = 0.41, 95% CI 0.28–0.54), with four missing studies.

Table 2

Mental health component of quality of life: effect sizes in meta-analysis of studies comparing psychotherapy with a control group

Number of
comparisons		 Effect size Heterogeneity b
Comparison a g 95% CI I 2 95% CI P
All studies 21 0.42*** 0.33–0.51 23 0–54
    Adjusted values 26 0.37 0.28–0.47
Effect size for depression 20 0.48*** 0.36–0.60 55 17–72
    Adjusted values 24 0.41 0.28–0.54
One effect size per study (highest) 18 0.43*** 0.32–0.53 29 0–59
One effect size per study (lowest) 18 0.40*** 0.31–0.50 17 0–53
Subgroup analyses
    MDD
        Yes 6 0.49*** 0.34–0.64 0 0–61 0.277
        No 15 0.39*** 0.28–0.50 34 0–63
    Comorbidity
        Yes 7 0.34** 0.12–0.56 46 0–76 0.428
        No 14 0.43*** 0.35–0.52 1 0–48
    Control group
        Care as usual 13 0.38*** 0.25–0.51 37 0–66 0.242
        Waiting list 7 0.48*** 0.36–0.61 0 0–58
    Target group
        Adults in general 13 0.43*** 0.33–0.53 24 0–60 0.350
        Women 4 0.54** 0.18–0.90 37 0–78
        Older adults 4 0.29** 0.09–0.48 4 0–69
    CBT v. other
        CBT 13 0.40*** 0.28–0.53 35 0–65 0.585
        Other 8 0.45*** 0.33–0.57 0 0–56

a. CBT, cognitive–behavioural therapy; MDD, major depressive disorder.

a. The data presented here are from analysis using the random effects model.

b. Variance between studies as a proportion of the total variance; heterogeneity tested using the I 2 statistic.

* P<0.05,

** P<0.01,

*** P<0.001.

Meta-regression analysis indicated a significant association between the effect size of the mental health component of QoL and the effect size of depressive symptoms at post-treatment measurement (slope 0.49, 95% CI 0.19–0.80, P = 0.003). The results suggested that with each increase in effect size of depressive symptom severity by 1 the effect size for the mental health component of QoL increased by 0.49 (Fig. 3). The effect size of the mental health component was not significantly related to the number of treatment sessions (slope 0.01, 95% CI −0.01 to 0.03, P = 0.544). The multivariate meta-regression analysis demonstrated that only the effect size of depression severity was a significant predictor of the effect of psychotherapy on the mental health component QoL (b = 0.50, 95% CI 0.16–0.83, P = 0.007; see Table 4).

Fig. 3

Relationship between effect sizes for depressive symptom severity and the mental health component of quality of life.

Physical health

Fourteen comparisons were included in the meta-analysis of the physical health component of QoL (Table 3). The mean effect size was g = 0.27 (95% CI 0.07–0.46). We detected high between-study heterogeneity (I 2 = 70, 95% CI 41–81). We repeated the analysis after removing an outlier with an effect size of g = 2.16. Reference Scheidt, Waller, Endorf, Schmidt, Konig and Zeeck30 The pooled effect size decreased (g = 0.16, 95% CI 0.05–0.27); in addition heterogeneity was low (I 2 = 4, 95% CI 0–49). We adjusted for publication bias and the mean effect size decreased to g = 0.13 (95% CI 0.01–0.25), with two studies missing The meta-analysis including the largest effect sizes of each study resulted in an overall effect size of g = 0.16 (95% CI 0.04–0.28). Next we included the smallest effect sizes, resulting in an overall effect size of g = 0.16 (95% CI 0.04–0.27). Finally we conducted a number of subgroup analyses, but none of the included moderators was significantly associated with the effect size of the physical health component of QoL. The mean effect size of depression severity was g = 0.52 (95% CI 0.38–0.66). Heterogeneity was moderate (I 2 = 38, 95% CI 0–66). After adjustment for publication bias the mean effect size decreased to g = 0.44 (95% CI 0.28–0.59), with three missing studies.

Table 3

Physical health component of quality of life: effect sizes in meta-analysis of studies comparing psychotherapy with a control group

Number of
comparisons		 Effect size Heterogeneity b
Comparison a g 95% CI I 2 95% CI P
All studies 14 0.27** 0.07–0.46 70 41–81
Outlier removed c 13 0.16** 0.05–0.27 4 0–49
    Adjusted values 15 0.13 0.01–0.25
Effect size for depression 14 0.52*** 0.38–0.66 38 0–66
    Adjusted values 17 0.44 0.28–0.59
One effect size per study
    Highest 12 0.16 0.04–0.28 18 0–58
    Lowest 12 0.16 0.04–0.27 16 0–57
Subgroup analyses
    MDD
        Yes 4 0.11 −0.11–0.32 13 0–72 0.528
        No 9 0.19** 0.05–0.32 18 0–62
    Comorbidity
        Yes 6 0.18* 0.02–0.33 13 0–66 0.843
        No 7 0.15 −0.02–0.33 22 0–67
    Treatment type
        Individual 10 0.17* 0.03–0.31 32 0–66 0.934
        Group 3 0.18 −0.07–0.43 0 0–90
    Target group
        Adults in general 7 0.16* 0.04–0.28 0 0–58 0.952
        Women 3 0.07 −0.49–0.62 65 0–88
        Older adults 3 0.16 −0.05–0.36 0 0–72
    CBT v. other
        CBT 10 0.18** 0.07–0.30 1 0–53 0.700
        Other 3 0.12 −0.18–0.41 46 0–84

CBT, cognitive–behavioural therapy; MDD, major depressive disorder.

a. The data presented here are from analysis using the random effects model.

b. Variance between studies as a proportion of the total variance; heterogeneity tested using the I 2 statistic.

c. Outlier's effect size g=2.16 (Scheidt et al). Reference Scheidt, Waller, Endorf, Schmidt, Konig and Zeeck30

* P<0.05,

** P<0.01,

*** P<0.001.

Univariate meta-regressions showed no significant association between the effect size of physical health component and the effect size of depressive symptoms (slope 0.35, 95% CI −0.12 to 0.82, P = 0.129) or the number of treatment sessions (slope 0.00, 95% CI −0.03 to 0.03, P = 0.968). Similarly, the multivariate meta-regression demonstrated that none of the predictors was significantly related to the effect of psychotherapy for depression on the physical health component of QoL at post-treatment assessment (Table 4).

Table 4

Study characteristics predicting the effect size of quality of life: multivariate meta-regression

B 95% CI s.e. P
Global component
    Depression effect size 0.37 −0.11 to 0.85 0.23 0.122
    Number of sessions −0.01 −0.05 to 0.02 0.02 0.380
    Target group: adults v. elderly 0.00 −0.48 to 0.48 0.23 0.999
    Control group: care as usual v. waiting list −0.05 −0.34 to 0.24 0.14 0.732
    Control group: other v. waiting list 0.03 −0.26 to 0.28 0.13 0.824
    CBT v. other 0.00 −0.24 to 0.22 0.11 0.994
    Life review v. other −0.23 −0.71 to 0.25 0.23 0.326
    MDD 0.12 −0.08 to 0.32 0.11 0.272
    Comorbidity −0.22 −0.53 to 0.08 0.15 0.143
    Internet-based treatment −0.11 −0.38 to 0.15 0.13 0.370
    Constant 0.34 −0.17 to 0.85 0.24 0.177
Mental health component
    Depression effect size 0.50 0.16 to 0.83 0.15 0.007
    Number of sessions −0.01 −0.04 to 0.02 0.01 0.466
    Target group: adults v. elderly 0.07 −0.24 to 0.38 0.14 0.621
    Target group: women v. elderly 0.22 −0.27 to 0.70 0.22 0.345
    CBT v. other 0.03 −0.23 to 0.20 0.09 0.783
    MDD 0.18 −0.17 to 0.22 0.18 0.319
    Comorbidity −0.06 −0.30 to 0.17 0.11 0.580
    Constant 0.21 −0.15 to 0.57 0.17 0.223
Physical health component
    Depression effect size 0.34 −1.05 to 1.73 0.50 0.535
    Number of sessions 0.01 −0.30 to 0.25 0.09 0.902
    Target group: adults v. elderly −0.18 −2.12 to 1.17 0.70 0.815
    Target group: women v. elderly −0.29 −1.66 to 1.07 0.49 0.587
    CBT v. other −0.06 −1.25 to 1.14 0.43 0.903
    MDD −0.05 −1.08 to 0.99 0.37 0.902
    Comorbidity 0.05 −1.61 to 1.71 0.60 0.935
    Individual treatment −0.04 −0.91 to 0.84 0.32 0.912
    Constant 0.07 −3.54 to 3.68 1.30 0.980

CBT, cognitive–behavioural therapy; MDD, major depressive disorder.

Discussion

We examined the effects of psychotherapy on QoL of people with depression, separately for global QoL and for its mental and physical health components. The results were in line with previous findings, suggesting that psychotherapy for depression is beneficial not only for depressive symptoms but also for quality of life. Reference Papakostas, Petersen, Mahal, Mischoulon, Nierenberg and Fava9,Reference McKnight and Kashdan13 Psychotherapy resulted in larger improvements in QoL than control conditions. The largest effect size was identified for the mental health component, whereas the effect size for global QoL was moderate. The smallest effect size was detected for the physical health component, which, however, included a limited number of comparisons. Nevertheless, even after excluding an outlier and adjusting for publication bias, the effect size for the physical health component remained statistically significant. Overall, it can be concluded that psychotherapy has a positive impact on various domains of a patient's life, such as mental functioning, social and work-related relationships, level of discomfort and engagement in everyday activities. Our findings, in conjunction with previous work, demonstrate the efficacy of psychotherapy for outcomes associated with depression. Reference Renner, Cuijpers and Huibers31 Particularly, the magnitude of the improvement in QoL is comparable – even though somewhat smaller – to that in social functioning of patients with depression. Reference Renner, Cuijpers and Huibers31 The influence of psychotherapy on domains of patients' lives other than the depressive symptoms is important because it can reduce the overall burden caused by the disease and decrease the risk of future depressive episodes. Reference Ezquiaga, Garcia-Lopez, de Dios, Leiva, Bravo and Montejo12,Reference Eaton, Martins, Nestadt, Bienvenu, Clarke and Alexandre32

We examined the association between QoL and depressive symptom severity by conducting various meta-regression analyses. The results were different for the global QoL and each of the two specific components of QoL. We found a significant positive relationship between the effect sizes for the mental health component and the effect sizes for depressive symptoms in the multivariate model. Nevertheless, the effect sizes for the physical health component were not related to the effect sizes for depressive symptoms. Finally, the relationship between the effect sizes for global QoL and depressive symptom severity were not statistically significant in the multivariate model. Overall, changes in QoL were not fully explained by changes in depressive symptoms. We can thus infer that decreased depressive symptom severity at the end of the treatment is not necessarily a manifestation of improvement in QoL of the patient or vice versa. This is an indication that QoL and depressive symptoms are two different constructs and that it is informative to use both as treatment outcomes. It should be highlighted that the applied research design did not allow us to determine causal or temporal relationships between QoL and depressive symptoms. A longitudinal design with repeated measurements of QoL and depressive symptoms is needed to examine whether changes in depressive symptoms lead to changes in QoL or vice versa. Reference Kazdin33

The effect sizes for depressive symptom severity were larger than the effect sizes for QoL. This finding is in line with previous studies demonstrating that impairments in QoL persist even after patients reach remission. Reference Ishak, Greenberg, Balayan, Kapitanski, Jeffrey and Fathy14,Reference Zimmerman, Chelminski, McGlinchey and Young15 As a result, distortion in daily life may endure even when deficits related to depressive symptoms have ceased. Reference Papakostas, Petersen, Mahal, Mischoulon, Nierenberg and Fava9 A possible explanation is that improvements in QoL follow a slower pace than those in depressive symptoms. Reference McKnight and Kashdan13 In addition, participants were eligible for the clinical trials because they experienced depressive symptoms and not because they reported low QoL. Therefore, they may not all have had low QoL to start with and thus had less to gain in terms of improvement in QoL. Finally, the detected difference in the effect sizes may also reflect the target of psychotherapy on reducing depressive symptom severity.

We found that the effects of psychotherapy on global QoL were larger for studies including exclusively participants with a diagnosis of major depressive disorder at baseline. Previous research has shown that deterioration of QoL is proportional to the severity of depression. Reference Demyttenaere, De Fruyt and Huygens34,Reference Berlim, Fleck, Ritsner and Awad35 Psychotherapy is therefore an effective treatment for people who experience both severe depressive symptoms and serious distress in their QoL. Furthermore, studies including adult patients yielded larger effect sizes than those including older adults. Older patients with depression demonstrate extensive age-related needs that may obstruct the efficacy of psychotherapy. Reference Fiske, Wetherell and Gatz36 In addition, debilitated QoL in older adults may be related to risk factors other than depression, which are not the explicit target of psychotherapy. Reference Rejeski and Mihalko37

Limitations of the study

The concept of QoL is inherently subjective and consequently is hard to measure with precision. It is thus possible that the different measures of QoL assessed slightly different constructs or parts of life. This limitation, however, applies mainly to the meta-analysis of global QoL where various instruments were included. The meta-analyses of the mental and physical health components were measured predominantly with the Medical Outcome Study Short Form. In addition, the number of studies in the meta-analysis of the physical health component was limited. Thus, we may have lacked adequate power to detect small effect sizes. A larger number of studies would allow us to interpret the results with more confidence, and therefore we strongly recommend the administration of measures of QoL in future clinical trials. Reference Ishak, Greenberg, Balayan, Kapitanski, Jeffrey and Fathy14 Another limitation relates to the quality of the included studies, which was not ideal. Researchers are encouraged to follow precisely the recommended guidelines for conducting and reporting randomised trials. Reference Schulz, Altman and Moher38 Moreover, a concern for every meta-analysis is the prevalence of publication bias. The test of publication bias that we performed examines only whether the funnel plot is symmetrical or whether studies with small sizes are missing. This procedure may have limited power to detect moderate publication bias and accordingly our results may overestimate the true effect size of psychotherapy on QoL. Reference Sterne, Gavaghan and Egger39 Furthermore, we examined only the short-term effects of psychotherapy; there is evidence that psychotherapy has long-term effects on depressive symptoms, Reference Cuijpers, Hollon, van Straten, Bockting, Berking and Andersson40 and it is therefore important to examine the possible long-term effects on QoL as well. Finally, we examined the effects of psychotherapy on QoL in comparison with control conditions. A meta-analysis focusing on the respective comparison between psychotherapy and pharmacotherapy for depression would be of importance.

Implications

Overall, this meta-analysis demonstrates that psychotherapy for depression is related to improvements in QoL. This evidence amplifies the notion that psychotherapy is beneficial not only for reducing depressive symptoms but also for improving additional outcomes related to depression. These effects are expected to reduce the enormous burden caused by depression and improve the lives of people with the disorder. Finally, the results indicate that QoL and depressive symptoms are two different constructs, and thus QoL could be assessed as an additional treatment outcome. Since the effects of psychotherapy are different for each component of QoL, it is informative to use specific scores for each domain, and not only an overall score for global QoL.

Acknowledgements

We thank Eirini Karyotaki, David Turner and Erica Weitz for their contribution in various parts of this meta-analysis.

Footnotes

Declaration of interest

None.

References

1 Hasin, DS, Goodwin, RD, Stinson, FS, Grant, BF. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 2005; 62: 1097–106.CrossRefGoogle ScholarPubMed
2 Waraich, P, Goldner, EM, Somers, JM, Hsu, L. Prevalence and incidence studies of mood disorders: a systematic review of the literature. Can J Psychiatry 2004; 49: 124–38.Google Scholar
3 Mathers, CD, Loncar, D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006; 3: e442.Google Scholar
4 Olesen, J, Gustavsson, A, Svensson, M, Wittchen, HU, Jonsson, B. The economic cost of brain disorders in Europe. Eur J Neurol 2012; 19: 155–62.Google Scholar
5 Sobocki, P, Jonsson, B, Angst, J, Rehnberg, C. Cost of depression in Europe. J Ment Health Policy Econ 2006; 9: 87–98.Google Scholar
6 Greenberg, PE, Kessler, RC, Birnbaum, HG, Leong, SA, Lowe, SW, Berglund, PA, et al. The economic burden of depression in the United States: how did it change between 1990 and 2000? J Clin Psychiatry 2003; 64: 1465–75.Google Scholar
7 Saarni, SI, Suvisaari, J, Sintonen, H, Pirkola, S, Koskinen, S, Aromaa, A, et al. Impact of psychiatric disorders on health-related quality of life: general population survey. Br J Psychiatry 2007; 190: 326–32.Google Scholar
8 Ustun, TB, Ayuso-Mateos, JL, Chatterji, S, Mathers, C, Murray, CJL. Global burden of depressive disorders in the year 2000. Br J Psychiatry 2004; 184: 386–92.Google Scholar
9 Papakostas, GI, Petersen, T, Mahal, Y, Mischoulon, D, Nierenberg, AA, Fava, M. Quality of life assessments in major depressive disorder: a review of the literature. Gen Hosp Psychiatry 2004; 26: 13–7.Google Scholar
10 Cuijpers, P, Geraedts, AS, van Oppen, P, Andersson, G, Markowitz, JC, van Straten, A. Interpersonal psychotherapy for depression: a meta-analysis. Am J Psychiatry 2011; 168: 581–92.Google Scholar
11 Cuijpers, P, Smit, F, Bohlmeijer, E, Hollon, SD, Andersson, G. Efficacy of cognitive–behavioural therapy and other psychological treatments for adult depression: meta-analytic study of publication bias. Br J Psychiatry 2010; 196: 173–8.Google Scholar
12 Ezquiaga, E, Garcia-Lopez, A, de Dios, C, Leiva, A, Bravo, M, Montejo, J. Clinical and psychosocial factors associated with the outcome of unipolar major depression: a one year prospective study. J Affect Disord 2004; 79: 63–70.Google Scholar
13 McKnight, PE, Kashdan, TB. The importance of functional impairment to mental health outcomes: a case for reassessing our goals in depression treatment research. Clin Psychol Rev 2009; 29: 243–59.Google Scholar
14 Ishak, WW, Greenberg, JM, Balayan, K, Kapitanski, N, Jeffrey, J, Fathy, H, et al. Quality of life: the ultimate outcome measure of interventions in major depressive disorder. Harv Rev Psychiatry 2011; 19: 229–39.Google Scholar
15 Zimmerman, M, Chelminski, I, McGlinchey, JB, Young, D. Diagnosing major depressive disorder VI: performance of an objective test as a diagnostic criterion. J Nerv Ment Dis 2006; 194: 565–9.Google Scholar
16 Cuijpers, P, van Straten, A, Warmerdam, L, Andersson, G. Psychological treatment of depression: a meta-analytic database of randomized studies. BMC Psychiatry 2008; 8: 36.Google Scholar
17 Higgins, J, Green, S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. Cochrane Collaboration, 2011.Google Scholar
18 Higgins, JP, Altman, DG, Gotzsche, PC, Juni, P, Moher, D, Oxman, AD, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928.CrossRefGoogle ScholarPubMed
19 Borenstein, M, Hedges, L, Higgins, J, Rothstein, H. Comprehensive Meta-analysis Version 2: 104. Biostat, 2005.Google Scholar
20 Harbord, R, Higgins, J. Metareg: Stata Module to Perform Meta-analysis Regression. Statistical Software Components, 2009.Google Scholar
21 Hedges, LV, Olkin, I. Statistical Method for Meta-analysis. Academic Press, 2014.Google Scholar
22 Hedges, LV, Vevea, JL. Fixed-and random-effects models in meta-analysis. Psychol Methods 1998; 3: 486.Google Scholar
23 Higgins, , Thompson, SG, Deeks, JJ, Altman, DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557–60.Google Scholar
24 Ioannidis, JPA, Patsopoulos, NA, Evangelou, E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ 2007; 335: 914–6.Google Scholar
25 Orsini, N, Bottai, M, Higgins, J, Buchan, I. Heterogi: Stata Module to Quantify Heterogeneity in a Meta-analysis. Statistical Software Components, 2006.Google Scholar
26 Duval, S, Tweedie, R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455–63.Google Scholar
27 Borenstein, M, Hedges, LV, Higgins, JP, Rothstein, HR. Introduction to Metaanalysis. Wiley, 2011.Google Scholar
28 Viechtbauer, W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat 2005; 30: 261–93.Google Scholar
29 Cohen, J. Statistical power analysis. Curr Dir Psychol Sci 1992; 1: 98–101.Google Scholar
30 Scheidt, CE, Waller, E, Endorf, K, Schmidt, S, Konig, R, Zeeck, A, et al. Is brief psychodynamic psychotherapy in primary fibromyalgia syndrome with concurrent depression an effective treatment? A randomized controlled trial. Gen Hosp Psychiatry 2013; 35: 160–7.Google Scholar
31 Renner, F, Cuijpers, P, Huibers, MJ. The effect of psychotherapy for depression on improvements in social functioning: a meta-analysis. Psychol Med 2014; 44: 2913–26.Google Scholar
32 Eaton, WW, Martins, SS, Nestadt, G, Bienvenu, OJ, Clarke, D, Alexandre, P. The burden of mental disorders. Epidemiol Rev 2008; 30: 1–14.Google Scholar
33 Kazdin, AE. Mediators and mechanisms of change in psychotherapy research. Annu Rev Clin Psychol 2007; 3: 1–27.Google Scholar
34 Demyttenaere, K, De Fruyt, J, Huygens, R. Measuring quality of life in depression. Curr Opin Psychiatry 2002; 15: 89–92.Google Scholar
35 Berlim, M, Fleck, MA. Quality of life and major depression. In Quality of Life Impairment in Schizophrenia, Mood and Anxiety Disorders (eds Ritsner, M, Awad, AG): 241–52. Springer Netherlands, 2007.Google Scholar
36 Fiske, A, Wetherell, JL, Gatz, M. Depression in older adults. Annu Rev Clin Psychol 2009; 5: 363–89.Google Scholar
37 Rejeski, WJ, Mihalko, SL. Physical activity and quality of life in older adults. J Gerontol A Biol Sci Med Sci 2001; 56: 23–35.Google Scholar
38 Schulz, KF, Altman, DG, Moher, D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Ann Intern Med 2010; 152: 726–32.Google Scholar
39 Sterne, JA, Gavaghan, D, Egger, M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol 2000; 53: 1119–29.Google Scholar
40 Cuijpers, P, Hollon, SD, van Straten, A, Bockting, C, Berking, M, Andersson, G. Does cognitive behaviour therapy have an enduring effect that is superior to keeping patients on continuation pharmacotherapy? A meta-analysis. BMJ Open 2013; 3: e002542.Google Scholar
41 Andersson, G, Bergstrom, J, Hollandare, F, Carlbring, P, Kaldo, V, Ekselius, L. Internet-based self-help for depression: randomised controlled trial. Br J Psychiatry 2005; 187: 456–61.Google Scholar
42 Barlow, JP. A Group Treatment for Depression in the Elderly. University of Houston, 1986.Google Scholar
43 Beeber, LS, Holditch-Davis, D, Perreira, K, Schwartz, TA, Lewis, V, Blanchard, H, et al. Short-term in-home intervention reduces depressive symptoms in early Head Start Latina mothers of infants and toddlers. Res Nurs Health 2010; 33: 60–76.Google Scholar
44 Berger, T, Hammerli, K, Gubser, N, Andersson, G, Caspar, F. Internet-based treatment of depression: a randomized controlled trial comparing guided with unguided self-help. Cogn Behav Ther 2011; 40: 251–66.Google Scholar
45 Burns, A, O'Mahen, H, Baxter, H, Bennert, K, Wiles, N, Ramchandani, P, et al. A pilot randomised controlled trial of cognitive behavioural therapy for antenatal depression. BMC Psychiatry 2013; 13: 33.CrossRefGoogle ScholarPubMed
46 Carlbring, P, Hagglund, M, Luthstrom, A, Dahlin, M, Kadowaki, A, Vernmark, K, et al. Internet-based behavioral activation and acceptance-based treatment for depression: a randomized controlled trial. J Affect Disord 2013; 148: 331–7.Google Scholar
47 Chiesa, A, Mandelli, L, Serretti, A. Mindfulness-based cognitive therapy versus psycho-education for patients with major depression who did not achieve remission following antidepressant treatment: a preliminary analysis. J Altern Complement Med 2012; 18: 756–60.CrossRefGoogle Scholar
48 Cramer, H, Salisbury, C, Conrad, J, Eldred, J, Araya, R. Group cognitive behavioural therapy for women with depression: pilot and feasibility study for a randomised controlled trial using mixed methods. BMC Psychiatry 2011; 11: 82.Google Scholar
49 Dekker, RL, Moser, DK, Peden, AR, Lennie, TA. Cognitive therapy improves three-month outcomes in hospitalized patients with heart failure. J Card Fail 2012; 18: 10–20.Google Scholar
50 Dindo, L, Recober, A, Marchman, JN, Turvey, C, O'Hara, MW. One-day behavioral treatment for patients with comorbid depression and migraine: a pilot study. Behav Res Ther 2012; 50: 537–43.CrossRefGoogle ScholarPubMed
51 Dobkin, RD, Menza, M, Allen, LA, Gara, MA, Mark, MH, Tiu, J, et al. Cognitive-behavioral therapy for depression in Parkinson's disease: a randomized, controlled trial. Am J Psychiatry 2011; 168: 1066–74.CrossRefGoogle ScholarPubMed
52 Dowrick, C, Dunn, G, Ayuso-Mateos, JL, Dalgard, OS, Page, H, Lehtinen, V, et al. Problem solving treatment and group psychoeducation for depression: multicentre randomised controlled trial. Outcomes of Depression International Network (ODIN) Group. BMJ 2000; 321: 1450–4.CrossRefGoogle ScholarPubMed
53 Duarte, PS, Miyazaki, MC, Blay, SL, Sesso, R. Cognitive-behavioral group therapy is an effective treatment for major depression in hemodialysis patients. Kidney Int 2009; 76: 414–21.Google Scholar
54 Fledderus, M, Bohlmeijer, ET, Pieterse, ME, Schreurs, KM. Acceptance and commitment therapy as guided self-help for psychological distress and positive mental health: a randomized controlled trial. Psychol Med 2012; 42: 485–95.Google Scholar
55 Folke, F, Parling, T, Melin, L. Acceptance and commitment therapy for depression: a preliminary randomized clinical trial for unemployed on long-term sick leave. Cogn Behav Pract 2012; 19: 583–94.Google Scholar
56 Freedland, KE, Skala, JA, Carney, RM, Rubin, EH, Lustman, PJ, Davila-Roman, VG, et al. Treatment of depression after coronary artery bypass surgery: a randomized controlled trial. Arch Gen Psychiatry 2009; 66: 387–96.Google Scholar
57 Harley, R, Sprich, S, Safren, S, Jacobo, M, Fava, M. Adaptation of dialectical behavior therapy skills training group for treatment-resistant depression. J Nerv Ment Dis 2008; 196: 136–43.Google Scholar
58 Haringsma, R, Engels, GI, Cuijpers, P, Spinhoven, P. Effectiveness of the Coping With Depression (CWD) course for older adults provided by the community-based mental health care system in the Netherlands: a randomized controlled field trial. Int Psychogeriatr 2006; 18: 307–25.Google Scholar
59 Hoifodt, RS, Lillevoll, KR, Griffiths, KM, Wilsgaard, T, Eisemann, M, Waterloo, K, et al. The clinical effectiveness of web-based cognitive behavioral therapy with face-to-face therapist support for depressed primary care patients: randomized controlled trial. J Med Internet Res 2013; 15: e153.Google Scholar
60 Hunter, SB, Watkins, KE, Hepner, KA, Paddock, SM, Ewing, BA, Osilla, KC, et al. Treating depression and substance use: a randomized controlled trial. J Subst Abuse Treat 2012; 43: 137–51.Google Scholar
61 Johansson, R, Sjoberg, E, Sjogren, M, Johnsson, E, Carlbring, P, Andersson, T, et al. Tailored vs. standardized internet-based cognitive behavior therapy for depression and comorbid symptoms: a randomized controlled trial. PLoS One 2012; 7: e36905.Google Scholar
62 Johansson, R, Ekbladh, S, Hebert, A, Lindstrom, M, Moller, S, Petitt, E, et al. Psychodynamic guided self-help for adult depression through the internet: a randomised controlled trial. PLoS One 2012; 7: e38021.Google Scholar
63 Kessler, D, Lewis, G, Kaur, S, Wiles, N, King, M, Weich, S, et al. Therapist-delivered Internet psychotherapy for depression in primary care: a randomised controlled trial. Lancet 2009; 374: 628–34.Google Scholar
64 King, M, Sibbald, B, Ward, E, Bower, P, Lloyd, M, Gabbay, M, et al. Randomised controlled trial of non-directive counselling, cognitive-behaviour therapy and usual general practitioner care in the management of depression as well as mixed anxiety and depression in primary care. Health Technol Assess 2000; 4: 1–83.Google Scholar
65 Korte, J, Bohlmeijer, ET, Cappeliez, P, Smit, F, Westerhof, GJ. Life review therapy for older adults with moderate depressive symptomatology: a pragmatic randomized controlled trial. Psychol Med 2012; 42: 1163–73.Google Scholar
66 Laidlaw, K, Davidson, K, Toner, H, Jackson, G, Clark, S, Law, J, et al. A randomised controlled trial of cognitive behaviour therapy vs treatment as usual in the treatment of mild to moderate late life depression. Int J Geriatr Psychiatry 2008; 23: 843–50.Google Scholar
67 Lamers, F, Jonkers, CC, Bosma, H, Kempen, GI, Meijer, JA, Penninx, BW, et al. A minimal psychological intervention in chronically ill elderly patients with depression: a randomized trial. Psychother Psychosom 2010; 79: 217–26.Google Scholar
68 Pot, AM, Bohlmeijer, ET, Onrust, S, Melenhorst, AS, Veerbeek, M, De Vries, W. The impact of life review on depression in older adults: a randomized controlled trial. Int Psychogeriatr 2010; 22: 572–81.Google Scholar
69 Pots, WT, Meulenbeek, PA, Veehof, MM, Klungers, J, Bohlmeijer, ET. The efficacy of mindfulness-based cognitive therapy as a public mental health intervention for adults with mild to moderate depressive symptomatology: a randomized controlled trial. PLoS One 2014; 9: e109789.Google Scholar
70 Preschl, B, Maercker, A, Wagner, B, Forstmeier, S, Banos, RM, Alcaniz, M, et al. Life-review therapy with computer supplements for depression in the elderly: a randomized controlled trial. Aging Ment Health 2012; 16: 964–74.Google Scholar
71 Rohricht, F, Papadopoulos, N, Priebe, S. An exploratory randomized controlled trial of body psychotherapy for patients with chronic depression. J Affect Disord 2013; 151: 85–91.Google Scholar
72 Savard, J, Simard, S, Giguere, I, Ivers, H, Morin, CM, Maunsell, E, et al. Randomized clinical trial on cognitive therapy for depression in women with metastatic breast cancer: psychological and immunological effects. Palliat Support Care 2006; 4: 219–37.Google Scholar
73 Serfaty, MA, Haworth, D, Blanchard, M, Buszewicz, M, Murad, S, King, M. Clinical effectiveness of individual cognitive behavioral therapy for depressed older people in primary care: a randomized controlled trial. Arch Gen Psychiatry 2009; 66: 1332–40.Google Scholar
74 Serrano, JP, Latorre, JM, Gatz, M, Montanes, J. Life review therapy using autobiographical retrieval practice for older adults with depressive symptomatology. Psychol Aging 2004; 19: 270–7.Google Scholar
75 Serrano Selva, JP, Latorre Postigo, JM, Ros Segura, L, Navarro Bravo, B, Aguilar Corcoles, MJ, Nieto Lopez, M, et al. Life review therapy using autobiographical retrieval practice for older adults with clinical depression. Psicothema 2012; 24: 224–9.Google ScholarPubMed
76 Strong, V, Waters, R, Hibberd, C, Murray, G, Wall, L, Walker, J, et al. Management of depression for people with cancer (SMaRT oncology 1): a randomised trial. Lancet 2008; 372: 40–8.Google Scholar
77 Talbot, NL, Chaudron, LH, Ward, EA, Duberstein, PR, Conwell, Y, O'Hara, MW, et al. A randomized effectiveness trial of interpersonal psychotherapy for depressed women with sexual abuse histories. Psychiatr Serv 2011; 62: 374–80.Google Scholar
78 Vernmark, K, Lenndin, J, Bjarehed, J, Carlsson, M, Karlsson, J, Oberg, J, et al. Internet administered guided self-help versus individualized e-mail therapy: a randomized trial of two versions of CBT for major depression. Behav Res Ther 2010; 48: 368–76.Google Scholar
79 Warmerdam, L, van Straten, A, Twisk, J, Riper, H, Cuijpers, P. Internet-based treatment for adults with depressive symptoms: randomized controlled trial. J Med Internet Res 2008; 10: e44.Google Scholar
80 Wiles, N, Thomas, L, Abel, A, Ridgway, N, Turner, N, Campbell, J, et al. Cognitive behavioural therapy as an adjunct to pharmacotherapy for primary care based patients with treatment resistant depression: results of the CoBalT randomised controlled trial. Lancet 2013; 381: 375–84.Google Scholar
81 Wong, DF. Cognitive behavioral treatment groups for people with chronic depression in Hong Kong: a randomized wait-list control design. Depress Anxiety 2008; 25: 142–8.Google Scholar
82 Wuthrich, VM, Rapee, RM. Randomised controlled trial of group cognitive behavioural therapy for comorbid anxiety and depression in older adults. Behav Res Ther 2013; 51: 779–86.Google Scholar
83 Zilcha-Mano, S, Dinger, U, McCarthy, KS, Barrett, MS, Barber, JP. Changes in well-being and quality of life in a randomized trial comparing dynamic psychotherapy and pharmacotherapy for major depressive disorder. J Affect Disord 2014; 152–154: 538–42.Google Scholar
84 Snarski, M, Scogin, F, DiNapoli, E, Presnell, A, McAlpine, J, Marcinak, J. The effects of behavioral activation therapy with inpatient geriatric psychiatry patients. Behav Ther 2011; 42: 100–8.Google Scholar
Figure 0

Fig. 1 Study selection.

Figure 1

Fig. 2 Standardised effect sizes (Hedges' g) of psychotherapy for depression compared with control conditions on global quality of life. CBT, cognitive–behavioural therapy (em, email therapy; sh, guided self-help; st, standard treatment; tai, tailored treatment); PST, problem-solving therapy; SUP, supportive therapy.

Figure 2

Table 1 Global quality of life: effect sizes in meta-analysis of studies comparing psychotherapy with a control group

Figure 3

Table 2 Mental health component of quality of life: effect sizes in meta-analysis of studies comparing psychotherapy with a control group

Figure 4

Fig. 3 Relationship between effect sizes for depressive symptom severity and the mental health component of quality of life.

Figure 5

Table 3 Physical health component of quality of life: effect sizes in meta-analysis of studies comparing psychotherapy with a control group

Figure 6

Table 4 Study characteristics predicting the effect size of quality of life: multivariate meta-regression

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