Effectiveness of Metacognitive Therapy on Depressive Symptom Severity: A Systematic Review, Meta-Regression and Meta-Analysis

doi:10.21203/rs.3.rs-7742545/v1

Download PDF

Systematic Review

Effectiveness of Metacognitive Therapy on Depressive Symptom Severity: A Systematic Review, Meta-Regression and Meta-Analysis

https://doi.org/10.21203/rs.3.rs-7742545/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Metacognitive therapy (MCT) is a modern therapeutic approach gaining wider use for psychological problems. This review evaluate the efficacy of metacognitive therapy on depressive symptom severity.

Method

This review followed Cochrane and PRISMA-2020 guidelines. A comprehensive literature search revealed 51 studies on the efficacy of MCT, of which 33 were trials. A random-effects model was used to assess treatment efficacy. We calculated effect sizes using (Hedge's g) SMD for the percentage change index. (PROSPERO registration (CRD420251024202)).

Results

Within-group aggregate effect sizes for the depression outcome measure, mean change for studies was Hedge's g= (-2.29), 95% CI (-2.75 | -1.83), (P < 0.001). Between-group analyses combined effect size for depression outcome, the mean change of aggregate effect size for the studies comparing MCT to the control group was Hedge's g=(-1.28), 95% CI (-1.71 | -0.86). Both effect sizes were highly significant (Ps < 0.000).

Conclusions

Findings indicate that metacognitive therapy is an effective intervention for depressive disorders and is superior to waitlist control conditions. Interpretation is restricted by small sample sizes and the lack of active comparison groups. Future investigations should employ larger samples and include head-to-head comparisons with other evidence-based psychotherapies.

Metacognitive Therapy

Depressive Symptoms

Depression

MCT

Depression causes profound suffering and increases the risk of premature death from physical illnesses and suicide. It affects not only individuals but also families and communities, disrupting social interactions and reducing economic productivity (1). Globally, depression is a major contributor to disability, with over one in five children and adolescents experiencing depressive episodes or symptoms (2). It affects approximately 3.8% of the world’s population—around 280 million people—with higher prevalence among adults (5%) and individuals over 60 (5.7%), and slightly higher rates observed in women than in men (3).

Several specific reasons can contribute to depression, including genetic, biological, environmental (psychosocial), life events, personality traits, learned behaviors, negative thinking, and substance use (4). Taking these different factors into account, there are several treatment options for depression, including medication-based therapies, non-drug approaches (such as cognitive behavioral therapy (CBT), interpersonal therapy, humanistic therapies, lifestyle modifications, and dietary supplements), as well as other interventions like transcranial magnetic stimulation (TMS) and electroconvulsive therapy (ECT) (4, 5). Depression is increasingly recognized as a chronic, often recurrent illness. Over 50% of individuals who have a major depressive episode will experience another, with recurrence risks exceeding 90% after multiple episodes. While many recover fully, about 20–33% still face lingering symptoms or functional impairments (6).

There is a pressing need for more effective depression treatments and to lower relapse rates. MCT has been investigated for its capacity to modify maladaptive emotion-regulation processes—principally rumination and worry—that sustain depressive disorders. MCT targets depressive symptoms by eliminating Cognitive Attentional Syndrome (CAS), a harmful pattern that perpetuates negative thinking and diminishes attentional flexibility and control over negative emotional experiences (7, 8). Earlier studies show a theoretical connection model between positive and negative metacognitive beliefs, rumination, and depression. The model posits that overcoming depression requires reducing rumination, worry, and dysfunctional thoughts, as well as altering metacognitive beliefs (8, 9). Numerous studies confirm the effectiveness of metacognitive therapy on depressive symptom severity. From inception to March 7, 2018, a systematic review of 49 randomized RCTs (N = 2,609) indicated that MCT effectively treated depression compared to a waitlist (SMD=-2.80) (10). A different systematic review, spanning 1994 to March 2014, found that MCT works well for anxiety and depressive disorders and shows superior outcomes compared with waitlist control groups and CBT (11). Across 25 studies included in a systematic review, MCT demonstrated effectiveness for multiple psychological complaints (12).

However, it's important to note that many secondary studies were not thoroughly assessed for selection and publication bias, and some were affected by selection bias. Despite their limitations, several systematic reviews based on primary studies have been conducted and published (10, 11, 12, 13, 14, 15, 16, 17, 18, 19). We analyzed the effectiveness of MCT on the severity of depressive symptoms.

This systematic review followed Cochrane and PRISMA-2020 guidelines and was registered on PROSPERO (CRD420251024202).

2.1. Search strategy

Briefly, two authors (ZT, MR) independently searched Cochrane CENTRAL, Embase, PubMed, Web of Science, Scopus, PsycINFO, Google Scholar, WHO ICTRP, and ClinicalTrials.gov for peer-reviewed studies published between January 1, 1990, and January 31, 2025, with no language limitations. The search keywords included of “metacognitive therap” OR “meta-cognitive therap” OR “metacognitive treatment” OR “metacognitive intervention” AND “depression” OR “depressive disorder” OR “depress*” OR “depressive symptom” OR “emotional distress” OR “emotional disorder*” OR “mood disorder*”. Grey literature was searched via databases (scopus and wos), proquest theses and dissertations, targeted websites (www.allconferences.com, www.conferencealerts.com, and www.opengray.eu), expert consultation, and screening of included studies' reference lists. Full search terms are detailed in Appendix S1.

2.2. Eligibility criteria

We considered studies employing any research design—including RCTs, NRCTs, quasi-experimental designs, intervention studies, and pilot investigations—that evaluated Adrian Wells’s MCT with respect to depressive symptom severity in adult patient samples. Eligible studies recruited participants aged 18 years or older. The primary outcome of interest was the severity of depressive symptoms, assessed using validated rating instruments administered after the intervention. Exclusion criteria were: studies involving children or adolescents (participants < 18 years); RCTs with fewer than five participants; investigations in which MCT was delivered concurrently with other therapies (e.g., cognitive behavioral therapy) such that effects of MCT could not be isolated; studies focusing solely on discrete MCT techniques rather than the full therapeutic protocol; and conference abstracts, dissertations, theses, or papers published in non–peer-reviewed outlets.

2.3. Screening and selection process

Studies retrieved from electronic searches were imported into EndNote 20 and deduplicated. ZT and MR independently screened titles and abstracts for the inclusion criteria and separately reviewed full texts of potentially eligible studies. Any disagreements on inclusion were resolved by consensus.

2.4. Data extraction

ZT and MR extracted the data separately using a pre-established electronic form. study characteristics (authors, journal, country, year, total and group sample sizes, intervention definition and duration, follow-up duration, and primary outcome); baseline demographic and clinical data (mean age); outcome measures (validated depression assessment instruments, treatment effect means and corresponding standard deviations); risk-of-bias evaluations conducted with the Cochrane Risk of Bias tools appropriate for randomized and observational studies; and evidence certainty was assessed using GRADE by two reviewers (ZT, MR), with disagreements resolved through consensus. Missing data on effect size, means, or standard deviations were requested from study authors via email. In one case, the corresponding author of a two-year follow-up study was contacted, but after three emails over three weeks, no response was received, and the study was excluded. The extracted information was transferred into an Excel spreadsheet.

2.5. Statistical analyses

Characteristics of the included studies were summarized narratively. Graphic examinations and meta-analysis were performed utilizing STATA 17. Hedge’s g was calculated for every study, accompanied by 95% confidence intervals and tests of statistical significance, to determine intervention effectiveness. Negative Hedge’s g values signify that the intervention had a favorable impact, demonstrating greater improvements in positive outcomes, specifically reduced depression symptom severity, in the intervention group compared to the control group. Effect sizes, as measured by Hedge’s g, are conventionally interpreted with absolute values of 0.20, 0.50, and 0.80 representing small, medium, and large effects, respectively (20). A mixed-effects model was used to account for heterogeneity and conservatively address within- and between-study variance. Meta-regressions examined the continuous moderators of follow-up time. Moderator analyses were conducted whenever feasible to maintain transparent and consistent reporting, irrespective of meta-analytic significance or heterogeneity. Heterogeneity was evaluated using the Q statistic and further quantified through the I² index, with thresholds of 25%, 50%, and 75% indicating low, moderate, and high levels of heterogeneity, respectively (21).

A leave-one-out sensitivity analysis was performed, and sensitivity plots were generated to assess the influence of individual studies on the overall meta-analysis results. All sensitivity analyses were carried out using Stata version 17.

The small study effect was evaluated through the assessment of funnel plot asymmetry, both visually and using Egger’s regression test (22). A non-significant Egger’s test combined with a symmetrical funnel plot indicated a low likelihood of publication bias. The trim and fill method (23), was also used; minimal bias was indicated if no studies were trimmed or if trimming resulted in negligible changes to the observed effect sizes. Concordance between these methods, indicating absence or little bias, supports the reliability of the meta-analytic results.

2.6. Risk of bias assessment

Risk of bias was separately assessed by two reviewers (ZT, MR), with discrepancies resolved by consensus. The Cochrane Risk of Bias tool was used to evaluate methodological quality across key domains, including selection, performance, detection, attrition, and reporting biases.

2.7. Appraising the robustness of evidence

The certainty of evidence was evaluated using the GRADE approach, which classifies evidence as high, moderate, low, or very low. Two reviewers (ZT and MR) independently assessed the outcome. Evidence from randomized controlled trials was initially rated as high quality but could be downgraded according to predefined criteria, including risk of bias, inconsistency (I² >50% and P < 0.10), indirectness, imprecision, and publication bias.

3.1.Study characteristics

Figure 1 displays the searching and selection process. Of 6067 identified reports, 4052 were excluded due to duplication. Out of 2,015 reports identified, 1,865 were excluded after screening titles and abstracts. Among the 150 reports assessed in full, 99 were excluded for not meeting the eligibility criteria. Ultimately, 51 reports were included in the quantitative synthesis.

Table 1 summarizes the features of 51 studies: 35.3% lack a control group, while 64.7% have either a control group or a waiting list. Most studies were in Europe (49%), Asia (47%), and Australia (4%). Studies had a follow-up duration range of 1 week to 36 weeks, and an approximately equal distribution of men and women. All studies included adult participants over 18 years old, with depression (63%).

Table 1
Participant and Intervention Characteristics
Study Country	N [nintervention: ncontrol]	Mean age	Intervention	Control group	Follow_up time Month	Study Duration Month	Population targeted	outcomes	Effect size	Measurement scale
Odin Hjemdal_2019 Norway	34:34	33.7	Metacognitive therapy	No/C	12	2.5	Magor dipression	Depression symptom sevearity	Mean (SD)	BDI
Dammen T_2015 Norway	11:11	42.27	Metacognitive therapy	No/C	6	2.5	Dipression	Depression symptom sevearity	Mean (SD)	BDI
Costas Papageorgiou_2014 UK	10:10	41.7	Metacognitive therapy	No/C	6	3.5	Magor dipression	Depression symptom sevearity	Mean (SD)	BDI
Adrian Wells_2006 UK	10:10	50	Metacognitive therapy	No/C	12	13	GAD	Depression symptom sevearity	Mean (SD)	BDI
Odin Hjemdal_2016 Norway	10:10	28.4	Metacognitive therapy	No/C	6	2.5	Dipression	Depression symptom sevearity	Mean (SD)	BDI
Dammen T_2016 Norway	10:10	43.2	Metacognitive therapy	No/C	24	2.5	Dipression	Depression symptom sevearity	Mean (SD)	BDI
Lotta Winter_2020 Germany	20:20	42.1	Metacognitive therapy	No/C	6	2.5	Magor dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Julia Kroener_2024 Germany	7:7	55	Metacognitive therapy	No/C	3	4.2	Alcohol use disorder	Depression symptom sevearity	Mean (SD)	BDI_II
Denise Bevan_2013 UK	6:6	34	Metacognitive therapy	No/C	0	3	Postpartum depression	Depression symptom sevearity	Mean (SD)	BDI_II, HADS_D, PHQ_9
Laura Ferraro_2019 Italy	53:53	51.2	Metacognitive therapy	No/C	3	5	Tinnitus	Depression symptom sevearity	Mean (SD)	HADS_D
Svein Haseth_2019 Norway	23:23	29.7	Metacognitive therapy	No/C	3	5.5	comorbid	Depression symptom sevearity	Mean (SD)	PHQ_9
Eivind R Strand_2023 Norway	17:17	31.1	Metacognitive therapy	No/C	0	2.5	Magor dipression	Depression symptom sevearity	Mean (SD)	PHQ_9
Ziba Hamidi_2020 Iran	10:10	35	Metacognitive therapy	No/C	0	2.5	PTSD	Depression symptom sevearity	Mean (SD)	DASS_21
Adrian Wells_2012 UK	10:10	34.5	Metacognitive therapy	No/C	12	2	Resistant depression	Depression symptom sevearity	Mean (SD)	BDI, Hamilton
Stian Solem_2019 Norway	39:39	49	Metacognitive therapy	No/C	36	2.5	Depression	Depression symptom sevearity	Mean (SD)	BDI, Hamilton
Lotta Winter_2019 Germany	30:30	41.1	Metacognitive therapy	No/C	6	4	Major depression	Depression symptom sevearity	Mean (SD)	Hamilton
Peter L_Fisher_2019 UK	27:27	51.15	Metacognitive therapy	No/C	6	1.5	Cancer	Depression symptom sevearity	Mean (SD)	HADS_D
Pia Callesen_2019 Denmark	124:124	42.1	Metacognitive therapy	No/C	6	1.5	Mixed Disorder	Depression symptom sevearity	Mean (SD)	HADS_D
Roger Hagen_2017 Norway	20:19	32.2	Metacognitive therapy	C	0	2.5	Depression	Depression symptom sevearity	Mean (SD)	BDI
Sheikh Shima_2020 Iran	20:20	56.7	Metacognitive therapy	C	3	2	T2DM	Depression symptom sevearity	Mean (SD)	BDI
Elham Zahedian_2021 Iran	12:12	50.75	Metacognitive therapy	C	1	2	DIP in Breast Disease	Depression symptom sevearity	Mean (SD)	BDI
Sverre Urnes Johnson_2017 Norway	36:38	42	Metacognitive therapy	C	12	2	PTSD, SPh, Panic	Depression symptom sevearity	Mean (SD)	BDI_II
Hans M_2009 Norway	15:13	37.2	Metacognitive therapy	C	0	2.5	Dipression, GAD	Depression symptom sevearity	Mean (SD)	BDI
Farzin Bagheri Sheykhangafshe_ 2024 Iran	18:18	26.41	Metacognitive therapy	C	0	3	SAD	Depression symptom sevearity	Mean (SD)	BDI
kimia bayegan_2020 Iran	15:15	68	Metacognitive therapy	C	3	2	Dipression in T2DM	Depression symptom sevearity	Mean (SD)	BDI
Behjat Mohammadnejadi_2222_Iran	15:15	35	Metacognitive therapy	C	2	2	Dipression	Depression symptom sevearity	Mean (SD)	BDI
Mehdi Zemestani_2015 Iran	15:15	24.2	Metacognitive therapy	C	3	2	Dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Pia Callesen_2020 Denmark	56:57	35	Metacognitive therapy	C	6	3	Magor dipression	Depression symptom sevearity	Mean (SD)	BDI_II, Hamilton
Gholam Reza Kheirabadi_2020 Iran	16:8	39	Metacognitive therapy	C	0	2.5	Magor dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Ahmad Ashouri_2013 Iran	10:13	32.48	Metacognitive therapy	C	12	12	Magor dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Naser Karimi_2023 Afghanistan	15:15	38.15	Metacognitive therapy	C	3	3	Substance Use Disorder	Depression symptom sevearity	Mean (SD)	BDI_II
Zarin Sadat Larijani_2014 Iran	13:15	20	Metacognitive therapy	C	0	2	Dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Ali Taheri Dagian_2021 Iran	14:15	35	Metacognitive therapy	C	3	3	Dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Hossein Shareh_2014 Iran	12:12	30	Metacognitive therapy	C	0	2	Dipression	Depression symptom sevearity	Mean (SD)	BDI_II
Adrian Wells_2014 UK	11:10	40.6	Metacognitive therapy	C	3	5	PTSD	Depression symptom sevearity	Mean (SD)	BDI_II
Hossein Shareh_2010 Iran	7:6	24.71	Metacognitive therapy	C	0	2.5	OCD	Depression symptom sevearity	Mean (SD)	BDI_II
Haddadi S_2022 Iran	18:18	56.7	Metacognitive therapy	C	3	2	Depression and PTG	Depression symptom sevearity	Mean (SD)	BDI_II
Sepideh Rajezi_2024 Iran	14:13	29	Metacognitive therapy	C	1	2.5	OCD	Depression symptom sevearity	Mean (SD)	BDI_II
Amir Mohsen Rahnejat_2021 Iran	17:23	48.26	Metacognitive therapy	C	3	3	PTSD	Depression symptom sevearity	Mean (SD)	BDI_II
Behjat Mohammadnezhady_2023Iran	15:15	35.5	Metacognitive therapy	C	2	2	Depression	Depression symptom sevearity	Mean (SD)	BDI_II
Sepideh Batebi_2020 Iran	17:15	37.46	Metacognitive therapy	C	3	3	Functional Dyspepsia	Depression symptom sevearity	Mean (SD)	Hamilton
Borjali M_2021 Iran	15:15	42.5	Metacognitive therapy	C	1	2	Magor dipression	Depression symptom sevearity	Mean (SD)	Hamilton
Sandra af Winklerfelt Hammarberg_2023 Sweden	31:33	35.7	Metacognitive therapy	C	6	6	GAD	Depression symptom sevearity	Mean (SD)	PHQ_9
Anja Schaich_2022 Germany	62:60	41.66	Metacognitive therapy	C	12	6	Major depressive disorder	Depression symptom sevearity	Mean (SD)	PHQ_9, Hamilton, QIDS_C
Tahereh Haji Seyed Javadi_2018 Iran	12:12	44.92	Metacognitive therapy	C	3	2	Breast Cancer	Depression symptom sevearity	Mean (SD)	DASS_21
Behnam Salahshour Bonab_2023 Iran	20:20	32.5	Metacognitive therapy	C	0	2.5	prehypertension	Depression symptom sevearity	Mean (SD)	DASS_21
Mohammad Elahi_2023 Iran	25:25	32.46	Metacognitive therapy	C	0	2.5	Drug addicts	Depression symptom sevearity	Mean (SD)	DASS_21
Fatima Abdi Sarkami_2020 Iran	15:15	62.93	Metacognitive therapy	C	0	2	Depression	Depression symptom sevearity	Mean (SD)	Yesavag
Adrian Wells_2012 UK	10:10	33.4	Metacognitive therapy	C	6	2	PTSD	Depression symptom sevearity	Mean (SD)	BDI_II
Jenine Anne Wenn_2018 Australia	12:10	61	Metacognitive therapy	C	6	1.5	prolonged grief	Depression symptom sevearity	Mean (SD)	DASS_21
Jennifer Jordan_2014 Australi	23:25	37.2	Metacognitive therapy	C	6	3	Depression	Depression symptom sevearity	Mean (SD)	QIDS_S, QIDS_C

The outcome measures were questionnaires specific to the disorder under study and had adequate psychometric properties. The BDI was the most frequently used measurement scale (60.8%), followed by DASS_21, HADS_D (7.8%), PHQ_9 (9.8%), Hamilton (13.7%), QIDS_C (3.9%), QIDS_S, and Yesavag (2%). Average scores on the measurement scales were similar but showed variability: BDI (0–63), HADS-D, QIDS, QIDS-C, and PHQ-9 (0–27), DASS-21 (0–42), and Hamilton (0–24).

Studies examined the impact of MCT on the severity of depressive symptoms, using Wells' adaptable disorder-specific manuals to tailor treatment to individual client needs. Therapy session duration ranged from 6 to 12 sessions across studies.

3.2. Risk of bias within studies

Figures 2 and 3 show the cochrane risk of bias assessments for the included studies. Most studies were judged as having a high risk of bias across domains and none were rated as low risk of bias. Due to the limited number of high-quality studies (n = 1), a quality assessment of the effect was not performed.

3.3. Meta-analysis

Figure 4 displays within-group aggregate effect sizes for depression outcome measures. Mean change for studies was Hedge's g= (-2.29), 95% CI (-2.75 | -1.83), (P < 0.001).

Figure 5 displays between-group analyses aggregate effect size for depression outcome. The mean change of aggregate effect size for the studies comparing MCT to control group was Hedge's g=(-1.28), 95% CI (-1.71 | -0.86). Both effect sizes were highly significant (Ps < 0.000). MCT leads to a decrease in the mean change of depression symptom severity.

3.4. Heterogeneity

Figure 6 shows highly severe within-group heterogeneity (I2 = 86.66%, H2 = 7.5, Q = 197.17, P < .001). Figure 7 displays highly severe between-group analysis heterogeneity (I2 = 93.73%, H2 = 15.96, Q = 385.63, P < .001).

3.5. Meta-regression

Random-effects metaregression was performed using Stata 17 to assess whether follow-up time influenced the effect of MCT on depressive symptom severity. Figure 8 displays that shorter follow-up periods are associated with larger effect sizes in within-group studies. In contrast, Fig. 9 displays that longer follow-up periods are associated with larger effect sizes in between-group studies. (Table 2)

Table 2
Summary of metaregression, heterogeneity statistics, and publication bias results
	Meta_Regression on follow_up time					Heterogeneity					Publication Bias
	Coeff	Std.err	Z	P	95%CI	H2	T2	I2 (%)	Q	R2%	Egger’s Test	Trim and Fill
Within-group analysis	-0.044	0.02	-2.18	0.02	(-0.08\| -0.004)	5.96	0.74	83.22	150.87	22.36	-2.51	-2.28
Between-group analyses	0.1	0.053	1.91	0.05	(-0.002\| 0.2)	14.33	1.45	93.02	304.61	8.52	-7.37	-1.07
Q = heterogeneity statistic; T2 = between-study variance I2 = percent of the observed variance reflecting differences in true effect sizes, rather than sampling error. * p < .050, p < .010, *p < .001. Publication bias statistics were reported only when at least three studies were available.

3.6. Assessing sensitivity analysis

We performed sensitivity analyses using the leave-one-out meta-analysis method. The exclusion of any single study did not significantly alter the treatment effect, significance, or heterogeneity levels (Figour 10, 11).

3.7. Assessing small study effect

Figures 12, 13 display the result of the funnel plots for the hedge's g-statistic. The distributions are practically dissymmetric, with outliers. Therefore, visually, there seems to be solid evidence for publication bias for depression. In order not to rely on visual inspection only, we used Egger's regression test for funnel plot asymmetry. The following results were obtained: (a) within-group analysis, Z = − 2.51, p = .012; (b) between-group analysis, Z = − 7.37, p = .000; Therefore, statistically, there is an indication of asymmetry and potential publication bias for depression. Nevertheless, we still applied the trim-and-fill method to obtain adjusted overall means that are supposed to be free of publication bias. These Hedge's g within-group analysis − 2.28 instead of − 2.28, with a confidence interval ranging between − 2.74 and − 1.82, and for between-group analysis, − 1.07 instead of − 1.28, with a confidence interval ranging between − 1.5 and − 0.60, continue indicating that a relatively large reduction of problematic outcomes has taken place. Although Egger's test suggested publication bias, trim and fill analysis did not, despite small study effects in both analyses. (Figs. 14,15) (Table 2)

3.8. Assessing certainty of evidence

Table 3 shows that the overall certainty of evidence for the effect of MCT on depression was moderate for between-group analysis, according to GRADE assessments. This is due to the risk of bias, inconsistency, indirectness, and publication bias from small study effects.

Table 3
Assessing certainty of evidence
Outcome	Study Design	N	Effect Size SMD (95%CI)	GRADE Critria					Overall Score	Interpretation of Magnitude of Effect
Outcome	Study Design	N	Effect Size SMD (95%CI)	Risk of Bias	Inconsistency	Indirectness	Imprecision	Publication Bias	Overall Score	Interpretation of Magnitude of Effect
Depression	Trial	33	-1.28 (-1.71\|-0.86)	Low	Moderate	Moderate	Moderate	Moderate	Moderate	Moderate effect
Depression	Observational	18	-2.29 (-2.75\|-1.83)	Very low	Moderate	Moderate	Very low	Very low	Low	Trivial effect

4.1.Summary of main results

This meta-analysis investigates the effect of MCT on depression symptom severity by assessing the efficacy of 51 studies compared to control conditions. Due to the study design, we computed both within- and between-group effect sizes.

Our results indicate MCT effectively reduces depression symptom severity and outperforms both waitlist and active treatment control conditions. Significant statistical heterogeneity was present in both within-group and between-group analyses. This heterogeneity may be due to the variety of statistical analyses used; however, sensitivity analysis revealed similar effect sizes between studies that used completer analyses and those that used intent-to-treat analyses.

A small study effect was statistically detected for depression, likely due to the presence of an outlier. Our re-analyses confirmed that the large effect of meta-cognitive therapy is insubstantial (a) after removing the outlying value and (b) after imputing missing effect sizes using the trim-and-fill method to obtain an adjusted summary measure. Together, these results support the use of MCT as an evidence-based intervention to treat depression.

This result corroborates previous meta-analyses conducted on group designs that showed MCT has a significant effect on depression, as a stand-alone treatment. Additionally, meta-regression in between-group analysis at the longest follow-up showed SMD recovery or improvement. This finding is notable because the effects of psychological or psychopharmacological interventions often diminish after treatment ends.

Although the primary studies may have had high methodological quality, insufficient reporting led us to rate many domains as unclear. Consequently, we assessed the overall methodological quality as a moderate effect for between-group analysis and a trivial effect for within-group analysis.

4.2. Agreements and disagreements with other studies or reviews

MCT has demonstrated promising results in the treatment of depression, as evidenced by multiple studies and reviews. A meta-analysis conducted by Philipp in 2018, encompassing data from 49 trials and a total of 2,609 patients, indicated that MCT was significantly more effective in treating depression compared to a waitlist control group (SMD=-2.80), highlighting the substantial advantage of MCT over simply waiting for treatment (10).

Further bolstering these findings, reviews conducted by Normann in both 2014 and 2018 also revealed the superiority of MCT over waitlist control conditions. These reviews indicated significant improvements in depression symptoms for patients undergoing MCT (SMD = 1.81) and (SMD = 2.06) (11, 12). The consistency of these results across multiple reviews strengthens the evidence base supporting the efficacy of MCT in alleviating depressive symptoms when compared against a control group receiving no active treatment.

More recently, Andersson (2024) synthesized the results of 21 studies, providing further evidence of MCT's effectiveness. The analysis revealed that MCT not only outperformed waitlist control groups (SMD = 1.84) but was also more effective than other CBT (SMD = 0.43) (19). This suggests that MCT may offer unique advantages over other established therapeutic approaches for depression, although more research is needed to fully understand the mechanisms behind this difference.

Future research efforts must prioritize the use of well-designed, adequately powered randomized controlled trials. These trials should also aim to minimize the risk of bias to provide the most accurate and reliable assessment of MCT's effectiveness. Specifically, future studies should focus on directly comparing MCT to other empirically supported treatments for depression to determine its relative efficacy and identify the specific patient populations for whom MCT may be most beneficial.

4.3. Limitations of the studies

The included studies were limited by small sample sizes, increasing the risk of confounding due to factors such as few therapists, lack of blinding, and absent reliability and adherence checks. Additionally, the risk of assessment bias suggests potential selection and reporting biases that may have affected the results.

This study confirms the effects of MCT on depression symptom severity and informs evidence-based clinical practice. It highlights the underexploited potential of meta-analyses of trial studies in clinical psychology research and emphasizes their relevance in psychopathological research. This approach complements meta-analyses of group designs, enhancing clinical decision-making in treatment efficacy.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interest

The authors state that they have no financial or personal relationships that could have influenced the work.

Footnotes

Not applicable.

Funding

No specific grant from funding bodies in the public, private, or non-profit sectors was received for this research.

Author Contribution

Mehdi Rabiei: Writing (review & editing, original draft), Methodology, Formal analysis, Data curation, Conceptualization. Zahra Torabi: Writing (review & editing, original draft), Supervision, Methodology, Formal analysis, Conceptualization.

Acknowledgement

My sincere appreciation goes to Professor Abbasali Keshtkar for his continuous support, insightful guidance, and encouragement during this research.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

American Psychiatric Association D, American Psychiatric Association D. Diagnostic and statistical manual of mental disorders: DSM-5. American psychiatric association Washington, DC; 2013.
Lu B, Lin L, Su X. Global burden of depression or depressive symptoms in children and adolescents: A systematic review and meta-analysis. J Affect Disord. 2024.
Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx). https://vizhub.healthdata.org/gbd-results/ (Accessed 4 March 2023).
Beck AT, Alford BA, Depression. Causes and treatment: University of Pennsylvania; 2009.
Goldman LS, Nielsen NH, Champion HC, Council on Scientific Affairs AMA. Awareness, diagnosis, and treatment of depression. J Gen Intern Med. 1999;14(9):569–80.
Kupfer DJ, Frank E, Perel JM, Cornes C, Mallinger AG, Thase ME, et al. Five-year outcome for maintenance therapies in recurrent depression. Arch Gen Psychiatry. 1992;49(10):769–73.
Wells A. Emotional disorders and metacognition: Innovative cognitive therapy. Wiley; 2002.
Wells A. Metacognitive therapy for anxiety and depression. Guilford Press; 2011.
Papageorgiou C, Wells A. An empirical test of a clinical metacognitive model of rumination and depression. Cogn therapy Res. 2003;27:261–73.
Philipp R, Kriston L, Lanio J, Kuehne F, Haerter M, Moritz S, et al. Effectiveness of metacognitive interventions for mental disorders in adults—A systematic review and meta-analysis (METACOG). Clin Psychol Psychother. 2019;26(2):227–40.
Normann N, van Emmerik AA, Morina N. The efficacy of metacognitive therapy for anxiety and depression: A meta-analytic review. Depress Anxiety. 2014;31(5):402–11.
Normann N, Morina N. The efficacy of metacognitive therapy: a systematic review and meta-analysis. Front Psychol. 2018;9:2211.
Zhang Z, Wang P, Gu J, Zhang Q, Sun C, Wang P. Effect of metacognitive therapy on depression in patients with chronic disease: a protocol for a systematic review and meta-analysis. BMJ open. 2024;14(4):e075959.
Hunot V, Moore TH, Caldwell DM, Furukawa TA, Davies P, Jones H et al. 'Third wave'cognitive and behavioural therapies versus other psychological therapies for depression. Cochrane Database Syst Reviews. 2013(10).
Rochat L, Manolov R, Billieux J. Efficacy of metacognitive therapy in improving mental health: A meta-analysis of single‐case studies. J Clin Psychol. 2018;74(6):896–915.
Churchill R, Moore TH, Furukawa TA, Caldwell DM, Davies P, Jones H et al. 'Third wave'cognitive and behavioural therapies versus treatment as usual for depression. Cochrane Database Syst Reviews. 2013(10).
Zanini C, Enrico P, Pescuma V, Favalli V, Bressi C, Brambilla P et al. Assessing the efficacy of metacognitive therapy as monotherapy or adjunctive treatment in patient suffering from major depression and dysthimia: A comprehensive review of clinical trials. J Affect Disord. 2024.
Akbari M, Hosseini ZS. A meta-analysis of comparing the efficacy of the third wave of behavior therapies on depression in Iran: Comparing acceptance and commitment therapy, mindfulness-based therapy and metacognitive therapy (2001–2017). Clin Psychol Personality. 2020;17(1):175–89.
Andersson E, Aspvall K, Schettini G, Kraepelien M, Särnholm J, Wergeland GJ et al. Efficacy of metacognitive interventions for psychiatric disorders: a systematic review and meta-analysis. Cogn Behav Ther. 2024:1–27.
Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis: Wiley; 2021.
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.
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(2):455–63.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
08 Jan, 2026
Reviews received at journal
08 Jan, 2026
Reviews received at journal
27 Dec, 2025
Reviews received at journal
12 Dec, 2025
Reviews received at journal
11 Dec, 2025
Reviewers agreed at journal
17 Nov, 2025
Reviewers agreed at journal
15 Nov, 2025
Reviewers agreed at journal
14 Nov, 2025
Reviewers agreed at journal
13 Nov, 2025
Reviewers invited by journal
13 Nov, 2025
Editor assigned by journal
01 Oct, 2025
Submission checks completed at journal
01 Oct, 2025
First submitted to journal
29 Sep, 2025

You are reading this latest preprint version

Effectiveness of Metacognitive Therapy on Depressive Symptom Severity: A Systematic Review, Meta-Regression and Meta-Analysis

Status:

Version 1

Abstract

Background

Method

Results

Conclusions

Figures

1.Background

2. Materials and methods

2.1. Search strategy

2.2. Eligibility criteria

2.3. Screening and selection process

2.4. Data extraction

2.5. Statistical analyses

2.6. Risk of bias assessment

2.7. Appraising the robustness of evidence

3. Results

3.1.Study characteristics

3.2. Risk of bias within studies

3.3. Meta-analysis

3.4. Heterogeneity

3.5. Meta-regression

3.6. Assessing sensitivity analysis

3.7. Assessing small study effect

3.8. Assessing certainty of evidence

4. Discussion

4.1.Summary of main results

4.2. Agreements and disagreements with other studies or reviews

4.3. Limitations of the studies

Conclusions

Declarations

Ethics approval and consent to participate

Competing interest

Footnotes

Funding

Author Contribution

Acknowledgement

Availability of data and materials

References

Additional Declarations

Status:

Version 1