Introduction
In 2020, an estimated 17% of the U.S. adolescent population had a major depressive episode, with females having a prevalence of 25.2% and adolescents with two or more races having a startling 29.9% prevalence (National Institute of Mental Health, 2022). The prevalence of adolescents diagnosed with a major depressive episode (MDE) has increased significantly from 2005 to 2011, estimating that 1 in 11 reported a MDE (Mojtabai et al., Reference Mojtabai, Olfson and Han2016). Globally, adolescent depression has a high disease burden with 34% of adolescents globally at risk of developing clinical depression and females from the Middle East, Africa and Asia having the highest risk of developing depression (Shorey et al., Reference Shorey, Ng and Wong2022). Adolescent depression has been associated with increased morbidity (GBD 2017 Risk Factor Collaborators, 2018), increased risk of suicide (Maughan et al., Reference Maughan, Collishaw and Stringaris2013), difficulty with school performance (Clayborne et al., Reference Clayborne, Varin and Colman2019), and poor social functioning (Kupferberg et al., Reference Kupferberg, Bicks and Hasler2016). The high disease burden of depression for adolescents is substantial, adversely affecting families and close friends. Half of adolescents who experience a MDE are more likely to have a recurrent episode within five years (Curry et al., Reference Curry, Silva, Rohde, Ginsburg, Kratochvil, Simons and March2011) and struggle with current or long-term employment (Clayborne et al., Reference Clayborne, Varin and Colman2019). Thus, providing effective and appropriate therapy for depressed adolescents (behavioural and medication or a combination) is critical.
Cognitive behavioural therapy (CBT) is a widely accepted and useful intervention for adolescents with depression (Das et al., Reference Das, Salam, Lassi, Khan, Mahmood, Patel and Bhutta2016) and is a recommended psychotherapy intervention for child and adolescent depression within the American Pschological Association (2019) and National Institute for Health and Care Excellence (2019) treatment guidelines. CBT has been used with varying effectiveness for adolescents as described in a 2007 meta-analysis (Klein et al., Reference Klein, Jacobs and Reinecke2007). CBT remains the best psychological intervention for depression compared with interpersonal psychotherapy, for example (Weisz et al., Reference Weisz, Kuppens, Ng, Eckshtain, Ugueto, Vaughn-Coaxum and Fordwood2017). Often CBT is combined with pharmacological intervention to improve treatment outcomes. However, with the increasing risk of suicide as a major side-effect of many medications (Cipriani et al., Reference Cipriani, Zhou, Del Giovane, Hetrick, Qin, Whittington and Xie2016), psychological interventions such as CBT are typically first-line treatment. There are many variations with regard to CBT protocols based on contextual factors such as setting (e.g. school, medical clinic, health department), use of pharmacological therapy, precise population of adolescents (e.g. incarcerated youth, high school vs middle school) and it may or may not include the family. A recent meta-analysis found that individual and group CBT are effective for anxiety disorders among children and adolescents (Sigurvinsdóttir et al., Reference Sigurvinsdóttir, Jensínudóttir, Baldvinsdóttir, Smárason and Skarphedinsson2020). Another meta-analysis concluded that CBT is effective for youth with subclinical depression (Oud et al., Reference Oud, de Winter, Vermeulen-Smit, Bodden, Nauta, Stone and Stikkelbroek2019). However, a gap remains in our current understanding of how CBT directly benefits adolescents with MDE, and whether combining CBT with pharmacological interventions improves treatment outcomes.
Therefore, the aim of this network meta-analysis is to synthesize new evidence in order to quantify the effectiveness of CBT interventions for adolescents with MDE, with the goal of head-to-head comparison and ranking treatment options with a particular focus on pharmacological interventions. This meta-analysis is innovative and differs from prior reviews given its clear focus on adolescents and direct and indirect examination of treatment effects using an advanced statistical modeling (e.g. network meta-analysis). Specifically, we set out to identify if various treatment options (e.g. CBT only, CBT+Medication, or Medication alone) from current clinical trials have the same short- and/or long-term effects on global functioning, and internalizing and externalizing symptoms in adolescents with MDE.
Method
Literature search and study selection
We first searched the Database of Abstracts of Reviews of Effects (DARE) and the Cochrane Database of Systematic Reviews (CDSR) to identify the presence of similar reviews. Then, the databases PubMed, CINAHL, PsycINFO, Scopus, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov and ProQuest Dissertations were chosen as the primary data sources for this review. Similar to prior meta-analyses, we used an exhaustive approach (Cooper, Reference Cooper2010) for the literature search using a variety of databases with multiple variations of search terms related to CBT in adolescent depression. An academic health centre reference librarian helped build a combination of index and MeSH terms, which was used according to the requirements of each database (see Table S1 in Supplementary material). In addition, we performed several snowball searches based on related previous reviews (Sigurvinsdóttir et al., Reference Sigurvinsdóttir, Jensínudóttir, Baldvinsdóttir, Smárason and Skarphedinsson2020; Wang et al., Reference Wang, Whiteside, Sim, Farah, Morrow, Alsawas and Murad2017). We also conducted ancestral searches in retrieved studies and consulted experts in the field for leads on relevant studies.
To be eligible, the study needed to include (i) an RCT design; (ii) a sample of adolescents aged between 9 and 18 years; (iii) a CBT intervention for adolescents; (iv) a comparison group to be CBT+Medication, or Medication only, (v) feature keywords in the title and/or abstract of full-length publications; and (vi) published in English. No restrictions were applied on publication status or date. The last search was updated in July 2022.
We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Moher et al., Reference Moher, Liberati, Tetzlaff and Altman2009) to guide the process of identification, selection and appraisal of the included studies. Each included study was reviewed twice against the study inclusion criteria by two independent reviewers. We developed a structured matrix for abstracting selected studies and building a database including extensive details regarding each research record.
In total, 7043 studies were identified from all databases. Of these, 3605 duplicates were removed, 2982 studies were excluded after title/abstract screening, and 609 studies were removed after full text review due to not meeting inclusion criteria, leaving a total of 83 studies. With the study purpose of comparing: (a) CBT for adolescents (CBT-A) vs CBT-A+Medication, (b) CBT-A vs Medication, and (c) CBT-A+Medication vs Medication, we further excluded studies that included alternative therapy (n=55), attention control (n=17), usual care (n=30) and CBT+placebo (n=1) as the control group. We also excluded another 10 studies that focused on CBT for both adolescents and parents. In case of multiple studies that used the same dataset, we selected the primary study that included the most complete data with the longest follow-up and was usually published in a later year. For example, TADS 2007, not TADS 2004, was selected. Thus, we did not miss important information about long-term follow-up outcomes.
A total of 14 studies were included in the final analyses. Figure S1 in the Supplementary material shows the PRISMA diagram of sample size evolution. These records were also re-reviewed by two independent researchers to ensure they met study inclusion criteria. The final 14 unique studies (with 35 comparison trials) with their descriptive characteristics are presented in Table 1.
Table 1. A summary of characteristics of the 14 studies and their participants
Variables and coding
The effect size analysed in this network meta-analysis was based on Cohen’s d (Cohen, Reference Cohen1988) a standardized mean difference of the outcome between the treatment and comparison groups. Because the research designs in all the 14 included studies (Brent et al., Reference Brent, Emslie, Clarke, Wagner, Asarnow, Keller and Zelazny2008; Byford et al., Reference Byford, Barrett, Roberts, Wilkinson, Dubicka, Kelvin and Goodyer2007; Clarke et al., Reference Clarke, Debar, Lynch, Powell, Gale, O’Connor and Hertert2005; Clarke et al., Reference Clarke, DeBar, Pearson, Dickerson, Lynch, Gullion and Leo2016; Goodyer et al., Reference Goodyer, Dubicka, Wilkinson, Kelvin, Roberts, Byford and Harrington2007; Hilton et al., Reference Hilton, Rengasamy, Mansoor, He, Mayes, Emslie and Brent2013; Jacobs et al., Reference Jacobs, Becker-Weidman, Reinecke, Jordan, Silva, Rohde and March2010; Kennard et al., Reference Kennard, Emslie, Mayes, Nightingale-Teresi, Nakonezny, Hughes and Jarrett2008; Kim et al., Reference Kim, Han, Lee and Renshaw2012; March et al., Reference March, Silva, Petrycki, Curry, Wells, Fairbank and Severe2007; Melvin et al., Reference Melvin, Tonge, King, Heyne, Gordon and Klimkeit2006; Melvin et al., Reference Melvin, Dudley, Gordon, Klimkeit, Gullone, Taffe and Tonge2017; Riggs et al., Reference Riggs, Mikulich-Gilbertson, Davies, Lohman, Klein and Stover2007; Wilkinson and Goodyer, Reference Wilkinson and Goodyer2008) were pre-test–post-test–control group designs, we followed Morris’s alternative approach to obtain an estimate of Cohen’s d using the pooled pre-test standard deviation (Morris, Reference Morris2008). The cognitive behavioural outcome measures in this network meta-analysis were depression, internalizing and externalizing symptoms (Internal & External), and global functioning (Global). Internalizing symptoms refer to problems of withdrawal, somatic complaints and anxiety, while externalizing symptoms exhibit themselves in delinquent and aggressive behaviour (Levesque, Reference Levesque and Levesque2011). Global functioning refers to the level of general functioning for adolescents in all areas (at home, at school, and with peers) (Aas, Reference Aas2011). The lower the outcome measure score, the better the cognitive behaviour. Thus, a negative value of d indicates a greater improvement of the cognitive behavioural outcome from pre-test to post-test in treatment group than that in comparison group. For calculating the effect size, the quantitative information about means and standard deviations was extracted from tables and figures of descriptive statistics reported in the included studies. In case of figures the quantitative information was digitized using the WebPlotDigitizer, version 3.9 (Rohatgi, Reference Rohatgi2015).
Besides the quantitative information for calculating the effect size, we coded several variables related to characteristics of the trials (e.g. year, authors, location), samples (e.g. age, gender, ethnicity, diagnosis), and methods (e.g. design, setting, measures, comparisons, treatment characteristics). Variables were initially coded by two independent reviewers. The coded variables were then cross-reviewed by two independent reviewers to identify and resolve any conflicts. The study team made several review rounds on the final coded sheet and ensured all studies were coded using the same standards. For example, while initial codes included several measures, researchers aggregated them into three major categories (depression, internalizing and externalizing symptoms, and global functioning).
Data analysis
All the extracted data were entered onto a Microsoft Office Excel spreadsheet for analysis in R. The variance of the effect size was estimated using Morris’s formula (Reference Morris2008) as follows:
$${\sigma ^2}\left( d \right) = 2\left( {c_p^2} \right)\left( {1 - \rho } \right)\left( {{{{n_T} + {n_C}} \over {{n_T}{n_C}}}} \right)\left( {{{{n_T} + {n_C} - 2} \over {{n_T} + {n_C} - 4}}} \right)\left( {1 + {{{d^2}} \over {2\left( {1 - \rho } \right)\left( {{{{n_T} + {n_C}} \over {{n_T}{n_C}}}} \right)}}} \right) - {d^2}$$
where ρ is the population correlation between the pre-test and post-test scores. The value of ρ is usually unavailable in the studies, so it was set as 0.45 as suggested by Morris (Reference Morris2008).
Network meta-analyses were conducted for head-to-head comparisons among three treatment comparators: CBT, Medication, and CBT+Medication. Their effects on three outcome measures: Depression, Internal & External, and Global, were presented in forest plots and tested using fixed-effects models or random-effects models if there was a substantial amount of heterogeneity among effect sizes across the studies tested by Higgins’ I 2 (Higgins and Thompson, Reference Higgins and Thompson2002; Higgins et al., Reference Higgins, Thompson, Deeks and Altman2003). According to Borenstein et al. (Reference Borenstein, Hedges, Higgins and Rothstein2009), I 2 =0–40% may suggest low, 30–60% moderate, 50–90% substantial, and 75–100% considerable heterogeneity. In addition to forest plots of head-to-head comparisons among treatment effects, the treatment ranking p-scores were also estimated for measuring the probability that a treatment is better than the competing treatments (Rücker and Schwarzer, Reference Rücker and Schwarzer2015). A treatment with a p-score of 1 is ranked as the best treatment among all the competing treatments, and 0 ranked the worst. All the network meta-analyses within a frequentist framework were conducted using an R package, netmeta (Rücker et al., Reference Rücker, Krahn, König, Efthimiou and Schwarzer2019).
To quantify the overall heterogeneity and inconsistency across the whole network, the DerSimonian–Laird’s τ 2 (Reference DerSimonian and Laird1986), Higgins’ I 2 (2002), and Cochran’s Q total (Reference Cochran1950) were calculated. The Q total can be further decomposed into Q within designs for assessing the heterogeneity between studies with the same design (i.e. the subset of treatments compared in a study) and Q between designs for assessing the design inconsistency. According to Borenstein et al. (Reference Borenstein, Hedges, Higgins and Rothstein2009), τ 2=0.04 indicates low, 0.09 moderate, and 0.16 high heterogeneity; while I 2=0–40% may suggest low, 30–60% moderate, 50–90% substantial, and 75–100% considerable heterogeneity. A statistically significant Q total, Q within designs or Q between designs at the α=0.05 level was also used to indicate heterogeneity and inconsistency. In addition to forest plots of head-to-head comparisons between treatment effects estimated from network meta-analysis, the treatment ranking p-scores (Rücker and Schwarzer, Reference Rücker and Schwarzer2015) were also estimated for measuring the probability that a treatment is better than the competing treatments. A treatment with a p-score of 1 is ranked as the best treatment and 0 is ranked as the worst, and the mean is always 0.5.
Publication bias
The publication bias was tested using funnel plots (Light and Pillemer, Reference Light and Pillemer1984). A symmetric funnel plot with a non-significant Egger test (Egger et al., Reference Egger, Smith, Schneider and Minder1997) suggests no publication bias among the included studies.
Results
The 14 studies with 35 comparison trials involving 2216 unique participants were included in the network meta-analysis. Table 1 shows a summary of the characteristics of the 14 studies and their participants. The mean age of participants in the 14 studies was 15 and 53% of the participants were female. Six studies were non-U.S.-based, and most samples were recruited from clinical settings (93%). Racial and ethnic backgrounds of study samples were not provided in 36% of the studies, and in the remaining nine studies, five included a sample with 25% or greater racial and ethnic minorities. Suicidality was addressed in half of the studies.
Table 2 displays the effect size of each outcome for the 35 trials within the 14 studies. Three pairs of network meta-analyses were conducted, one for each of the three outcomes: Depression, Internal & External, and Global. For each outcome, its short-term effect d 0 (i.e. immediate effect at the end of intervention) and long-term effect d 1 (i.e. lasting effect at the longest follow-up) were analysed separately.
Table 2. The effect sizes of each outcome for the 35 trials within the 14 studies
a d 0, short-term effect size at the end of intervention;
b d 1, long-term effect size at the longest follow-up.
Depression
There were 14 comparison trials within 10 studies (Brent et al., Reference Brent, Emslie, Clarke, Wagner, Asarnow, Keller and Zelazny2008; Clarke et al., Reference Clarke, Debar, Lynch, Powell, Gale, O’Connor and Hertert2005; Goodyer et al., Reference Goodyer, Dubicka, Wilkinson, Kelvin, Roberts, Byford and Harrington2007; Kennard et al., Reference Kennard, Emslie, Mayes, Nightingale-Teresi, Nakonezny, Hughes and Jarrett2008; Kim et al., Reference Kim, Han, Lee and Renshaw2012; March et al., Reference March, Silva, Petrycki, Curry, Wells, Fairbank and Severe2007; Melvin et al., Reference Melvin, Tonge, King, Heyne, Gordon and Klimkeit2006; Melvin et al., Reference Melvin, Dudley, Gordon, Klimkeit, Gullone, Taffe and Tonge2017; Riggs et al., Reference Riggs, Mikulich-Gilbertson, Davies, Lohman, Klein and Stover2007; Wilkinson and Goodyer, Reference Wilkinson and Goodyer2008) regarding treatment effect on depression. Ten of the 14 trials also had long-term follow-up ranging from 1 to 12 months. Supplementary Figure S2 displays two networks of direct comparison trials – one for short-term effect the other for long-term effect.
Short-term effect
Figure 1A displays the forest plot of the short-term effects of treatments on depression. The estimated treatment effects were based on a random-effects model because there was substantial overall heterogeneity among the effect sizes across the 14 studies (τ 2=0.18; I 2= 81.9%; Q total=55.10, d.f.=10, p<.001). There was also significant heterogeneity within designs (Q within designs=46.03, d.f.=8, p<.001) and inconsistency between designs (Q between designs=9.07, d.f.=2, p=.01). The forest plot shows that there was no significant difference between any two treatments because all the 95%CIs covered zero although the treatment ranking p-scores indicated that CBT+Medication had the highest p-score (0.97) followed by Medication (0.34) and CBT (0.18). From the symmetrical funnel plot with a non-significant Egger test (p=.56) (Supplementary material Fig. S3a), we found no indication of publication bias among the 14 short-term trials on depression.
Figure 1. Forest plots of treatment effects on depression. MD, standardized mean difference (d). A, short-term effect (end of intervention); B, long-term effect (longest follow-up).
Long-term effect
Figure 1B displays the forest plot of the long-term effects of treatments on depression. The estimated treatment effects were based on a random-effects model because there were substantial overall heterogeneity among the effect sizes across the 10 studies (τ 2=0.12; I 2=73.6%; Q total=22.77, d.f.=6, p<.001). There was also significant heterogeneity within designs (Q within designs=17.77, d.f.=4, p<.001) and marginal inconsistency between designs (Q between designs=4.99, d.f.=2, p=.08). The forest plot shows that there is no significant difference between any two treatments because all the 95% CIs covered zero although the treatment ranking p-scores indicated that CBT+Medication had the highest p-score (0.88) followed by CBT (0.42) and then Medication only (0.20). From the symmetrical funnel plot with a non-significant Egger test (p=.69) (Supplementary Fig. S3b), we found no indication of publication bias among the 10 long-term trials on depression.
Internalizing and externalizing symptoms
There were 10 comparison trials within eight studies (Brent et al., Reference Brent, Emslie, Clarke, Wagner, Asarnow, Keller and Zelazny2008; Byford et al., Reference Byford, Barrett, Roberts, Wilkinson, Dubicka, Kelvin and Goodyer2007; Clarke et al., Reference Clarke, Debar, Lynch, Powell, Gale, O’Connor and Hertert2005; Clarke et al., Reference Clarke, DeBar, Pearson, Dickerson, Lynch, Gullion and Leo2016; Hilton et al., Reference Hilton, Rengasamy, Mansoor, He, Mayes, Emslie and Brent2013; Kim et al., Reference Kim, Han, Lee and Renshaw2012; Melvin et al., Reference Melvin, Tonge, King, Heyne, Gordon and Klimkeit2006; Melvin et al., Reference Melvin, Dudley, Gordon, Klimkeit, Gullone, Taffe and Tonge2017) about treatment effect on internalizing and externalizing symptoms. Eight of the 10 trials also had long-term follow-up ranging from 1 to 23 months. Supplementary Fig. S4 displays two networks of direct comparison trials. One network is for short-term effect at the end of intervention and the other network is for long-term effect at the longest follow-up.
Short-term effect
Figure 2A displays the forest plot of the short-term effects of treatments on internalizing and externalizing symptoms. The estimated treatment effects were based on a fixed-effects model because there was no overall heterogeneity among the effect sizes across the 10 studies (τ 2=0; I 2=0%; Q total=4.47, d.f.=7, p=.72). There was also no heterogeneity within designs (Q within designs=4.33, d.f.=5, p=.50) or inconsistency between designs (Q between designs=0.14, d.f.=2, p=.93). The forest plot shows that there was no significant difference between any two treatments because all the 95% CIs covered zero. Although the treatment ranking p-scores indicated that CBT had a slightly higher p-score (0.68) than CBT+Medication (0.45) and Medication only (0.37), they all cluster around the p-score mean of 0.5 suggesting similar efficacy (Rücker and Schwarzer, Reference Rücker and Schwarzer2015). From the symmetrical funnel plot with a non-significant Egger test (p=.18) (Supplementary Fig. S5a), we found no indication of publication bias among the 10 short-term trials on internalizing and externalizing symptoms.
Figure 2. Forest plots of treatment effects on internalizing and externalizing symptoms. MD, standardized mean difference (d). A, short-term effect (end of intervention); B, long-term effect (longest follow-up).
Long-term effect
Figure 2B displays the forest plot of the long-term effects of treatments on internalizing and externalizing symptoms. The estimated treatment effects were based on a fixed-effects model because there was no overall heterogeneity among the effect sizes across the eight studies (τ 2=0.002; I 2=6.4%; Q total=5.34, d.f.=5, p=.38). There was also no heterogeneity within designs (Q within designs=5.01, d.f.=3, p=.17) or inconsistency between designs (Q between designs=0.34, d.f.=2, p=.85). The forest plot shows that there was no significant difference between any two treatments because all the 95% CIs covered zero, and the treatment ranking p-scores close to the mean of 0.5 also indicated similar efficacy: CBT+Medication (0.62), Medication (0.54), and CBT (0.34). From the symmetrical funnel plot (Supplementary Fig. S5b), we found no indication of publication bias among the eight long-term trials on internalizing and externalizing symptoms. The Egger test was unavailable due to the insufficient number of trials.
Global functioning
There were 11 comparison trials within seven studies (Byford et al., Reference Byford, Barrett, Roberts, Wilkinson, Dubicka, Kelvin and Goodyer2007; Clarke et al., Reference Clarke, Debar, Lynch, Powell, Gale, O’Connor and Hertert2005; Goodyer et al., Reference Goodyer, Dubicka, Wilkinson, Kelvin, Roberts, Byford and Harrington2007; Jacobs et al., Reference Jacobs, Becker-Weidman, Reinecke, Jordan, Silva, Rohde and March2010; Kim et al., Reference Kim, Han, Lee and Renshaw2012; Melvin et al., Reference Melvin, Tonge, King, Heyne, Gordon and Klimkeit2006; Melvin et al., Reference Melvin, Dudley, Gordon, Klimkeit, Gullone, Taffe and Tonge2017) about treatment effect on global functioning. Seven of the 11 trials also had long-term follow-up ranging from 1 to 12 months. Supplementary Fig. S6 displays two networks of direct comparison trials. One network is for short-term effect at the end of intervention and the other network is for long-term effect at the longest follow-up.
Short-term effect
Figure 3A displays the forest plot of the short-term effects of treatments on global functioning. The estimated treatment effects were based on a random-effects model because there was moderate overall heterogeneity among the effect sizes across the 11 studies (τ 2=0.04, I 2= 50.5%; Q total=14.13, d.f.=7, p=.05). There was also heterogeneity within designs (Q within designs=11.85, d.f.=5, p=.04) although no inconsistency between designs (Q between designs=2.28, d.f.=2, p=.32). The forest plot shows that there was no significant difference between any two treatments because all the 95% CIs covered zero although the treatment ranking p-scores indicated that CBT+Medication (0.65) and Medication (0.65) had higher p-scores than CBT (0.20). From the symmetrical funnel plot with a non-significant Egger test (p=.91) (Supplementary Fig. S7a), we found no indication of publication bias among the 11 short-term trials on global functioning.
Figure 3. Forest plots of treatment effects on global functioning. MD, standardized mean difference (d). A, short-term effect (end of intervention); B, long-term effect (longest follow-up).
Long-term effect
Figure 3B displays the forest plot of the long-term effects of treatments on global functioning. The estimated treatment effects were based on a random-effects model because there was moderate overall heterogeneity among the effect sizes across the seven studies (τ 2=0.11; I 2=69.4%; Q total=13.08, d.f.=4, p=.01). There was also heterogeneity within designs (Q within designs=8.76, d.f.=2, p=.01) although no inconsistency between designs (Q between designs=4.33, d.f.=2, p=.11). The forest plot shows that CBT had a significantly more long-term effect on global functioning than both CBT+Medication (d=–0.89, 95% CI=[–1.51, –0.27]) and Medication (d=–0.94, 95% CI=[–1.61, –0.28]) although there was no significant difference between CBT+Medication and Medication (d=–0.06, 95% CI=[–0.45, 0.33]). Such findings echoed the treatment ranking p-scores which indicated that CBT (1.00) had a much higher p-score than CBT+Medication (0.31) and Medication only (0.19). From the symmetrical funnel plot (Supplementary Fig. S7b), we found no indication of publication bias among the seven trials on global functioning. The Egger test was unavailable due to the insufficient number of trials.
Discussion
The prevalence of adolescent depression is rising (Mojtabai et al., Reference Mojtabai, Olfson and Han2016) and its impact on adolescents’ school performance, social functioning, suicide risk, as well as adulthood psychosocial and health outcomes (Clayborne et al., Reference Clayborne, Varin and Colman2019; Keenan-Miller et al., Reference Keenan-Miller, Hammen and Brennan2007) are far reaching. Therefore, we conducted this systematic review and meta-analysis to evaluate and compare the effectiveness of CBT only, CBT+Medication, or Medication only in reducing duration and frequency of MDE in the adolescent population. This meta-analysis indicates there is no significant difference between any two treatments (CBT only, CBT+Medication, or Medication) for MDE, internalizing and externalizing symptoms in the short- or long-term effects. It is noteworthy that the CBT+Medication p-score (measure of certainty that one treatment is better than the other) for depression short-term effects and long-term effects was 50% larger than the p-scores for CBT or Medication alone. For global functioning, CBT had significantly longer lasting effect at the longest follow-up than CBT+Medication or Medication alone. These findings suggest the importance of CBT in improving global functioning but raises questions about the role of CBT over medication for treating adolescent depression, internalizing, and externalizing symptoms.
Overall, the results were clear. There were no difference in effectiveness of the three comparators for improving internalizing and externalizing symptoms, both in the short and long term. The absence of heterogeneity in effect sizes and quite similar p-scores further strengthens this observation that CBT alone, medication alone, or combination therapy all have similar effectiveness for internalizing and externalizing symptoms. However, there is more uncertainty in the results for MDE, with considerable heterogeneity in effects sizes and substantial differences in the p-scores for combination therapy (CBT+Medication) vs either alone. The uncertainty in our findings is consistent with the literature which shows that response rate for depression in youths treated with psychotherapy is 39% compared with 24% for control groups (Cuijpers et al., Reference Cuijpers, Karyotaki, Ciharova, Miguel, Noma, Stikkelbroek and Furukawa2021), indicating a relatively low response rate. Pharmacological treatment of depression in adolescents is also challenging. A recent meta-analysis examined comparative efficacy of anti-depressants in youth and found that most were not significantly better than placebo (Cipriani et al., Reference Cipriani, Zhou, Del Giovane, Hetrick, Qin, Whittington and Xie2016), which could re-ignite conversation about the most appropriate first-line treatment for adolescent depression. Our findings suggest that there is room for both modalities in the treatment of adolescent depression, and a need for other therapeutic techniques. Also, a focus on longer term outcomes, such as functioning, may be an important consideration for measuring treatment outcomes.
The results indicate that CBT may not be better in the short term, but there are long-term benefits of CBT over medication for overall functioning. This is somewhat consistent with what could be expected, as CBT focuses on teaching techniques to challenge cognitive distortions (e.g. catastrophizing) and enhance behavioural adaptation (e.g. identifying and coping with difficult emotions) that the adolescent can use in the present and carry forward into the future. As a result, after depressive symptoms, measures of global functioning are the most frequently measured in clinical trials because of their importance in predicting future outcomes. In addition, the growing focus on patient-centred research indicates a need for more attention to general wellbeing outcomes beyond the disease pathology (Krause et al., Reference Krause, Bear, Edbrooke-Childs and Wolpert2019). Data from 80 youth seeking care in the UK National Health Service identified that the types of goals that young people set when in therapy are often related to functioning such as being able to attend social events and to improve school performance (Bradley et al., Reference Bradley, Murphy, Fugard, Nolas and Law2013). In addition, improving global functioning in adolescents with depression has meaningful implications for meeting future developmental milestones (e.g. establishing career and independent living) and possibly future quality of life (Peters et al., Reference Peters, Jacobs, Feldhaus, Henry, Albano, Langenecker and Curry2016). Follow-up with 196 adolescents in the TADS study (March et al., Reference March, Silva, Petrycki, Curry, Wells, Fairbank and Severe2007), found that randomization to any of the three clinical arms (CBT, CBT+Medication, or Medication alone) was associated with improved functioning and success with developmental targets four years later (Peters et al., Reference Peters, Jacobs, Feldhaus, Henry, Albano, Langenecker and Curry2016). However, most meta-analytic work has necessarily focused on symptom improvement and not functioning. Our findings add insight into the benefits of CBT for both the short and longer term, and the broader, whole-person perspective.
The long-term effects of CBT for global functioning suggest that pediatric healthcare providers should establish strong connections with youth-focused psychotherapy services in their area, and consider collaborative models of care that integrate psychotherapy into the primary care setting. Telehealth services could be advantageous for reaching communities with reduced access to traditional mental health services, but research is needed to identify potential barriers (e.g. internet access, mistrust, establishing a therapeutic relationship). Additionally, evidence on models of care that will bring services to adolescents such as mental health models in school are needed. While CBT is the most studied psychotherapy modality, other therapies focused on interpersonal therapy and behavioural activation are promising. CBT that includes components of behavioural activation is associated with better outcomes than CBT alone (Oud et al., Reference Oud, de Winter, Vermeulen-Smit, Bodden, Nauta, Stone and Stikkelbroek2019) and behavioural activation interventions are more economical to deliver than CBT (Richards et al., Reference Richards, Ekers, McMillan, Taylor, Byford, Warren and Finning2016). Future research could explore innovative delivery models such as telehealth which may increase access to psychotherapy, and the delivery of behavioural activation interventions using community health workers. Lastly, there remains a need to determine the effectiveness of other psychotherapies compared with medication and combination therapy.
Limitations
Several limitations of this study should be noted. First, we recognize that family-based CBT (parent and adolescent) may be meaningfully different from CBT focused only on adolescents. We originally included evaluation of this additional approach as a study aim. However, due to the lack of needed comparisons to run the analysis, we decided to exclude the studies that focused on CBT for both adolescents and parents. There is a need for future research to examine the benefits and differences in outcomes based on a family-based CBT approach. Similarly, we did not compare other psychotherapies such as interpersonal therapy or behavioural activation. As CBT has the largest literature base, we made the decision to focus on this modality. Second, a lack of recent publications on this topic makes it difficult to assess if depression experiences may differ for the contemporary cohort of adolescents. Third, we were unable to further examine the important contextual factor of suicidality due to the lack of consistent reporting. Incomplete reporting on suicidality could reflect that the publications analysed within this study had a lower burden of symptoms. Fourth, due to the scope of this review study, we needed to exclude some studies with other control treatments (e.g. attention control, usual care, placebo, etc.). This could introduce potential selection bias and might limit the interpretation of our findings. Lastly, there were substantial heterogeneity and inconsistency in studies on depression and global functioning; thus, further investigation is warranted to explain this. One such further investigation would be meta-regression in multivariate meta-analysis with a larger number of studies.
Conclusions
Adolescent depression is on the rise and innovative efficacious treatment models are critically needed. Our findings emphasize the important role of CBT for improving global functioning, but we did not find CBT to be superior to medication for MDE and other outcomes. Improving the overall functioning of adolescents with depression supports their ability to meet future developmental milestones. More research is needed to develop or refine other psychotherapeutic techniques for adolescents. Ideally, newer treatment modalities would translate to community and clinical settings easily to support equitable access for all adolescents with depression.
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/S1352465822000662
Data availability statement
The data that support the findings of this study are available from the corresponding author (L.D.) upon reasonable request.
Acknowledgements
None.
Author contributions
Latefa Dardas: Conceptualization (lead), Data curation (equal), Formal analysis (equal), Methodology (equal); Hanzhang Xu: Conceptualization (supporting), Formal analysis (equal), Methodology (equal); Michelle Franklin: Conceptualization (supporting), Data curation (equal), Formal analysis (supporting), Methodology (equal); Jewel Scott: Conceptualization (equal), Data curation (equal), Formal analysis (supporting), Methodology (equal); Ashlee Vance: Conceptualization (supporting), Data curation (equal), Methodology (equal); Brittney van de Water: Conceptualization (equal), Data curation (equal), Formal analysis (supporting), Methodology (equal); Wei Pan: Conceptualization (equal), Formal analysis (lead), Investigation (equal), Methodology (equal).
Dr Dardas and Dr Pan conceptualized and designed the study, carried out the analyses, and reviewed and revised the manuscript. Dr Xu collected data, carried out the initial analysis, and reviewed and revised the manuscript. Dr Franklin and Dr Scott collected data, drafted the initial manuscript, and reviewed and revised the manuscript. Dr Vance and Dr van de Water collected data and reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
Financial support
None.
Conflicts of interest
The authors have no conflicts of interest to disclose.
Ethical standards
Authors have abided by the Ethical Principles of Psychologists and Code of Conduct as set out by the BABCP and BPS. An IRB review and exemption was obtained from the University of Jordan School of Nursing.
Comments
No Comments have been published for this article.