Introduction
Major depressive disorder (MDD) is the most common mental health disorder (Moffitt et al., Reference Moffitt, Caspi, Taylor, Kokaua, Milne, Polanczyk and Poulton2010). It is associated with reduced daily functioning (DF) (Adler et al., Reference Adler, McLaughlin, Rogers, Chang, Lapitsky and Lerner2006; de Jonge et al., Reference de Jonge, Bockting, van Oppen, Van, Peen, Kikkert and Dekker2018; Moffitt et al., Reference Moffitt, Caspi, Taylor, Kokaua, Milne, Polanczyk and Poulton2010; ten Doesschate, Bockting, Koeter, & Schene, Reference ten Doesschate, Bockting, Koeter and Schene2010) and impaired cognitive functioning (CF) (Ahern & Semkovska, Reference Ahern and Semkovska2017; Keyes, Platt, Kaufman, & McLaughlin, Reference Keyes, Platt, Kaufman and McLaughlin2017; Rock, Roiser, Riedel, & Blackwell, Reference Rock, Roiser, Riedel and Blackwell2014; Semkovska et al., Reference Semkovska, Quinlivan, O'Grady, Johnson, Collins, O'Connor and Gload2019). Notably, impaired CF is not limited to the acute phase of MDD but persists when MDD has remitted, while the level of CF impairment appears to worsen with repeated episodes (Semkovska et al., Reference Semkovska, Quinlivan, O'Grady, Johnson, Collins, O'Connor and Gload2019). Further, impaired CF associated with MDD has been found to predict the level of DF, independently of mood symptoms (Jaeger, Berns, Uzelac, & Davis-Conway, Reference Jaeger, Berns, Uzelac and Davis-Conway2006; McIntyre et al., Reference McIntyre, Cha, Soczynska, Woldeyohannes, Gallaugher, Kudlow and Baskaran2013). Moreover, impaired CF is believed to be an important factor in the maintenance of a vicious cycle of depressive symptomatology (DS), reduced DF and MDD recurrence (Ahern, Bockting, & Semkovska, Reference Ahern, Bockting and Semkovska2019; Jaeger et al., Reference Jaeger, Berns, Uzelac and Davis-Conway2006; Majer et al., Reference Majer, Ising, Künzel, Binder, Holsboer, Modell and Zihl2004). Thus, addressing CF might improve outcomes (Ahern et al., Reference Ahern, Bockting and Semkovska2019). A promising method in the treatment of MDD and elevated depressive symptoms, which indeed addresses CF, is cognitive remediation (Cella et al., Reference Cella, Price, Corboy, Onwumere, Shergill and Preti2020; Motter et al., Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016). This involves drill-and-practice exercises and/or cognitive strategy training. Cognitive remediation aims to improve CF by means of enhancing neuroplasticity (Robertson & Murre, Reference Robertson and Murre1999), or to compensate for impaired CF in daily life (Twamley, Vella, Burton, Heaton, & Jeste, Reference Twamley, Vella, Burton, Heaton and Jeste2012). Therapy delivery format is variable, and includes computerized (e.g. online training) and non-computerized (e.g. offline work with a therapist), and individual and group formats.
A first meta-analysis on the effectiveness of (computerized) cognitive remediation in MDD (N = 9; n = 539) suggests that it improves DS and CF as well as DF (Motter et al., Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016). However, as the authors acknowledge, the small number of studies and patients included limited their meta-analysis. Given the increasing number of studies (Semkovska, Lambe, Lonargáin, & McLoughlin, Reference Semkovska, Lambe, Lonargáin and McLoughlin2015; Trapp, Engel, Hajak, Lautenbacher, & Gallhofer, Reference Trapp, Engel, Hajak, Lautenbacher and Gallhofer2016), an updated meta-analysis is warranted. Further, the previous meta-analysis (Motter et al., Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016) could not examine the effect of therapy delivery format, clinical patient characteristics or effects at follow-up. In addition, they did not perform a sensitivity analysis excluding non-randomized studies, or any assessment of risk of bias or certainty, besides risk of publication bias.
The current meta-analysis therefore aimed to update and expand on the previous meta-analysis (Motter et al., Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016) in order to evaluate the effectiveness of cognitive remediation in depression. We primarily aimed to investigate the effects on DS, CF and DF (e.g. work, social functioning, quality of life). Secondarily, the aim was to conduct subgroup- and moderator analyses to assess the influence of therapy delivery format (computerized v. non-computerized, group v. individual); patients' clinical status (current v. remitted depression); control group [placebo v. waitlist/treatment as usual (TAU) control group]; baseline depressive symptom severity; and diagnosis (clinical MDD v. no formal clinical diagnosis, i.e. depression based on elevated depressive symptoms). We assessed effects at follow-up as well.
Methods
Search strategy and selection criteria
We conducted a meta-analysis in accordance with PRISMA guidelines and its protocol was prospectively registered on PROSPERO (CRD42019124316). Databases PubMed, PsycINFO, Embase and Cochrane Library were searched for relevant studies published from their origin through 2 February 2019. The search was updated on 8 November 2020. The search strategy included key words and MeSH terms related to cognitive remediation and depression (Appendix II). References and citations of included studies, relevant reviews and meta-analyses were searched for additional studies.
To be included, studies needed to be a randomized or non-randomized clinical trial, testing the effectiveness of cognitive remediation, as compared to a non-cognitive remediation control group (e.g. placebo, waitlist, TAU), in current or remitted patients with primary depression, aged ⩾18 years and reporting sufficient statistics to calculate effect sizes. For example, antidepressant medication and cognitive behavioural therapy were considered TAU. Depression was operationalized as an MDD diagnosis confirmed by a clinician, clinical interview or elevated symptoms/disorder based on any instrument aimed at assessing MDD. We utilized tolerant diagnostic criteria in order to remain inclusive and to include studies with a relatively broad range of baseline depressive symptom severity. The rationale for this was to promote the generalizability of the results, and to enable exploring the effect of baseline depressive symptom severity. Statistics were considered sufficient if post-cognitive remediation summary means (M) and standard deviations (s.d.) on either DS, CF or DF were reported. In case of mixed samples (e.g. schizophrenia, bipolar disorder, MDD), we required statistics for the depression sub-sample. There were no limitations with regard to publication year; we aimed to include all relevant peer-reviewed studies published to this date. Exclusion criteria were coexisting psychotic disorders, brain injuries, other neurological disorders, recent/consecutive electroconvulsive therapy and any form of transcranial stimulation as this might affect cognitive remediation results (Jahshan, Rassovsky, & Green, Reference Jahshan, Rassovsky and Green2017). Papers written in English, French and Dutch language were included.
After removing duplicates, two authors (AML and MB) independently screened titles and abstracts and selected studies with potential for inclusion. Selected studies were reviewed independently full-text (AML, MS and MB). Any disagreements were resolved through consensus (AML, MS and MB).
Data-analysis
Extracted data for the cognitive remediation and control conditions were: number, gender and age of patients; diagnostic instruments and criteria for depression; current/remitted depression; intervention characteristics; instruments to assess DS, CF and DF; M and s.d. of DS, CF and DF measures post-intervention and at follow-up; DS measures at baseline; time from end of treatment to post-intervention and follow-up assessments; data on quality, including randomization. In case of multiple comparisons within the same study, all relevant comparisons were included in the meta-analysis. In order to justify the weight of the respective comparisons by the true number of participants, participants (n) included in both comparisons were equally divided across the comparisons (i.e. two cognitive remediation samples were each compared to half of the same control sample, and vice versa when two relevant control samples were included, they were each compared to half of the same cognitive remediation sample) (Higgins et al., Reference Higgins, Thomas, Chandler, Cumpston, Li, Page and Welch2020). In case of data overlap, only the most recent study was included to ensure statistical independence.
Outcome measures were divided into three main outcome categories: DS, CF and DF. Measures of cognitive domains by means of objective standardized cognitive tests were considered CF outcomes. Measures of aspects of (satisfaction with) functioning in daily live, e.g. quality of life, administration tasks and social interactions, were categorized as DF outcomes. CF outcomes were further divided into standardized cognitive domains, namely Attention; Processing speed; Motor speed; Working memory; Verbal learning and memory; Visual learning and memory; Executive functioning; Verbal fluency; Global/intellectual functioning (Lezak, Howieson, Bigler, & Tranel, Reference Lezak, Howieson, Bigler and Tranel2012). DF outcomes were divided into subjective and objective. Subjective DF was operationalized as self-reported DF, e.g. a questionnaire on quality of life filled in by a patient. Objective DF was operationalized as clinician-rated DF, e.g. results on an advanced finances task rated by a clinician.
Categories were defined by authors AML and MS. Data were extracted and categorized by AML. Data extractions and categorizations were cross-checked by MS and MB. If any relevant information was found to be missing, the corresponding authors of the respective articles were contacted to request the information and reminded twice.
AML rated the risk of bias and MB cross-checked the ratings using the Cochrane Risk of Bias tool, as recommended by the GRADE system (Guyatt et al., Reference Guyatt, Oxman, Akl, Kunz, Vist, Brozek and DeBeer2011). For each study, seven criteria were scored as low risk of bias (0 points), unclear risk of bias (1 point) or high risk of bias (2 points). A study was rated to have low risk of bias (total points <6) or high risk of bias (total points >6). We assessed the overall certainty of evidence for the three main outcome categories using the GRADE framework.
We used Comprehensive Meta-Analysis Software (version 3) (Borenstein, Hedges, Higgins, & Rothstein, Reference Borenstein, Hedges, Higgins and Rothstein2013) to calculate effect sizes (Hedges' g) based on means and standard deviations, and number of patients in both conditions at the first post-intervention assessment (cognitive remediation compared to control). For follow-up analyses, we used the first follow-up time-point (i.e. any additional assessment after the first post-intervention assessment) as a starting point to assess effects at follow-up. We similarly calculated effect sizes based on means, standard deviations and number of patients in both conditions. For the analyses on DS, CF and DF, the mean of the effect sizes per study on DS, CF or DF outcomes, respectively, was used. For the analyses on CF domains and DF sub-categories, the mean of the effect sizes per study per domain/sub-category was used. For each outcome, a positive effect size indicated greater improvement in the cognitive remediation condition compared to the control condition. Effect sizes were weighted by their inverse variance in order to give more weight to studies with larger sample sizes. To determine statistical significance, two-sided 95% confidence intervals were used. Weighted, mean effect sizes of 0.2–0.49 were considered small; 0.5–0.79 medium; and >0.8 large (Cohen, Reference Cohen1988). The I 2 index was used to quantify heterogeneity. Percentages of <40% were considered small; 30–60% moderate; 50–90% substantial; and 75–100% considerable heterogeneity (Higgins et al., Reference Higgins, Thomas, Chandler, Cumpston, Li, Page and Welch2020). We used a random-effects model, and mixed (random within and fixed across subgroups) effects model for categorical subgroup analyses, because of the a priori assumption that there would be substantial variability between the included studies (Borenstein, Hedges, Higgins, & Rothstein, Reference Borenstein, Hedges, Higgins and Rothstein2009).
To assess the moderating effect of baseline depressive symptom severity, Montgomery-Asberg Depression Rating Scale (Montgomery & Åsberg, Reference Montgomery and Åsberg1979) or Beck Depression Inventory-II (Beck, Steer, & Brown, Reference Beck, Steer and Brown1996) scores were transformed to Hamilton Depression Rating Scale (HDRS-17) (Hamilton, Reference Hamilton1967) scores (Heo, Murphy, & Meyers, Reference Heo, Murphy and Meyers2007; Vittengl, Clark, Kraft, & Jarrett, Reference Vittengl, Clark, Kraft and Jarrett2005). Mean HDRS-17 <8 was considered minimal; HDRS-17 8–15 moderate; and HDRS-17 >15 severe symptoms. Subgroup analyses were performed by clustering studies into contrasting subgroups. To ensure adequate power, a minimum of three studies per subgroup was required. Continuous moderators were analysed by simple meta-regression. Meta-regression analyses were not performed if the number of studies was <10.
Publication bias for the three main outcome categories was assessed by inspecting funnel plots and using Egger's test for their symmetry, and Duval and Tweedie's trim and fill procedure. Sensitivity analyses for the effect on DS, CF and DF were performed excluding outliers defined as individual studies showing an effect size with a 95% confidence interval that did not show any overlap with the 95% confidence interval of the overall, i.e. pooled, effect (Harrer, Cuijpers, Furukawa, & Ebert, Reference Harrer, Cuijpers, Furukawa and Ebert2019); studies with high risk of bias; insufficient sequence generation; small number of patients (n in either one of the conditions <5); large number of days from end of treatment to post-intervention assessment (>14 days); and studies with participants without a formal clinical MDD diagnosis.
Results
Study characteristics
We identified 5221 records, and included 21 studies with 438 patients allocated to a cognitive remediation condition and 540 patients allocated to a control condition (Alvarez, Cortés Sotres, León, Estrella, & Sánchez Sosa, Reference Alvarez, Cortés Sotres, León, Estrella and Sánchez Sosa2008; Anguera, Gunning, & Areán, Reference Anguera, Gunning and Areán2017; Bowie et al., Reference Bowie, Gupta, Holshausen, Jokic, Best and Milev2013; Elgamal, McKinnon, Ramakrishnan, Joffe, & MacQueen, Reference Elgamal, McKinnon, Ramakrishnan, Joffe and MacQueen2007; Hoorelbeke & Koster, Reference Hoorelbeke and Koster2017; Hoorelbeke, van den Bergh, de Raedt, Wichers, & Koster, Reference Hoorelbeke, van den Bergh, de Raedt, Wichers and Koster2021; Listunova et al., Reference Listunova, Kienzle, Bartolovic, Jaehn, Grützner, Wolf and Roesch-Ely2020; Morimoto et al., Reference Morimoto, Wexler, Liu, Hu, Seirup and Alexopoulos2014, Reference Morimoto, Altizer, Gunning, Hu, Liu, Cote and Alexopoulos2020; Moshier, Molokotos, Stein, & Otto, Reference Moshier, Molokotos, Stein and Otto2015; Moshier & Otto, Reference Moshier and Otto2017; Naismith et al., Reference Naismith, Diamond, Carter, Norrie, Redoblado-Hodge, Lewis and Hickie2011; Owens, Koster, & Derakshan, Reference Owens, Koster and Derakshan2013; Pratap et al., Reference Pratap, Renn, Volponi, Mooney, Gazzaley, Arean and Anguera2018; Semkovska & Ahern, Reference Semkovska and Ahern2017; Semkovska et al., Reference Semkovska, Lambe, Lonargáin and McLoughlin2015; Trapp et al., Reference Trapp, Engel, Hajak, Lautenbacher and Gallhofer2016; Twamley et al., Reference Twamley, Thomas, Burton, Vella, Jeste, Heaton and McGurk2019; Wanmaker, Geraerts, & Franken, Reference Wanmaker, Geraerts and Franken2015; Wanmaker, Hopstaken, Asselbergs, Geraerts, & Franken, Reference Wanmaker, Hopstaken, Asselbergs, Geraerts and Franken2014; Yamaguchi et al., Reference Yamaguchi, Sato, Horio, Yoshida, Shimodaira, Taneda and Ito2017) (Fig. 1).
PRISMA flow diagram.
The total number of comparisons was 24.Footnote †Footnote 1 Three studies had high risk of bias (Elgamal et al., Reference Elgamal, McKinnon, Ramakrishnan, Joffe and MacQueen2007; Morimoto et al., Reference Morimoto, Wexler, Liu, Hu, Seirup and Alexopoulos2014; Owens et al., Reference Owens, Koster and Derakshan2013) (Appendix I – eTable 1). Results on relevant outcomes were categorized into DS, CF and DF categories and sub-categories (Appendix I – eTable 2). Twenty-one comparisons included DS, 19 included CF and 12 included DF outcomes (see Table 1 for further study details).
Study characteristics
MDD, major depressive disorder; TAU, treatment as usual; PST, Problem Solving Therapy; Abbreviations of clinical instruments, in alphabetical order: BACS, Brief Assessment of Cognition in Schizophrenia; BDI, Beck Depression Inventory; BRIEF-A, Behavior Rating Inventory of Executive Function Adult Version; BVMT-R, Brief Visual Memory Test Revised; CBT, Cognitive Behavioral Therapy; CFQ, Cognitive Failures Questionnaire; COWAT, Controlled Oral Word Association Test; CPT-IP, Continuous Performance Test – Identical Pairs; CVLT, California Verbal Learning Test; Degraded CPT, Degraded Continuous Performance Test; D-KEFS, Delis-Kaplan Executive Functioning System; DS, Digit Span; DSM-IV, Diagnostic and Statistical Manual of mental disorders IV; GAF, Global Assessment of Functioning; HDRS-17, Hamilton Depression Rating Scale-17; HDRS-24, Hamilton Depression Rating Scale-24; HVLT, Hopkins Verbal Learning Test; ICD-10, International Classification of Diseases and related health problems-10; ILSS, Independent Living Skills Survey; LIFE-RIFT, Longitudinal Interval Follow-up Evaluation Range of Impaired Functioning Tool; LNS, Letter Number Sequencing Test; MADRS, Montgomery-Asberg Depression Rating Scale; MINI, Mini International Neuropsychiatric Interview; MINI-ICF, Mini – Internal Classification of Functioning, Disability and Health MIST, Memory for Intentions Test; NAB, Neuropsychological Assessment Battery; PASAT, Paced Auditory Serial Addition Test; PHQ-9, Patient Health Questionnaire-9; PIQ, Performance Intelligence Quotient; RAVLT, Rey Auditory Verbal Learning Test; RDQ, Remission of Depression Questionnaire; ROCF, Rey-Osterrieth Complex Figure test; RRS, Ruminative Response Scale; RS, Resilience Scale; Ruff's 2&7 SAT, Ruff's 2&7 Selective Attention Test; R, Revised; SCID, Structured Clinical Interview for the DSM; SCT, Symbol Coding Task; SDS, Sheehan Disability Scale; SLOF, Specific Level of Functioning Scale; Spat; S, Spatial Span; SSPA, Social Skills Performance Assessment; Stroop CWT, Stroop Color Word Test; TMT-A, Trail Making Test part A; TMT-B, Trail Making Test part B; TOVA, Test of Variables of Attention; VIQ, Verbal Intelligence Quotient; VTS, Vienna Test System; WAIS, Wechsler Adult Intelligence Scale; WCST, Wisconsin Card Sorting Test; WHODAS, World Health Organization Disability Assessment Schedule; WM, Working Memory; WMS, Wechsler Memory Scale; QLDS, Quality of Life in Depression Scale; QOLI, Quality Of Life Interview; UM LNS, University of Maryland Letter Number Span; UPSA-Brief, University of California, San Diego, Performance-based Skills Assessment-Brief.
*In Alvarez et al. (Reference Alvarez, Cortés Sotres, León, Estrella and Sánchez Sosa2008), the control sample was split into two (a and b) in order to perform analyses using both cognitive remediation samples. The original control sample consisted of 11 patients. **In Listunova et al. (Reference Listunova, Kienzle, Bartolovic, Jaehn, Grützner, Wolf and Roesch-Ely2020), the control sample was split into two (a and b) in order to perform analyses using both cognitive remediation samples. The original control sample consisted of 19 patients. ***In Pratap et al. (Reference Pratap, Renn, Volponi, Mooney, Gazzaley, Arean and Anguera2018) the cognitive remediation sample was split into two (a and b) in order to perform analyses using both control samples. The original cognitive remediation sample consisted of 79 patients.
Main effects on depressive symptomatology, cognitive and daily functioning
The direction of the effect was favourable and significant for all three outcome categories. There was a small significant effect on DS (g = 0.28; 95% CI 0.09–0.46), a medium significant effect on CF (g = 0.60; 95% CI 0.37–0.83) and a small significant effect on DF (g = 0.22; 95% CI 0.06–0.39). Heterogeneity was moderate for both DS (I 2 = 40%) and CF (I 2 = 44%), and small for DF (I 2 = 3%) (see Table 2 and Fig. 2).
Forest plots of three main outcomes: (a) Forest plot effect on depressive symptomatology, (b) Forest plot effect on cognitive functioning, (c) Forest plot effect on daily functioning. CR, cognitive remediation; CI, confidence interval.
Main effects and subgroup analyses of cognitive remediation on depressive symptomatology, cognitive and daily functioning
N, number of comparisons; n, number of patients; CI, confidence interval; TAU, treatment as usual.
*This p value indicates the between-group difference in the subgroup analyses.
Subgroup analyses
With regard to therapy delivery format, only one study had a full non-computerized format, and only one other study had a full group format. As we required a minimum of three studies per subgroup, we did not perform subgroup analyses based on therapy format. Only two studies included patients with minimal depressive symptom severity at baseline; thus, only subgroups of moderate and severe baseline depressive symptom severity were analysed. There were not enough studies to perform subgroup analyses based on diagnosis for the effects on CF and DF.
Depressive symptomatology
Subgroup analyses showed that there was a significantly larger effect on DS in patients with severe baseline depressive symptoms compared to patients with moderate baseline depressive symptoms: there was no significant effect on DS in patients with moderate baseline symptoms, while in those with severe baseline symptoms, there was a small significant effect (g = 0.48). With regard to the effect on DS, difference in effect size between other subgroups did not reach statistical significance (Table 2).
Cognitive functioning
Significant effects for CF domains were: small for Attention (g = 0.36), Processing speed (g = 0.26) and Verbal learning and memory (g = 0.47); and medium for Working memory (g = 0.54) (Table 2). There were insufficient studies reporting outcomes on Motor speed (N = 1) and Global/intellectual functioning (N = 2) to meta-analyse these effects. There were no significant effects on Visual learning and memory; Executive functioning; or Verbal fluency. Subgroup analyses revealed that the effect on CF was significantly larger in comparison to placebo control groups (large significant effect, g = 0.84), than in comparison to Waitlist/TAU control groups (small significant effect, g = 0.39). There were no significant differences in effect size for CF between other subgroups.
Daily functioning
There was a small significant effect on subjective DF (g = 0.22) and no significant effect on objective DF (Table 2). Subgroup analyses revealed no significant differences in effect sizes for DF between subgroups.
Meta-regression analyses
We performed simple meta-regression analyses on the moderating effects of baseline depressive symptom severity (mean HDRS-17 scores) and post-hoc on age (mean age), gender (percentage female) and cognitive remediation duration (in minutes). There were no significant effects.Footnote 2
Effects at follow-up
As a number of studies provided outcomes at follow-up (i.e. any additional assessment after the first post-intervention assessment, ranging from 1 to 3 months after the first post-intervention assessment), we opted to perform post-hoc analyses on the effects of cognitive remediation v. control on DS, CF and DF at follow-up. We took the first follow-up time-point. There were no significant effects of cognitive remediation compared to control at follow-up.Footnote 3 As we found no significant durable effects at the first follow-up time point, we did not further analyse effects at follow-up.
Publication bias and certainty of the evidence
Inspection of the funnel plots and Egger's test did not indicate publication bias for DS (p = 0.67), CF (p = 0.40) or DF (p = 0.48). Duval and Tweedie's trim and fill procedure under the random-effects model indicated no publication bias either for DS, CF or DF. For DS and DF, the pooled effect sizes were downgraded using the GRADE assessment to very low, and for CF to low certainty of evidence (Appendix III).
Sensitivity analyses
Sensitivity analyses excluding two outliers (Hoorelbeke et al., Reference Hoorelbeke, van den Bergh, de Raedt, Wichers and Koster2021; Morimoto et al., Reference Morimoto, Altizer, Gunning, Hu, Liu, Cote and Alexopoulos2020); studies with high risk of bias (Appendix I – eTable 1), one study with small sample size (Yamaguchi et al., Reference Yamaguchi, Sato, Horio, Yoshida, Shimodaira, Taneda and Ito2017), studies with >14 days from end of treatment to post-intervention assessment (Hoorelbeke et al., Reference Hoorelbeke, van den Bergh, de Raedt, Wichers and Koster2021; Yamaguchi et al., Reference Yamaguchi, Sato, Horio, Yoshida, Shimodaira, Taneda and Ito2017); or studies with participants without a formal clinical MDD diagnosis (Moshier et al., Reference Moshier, Molokotos, Stein and Otto2015; Owens et al., Reference Owens, Koster and Derakshan2013; Pratap et al., Reference Pratap, Renn, Volponi, Mooney, Gazzaley, Arean and Anguera2018; Wanmaker et al., Reference Wanmaker, Hopstaken, Asselbergs, Geraerts and Franken2014) did not affect the overall results on the three main outcome categories DS, CF and DF. Further, excluding studies with unclear or insufficient sequence generation (Appendix I – eTable 1) did not affect the results on CF. However, the effect on DS changed from a small significant effect (g = 0.28) to a non-significant, smaller effect (g = 0.18; 95% CI −0.05 to 0.42; N = 12; n = 204; I 2 = 31%; 95% CI 0–65%), and the effect on DF changed from a small significant effect (g = 0.22) to a non-significant, smaller effect as well (g = 0.20; 95% CI −0.03 to 0.43; N = 8; n = 295; I 2 = 0%; 95% CI 0–68%). Thus, confounding bias might have affected the effects on DS and DF.
Discussion
We performed a meta-analysis to assess the effectiveness of cognitive remediation in depression. Our results indicate a small significant effect on DS, a medium significant effect on CF and a small significant effect on DF. Significant effects for CF domains were small for Attention, Processing speed and Verbal learning and memory, and medium for Working memory. For DF sub-categories, there was a small significant effect for Subjective DF. However, we found no indication that these beneficial effects are sustainable as the meta-analysis did not identify any significant effects of cognitive remediation on DS, CF or DF at follow-ups up to 3 months after the post-intervention assessments.
Our findings of small significant effects on DS and DF, and medium significant effect on Working memory are consistent with the only previous meta-analysis on the subject (Motter et al., Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016). However, for Attention, Motter et al. (Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016) did find a moderate significant effect whereas we identified a small significant effect. Processing speed outcomes were not meta-analysed separately but merged with Attention outcomes in the previous work (Motter et al., Reference Motter, Pimontel, Rindskopf, Devanand, Doraiswamy and Sneed2016), whereas we have quantified separately the effects of these two cognitive domains. Further, in contrast to our findings, they found no significant effect on Verbal memory. These differences might be explained by a limited number of studies and participants included in the previous meta-analysis, relative to the present meta-analysis.
In subgroup analyses, we found that effects on DS were significantly larger in the subgroup with patients with severe depressive baseline symptoms compared to those with moderate symptoms: there was a small significant effect in patients with severe depressive baseline symptoms, and no significant or sizable effect in patients with moderate symptoms. This is not surprising, since more depressive symptoms mean more room for improvement. This finding emphasizes the importance of taking baseline symptoms into account, as has been argued extensively by others before (e.g. Nunes et al., Reference Nunes, Pavlicova, Hu, Campbell, Miele, Hien and Klein2011). Future studies should consider that only in severely depressed individuals, cognitive remediation appears to improve DS. Moreover, this finding suggests that patient characteristics impact on the effectiveness of cognitive remediation. Gaining more knowledge on the association between individual patient characteristics and the effectiveness of cognitive remediation may ultimately lead to personalized cognitive remediation interventions. The potential in this area should be noted.
Although CF improved in comparison to both placebo and waitlist/TAU control conditions, improvement was significantly more pronounced in comparison to placebo than in comparison to waitlist/TAU with large and moderate effect size, respectively. This could be explained by that placebo control conditions were by definition specifically designed in order not to improve CF, while this was not the case for waitlist and/or TAU control conditions. We could not demonstrate any significant effect of clinical status (current v. remitted depression) or diagnosis (clinical MDD v. no clinical diagnosis).
Further, we found no moderating effect of cognitive remediation duration on the effectiveness achieved. Relatively short programmes may be sufficiently effective. However, the absence of a duration effect might have been attributable to the variation in the design of the remediation programmes.
Some limitations of the meta-analysis should be noted. A limitation with regard to the effect on DF is that this outcome measure was very heterogeneous in terms of what instruments were used and what these instruments aimed to measure. Although we tried to categorize DF outcomes as subjective and objective in order to promote homogeneity, there was still a great variety of outcomes included within these categories. Statistical heterogeneity was, however, low (I 2 = 3%). The same could be said for CF, because instruments aimed at assessing various cognitive domains were included though all instruments explicitly aimed to assess CF. Although most studies included had low risk of bias and excluding studies with high risk of bias did not change the results, some studies reported non-random or unclear sequence generation. Our results indicate that confounding in the observational studies may have biased the results for DS and DF: when the analyses on DS and DF were restricted to randomized studies, the effect sizes were lower and no longer significant. According to the GRADE assessment, the pooled effect size for DS and DF was downgraded to very low, and for CF to low certainty of evidence. Both including varying cognitive remediation interventions and studies among patients with a broad range of depression severity likely improves the generalizability of our results. However, the other side of the coin is that such liberal inclusion decreases the specificity with which our results apply to a specific cognitive remediation format and specific population. Notably, interventions were not only diverse qualitatively, but also the quantity (duration) of cognitive remediation varied considerably. Unfortunately, there were not enough studies on cognitive remediation interventions with a fully non-computerized, or group format to perform any subgroup analyses on therapy delivery format as we aimed to. Our findings should be interpreted cautiously, keeping in mind that the vast majority of included studies had a fully computerized and individual format, although some studies combined computerized and non-computerized, and individual and group interventions. There were not enough studies to include a subgroup with minimal depressive symptoms in any of the subgroup analyses on symptom severity at baseline, or to perform subgroup analyses on diagnosis for CF and DF.
Furthermore, cognitive impairment has been shown to increase with the number of depressive episodes (Semkovska et al., Reference Semkovska, Quinlivan, O'Grady, Johnson, Collins, O'Connor and Gload2019), and thus cognitive remediation might be especially relevant for patients with recurrent depression. However, none of the included studies recruited exclusively patients with recurrent depression. Also, only two of the included studies report evident cognitive impairment at baseline as an inclusion criterion (Listunova et al., Reference Listunova, Kienzle, Bartolovic, Jaehn, Grützner, Wolf and Roesch-Ely2020; Yamaguchi et al., Reference Yamaguchi, Sato, Horio, Yoshida, Shimodaira, Taneda and Ito2017). Similarly to the larger effect on DS found in patients with severe depressive symptoms at baseline, the effects of cognitive remediation might be more pronounced in patients with evident cognitive impairment. Both these factors might have impacted the current meta-analysis results. It would be relevant for future studies to focus on the effectiveness of cognitive remediation specifically in samples with recurrent depression and/or evident cognitive impairment, and to study whether effects are different compared to samples with single-episode depression and/or no evident cognitive impairment. Further, the number of studies that provided follow-up data was limited. It should also be noted that sample sizes were often small. Future studies should include more participants and thereby increase power.
In conclusion, our findings indicate that cognitive remediation in depression substantially improves CF; more specifically, Attention, Processing speed, Working memory and Verbal learning and memory. We found small significant effects on DS and subjective DF as well. However, these might be overestimations due to confounding bias. Further, our findings indicate that it is important to consider baseline depressive symptom severity: cognitive remediation improved DS in those with severe baseline symptoms but not in those with moderate baseline symptoms. The effects on DS, CF and DF disappeared at follow-up. Given that the endurance of the effects of cognitive remediation is under discussion, it is critical to study how interventions can be innovated or combined with other interventions in order for their effects to last. Development of cognitive remediation protocols that aim for sustainable effects is crucial. More high-quality, well-powered, randomized controlled trials are needed that include long-term follow-ups. The effect of cognitive remediation on DS, DF, as well as optimal therapy delivery format needs to be determined.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291721001100.
Acknowledgements
The authors thank the authors who replied to their e-mail requests for additional data and/or information.
Author contributions
AML, MS, MB, DD and CLB designed the study. AML searched the databases. AML and MB screened records for inclusion and conflicts were dissolved through discussion with MS. Full-text screening was done by AML, MS and MB and conflicts were dissolved trough discussion among them. AML corresponded with the authors in case of missing data/information. AML extracted data supervised by MB and MS. MB and MS cross-checked data extraction. AML analysed and interpreted the data, supervised by MS, MB, GJG, HB and CLB. MB cross-checked the analyses. AML drafted the manuscript. All authors reviewed and revised the manuscript. The final manuscript was approved for submission by all authors.
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Conflict of interest
None.
Open access
