Comparative Efficacy and Durability of Psychotherapeutic Interventions in the Treatment of Depressive Disorders: A Systematic Review and Network Met

Comparative Efficacy and Durability of Psychotherapeutic Interventions in the Treatment of Depressive Disorders: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

OR

Comparative Efficacy and Durability of Psychotherapeutic Interventions on Depressive Symptoms and Quality of Life of Patients with Depressive Disorders: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

OR

Effectiveness and Durability of Psychological Interventions on Depressive Symptoms and Quality of Life: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

Abstract

Background: Depressive disorders in adults are a major global health issue that significantly affects quality of life. Although, various psychotherapeutic interventions showed the efficacy in treating depressive disorders, there is no recent network meta-analysis (NMA) to re-evaluate the efficacy and durability of psychological interventions in the management of depressive symptoms and enhancing Quality of life.

Objectives: To evaluate the comparative efficacy and durability of various psychotherapeutic interventions in treating depressive disorders and improving quality of life using a network meta-analysis (NMA) of randomized controlled trials (RCTs).

Methods: We searched PubMed, Web of Science, Scopus, EBSCOhost, and PsycNET up to June 23, 2024, for RCTs comparing psychotherapeutic interventions for adult depressive disorders. Risk of bias was assessed using the Cochrane RoB 2 tool. Effect sizes were calculated using random-effects models with the 'netmeta' R package, and treatment hierarchies were established through rank probabilities.

Results: A total of 12,076 studies were initially identified, and 77 RCTs were analysed to evaluate the efficacy of seven interventions for depressive disorders. The result revealed a range of moderate to high efficacy across interventions, with Standardized Mean Differences (SMDs) varying from -0.553 to -1.323. ACT was most effective in reducing depressive symptoms significantly (SMD = -1.323; 95% CI: -1.615 to -1.031), and moderately improving quality of life (SMD = -0.460; 95% CI: -0.812 to -0.109). Long-term follow-ups highlighted ACT's sustained benefits for depressive symptoms (SMD = 1.286; 95% CI: 0.967 to 1.606). The analysis revealed substantial heterogeneity in treatment effects (I = 99.5%, = 0.1489), emphasising the need for personalized therapeutic interventions in managing depressive disorders.

Conclusions: This meta-analysis highlights the importance of selecting evidence-based psychotherapies for depression, considering short-term and long-term outcomes. Findings show outcome variability across interventions, emphasizing on personalized treatment approaches. Clinical decisions should consider patient characteristics, symptoms, and therapy accessibility. Further research on therapeutic mechanisms and personalized strategies is required.

Keywords: Depressive Disorders, Quality of Life, Psychotherapeutic Interventions, RCTs, Network Meta-analysis

Introduction

Depressive disorders are a widely prevalent, debilitating, and complex mental health conditions characterized by a range of heterogeneous symptoms, such as persistent low mood, anhedonia, feelings of worthlessness and guilt, poor concentration, changes in appetite, fatigue, psychomotor agitation or retardation, sleep disturbances, and suicidal thoughts (DSM-5; American Psychiatric Association, 2013). Depressive disorders have an episodic or long-term recurrent course with lifelong implications for reduced quality of life and excess mortality (Schneider et al., 2019). Functional impairment, particularly lowered productivity and symptom severity, contributes to the global burden of depressive disorders, making it a significant public health concern (Steinert et al. 2014).

Recent epidemiological data indicate that depressive disorders account for a significant percentage (37.3%) of mental illnesses globally (Global Burden of Diseases Study, 2019). These disorders are ranked 13th among the top 25 primary causes of disability-adjusted life-years (DALYs), affecting approximately 5% of the worlds adult population (World Health Organization, 2023). The prevalence and severity of depressive disorders emphasizes the need for continued research, improved diagnostic methods, and development of effective interventions. These efforts are crucial for mitigating personal, social, and economic burdens associated with depressive disorders and for improving global mental health outcomes.

Several evidence-based psychotherapeutic interventions have been established for the treatment (Cuijpers et al., 2019), prevention (van Zoonen et al., 2014), and aftercare (Biesheuvel-Leliefeld et al., 2015) of depressive disorders. Various meta-analyses have evaluated the efficacy of different therapeutic interventions, including cognitive behavior therapy (Cuijpers et al., 2013a), behavioral activation therapy (Cuijpers et al., 2007; Ekers et al., 2008), interpersonal therapy (Cuijpers et al., 2011; de Mello et al., 2005), problem-solving therapy (Malouff et al., 2007; Cuijpers et al., 2007), Mindfulness-Based Cognitive Therapy (Williams et al., 2014), Acceptance and Commitment Therapy (Ferreira et al., 2022), and dialectical behavior therapy (Harley et al., 2008). However, most of the earlier meta-analyses have been impacted by factors varying among various studies, such as duration of treatment, treatment type, type of control group, or initial symptom severity (Shadish & Sweeney, 1991), and therefore may overestimate treatment effects (Spielmans et al., 2007). However, these meta-analyses require a word of caution because of the potential impact of confounding factors such as treatment duration, type of control group, and initial symptom severity (Shadish & Sweeney, 1991). These factors might have caused earlier studies (Spielmans et al., 2007) to overestimate the effects of treatment, so we need a more nuanced way to judge how well psychotherapies treat depressive disorders.

Cuijpers et al. (2011) observed that various psychotherapeutic techniques produce superior outcomes compared to waiting lists, usual care, and placebos, with moderate to large effect sizes (d = -0.66, 95% CI [-0.73, -0.60]). De Maat et al. (2006) and Cuijpers et al. (2013b) found that psychotherapies demonstrate comparable effectiveness to pharmacotherapies for depression, with the combination of both therapeutic approaches yielding superior results compared to either modality alone.

Contrary to these findings, Barth et al. (2013) reported that different psychotherapeutic methods had negligible to no relative effects on depressive symptoms (range d = 0.01, d = -0.30), with most failing to achieve statistical significance. The exception was interpersonal therapy (IPT), which demonstrated significantly better outcomes than supportive therapy (d = -0.30, 95% CI [-0.54, -0.05]). Similarly, van Bronswijk et al. (2018) found a modest general effect size of 0.42 (95% CI 0.29-0.54) in favour of psychotherapy plus treatment-as-usual (TAU) over TAU alone. Furthermore, Cuijpers et al. (2021) observed no appreciable differences between the most popular forms of psychotherapy, concluding that they can all be efficacious in treating adult depression and improving the quality of life. The authors suggested that factors such as therapeutic alliance, patient characteristics, individual preferences, and treatment availability may have a greater influence on the selection of psychotherapy type for depressive disorders than the specific therapeutic approach itself.

Despite significant advancements in the treatment of depression, a critical gap persists in understanding the comparative efficacy of various psychotherapeutic techniques for alleviating depressive symptoms and improving quality of life in adults. Although progress has been made in the management of depression, less than 50% of patients with depressive disorders respond to first-line antidepressant treatment or psychotherapy (Cuijpers et al., 2013; Kolovos et al., 2016). Considering its treatment-resistant and recurring nature, there is a dearth of information about the efficacy and durability of various available psychotherapeutic approaches in the treatment of depressive disorders.

Network meta-analysis (NMA) offers a superior approach to assess the relative effectiveness of various psychotherapies for depressive disorders compared with traditional systematic reviews and meta-analyses. NMA is a promising statistical tool that can compare direct and indirect evidence through synthesis of information from various clinical trials; it can examine the relative effectiveness of various psychotherapies, and it also provides rank probabilities or treatment hierarchy that can have significant implications for clinical treatment decisions (Cipriani et al., 2013).

This statistical method synthesizes direct and indirect evidence from multiple clinical trials, allowing for the examination of relative effectiveness and the generation of treatment hierarchies through rank probabilities. These features make NMA a valuable tool for informing clinical treatment decisions (Cipriani et al., 2013). However, previous NMAs have been limited by a low proportion of studies with a low risk of bias (30.8%) and significant inconsistencies between direct and indirect evidence (Cuijpers et al., 2021). Additionally, insufficient statistical power due to the small sample sizes in randomized controlled trials (RCTs) has hindered the detection of differential effects among psychotherapeutic methods (Cuijpers, 2016). Addressing these limitations, the current network meta-analysis of RCTs aimed to provide a comprehensive evaluation of psychotherapeutic interventions and estimate their efficacy and durability in treating depressive disorders and quality of life in adults.

Methods

2.1 Protocol Registration

The current systematic review and network meta-analysis was registered with PROSPERO (CRDxxxxxxx), and conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Network Meta-Analyses PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IdXR0b248L0F1dGhvcj48WWVhcj4yMDE1PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA (Hutton et al., 2015).

2.2 Data Sources, Search strategy and Selection Process

We systematically conducted literature search in PubMed, Web of Science, Scopus, EBSOhost, PsychNet electronic databases until 23rd June 2024, combining terms regarding psychotherapy (e.g. Mindfulness-Based Cognitive Therapy, Cognitive Behavioral Therapy, Interpersonal Therapy, Dialectical Behavior Therapy, Behavioral Activation, problem-solving therapy, Acceptance and Commitment Therapy) and depressive disorders (keywords and text words) (Supplementary 1), with filters for randomized controlled trials. The predefined search keywords were used along with Boolean Operators (i.e. AND, OR). A comprehensive review of reference lists from previous meta-analyses and selected articles was conducted to ensure thorough coverage of relevant literature. Literature search was managed to remove the duplication using Zotero 6.0.36 reference managing software.

The two authors independently evaluated each article by screening the titles and abstracts for inclusion. On meeting inclusion criteria, it was followed by a thorough full-text assessment, if deemed necessary. Disagreements were resolved through discussion or consultation with a third independent reviewer.

2.3 Eligibility Criteria

Inclusion and Exclusion Criteria

Only English-language publications, reporting randomized controlled-design studies with adult participants (age 18-65 years), diagnosed with depression, as per standardized diagnostic criteria (DSM-III, DSM-III-R, DSM-IV, DSM-5, and ICD-10) were included in this study. Any type of psychotherapeutic intervention in any delivery-format (viz., individual, group) or treatment setting (viz., telephone, face-to-face, or self-help including internet) were included. In addition, studies that compare these psychotherapies against standard control conditions such as waitlist, usual care, TAU, or placebo were also included.

On the other hand, observational studies, case studies and non- randomized trials were excluded to maintain the rigor of the analysis. Studies focusing exclusively on populations with specific characteristics (e.g., postpartum depression, depression secondary to chronic physical or mental illnesses) were excluded. Studies with very short follow-up periods that do not allow assessment of the long-term efficacy of treatments were also excluded from the analysis.

Risk of Bias Assessment of included studies

The Cochrane risk-of-bias tool for randomized trial (RoB2, 2019 Version) was applied to evaluate the risk of bias (RoB) (Sterne et al., 2019). The Cochrane RoB2 tool examines various aspects of study design and execution, including random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, completeness of outcome data, selective reporting, and other potential sources of bias (figure 2.1, 2.2). This comprehensive evaluation ensures a thorough assessment of the methodological quality and potential biases present in the included RCTs.

2.5 Data Extraction

Pertinent information regarding name of author(s), year of publication, country, tools used to measure depressive symptoms, research design, duration of therapy (in weeks), number of sessions, type of treatments, number of participants in treatment and control group along with the follow-up duration (in months) were extracted from the selected articles. Such essential data were then entered into Microsoft Excel sheets on basis of the chosen outcomes. The first author extracted the data from the selected studies and the accuracy of the extracted data were reviewed by the second author. (Table 1)

2.6 Statistical Analysis

R software (version 4.2.1) was used to calculate the effect sizes and visualization with two packages, specifically 'metafor' ADDIN EN.CITE <EndNote><Cite><Author>Viechtbauer</Author><Year>2010</Year><RecNum>6421</RecNum><DisplayText>(Viechtbauer, 2010)</DisplayText><record><rec-number>6421</rec-number><foreign-keys><key app="EN" db-id="dvd90v2p6p9zrre0d2o50zsusrt0wdw25fwv" timestamp="1704691940">6421</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Viechtbauer, W.</author></authors></contributors><titles><title>Conducting meta-analyses in R with the metafor package</title><secondary-title>Journal of statistical software</secondary-title></titles><periodical><full-title>Journal of Statistical Software</full-title></periodical><pages>1-48</pages><volume>36</volume><number>3</number><dates><year>2010</year></dates><isbn>1548-7660</isbn><urls></urls><electronic-resource-num>https://doi.org/10.18637/jss.v036.i03</electronic-resource-num></record></Cite></EndNote>(Viechtbauer, 2010) and netmeta ADDIN EN.CITE <EndNote><Cite><Author>Balduzzi</Author><Year>2023</Year><RecNum>6424</RecNum><DisplayText>(Balduzzi et al., 2023)</DisplayText><record><rec-number>6424</rec-number><foreign-keys><key app="EN" db-id="dvd90v2p6p9zrre0d2o50zsusrt0wdw25fwv" timestamp="1704692172">6424</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Balduzzi, S.</author><author>Rcker, G.</author><author>Nikolakopoulou, A.</author><author>Papakonstantinou, T.</author><author>Salanti, G.</author><author>Efthimiou, O.</author><author>Schwarzer, G.</author></authors></contributors><titles><title>netmeta: An R package for network meta-analysis using frequentist methods</title><secondary-title>Journal of Statistical Software</secondary-title></titles><periodical><full-title>Journal of Statistical Software</full-title></periodical><pages>1-40</pages><volume>106</volume><number>2</number><dates><year>2023</year></dates><isbn>1548-7660</isbn><urls></urls><electronic-resource-num>https://doi.org/10.18637/jss.v106.i02</electronic-resource-num></record></Cite></EndNote>(Balduzzi et al., 2023) packages. cmicalc() function of the metafor package was used to calculate the effect size and variances of the selected studies. Morris's (2008) "dppc2" convention was used to compute the effect size because all of the studies used a pre-test-post-test control group design and most of them included the mean and standard deviations of both the treatment and control group's pre- and post-assessment measures. It was followed by, pairwise comparisons across the network, both direct and indirect, which were carried out using the "netmeta" package. It utilizes TE and seTE as the input for calculation of effect size and standard error of treatment effect for pairwise comparison of treatment and control group ADDIN EN.CITE <EndNote><Cite><Author>Harrer</Author><Year>2021</Year><RecNum>6426</RecNum><DisplayText>(Harrer et al., 2021)</DisplayText><record><rec-number>6426</rec-number><foreign-keys><key app="EN" db-id="dvd90v2p6p9zrre0d2o50zsusrt0wdw25fwv" timestamp="1704692185">6426</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Harrer, M.</author><author>Cuijpers, P.</author><author>Furukawa, T. </author><author>Ebert, D.</author></authors></contributors><titles><title>Doing meta-analysis with R: A hands-on guide</title></titles><edition>1st</edition><dates><year>2021</year></dates><pub-location>New York</pub-location><publisher>Chapman and Hall/CRC</publisher><isbn>1003107346</isbn><urls></urls><electronic-resource-num>https://doi.org/10.1201/9781003107347</electronic-resource-num></record></Cite></EndNote>(Harrer et al., 2021). A significance level of 5% was used for all statistical tests (two-tailed testing) and the computations were based on random effect model.

"TAU" served as a reference group in this study so that it could be compared to other treatment groups. Using the 'forest' function, a forest plot was made to visually depict the efficacy of 'TAU' in contrast to other intervention techniques (Balduzzi et al., 2023). Egger's regression test was performed to assess the symmetry of the plots and funnel() function was used to assess presence of possible publication bias in the current network meta-analysis. A network diagram showing the arrangement of every intervention for every outcome was made using the function netgraph(). The graphical representation shows direct comparisons between each intervention as lines, and nodes, which indicate the interventions (Balduzzi et al., 2023). Moreover, heat maps illustrating network inconsistencies were created using the netheat() program (Balduzzi et al., 2023). Lastly, the netrank() function was used to create a treatment hierarchy of interventions for netmeta or rankogram object using p-score (Balduzzi et al., 2023).

3.Results

3.1 Study Retrieval Results

A total of 12,076 studies were identified until 23rd June 2023. After removing 4,022 duplicates and applying pre-decided inclusion and exclusion criteria, 77 studies were included in this network meta-analysis. The PRISMA2020 flow diagram in Figure 1 provides a visual representation of the study-selection process.

3.2 Study Characteristics

The current network meta-analysis included 77 studies published between the year 2003 and 2023, reflecting a significant geographic distribution of studies. The United States accounted for the highest number (k=15) of studies, followed by the Netherlands (k=13), the United Kingdom (k=7), China (k=6), Japan (k=4) and Germany (k=4). All other studies were conducted from other countries, including Brazil, Iran, Finland, Canada, Australia, Belgium, Colombia, India, Indonesia, Italy, Nigeria, Norway, Romania, Sweden, Taiwan, Turkey, Uganda, and Turkey. Among the studies reviewed, 29 assessed post-follow-up depression, and 13 examined quality of life (QoL) outcomes.

The current study included seven distinct psychotherapeutic interventions for treatment of depressive disorders. Cognitive Behavioral Therapy (CBT) was the most frequently examined intervention, featured in 24 studies, followed by Mindfulness-Based Cognitive Therapy (MBCT) in 20 studies and Acceptance and Commitment Therapy (ACT) in 10 studies. Other interventions such as Behavioral Activation (BA), Interpersonal Therapy (IPT), and Dialectical Behavior Therapy (DBT) were included in twelve, seven, and four studies, respectively. Problem-Solving Therapy (PST) was explored in six studies.

This network meta-analysis encompassed 77 studies, of which 22.33% (n=29) conducted follow-up studies to evaluate the durability of treatment effects. The follow-up durations exhibited considerable variability, ranging from post-treatment assessments to a maximum of 26 months. The distribution of follow-up periods was as follows: 10.01% (n=13) of studies conducted assessments within 3 months post-treatment, emphasizing short-term outcomes; 2.31% (n=3) implemented follow-ups between 4 and 5 months; and 30.3% (n=10) evaluated outcomes at 6 months, indicating a focus on medium-term effects. Long-term effects were assessed in 15.2% (n=5) of studies at 12 months post-treatment, while only one study (3.0%) extended the follow-up to 26 months, providing insights into the sustained impact of psychotherapy over a two-year period.

The Beck Depression Inventory (BDI-II) and Hamilton Rating Scale for Depression (HRSD) emerged as the predominant instruments for assessing depressive symptoms, providing standardized measures across diverse study populations. Table 1 presents a comprehensive overview of the characteristics of the included studies, and delineates the distribution of interventions and follow-up periods. This systematic approach to data collection and analysis enhances the robustness of network meta-analysis, facilitating a nuanced understanding of the comparative efficacy and durability of various psychotherapeutic interventions for depression.

3.3 Risk of Bias Assessment

Figure 2 and 2.1 summarizes the RoB of included studies. Most studies were low risk of bias (k=40) (Alsubaie et al. 2020, Geschwind et al. 2012, Musa et al. 2020 etc.). Only one study had a high risk of bias due to deviations from intended interventions and measurement of the outcome (Asl et al., 2014). Rest of the studies (k=36) had moderate risk of bias for each of the five domains as well as for the judgment of overall bias. Often, it is not feasible to blind participants or the outcome assessors in group psychotherapy session such as MBCT, therefore some studies have been reported to have high or unclear risk of bias (Asl et al., 2014, Barnhofer et al. 2009, He et al. 2019 etc.). (Figure 2 and 2.1)

3.4 Comparative Efficacy of Psychotherapies for Adult Depression

3.4.1 Immediate Treatment Efficacy: Pre-Post Treatment Analysis

In the present network meta-analysis, which compared 77 studies, and 87 pairwise comparisons across seven psychological treatments, the efficacy of various psychotherapeutic interventions was evaluated against treatment-as-usual (TAU) for treatment of depressive symptoms using a random effects model. The results revealed significant heterogeneity in treatment effectiveness (I = 99.6%, 2 = 0.2322), indicating a substantial variability in treatment efficacy across studies. Acceptance and Commitment Therapy (ACT) showed the best efficacy, with a standardized mean difference (SMD) of -1.323 (95% confidence interval [CI]: -1.615 to -1.031, p < 0.0001), showing a substantial reduction in depressive symptoms. In contrast, Cognitive Behavioral Therapy (CBT) demonstrated significant improvements in depressive symptoms, with a SMD of -0.8382 (95% CI: -1.028 to -0.648, p < 0.0001). Interpersonal Therapy (IPT) produced a strong effect as well, with a SMD of -0.9660 (95% CI: -1.3339 to -0.5981, p < 0.0001). Problem-Solving Therapy (PST) showed similar efficacy, with a SMD of -0.973 (95% CI: -1.292 to -0.653, p < 0.0001). Mindfulness-Based Cognitive Therapy (MBCT) demonstrated moderate efficacy, with a SMD of -0.553 (95% CI: -0.759 to -0.347, p < 0.0001). Dialectical Behavior Therapy (DBT) did not yield statistically significant results compared to TAU, with a SMD of -0.333 (95% CI: -0.831 to 0.164, p = 0.1893). Overall, the findings indicated that ACT, BA, CBT, IPT, PST, and MBCT, all significantly reduced depressive symptoms compared to TAU, with ACT demonstrating the greatest therapeutic effect.

3.4.2 Durability of Treatment Efficacy: Post-Follow-Up Treatment Analysis

In this network meta-analysis, 29 studies and 33 pairwise comparisons were evaluated to determine the long-term effects of various psychotherapeutic interventions in treatment of depressive symptoms post-treatment. The random effects model elucidated differing levels of efficacy in maintaining treatment gains over a span of time.

Acceptance and Commitment Therapy (ACT) demonstrated most significant long-term benefit, with a standardized mean difference (SMD) of 1.286 (95% CI [0.967, 1.606], p < 0.0001), with a strong positive impact on depressive symptoms post-treatment. However, Behavioral Activation (BA) revealed a slight negative effect size (SMD = -0.373, 95% CI [-0.925, 0.179], p = 0.185), which did not reach statistical significance. Similarly, Cognitive Behavioral Therapy (CBT) showed minimal positive effect (SMD = 0.1686, 95% CI [-0.0772, 0.4144], p = 0.1788), suggesting minimal improvement after follow-up. Further, Interpersonal Therapy (IPT) (SMD = -0.011, 95% CI [-0.392, 0.370], p = 0.956), Mindfulness-Based Cognitive Therapy (MBCT) (SMD = -0.006, 95% CI [-0.277, 0.266], p = 0.968), and Problem-Solving Therapy (PST) (SMD = 0.021, 95% CI [-0.322, 0.364], p = 0.904) did not show significant follow-up effects.

Overall, while ACT maintained its strong post-treatment efficacy, other therapies showed mixed results in terms of long-term effectiveness. Heterogeneity in treatment effects was considerable (I = 99.5%, = 0.1489), indicating substantial variability across studies.

3.5 Comparative Efficacy of Psychotherapies for Quality of Life

3.5.1 Immediate Treatment Efficacy: Pre-Post Treatment Analysis

In this analysis, 13 studies were included, with 17 pairwise comparisons across seven different therapeutic interventions. In the Quality of Life (QoL) pre-post analysis, a random effects model was employed to evaluate the efficacy of various psychotherapeutic interventions compared to treatment-as-usual (TAU). The results indicated significant variability in treatment effects (I = 99.1%, tau = 0.1694), reflecting high heterogeneity across studies.

Behavioral Activation (BA) demonstrated the most substantial improvement in QoL, with a standardized mean difference (SMD) of 1.242 (95% CI [0.639, 1.843], p < 0.0001), indicating a significant improvement in QoL post-treatment. Interpersonal Therapy (IPT) also showed a large positive effect, with a SMD of 1.190 (95% CI [0.173, 2.207], p = 0.022). Problem-Solving Therapy (PST) exhibited a moderate effect, with a SMD of 0.5770 (95% CI [0.0927, 1.0614], p = 0.0195), suggesting significant improvements in QoL. Acceptance and Commitment Therapy (ACT) demonstrated a small but significant effect (SMD = 0.852, 95% CI [0.006, 1.697], p = 0.048). Cognitive Behavioral Therapy (CBT) showed a borderline significant improvement in QoL, with a SMD of 0.384 (95% CI [-0.0001, 0.768], p = 0.050). In contrast, Mindfulness-Based Cognitive Therapy (MBCT) did not demonstrate a statistically significant effect on QoL, with a SMD of 0.129 (95% CI [-0.208, 0.467], p = 0.452).

Tests for heterogeneity and inconsistency confirmed significant variation both within and between designs (Q = 1188.97, p < 0.0001), highlighting considerable differences in treatment outcomes across the included studies.

3.5.2 Durability of Treatment Efficacy: Post-Follow-Up Treatment Analysis

The results of network meta-analysis evaluated the post-follow-up effects of six psychotherapeutic treatments on quality of life (QoL) across seven studies and nine pairwise comparisons.

In the post-follow-up analysis of quality of life (QoL) using a random effects model, various psychotherapeutic interventions were compared to treatment-as-usual (TAU). The results highlighted moderate heterogeneity (I = 94.6%, = 0.0175), indicating variability in the treatment effects across studies.

Acceptance and Commitment Therapy (ACT) showed a significant negative effect on QoL at follow-up, with a standardized mean difference (SMD) of -0.460 (95% CI [-0.812, -0.109], p = 0.010), indicating a decrease in QoL post-treatment. In contrast, Cognitive Behavioral Therapy (CBT) exhibited a significant positive effect, with a SMD of 0.4151 (95% CI [0.211, 0.619], p < 0.0001), suggesting improved QoL at follow-up. Problem-Solving Therapy (PST) also showed a significant improvement in QoL, with a SMD of 0.240 (95% CI [0.072, 0.409], p = 0.0053). However, Behavioral Activation (BA) and Mindfulness-Based Cognitive Therapy (MBCT), did not have significant effects on QoL, with SMDs of 0.0475 (95% CI [-0.232, 0.327], p = 0.739) and 0.039 (95% CI [-0.149, 0.226], p = 0.686), respectively.

The tests for heterogeneity showed significant variation within designs (Q = 73.52, p < 0.0001), but no significant inconsistency between designs (Q = 0.09, p = 0.768).

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