Abstract
Major depressive disorder is a leading cause of disability worldwide; identifying effective strategies to prevent depression relapse is crucial. This individual participant data meta-analysis addresses whether and for whom psychological interventions can be recommended for relapse prevention of major depressive disorder. One- and two-stage individual patient data meta-analyses were conducted on 14 randomized controlled trials (N = 1,720). The relapse risk over 12 months was substantially lower for those who received a psychological intervention versus treatment as usual, antidepressant medication, or evaluation-only control (hazard ratio, 0.60; 95% confidence interval, 0.48–0.74). The number of previous depression episodes moderated the treatment effect, with psychological interventions demonstrating greater efficacy for patients with three or more previous episodes. Our results suggest that adding psychological interventions to current treatment to prevent depression relapse is recommended. For patients at lower risk of relapse, less-intensive approaches may be indicated.
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Main
Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years worldwide1 and is characterized by high relapse rates2,3. The risk for relapse contributes largely to the overall burden of MDD, making relapse prevention a matter of urgent priority.
Antidepressant medication (ADM), psychological interventions2,4, or their combination are commonly employed to prevent depression relapse. These interventions can either be the same by which the patient achieved remission (continuation therapy) or altered (sequential)5. Recent systematic reviews and meta-analyses have shown that psychological interventions alone or in combination with antidepressants can be viable alternatives to antidepressants with sustained effects6,7,8, regardless of clinical risk factors9.
While a range of approaches for relapse prevention of depression are available, it remains unclear which intervention to offer to whom. A personalized strategy can help to reduce trial and error in determining the most accurate relapse prevention strategy for each patient10,11. Advances in personalization for patients with recurrent depression can be achieved by individual participant data meta-analysis (IPDMA). By pooling individual participant data (IPD) in a meta-analysis, moderators and predictors of relapse can be assessed more specifically compared with standard meta-analyses12,13. This adds power and precision over standard aggregate meta-analysis14.
To date, two IPDMAs have been conducted for depression relapse prevention15,16. Kuyken et al.16 compared mindfulness-based cognitive therapy (MBCT) with antidepressants or treatment as usual (TAU) alone. This study found that MBCT-based interventions (with TAU or tapering) were effective in reducing the risk of relapse versus control (hazard ratio (HR), 0.69; 95% confidence interval (CI), 0.58–0.82). Severity of depressive symptomatology was a moderator; patients with higher depression scores at baseline had a longer time to relapse when receiving MBCT compared with patients in control conditions. A more recent IPDMA has evaluated the effects of a psychological intervention while tapering ADM compared with ADM continuation9. No difference in time to relapse was observed between the two treatments, and no variable was identified that moderated outcome.
A broader set of psychological interventions (cognitive behavioral therapy (CBT), continuation cognitive therapy (C-CT), preventive cognitive therapy (PCT), and MBCT) and comparisons (psychological intervention versus active control, psychological intervention versus TAU) remain to be explored.
This study is an IPDMA of randomized controlled trials (RCTs) on psychological interventions for previously depressed patients compared with patients in antidepressant, TAU, or evaluation control. This IPDMA is crucial as it goes beyond previous IPDMAs by including a broader set of moderators and psychological interventions.
Results
Selected studies
After screening 15,792 references and reviewing 236 full-text articles, we included 28 studies (n = 4,053) that compared a psychological intervention for relapse prevention versus control. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses)17 flow chart is presented in appendix 6 in the Supplementary Information. Of the 28 included studies, authors of 18 (64%) studies agreed to provide IPD (n = 2,840). The remaining ten studies were included as aggregate data in a sensitivity analysis. An overview of author-specific reasons for not providing data is provided in appendix 4 in the Supplementary Information. Reasons for not being able to supply data were data lost due to lab closure18,19, data transfer, ethics regulations20,21, and unable to provide data either for no reason or in time22,23,24,25,26,27.
On receipt of data, two studies did not include time to relapse as an outcome measure28,29 and were therefore excluded from the analyses. Two studies included an active psychological control group (for example, CBT and psychological placebo); these studies were not included in this analysis30,31 due to too few studies to allow for comparisons and heterogeneity between control conditions.
An overview of the 14 studies that provided IPD and their intervention characteristics is provided in appendices 7 and 8 in the Supplementary Information. Table 1 provides summary statistics (at baseline) for the psychological intervention versus non-psychological intervention control conditions. The mean age of the participants was 45.1 years (s.d. 11.1, n = 1,724). The average number of previous episodes was 4.8 (s.d. 5.0, n = 1,614): 5% (n = 74 out of 1,614) had one episode, 20% (n = 327 out of 1,614) had two episodes, and 75% (n = 1,213 out of 1,614) had three or more episodes. The proportion of women was 73% (n = 1,258 out of 1,725).
Study and participant characteristics
Seventeen predefined sociodemographic and clinical characteristics were identified in the provided IPD: age, sex, ethnicity, education, employment, marital status, treatment group, number of previous episodes, age of onset, time in remission (months), duration of last episode (months), stable/unstable remission, previous psychological intervention, comorbid mental health condition, comorbid physical health condition, and baseline depression at point of randomization as measured with the Beck Depression Inventory (BDI)32 and Hamilton Depression Rating Scale (HAM-D)33 (appendix 9 in the Supplementary Information).
The 14 studies (n = 1,725) tested 4 different psychological interventions (PCT, CBT, MBCT and C-CT) and included 3 different control conditions (ADM, TAU and evaluation only). We were able to make two pairwise comparisons: Psychological interventions alone, with ADM, or TAU versus non-psychological control (TAU, ADM or evaluation only; 14 studies)34,35,36,37,38,39,40,41,42,43,44,45,46,47 and psychological interventions with TAU versus TAU (8 studies)34,36,38,39,40,43,46,47.
The risk of bias was low (appendix 10 in the Supplementary Information). Blinding participants and personnel was the only category with a consistently high risk of bias because it is impossible to blind respondents to condition in psychotherapy study designs. Domains that were well adhered to were complete outcome data, lack of selective outcome reporting, intention to treat analysis, blinding of outcome assessors, and identical post-timing. Areas that were less well adhered to (or where it was difficult to ascertain) were similar groups at baseline (no baseline differences) and compliance to intervention protocol, sequence generation, and allocation concealment.
Effects of psychological interventions versus control
Two-stage random-effects analysis found that psychological interventions were significantly better than control conditions in delaying the time to relapse: HR 0.60 (0.48–0.74), P ≤ 0.000, I2 = 14.9% (n = 1,720, 14 studies). Adding psychological interventions to TAU also significantly reduced the risk of relapse compared with TAU only: HR 0.62 (0.47–0.82), P= 0.005, I2 = 28.3% (n = 1,191, 8 studies). Subgroup analysis within the two-stage random-effects analysis found no difference in efficacy of psychological intervention type. Forest plots of subgroup analyses can be found in appendices 11–13 in the Supplementary Information.
Table 2 shows the results from the pairwise IPDMA, using two-stage approach on the IPD available. Forest plots of pairwise meta-analyses are shown in Fig. 1 and appendix 14 in the Supplementary Information. Fixed-effects analysis results were comparable (appendix 15 in the Supplementary Information). The I2 statistic was considered low across comparisons.
Predictors of depression relapse
Fixed-effects one-stage models were used for predictor analysis of the control group as I2 was low between studies. Among the predefined sociodemographic and clinical covariates, age, sex, marital status, previous episodes, age of onset, and residual depression symptoms (HAM-D33) had 60% availability in the dataset. Among these, bivariable fixed-effects models found that being married versus single, divorced, or widowed decreased the risk of relapse for patients randomized to control (Table 3). Furthermore, we found that more previous episodes; being single, divorced, separated, or widowed (marital status); a lower age of onset; and increased depressive symptoms individually significantly increased the risk of relapse at P < 0.10 in the control group. On incorporating all predictors in a multivariable model, marital status and age of onset were no longer significant at P < 0.10. Thus, the identified predictors for relapse (independent of therapy) were number of previous depressive episodes and residual depressive symptoms at baseline as measured with HAM-D. After adding the non-significant variables back into the model, we found none of them to be significant.
Moderators of intervention outcome
Table 4 provides an overview of the moderator analyses. No significant interaction effects were observed for our first pairwise comparison (psychological interventions versus non-psychological interventions). For the second pairwise comparison (psychological interventions added to TAU versus TAU only), we found a significant interaction effect for previous depressive episodes. Participants with three or more previous episodes had a lower risk of relapse when receiving a psychological intervention compared with participants with two or fewer previous episodes who received a psychological intervention (HR 0.55 (0.37–0.79), P = 0.006).
When evaluating the moderator effect of previous episodes in more detail, we note that psychological interventions were not more effective in reducing relapse for those with two episodes or fewer for psychological interventions versus TAU (two episodes, five studies, HR 0.85 (0.37–1.92), P = 0.613; one episode, one study, HR 1.48 (0.40–5.53), P = 0.556).
Sensitivity analyses
We conducted a sensitivity analysis to compare studies that provided IPD and where co-authors were named authors on this manuscript versus studies that did not provide IPD (and authors were not listed on this manuscript). We found no difference in effect (appendix 16 in the Supplementary Information). The funnel plot comparing all psychological interventions versus control conditions at 12 and 14 months showed little evidence for small study effects at 12 months (Egger’s test P value = 0.34), but there was evidence of such effects at 14 months (Egger’s test P value = 0.01), although the associated funnel plot did not show extreme asymmetry (appendices 17 and 18 in the Supplementary Information). Given that Klein et al.37 included participants with chronic depression, we also conducted a sensitivity analysis excluding this study. No differential results compared with the original analysis were observed.
Discussion
In this study, we conducted an IPDMA on psychological-relapse prevention interventions on relapse of depression. We aimed to identify predictors and moderators of treatment outcome to inform personalized treatment decisions. We found that psychological interventions combined with TAU or alone were superior to TAU and other control conditions. Consistent with previous meta-analyses, we observed no difference in efficacy between psychological intervention types (PCT, MBCT, C-CT and CBT)15,48,49,50,51. Patients with three or more previous episodes appear to benefit more from psychological interventions added to TAU compared with TAU or any other control.
The results of this IPDMA offer evidence for the effectiveness of psychological interventions for preventing depression relapse. Adding a psychological intervention to TAU during recovery or remission significantly reduces the risk of relapse. Moreover, assuming equal access to options for preventing depression relapse, patients have the option to choose among different psychological interventions to add to TAU as we did not find evidence for a difference in effect among the studied psychological interventions (MBCT, PCT, CBT and C-CT). However, this finding may be attributable to power issues as well given low sample sizes in certain treatments and needs to be interpreted with caution. Nevertheless, our finding is consistent with findings in previous meta-analyses15,48,49,50,51.
It is interesting that in our study we found a moderating effect for previous episodes in our psychological intervention with TAU versus TAU comparison. There was a greater effect of psychological interventions after three or more previous episodes. The previous literature on this has been mixed. While one previous aggregate data meta-analysis by Zhang et al.50 found this effect for patients taking part in MBCT compared with TAU, this was not observed for the Kuyken et al.16 IPDMA. This might be because some studies in this IPDMA included only participants with three or more previous episodes39,40,43,44. In addition, an aggregate data meta-analysis by Biesheuvel-Leliefeld et al.49 did not observe a moderating effect for previous episodes, and other previous analyses did not explore the potential moderating effect of previous episodes8,48,52, something we were able to achieve in this study. Note that our subgroup size was relatively small for two or fewer episodes (two episodes, n = 182 out of 1,191) and those with one previous episode (n = 32 out of 1,191). Therefore we must be careful to conclude that psychological interventions are not effective for those with two or fewer previous episodes. Still, the most convincing evidence is that psychological interventions can be more effective for those with three or more previous episodes.
It may also be surprising that residual symptoms did not help predict which preventive intervention would work best for whom. In contrast to prior meta-analyses and meta-reviews, we suggest our well-powered IPDMA offers a more reliable picture.
Our finding that age of onset was not significant when pooled in a model with depressive episodes and residual depressive symptoms does not mean that age of onset is not a meaningful variable to evaluate for risk of relapse. Age of onset may still be relevant if there is not yet a high number of previous episodes. Still, future research is needed to further disentangle the relationship between age of onset and depression relapse53. Further, it is possible that age of onset interacts with psychological treatment primarily when examined in ‘at risk’ populations. For example, early intervention in at-risk younger populations can mitigate the suffering in depression54. Such early intervention has the potential to reduce risk and number and duration of episodes.
There is a widely held assumption that demographic factors have little influence on depression relapse53,55. In our study, we observed that marital status did predict relapse when entered individually in a model, with those being married or in a partnership having a significantly lower risk of relapse. This is consistent with a larger literature showing that poor social support and poor social ties are related to a poorer course of depression56. Given our results, the assumption that demographic factors do not matter in predicting relapse may not be adequate. Perhaps there is a complex interplay of factors (for example, additive and/or synergistic) that increase the risk of relapse at any given time, or marital status (and potentially other demographic predictors) may be predictive for certain groups with recurrent depression57. For example, sex and marital status have been considered risk factors for relapse when they are present in those with multiple previous episodes of depression53. Further research regarding when and how marital status interacts with other predictors is warranted. This would include a more granular approach looking at clusters of risk factors for specific patient groups (those with more or fewer than two previous episodes or environmental and life stressors; both of these characteristics are not reported consistently and hence were not included).
Strengths and limitations
In this study, we employed an IPDMA to study the effects of psychological interventions versus control conditions for relapse prevention of depression. While we did not receive IPD from all the datasets that we requested (64% of included studies provided data), a sensitivity analysis suggested no evidence of data availability bias for this IPD meta-analysis. The included studies were of high quality, all including independent (blinded) outcome assessors.
The strength of our study lies in our IPDMA approach, which can add power and precision compared with standard meta-analytic approaches12,13. For this reason, IPDMAs are called the ‘gold standard’ of evidence synthesis. By using an IPDMA, we were able to estimate the relative efficacies of psychological interventions, predictors, and moderators of effect, improving our ability to suggest more personalized treatment strategies in the future. Moreover, compared with previous literature, our review aimed to evaluate all psychological intervention types versus control, offering an additional contribution to previous IPDMAs for recurrent depression focusing on MBCT or tapering studies alone9,16.
This study has several important limitations. First, the time to follow-up was censored at 12 months for consistency; it is unknown whether the performance of predictors and moderators may differ in the longer term. We recommend that future research include even longer follow-ups to explore this further.
Second, we included only study designs that randomized after remission (sequential or continuation study designs) and not studies that evaluated long-term effects of active interventions or the therapy patients received previously (via naturalistic follow-up), which can also offer certain long-term effects58. Moreover, we did not search gray-literature databases for potentially relevant trials.
Third, we included the largest number of predictors to date but were not able to include all the predictors we requested. Therefore, moderators such as childhood trauma, socioeconomic status, employment, and cognitive processing styles were not considered. It is recommended that future RCTs include these constructs. Moreover, we included only moderators with at least 60% of data available across studies. Capturing (more) baseline characteristics consistently would be important for future research.
Fourth, few studies were available for certain subgroup analyses, which meant that we were unable to conduct them. Multifactorial trials can help to identify which particular intervention element is most effective in reducing the risk of relapse58. This information can aid further personalization of relapse prevention interventions so that intervention components can be tailored to an individual’s profile. Moreover, IPD network meta-analysis could allow comparison of multiple treatment groups at the same time59.
Fifth, we did not find a significant difference in effect for psychological interventions versus TAU control for respondents with two or fewer episodes. This might be due to small sample size: 32 out of 1,191 had 1 episode, and 182 out of 1,191 had 2 episodes. In addition, rates of relapse are lower for those with fewer previous episodes, so we may have had reduced power to identify a significant difference60. Hence, it remains poignant to conduct more research into what works for patients with two or fewer previous episodes. We recommend future high-powered trials with longer follow-ups to assess whether and which psychological interventions can reduce the risk of depression relapse for patients with two or fewer episodes.
Furthermore, it would be relevant to identify whether there are different mechanisms of change at play for patients with two or fewer episodes. It would be relevant to study specific (potentially differential) mechanisms of change that can be targeted with intervention. Prospective ecological momentary assessment studies within trials may help to identify such mechanisms61.
Sixth, not all studies provided IPD. While our sensitivity analysis found no significant difference in effect, this result does raise important questions regarding data availability and data sharing and for prospective studies to adhere to the findability, accessibility, interoperability, and reuse principles of data access62.
Seventh, while we were able to include most frequently studied psychological interventions in this IPD (CBT, MBCT, PCT, and C-CT), we were not able to assess the effects of interpersonal psychotherapy (IPT) (as no study provided data) or other intervention types. Therefore, it is not possible for us to make any conclusions about what works for whom for this treatment type. Further research on IPT may be needed, as to the best of our knowledge only two trials are available on this treatment type18,63.
Finally, there is a potential of allegiance bias given that the individual trial authors who developed the interventions were invited as co-authors as part of the IPDMA. To minimize the potential of allegiance bias, we invited all authors of different relapse intervention treatments. Moreover, the study was led by a researcher (J.J.F.B.) with no conflict of interest. The analyses were overseen by an independent statistician (F.C.W.).
Conclusions
In summary, the results of this IPDMA show that psychological interventions are effective in reducing the risk of relapse for adults with depression, especially for those who need them the most. Our findings provide support for residual depression symptoms and number of depression episodes being the primary predictors of relapse in recurrent depression.
These results raise important implications for clinical practice. Psychological interventions (PCT, MBCT, CBT and C-CT) can be especially effective in preventing depression relapse for those with three or more previous episodes. Moreover, depression symptoms and previous depression episodes appear to be important parameters for risk stratification in clinical practice. Future research will need to explore the effects of interventions after a first and second episode using a wider range of predictors and estimate indirect associations via an individual participant data network meta-analysis.
Methods
Eligibility criteria
A full protocol of the study has been published64, and the protocol was registered on PROSPERO: CRD42019127844. The PRISMA-IPD statement was followed65. Studies that met our eligibility criteria (1) were RCTs; (2) compared a psychological intervention with any type of control condition (TAU, wait-list, antidepressants, psychological placebo, or psychological intervention; for example, study designs comparing MBCT with CBT);31 (3) were in adults (the mean age of participants had to be between 18– and 65 years) in remission from MDD, with remission being defined as either no or subthreshold depression symptoms for at least eight weeks or as defined by the authors of the study; and (4) were where the primary outcome of time to relapse was established by a clinical diagnostic interview. With regards to TAU, TAU often consists of no care at all or antidepressant continuation in primary care47,66,67 Only studies published in English were included.
Study identification and selection of studies
PubMed, PsychINFO, Embase, and Cochrane Central Register of Controlled Trials were searched on 23 January 2021. To identify eligible studies, index and free terms, jointly with Boolean operators, were used on four tiers: (1) depression disorder, (2) recurrence and relapse, (3) preventive interventions, and (4) RCTs (see appendix 1 in the Supplementary Information for search strings). We also reviewed the reference lists from previous meta-analyses and contacted members of the international task-force group to ask whether they knew any other studies on psychological-relapse prevention intervention for depression. Once the references were imported into Covidence (covidence.org), they were independently screened by J.J.F.B. and one other reviewer (M.E.B., C.L.B., or a research assistant). Full texts were screened independently by J.J.F.B., and a research assistant. A senior author (C.L.B.) was consulted about any conflict.
Data collection and data items
The first and last authors of the study were contacted, and if they did not respond we contacted the corresponding authors. The authors of included studies received a variable collection sheet that included the variables of interest to this IPDMA (appendix 2 in the Supplementary Information). Variables were selected on the basis of previous reviews (for example, refs. 53,68). Upon receipt of data, two independent reviewers led by J.J.F.B. checked the received data for accuracy in a two-stage process that assessed (1) whether the variables of interest were present in the provided dataset and (2) whether the received data reflected the data in the published article by calculating participant numbers, means, and standard deviations for selected variables and relapse rate for each of the provided datasets (see appendix 3 in the Supplementary Information for an overview).
Risk-of-bias assessment
The risk-of-bias assessments were conducted independently by three researchers, J.J.F.B., J. Gulpen, and M.E.B., using the updated Cochrane Risk of Bias tool by Furlan et al.69 including six items for risk of bias: (1) random sequence generation, (2) allocation concealment, (3) blinding of outcome assessors, (4) incomplete outcome data, (5) selective outcome reporting, and (6) other threats to validity (similar groups, cointerventions, compliance, and similar timing of outcome assessment). Studies were rated on each criterion as ‘low risk,’ ‘high risk,’ or ‘unclear risk.’ A minimum of five criteria with a low-risk rating qualified as the overall low risk of bias.
Statistical analysis
This IPDMA focused on the differential effects of psychological interventions and control on time to relapse (in weeks), with relapse being assessed via a diagnostic interview such as the Structured Clinical Interview for Depression (SCID)70. Studies were excluded from the analysis where the time to relapse was not measured or where the follow-up period post randomization was less than 12 months. The primary analyses used follow-up data to 12 months, with participants who had data beyond 12 months being censored at 12 months (see appendix 4 in the Supplementary Information for an overview of follow-up timings and censoring).
Analyses were conducted in Stata (v.15.1 and v.17). Two or more studies were required to conduct one pairwise comparison analysis71. This resulted in two pairwise comparisons: (1) psychological interventions (alone, with TAU, or with ADM) versus any non-psychological control group and (2) psychological interventions with TAU versus TAU only. One- and two-stage random-effects and fixed-effects analyses were conducted for the identified pairwise comparisons. We report on the two-stage random-effects meta-analysis as we expected and observed clinical heterogeneity in the primary studies (including different types of psychological interventions delivered in different settings). The Hartung–Knapp–Sidik–Jonkman method for random-effects meta-analysis was used72. Our effect size was the hazard ratio, which quantifies the relative risk of an event occurring between two groups or conditions over time.
Heterogeneity was assessed using the I2 statistic, based on two-stage meta-analyses; the I2 represents the proportion of variation across studies that is due to heterogeneity rather than sampling error, with 0% indicating no heterogeneity, 25% low heterogeneity, 50% moderate heterogeneity, and 75% high heterogeneity73.
One-stage meta-analyses were performed using a series of Cox proportional hazards models, with a fixed effect on study. The analysis consisted of two stages. First, predictors of relapse in the control group were identified to ascertain predictors of relapse independently of treatment or active intervention. Second, a series of interaction terms were created on the basis of the predictors identified in the control group to identify which treatment would work best for whom. The interaction terms were created between the binarized treatment group variable and a single predictor variable.
With regard to predictors, we previously set out to include predictors that had at least 40% of data available in at least three studies64. This resulted in very low sample sizes, especially when covariates were combined. We therefore set the minimum number of observations for each covariate in a comparison with 60%. This means that we expected no more than 40% of data in each covariate to be missing. This allowed for more highly powered and representative estimates of predictor and moderator effects.
Predictors of relapse were identified by entering each of the individual predictors into a multivariable time-to-event model using control-group data only. The independent variable was the predictor of interest, and time to relapse was the dependent variable. Study was included in the model to account for the clustering of patients in studies. All relapse predictors that predicted time to relapse at P < 0.10 were included in a Cox proportional hazards regression model. This regression model also included intervention allocation. To identify the final list of predictors, those significant at P < 0.05 were selected. To study interaction effects, a series of models were performed that added the interaction term for each predictor and treatment allocation (only one interaction term per model was included), thus allowing investigation of whether the predictor also acted as a moderator. An interaction term was deemed significant if it had a P value < 0.05.
Predictors were all centered to facilitate interpretation and improve model estimation. To avoid ecological bias74,75, which refers to the potential discrepancy between group-level associations (across trial) and individual-level associations (within trial), we added a fixed effect on study level in the one-stage IPDMA. The two-stage IPDMA already accounts for ecological bias by estimating within-study analysis first and then across-study (meta-analysis) in the second step74,75.
Sensitivity analyses
A further sensitivity analysis was conducted to investigate the possibility of inclusion bias. Here aggregate risk ratios (extracted from the published article) of included studies that did not provide IPD were combined with risk ratios as calculated from studies that provided IPD. The risk ratios for studies that provided IPD versus those that did not were compared via a one-stage random-effects subgroup analysis in Comprehensive Meta-Analysis (v.3). We identified little variation between study characteristics such as duration of follow-up, country of study, and year of publication and did not perform a further analysis to investigate whether these may have affected our results. To investigate small-sample-size effects, we inspected the funnel plot and conducted Egger’s test76 based on the two-stage IPD analysis. Treatment allocation for the Egger’s test was categorized by psychological intervention versus non-psychological intervention.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
This is individual participant data from randomized controlled trials, which cannot be shared publicly due to ethical and consent restrictions that are in place. For data access, please contact the corresponding author. Data access can be provided if these conditions are met: (1) there is approval from all co-authors for the data to be shared, (2) there is a data-sharing agreement in place (which adheres to the requirements for data sharing by the Amsterdam University Medical Centre), (3) individual studies have participant consent and ethics approvals in place to allow for further onward sharing, and (4) there is an analysis plan in place that all co-authors agree with. Upon data sharing, data can be used only for the specified purposes.
Code availability
Analysis code can be found at https://osf.io/fyr7h/.
References
Depression and Other Common Mental Disorders Global Health Estimates (WHO, 2017).
American Psychiatric Association Practice Guidelines for the Treatment of Patients With Major Depressive Disorder (APA, 2010); https://doi.org/10.1176/appi.books.9780890423387.654001
Frank, E. et al. Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Arch. Gen. Psychiatry 48, 851–855 (1991).
Depression in Adults: Recognition and Management (NICE, 2009); https://www.nice.org.uk/guidance/cg90/chapter/Key-priorities-for-implementation#ftn.footnote_6
Bockting, C., Breedvelt, J. J. F. & Brouwer, M. Relapse prevention. Comprehensive Clinical Psychology 2nd edn, Vol. 6 (ed. Andersson, G.) 177–193 (Elsevier, 2022); https://doi.org/10.1016/B978-0-12-818697-8.00224-7
Breedvelt, J. J. F. et al. Psychological interventions as an alternative and add-on to antidepressant medication to prevent depressive relapse: systematic review and meta-analysis. Br. J. Psychiatry 219, 538–545 (2020).
Guidi, J., Tomba, E. & Fava, G. A. The sequential integration of pharmacotherapy and psychotherapy in the treatment of major depressive disorder: a meta-analysis of the sequential model and a critical review of the literature. Am. J. Psychiatry 173, 128–137 (2016).
Guidi, J. & Fava, G. A. Sequential combination of pharmacotherapy and psychotherapy in major depressive disorder. JAMA Psychiatry https://doi.org/10.1001/jamapsychiatry.2020.3650 (2020).
Breedvelt, J. J. F., Warren, F. C., Segal, Z., Kuyken, W. & Bockting, C. L. Continuation of antidepressants vs sequential psychological interventions to prevent relapse in depression: an individual participant data meta-analysis. JAMA Psychiatry 78, 868–875 (2021).
DeRubeis, R. J. et al. The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS ONE 9, e83875 (2014).
Huibers, M. J. H. et al. Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the Personalized Advantage Index approach. PLoS ONE 10, e0140771 (2015).
Riley, R. D. et al. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 340, 521–525 (2014).
Riley, R. D. & Steyerberg, E. W. Meta-analysis of a binary outcome using individual participant data and aggregate data. Res. Synth. Methods 1, 2–19 (2010).
Stewart, L. A. & Tierney, J. F. To IPD or not to IPD? Advantages and disadvantages of systemic reviews using individual patient data. Eval. Health Prof. 25, 76–97 (2002).
Breedvelt, J. J. F. et al. Psychological interventions as an alternative and add-on to antidepressant medication to prevent depressive relapse: systematic review and meta-analysis. Br. J. Psychiatry 219, 538–545 (2021).
Kuyken, W. et al. Efficacy of mindfulness-based cognitive therapy in prevention of depressive relapse: an individual patient data meta-analysis from randomized trials. JAMA Psychiatry 73, 565–574 (2016).
Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021).
Frank, E. et al. Three-year outcomes for maintenance therapies in recurrent depression. Arch. Gen. Psychiatry 47, 1093–1099 (1990).
Katon, W. et al. A randomized trial of relapse prevention of depression in primary care. Arch. Gen. Psychiatry 58 241–247 (2001).
Brakemeier, E.-L. et al. Erratum to: ‘Cognitive-behavioral therapy as continuation treatment to sustain response after electroconvulsive therapy in depression: a randomized controlled trial.’ Biol. Psychiatry 76, 430 (2014).
Meadows, G. N. et al. Mindfulness-based cognitive therapy for recurrent depression: a translational research study with 2-year follow-up. Aust. N. Z. J. Psychiatry 48, 743–755 (2014).
Paykel, E. S. et al. Prevention of relapse in residual depression by cognitive therapy. A controlled trial. Arch. Gen. Psychiatry 56, 829–835 (1999).
Paykel, E. S. et al. Duration of relapse prevention after cognitive therapy in residual depression: Follow-up of controlled trial. Psychol. Med. 35, 59–68 (2005).
Perlis, R. H. et al. Effect of abrupt change from standard to low serum levels of lithium: a reanalysis of double-blind lithium maintenance data. Am. J. Psychiatry 159, 1155–1159 (2002).
Petersen, T. J. et al. The role of cognitive–behavioral therapy and fluoxetine in prevention of recurrence of major depressive disorder. Cogn. Ther. Res. 34, 13–23 (2007).
Stangier, U. et al. Maintenance cognitive–behavioral therapy and manualized psychoeducation in the treatment of recurrent depression: a multicenter prospective randomized controlled trial. Am. J. Psychiatry 170, 624–632 (2013).
Teismann, T. et al. A randomized controlled trial on the effectiveness of a rumination-focused group treatment for residual depression. Psychother. Res. 24, 80–90 (2014).
Biesheuvel-Leliefeld, K. E. M. et al. Effectiveness of supported self-help in recurrent depression: a randomized controlled trial in primary care. Psychother. Psychosom. 86, 220–230 (2017).
Hoorelbeke, K., Van den Bergh, N., De Raedt, R., Wichers, M. & Koster, E. H. W. Preventing recurrence of depression: long-term effects of a randomized controlled trial on cognitive control training for remitted depressed patients. Clin. Psychol. Sci. 9, 615–633 (2021).
Shallcross, A. J. et al. Relapse/recurrence prevention in major depressive disorder: 26-month follow-up of mindfulness-based cognitive therapy versus an active control. Behav. Ther. 87, 836–849 (2018).
Farb, N. et al. Prevention of relapse/recurrence in major depressive disorder with either mindfulness-based cognitive therapy or cognitive therapy. J. Consult. Clin. Psychol. 86, 200–204 (2018).
Beck, A., Steer, R. & Brown, G. Beck Depression Inventory-II (BDI-II) (The Psychological Corporation, 1996)
Hamilton, M. A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62 (1960).
Teasdale, J. D. et al. Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. J. Consult. Clin. Psychol. 68, 615–623 (2000).
Jarrett, R. B. et al. Preventing recurrent depression using cognitive therapy with and without a continuation phase. Arch. Gen. Psychiatry 58, 381–388 (2001).
Ma, S. H. & Teasdale, J. D. Mindfulness-based cognitive therapy for depression: replication and exploration of differential relapse prevention effects. J. Consult. Clin. Psychol. 72, 31–40 (2004).
Klein, D. N. et al. Cognitive–behavioral analysis system of psychotherapy as a maintenance treatment for chronic depression. J. Consult. Clin. Psychol. 72, 681–688 (2004).
Bockting, C. L. H. et al. Preventing relapse/recurrence in recurrent depression with cognitive therapy: a randomized controlled trial. J. Consult. Clin. Psychol. 73, 647–657 (2005).
Bondolfi, G. et al. Depression relapse prophylaxis with mindfulness-based cognitive therapy: replication and extension in the Swiss health care system. J. Affect. Disord. 122, 224–231 (2010).
Godfrin, K. A. & van Heeringen, C. The effects of mindfulness-based cognitive therapy on recurrence of depressive episodes, mental health and quality of life: a randomized controlled study. Behav. Res. Ther. 48, 738–746 (2010).
Jarrett, R. B., Minhajuddin, A., Gershenfeld, H., Friedman, E. S. & Thase, M. E. Preventing depressive relapse and recurrence in higher-risk cognitive therapy responders: a randomized trial of continuation phase cognitive therapy, fluoxetine, or matched pill placebo. JAMA Psychiatry 70, 1152–1160 (2013).
Hollandare, F. et al. Two-year outcome of internet-based relapse prevention for partially remitted depression. Behav. Res. Ther. 51, 719–722 (2013).
Williams, J. M. G. et al. Mindfulness-based cognitive therapy for preventing relapse in recurrent depression: a randomized dismantling trial. J. Consult. Clin. Psychol. 82, 275–286 (2014).
Huijbers, M. J. et al. Adding mindfulness-based cognitive therapy to maintenance antidepressant medication for prevention of relapse/recurrence in major depressive disorder: randomised controlled trial. J. Affect. Disord. 187, 54–61 (2015).
Bockting, C. L. H. et al. Effectiveness of preventive cognitive therapy while tapering antidepressants versus maintenance antidepressant treatment versus their combination in prevention of depressive relapse or recurrence (DRD study): a three-group, multicentre, randomised control. Lancet Psychiatry 5, 401–410 (2018).
Klein, N. S. et al. No sustainable effects of an internet-based relapse prevention program over 24 months in recurrent depression: primary outcomes of a randomized controlled trial. Psychother. Psychosom. 87, 55–57 (2018).
de Jonge, M. et al. Preventive cognitive therapy versus care as usual in cognitive behavioral therapy responders: a randomized controlled trial. J. Consult. Clin. Psychol. 87, 521–529 (2019).
Clarke, K., Mayo-Wilson, E., Kenny, J. & Pilling, S. Can non-pharmacological interventions prevent relapse in adults who have recovered from depression? A systematic review and meta-analysis of randomized controlled trials. Clin. Psychol. Rev. 39, 58–70 (2015).
Biesheuvel-Leliefeld, K. E. M. et al. Effectiveness of psychological interventions in preventing recurrence of depressive disorder: meta-analysis and meta-regression. J. Affect. Disord. 174, 400–410 (2015).
Zhang, Z., Zhang, L., Zhang, G., Jin, J. & Zheng, Z. The effect of CBT and its modifications for relapse prevention in major depressive disorder: a systematic review and meta-analysis. BMC Psychiatry 18, 50 (2018).
Sim, K., Lau, W. K., Sim, J., Sum, M. Y. & Baldessarini, R. J. Prevention of relapse and recurrence in adults with major depressive disorder: systematic review and meta-analyses of controlled trials. Int. J. Neuropsychopharmacol. 19, pyv076 (2016).
Vittengl, J. R., Clark, L. A., Dunn, T. W. & Jarrett, R. B. Reducing relapse and recurrence in unipolar depression: a comparative meta-analysis of cognitive–behavioral therapy’s effects. J. Consult. Clin. Psychol. 75, 475–488 (2007).
Burcusa, S. L. & Iacono, W. G. Risk for recurrence in depression. Clin. Psychol. Rev. 27, 959–985 (2007).
Beardslee, W. R. et al. Prevention of depression in at-risk adolescents: longer-term effects. JAMA Psychiatry 70, 1161–1170 (2013).
Buckman, J. E. J. et al. Risk factors for relapse and recurrence of depression in adults and how they operate: a four-phase systematic review and meta-synthesis. Clin. Psychol. Rev. 64, 13–38 (2018).
Klein, D. N. & Allmann, A. E. S. in Handbook of Depression (eds Gotlib, I. H. & Hammen, C. L.) 3rd edn. 64–83 (Guilford Press, 2014); https://doi.org/10.1097/00005053-200301000-00022
Coyne, J. C. Toward an interactional description of depression. Psychiatry 39, 28–40 (1976).
Furukawa, T. A. et al. Initial treatment choices to achieve sustained response in major depression: a systematic review and network meta-analysis. World Psychiatry 20, 387–396 (2021).
Cipriani, A., Higgins, J. P. T., Geddes, J. R. & Salanti, G. Conceptual and technical challenges in network meta-analysis. Ann. Intern. Med. 159, 130–137 (2013).
Solomon, D. A. et al. Multiple recurrences of major depressive disorder. Am. J. Psychiatry 157, 229–233 (2000).
Robberegt, S. J. et al. Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT. BMJ Open 12, e058560 (2022).
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Sheets, E. S. et al. Prevention of recurrence of major depression among emerging adults by a group cognitive–behavioral/interpersonal intervention. J. Affect. Disord. 147, 425–430 (2013).
Breedvelt, J. J. F. et al. Individual participant data (IPD) meta-analysis of psychological relapse prevention interventions versus control for patients in remission from depression: a protocol. BMJ Open 10, e034158 (2020).
Stewart, L. A. et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement. JAMA 313, 1657–1665 (2015).
Kendrick, T. et al. Randomised controlled trial to determine the clinical effectiveness and cost-effectiveness of selective serotonin reuptake inhibitors plus supportive care, versus supportive care alone, for mild to moderate depression with somatic symptoms in primary care. Health Technol. Assess. https://doi.org/10.3310/hta13220 (2009).
Moore, M. et al. Explaining the rise in antidepressant prescribing: a descriptive study using the general practice research database. BMJ 339, b3999 (2009).
Bockting, C. L., Hollon, S. D., Jarrett, R. B., Kuyken, W. & Dobson, K. A lifetime approach to major depressive disorder: the contributions of psychological interventions in preventing relapse and recurrence. Clin. Psychol. Rev. 41, 16–26 (2015).
Furlan, A. D. et al. 2015 updated method guideline for systematic reviews in the Cochrane back and neck group. Spine 40, 1660–1673 (2015).
First, M. B. Structured Clinical Interview for the DSM (SCID). Encycl. Clin. Psychol. https://doi.org/10.1002/9781118625392.wbecp351(2015).
Valentine, J. C., Pigott, T. D. & Rothstein, H. R. How many studies do you need? A primer on statistical power for meta-analysis. J. Educ. Behav. Stat. 35, 215–247 (2010).
IntHout, J., Ioannidis, J. P. A. & Borm, G. F. The Hartung–Knapp–Sidik–Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian–Laird method. BMC Med. Res. Methodol. 14, 25 (2014).
Higgins, J. P. T., Thompson, S. G., Deeks, J. J. & Altman, D. G. Measuring inconsistency in meta-analyses. Br. Med. J. 327, 557–560 (2003).
Hua, H. et al. One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information. Stat. Med. 36, 772–789 (2017).
Riley, R. D., Lambert, P. C. & Abo-Zaid, G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ Online 340, 521–525 (2010).
Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test measures of funnel plot asymmetry. Br. Med. J. 315, 629–634 (1997).
Acknowledgments
The authors thank J. Gülpen for support with the risk-of-bias assessments. This project was funded in part by internal funds from the Alliance for Public Health at the Amsterdam University Medical Centre—Mental Health Stream.
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J.J.F.B., M.E.B., W.F.W., C.P., P.v.O., C.L.B. and E. Karyotaki contributed to the concept and design of this study. Acquisition, analysis and interpretation of data was conducted by J.J.F.B., C.L.B., S.G., E. Karyotaki, F.C.W., M.E.B., F.J., F.H., D.N.K., M.d.J., N.K., Z.S., N.F., K.E.M.B.-L., R.J., J.V., M.T., H.M., W.K., A.J.S., C.v.H., K.H., E. Koster, M.J.H., M.W., A.S., P.C., P.v.O., S.G., M.W., A.J.S. and M.T. Both J.J.F.B. and C.L.B. drafted the manuscript. Thereafter, critical revision of the manuscript was provided by J.J.F.B., C.L.B., S.G., E. Karyotaki, F.C.W., M.E.B., F.J., F.H., D.N.K., M.d.J., N.K., Z.S., N.F., K.E.M.B.-L., R.J., J.V., M.T., H.M., W.K., A.J.S., C.v.H., K.H., E. Koster, M.J.H., M.W., A.S., P.C., P.v.O., S.G. and M.W. The statistical analyses were conducted by J.J.F.B., M.E.B., F.C.W. and C.L.B. Two authors obtained funding, namely J.J.F.B. and C.L.B. J.J.F.B. and C.L.B. had full access to all of the data in the study and take responsibility for the integrity of the data and accuracy of analysis.
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Ethical approval was obtained for each of the trials that provided individual patient data. As part of taking part in the original trial, patients provided informed consent that included consent to data sharing for research purposes.
Conflict of interest
F.C.W., E. Karyotaki, E. Koster, M.E.B., J.J.F.B., P.C., P.v.O., D.N.K., K.H., N.F., C.v.H. and S.G. have no conflict of interest to report. All authors with the exception of F.C.W. (independent statistician), E. Karyotaki. (systematic reviewer), M.E.B. (systematic reviewer), J.J.F.B. (systematic reviewer), S.G. (systematic reviewer), P.C. (systematic reviewer) and P.v.O. (systematic reviewer) were investigators on one or more of the original randomized clinical trials that contributed data to the individual patient data and secured grant funding for these trials. C.L.B. has presented clinical training workshops, some of which include a fee. C.L.B. receives royalties from her books and co-edited books, and she developed PCT on the basis of the cognitive model of A. T. Beck. M.W. founded the Oxford Mindfulness Centre and was its director until 2013. W.K. is its current director. A.S. is founder and clinical director of the Radboud UMC Centre for Mindfulness and H.M. is director of the Centre for Mindfulness, Hong Kong. M.J.H. is affiliated with the Radboud University-based mindfulness center. M.W. and Z.S. receive royalties for books on mindfulness-based cognitive therapy that they have co-authored. M.W., W.K., A.S., H.M. and Z.S. additionally receive payments for training workshops and presentations related to mindfulness-based cognitive therapy. W.K. donates all such fees to the Oxford Mindfulness Foundation, a charitable trust that supports the work of the Oxford Mindfulness Centre, as does A.S. to the Radboud UMC. Z.S. is a member of the scientific advisory board for Mindful Noggin, which is part of NogginLabs, a private company specializing in customized web-based learning. W.K. was an unpaid director of the Mindfulness Network Community Interest Company until 2015. R.J. is a paid consultant to the NIH, NIMH, and UpToDate. She holds stock equity in Amgen, Procter and Gamble, and Johnson and Johnson. J.V. is a paid reviewer for UpToDate. F.H. licenses a cognitive–behavioral relapse prevention manual for depression to Pear Therapeutics. F.J. receives payments for training workshops and presentations related to mindfulness-based cognitive therapy.
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Breedvelt, J.J.F., Karyotaki, E., Warren, F.C. et al. An individual participant data meta-analysis of psychological interventions for preventing depression relapse. Nat. Mental Health 2, 154–163 (2024). https://doi.org/10.1038/s44220-023-00178-x
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DOI: https://doi.org/10.1038/s44220-023-00178-x
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