Resilience-oriented cognitive behavioral interventions for depressive symptoms in children and adolescents: A meta-analytic review

Journal of Affective Disorders 270 (2020) 150–164

Contents lists available at ScienceDirect

Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Review article

Resilience-oriented cognitive behavioral interventions for depressive symptoms in children and adolescents: A meta-analytic review

T

Liang Maa,1, , Yingnan Zhanga,1, Cong Huangb,c, Zeshi Cuid ⁎

a

The First Hospital of China Medical University, Shenyang, China Department of Sports and Exercise Science, Zhejiang University, Hangzhou, China c Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, Sendai, Japan d School of Pharmacy, China Medical University, Shenyang, China b

ARTICLE INFO

ABSTRACT

Keywords: Resilience Cognitive behavioral Depressive symptoms Controlled trial Meta-analysis

Objective: The study aimed to evaluate whether resilience-oriented cognitive behavioral interventions (CBIs) which teach cognitive, problem-solving, and social skills are effective for addressing depressive symptoms in the school setting and to investigate factors that could moderate the intervention effects. Method: Electronic databases Medline, PsycINFO and Cochrane Central were searched to identify potentially relevant trials. The difference of change from baseline in depressive symptoms between intervention and control condition was assessed. Mean effect sizes (Hedges’g) were calculated using random-effects models. Study-specific characteristics relevant to participant demographics (age, gender, and risk status), intervention conditions (program type, intervention duration, group leader type, and use of homework), and study features (sample size, and methodological quality) were evaluated as potential moderators of the effect size. Results: 38 controlled studies were identified, including 24,135 individuals. At post-intervention, the mean effect size was considered significantly small (Hedges’g = 0.13) and subgroup analyses revealed significant effect sizes for programs administered to both universal and targeted samples, programs both with and without homework, and programs led by school personnel. The mean effect size was largely maintained at 6 months follow-up and subgroup analyses indicated significant effect sizes for programs administered to targeted samples, programs based on Penn Resiliency Program, programs with homework, and programs led by professional interventionists. Conclusion: This study reinforces the efficacy of resilience-oriented CBIs for addressing depressive symptoms in the school setting. Although more research is needed to confirm and extend the findings of this study, our findings suggest a range of directions in particular for further investigation.

1. Introduction

importance to alleviate the enormous burden of depression (KleineBudde et al., 2013; Luppa et al., 2007). Depression, often characterized by persistent sadness, loss of interest and inability to carry out daily activities, results from a complex interaction of social, psychological and biological factors (Krishnan, 2002; Mazure, 2010; Smith et al., 2009). Individuals who have experienced adverse life events are more likely to have elevated levels of depressive symptoms and develop depression (Alexopoulos, 2005; Hovens et al., 2015; Jefferis et al., 2011). Previous studies have suggested that resilience may act as a protective mechanism during times of adversity against the development of depression (Southwick and Charney, 2012; Southwick et al., 2005; Waugh and Koster, 2015). Resilience refers to the ability to employ a collection of

Depression is the leading cause of disability worldwide (Hawton et al., 2013; Moussavi et al., 2007), affecting 2%−8% of children and adolescents (Hazell, 2011). 20%−24% of youth will have experienced major depressive episodes by the age of 18 (Lewinsohn et al., 1998), rendering children and adolescents at a prominently higher risk for the etiology of depression than young adults (Hankin et al., 1998). Elevated but sub-clinical levels of depressive symptoms are also common in children and adolescents (Roberts et al., 1991), resulting in substantial impairment as well as increased risk for clinical depression (Gotlib et al., 1995). Effective prevention of depressive symptoms targeting children and adolescents is thus of great Corresponding author. E-mail address: [email protected] (L. Ma). 1 The authors contributed to this work equally ⁎

https://doi.org/10.1016/j.jad.2020.03.051 Received 13 January 2019; Received in revised form 7 January 2020; Accepted 20 March 2020 Available online 25 March 2020 0165-0327/ © 2020 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

multiple internal (personal characteristics or strengths) and external (qualities of wider family, social, and community environments) protective factors (assets and resources) that enable an individual to thrive in the face of adversity (Connor and Davidson, 2010). Previous empirical studies have provided consistent evidence in support of higher levels of resilience to be associated with lower levels of depressive symptoms in children and adolescents (Hjemdal et al., 2007; Wingo et al., 2010; Wu et al., 2017). In other studies, resilience protective factors have been found to be significant moderators between adversity and depression (Campbell-Sills et al., 2006; Ding et al., 2017; Jaureguizar et al., 2018). Given the potential impact of resilience on depression, researchers and mental health professionals have responded to this by developing and evaluating resilience-oriented depression prevention programs. Although the framework of resilience emphasizes the assets and resources both at personal (internal) and suprapersonal (external) levels (Yates and Masten, 2004), most depression prevention programs implemented to date have been cognitive behavioral interventions (CBIs), which largely focus on promoting personal resilience assets and resources such as positive cognitive styles. However, CBIs for depression have varied in important manners. Some were focused closely on Beck's approach to cognitive therapy which teaches cognitive restructuring and commonly includes a module on behavioral activation (Beck, 1976), whereas others have employed a broader definition, considering problem solving, behavioral activation, or inerpersonal/ social skills training to be part of a larger family of CBIs (Cuijpers et al., 2013). Based on the content of extant depression prevention programs, Stice et al. (2009) identified interventions as focusing on: (a) reducing negative cognitions (cognitive restructuring); (b) encouraging engagement in pleasant activities (behavioral activation); (c) promoting problem-solving skills (problem solving); and (d) promoting social skill development (social skills). Although each of these content areas has been considered useful to strengthen the capacity of resilience, only a portion of CBIs have combined these content areas. Whilst variation exists in the operationalization of resilience, it is generally accepted that the nature of resilience is multifactorial (Dray et al., 2015), suggesting the importance of an integrative approach in resilience-oriented CBIs. For this meta-analytic review, we distinguish a subtype of CBIs in which cognitive restructuring (with or without a module on behavioral activation) is an important content, and in which 2 other contents (problem solving, and social skills training) also have a prominent place. We restrict our focus to these CBIs because the nature of resilience is multifactorial and different content areas should relate more closely to disparate resilience protective factors. One example of this approach is the Penn Resiliency Program (PRP), which incorporates cognitive and social problem-solving strategies designed to prevent depression for youth in late childhood and early adolescence (Reivich et al., 2013). Although earlier meta-analytic reviews have in general provided support for a modest beneficial effect of depression prevention programs targeting children and adolescents (Horowitz and Garber., 2006; Stice et al., 2009; Merry et al., 2012; Hetrick et al., 2016), a latest metaanalytic review showed conflicting results (Caldwell et al., 2019). Evidence from some of these meta-analyses limited to CBIs was also mixed, with a great deal of heterogeneity in findings (Hetrick et al., 2016; Caldwell et al., 2019). One plausible source of such inconsistency and heterogeneity may be attributable to the broad definition of CBIs. For example, some interventions might only include one of the cognitive behavioral techniques in isolation (e.g., only cognitive restructuring, behavioral activation, problem solving, or social skills training), but was still included as being CBIs. This inclusive approach makes it difficult to give an overall appraisal of the resilience-oriented CBIs, which as defined earlier teach combined cognitive, problem-solving, and social skills. Restricting inclusion as we have done in this meta-analysis ensures greater homogeneity, therefore enabling us to explore the impact of this specific subtype of CBIs on depressive

symptoms. Two earlier meta-analytic reviews have focused exclusively on PRP (Bastounis et al., 2016; Brunwasser et al., 2009), but alternative resilience-oriented CBIs were not assessed. Given arising concerns on the attenuating effects of PRP under real-world conditions (Bastounis et al., 2016), it is evident to include and investigate alternative resilience-oriented CBIs that could be more effective and replicated for larger-scale roll-out. A comprehensive review and metaanalysis of resilience-oriented CBIs would help determine the efficacy for this specific subtype of CBIs. Furthermore, it would be worthwhile to explore factors that predict magnitude of resilience-oriented CBIs. Such information is important as this could help increase the yield of future intervention efforts by identifying specific contexts for which the largest intervention effects would occur. Although associations between potential moderators and intervention effects have been assessed for depression prevention programs in previous meta-analyses (Horowitz and Garber, 2006; Sockol, 2015; Stice et al., 2009), it would be of value to investigate whether such moderation effects are real specific to resilience-oriented CBIs. Plausible sources of these moderators result from participant demographics (e.g., age, gender, and risk status), intervention conditions (e.g., program type, intervention duration, group leader type, and use of homework), and study features (e.g., sample size, and methodological quality). Identification of moderators relevant to participant demographics would help determine for which population groups these interventions could be more effective. The effect of age, gender, and risk status was examined because youth of different ages, genders, and risk status may not respond equally to the interventions, possibly due to the diverse cognitive ability, inherent gender difference, and different motivation to engage in the prevention program content. Larger effects may emerge for older adolescents as they have better cognitive ability, female adolescents as they have greater depressive symptoms and higher rates of major depression which would make it easier to demonstrate prevention effects, and high-risk adolescents as they are more motivated to engage in the prevention program content (Stice et al., 2009). Likewise, examination of moderators relevant to intervention conditions would help determine in what intervention conditions these programs could produce stronger effects. As such, we examined four variables that could influence the effects of the interventions: intervention duration (including number of sessions, and total intervention time), use of homework, program type, and group leader type. It is possible that prevention programs would be more effective if they have more sessions, include longer time, and employ homework exercise, given that greater exposure to prevention program content may produce a larger opportunity for symptom reduction. It is also possible that prevention programs are more effective when delivered by dedicated interventionists versus classroom teachers. Compared with professional interventionists, classroom teachers typically receive less training and supervision due to competing demand for classroom responsibilities (Dane and Schneider, 1998). Meanwhile, despite the similarity in promoting protective factors, resilience-oriented CBIs take many forms and variation between specific programs such as PRP and RAP (Resourceful Adolescent Program; Shochet and Osgarby, 1999) could result in different effects. Furthermore, previous evidence suggests that intervention effects may be biased by larger effects reported by studies with small sample size and suboptimal methodological quality (Cuijpers et al., 2010; Sterne et al., 2000; Thornton and Lee, 2000). It would be helpful to assess whether the intervention effects are influenced by such study features. In summary, the primary purpose of this meta-analysis was to aggregate data of 38 controlled trials to determine whether resilienceoriented CBIs (which teach cognitive, problem-solving, and social skills) are effective for prevention of depressive symptoms in the school setting. We hypothesized that children and adolescents who participated in resilience-oriented CBIs would report lower levels of depressive symptoms as compared to those who did not. The secondary 151

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

purpose of this study was to assess whether proposed study characteristics would moderate the intervention effects.

number of sessions times the length of each session) included by a given program. Program type, group leader type, and homework use were coded as categorical variables. Program type was coded as PRP, RAP and others. Homework use was coded as yes if any task was assigned for practice at home. Group leader type was coded as professional interventionists if they were clinical psychologists, psychology graduate students, or advanced research assistants, otherwise were coded as school personnel (typically classroom teachers). Characteristics of study features included sample size, and methodological quality. Sample size was coded as a continuous variable, representing the number of total participants. Methodological quality was coded as a dichotomous variable (higher versus lower), representing whether the study was categorized as higher quality or lower quality following our main criteria (see the section: Risk of bias assessment). Data extraction was undertaken by the first (LM) and second author (YZ) independently. Disagreements were resolved by discussion and consensus.

2. Method This study was conducted in adherence to the PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions (Moher et al., 2009). 2.1. Data sources and search strategy Studies that assessed the efficacy of resilience-oriented CBIs aimed at preventing depressive symptoms in the school setting were identified through online searches. Electronic databases Medline, PsycINFO and Cochrane Central were initially searched from their inception until November 1 2017, and updated up to June 1 2019. Search strategy used in this meta-analysis is presented in the Appendix. Reference lists from relevant reviews and retrieved studies were reviewed and searched to identify additional publications.

2.4. Risk of bias assessment Risk of bias was rated by two reviewers (LM and CH) independently according to the recommendation of Cochrane Collaboration's Tool (Higgins et al., 2011). Discrepancies were resolved through discussion and consensus, and if necessary, the third reviewer (ZC) was consulted. As it is rarely possible for the participants to be blinded to the intervention conditions, we left out the assessment of performance bias (blinding of participants). Potential risk of bias was categorized into four domains as follows: whether the sequence was randomly generated (e.g., random computer generator was used), whether the allocation process was concealed (e.g., concealed envelope was used), whether the outcome was assessed by blinded assessors (e.g., the assessors were unaware of the intervention conditions), and whether incomplete outcome data were handled (e.g., intent-to-treat analysis was used). As a result, studies that met at least three of all criteria were categorized as higher quality, otherwise categorized as lower quality.

2.2. Eligibility criteria Studies were eligible for inclusion in this meta-analytic review if: a) The study targeted children and adolescents as participants. b) The study assessed an intervention of resilience-oriented CBIs in the school setting. We focused exclusively on a subtype of CBIs, which combine at least three core content areas: cognitive restructuring (with or without a module on behavioral activation), problem solving, and social skills training. Under this criterion, CBIs with limited content areas (e.g., cognitive restructuring only), interpersonal therapy, and third-wave cognitive behavioral therapy (e.g., mindfulness-based interventions), which might be promising in addressing depressive symptoms for students, were not included in the scope of this review. c) The study compared the intervention condition with a no-intervention, assessment-only, waiting list, attention control, or placebo control condition. To minimize the difficulty in understanding, head-to-head trials that compared the intervention with an active control condition such as psychological intervention, or physical exercise were excluded from this review. d) The study evaluated depression as a primary outcome and used a validated measure for the assessment of depressive symptoms. Programs that were primarily developed to target overall psychological well-being or other mental health issues (e.g., anxiety, or posttraumatic stress disorders) were excluded, such as FRIENDS (Barrett and Turner, 2011), REACH for RESILIENCE (Dadds and Roth, 2008), and ERASE-Stress (Gelkopf and Berger, 2009). e) The study was a randomized or quasi-randomized controlled trial.

2.5. Statistical analyses Prevention effects were defined as the difference in mean change from baseline on depressive symptoms between the intervention and control conditions. For each study, effect size (Hedges’g) was calculated by dividing the difference in the mean change from baseline between intervention and control conditions by the pooled standard deviation at baseline, corrected by Hedges’ formula: Mi,change Mc,change g=( ) × C, where the pooled standard deviation at SD P, baseline

baseline is defined as: SDp, baseline =

(ni

1) ×SD2i, baseline + (nc 1) ×SD2c, baseline ni + nc 2 3 = 1 4 × (n + n 2) 1 . i c

,

and the correction factor is defined as: C When studies employed more than one instrument to measure the same outcome variable, we computed an average effect size estimate across the different instruments. Several studies (Chaplin et al., 2006; Gillham et al., 2007; Possel et al., 2004, 2011) presented the results in the form of multiple subgroups (e.g., initially high-symptom versus lowsymptom children). For these studies, following the example of other meta-analyses (Horowitz and Garber, 2006; Sandra Jo et al., 2003), means and standard deviations were combined for the overall sample using the following formulas:

2.3. Data extraction An abstraction form was piloted and used to extract data relevant to participant demographics, intervention conditions and study features from the included studies. Characteristics of participant demographics included age, gender, and risk status. Age and gender were coded as continuous variables, representing the mean age of the sample and female percentage of the sample. Risk status was coded as a dichotomous variable (universal versus targeted), representing whether the study participants were included universally (e.g., all students from an intact setting) or on the basis of individual risk (e.g., identified students who were endorsing a known risk factor for the development of depression or having elevated depressive symptoms). Characteristics of intervention conditions included intervention duration, program type, group leader type, and homework use. Intervention duration was coded as a continuous variable, representing the total intervention time (the

Mcombined =

n1 × M1 + n2 × M2 , n1 + n2

(n1 SDcombined =

1) × SD12 + (n2

× (M12 + M22

1) × SD22 +

2 × M1 × M2) n1 + n2 1

n1 × n2 n1 + n2

.

A few other studies (Gillham et al., 2012; Kirsten et al., 2014; 152

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

Shochet et al., 2001) compared two variations of an intervention with the same control condition. For these studies, we pooled the means and standard deviations of the different intervention conditions in order to calculate one effect size. Three studies (Pattison and Lynd-Stevenson; Possel et al., 2013; Stallard et al., 2012) compared one intervention condition with multiple control conditions such as an attention control condition and a no-intervention control condition. In these cases, the means and standard deviations of the different control conditions were pooled before being compared with the intervention condition. A positive effect size represented greater reductions in depressive symptoms for the intervention condition as compared to the control condition. Effect sizes of 0.8, 0.5, and 0.2 were assumed to be large, moderate, and small (Cohen, 1977). To evaluate the pooled effect size, random-effects model and inverse variance weighted method were used, as the assumption of fixed-effects model was likely to be violated because of the non-negligible heterogeneity across the studies (Dersimonian and Laird, 1986). Heterogeneity was assessed through I2 statistic that was distinguished as low, moderate, and substantial with values of 25%, 50%, and 75% (Higgins et al., 2003). Influence analysis was conducted to evaluate the impact of individual study on the pooled effect size. In the influence analysis, pooled effect size was recalculated on exclusion of certain given study. Subgroup analyses were conducted on categorical characteristics and analysis of variance (ANOVA) was applied to test the difference between stratified subgroups. Meta-regression analyses were performed on continuous characteristics to assess whether they were associated with the effect size (Thompson and Sharp, 1999). Publication bias was assessed through visual inspection of funnel plots and Duval and Tweedie's trim-and-fill procedure (Duval and Tweedie, 2000). All the analyses were performed with Stata release 12 (StataCorp) and p ≤ 0.05 was considered statistically significant.

3.2. Characteristics of included studies

2.6. Power analysis

Major findings for the effect on depressive symptoms of the included trials are summarized in Table 1. At post-intervention, 22 of the included trials (58%) found significant reductions in depressive symptoms for the intervention condition relative to the control condition (either in the overall sample or a subsample). 16 trials (42%) found no significant evidence in favor of the intervention condition to control condition. The majority (n = 34) of included trials provided data on follow-up assessments. Eleven studies provided evidence that the intervention effects were maintained at 6 months follow-up. For studies with longer follow-ups, there were few indications of a difference between the intervention and control condition.

Descriptive information of included studies is presented in Table 1 and study-specific characteristics are presented in Table 2. In total, the trials included 24,135 participants with sample size ranging from 29 to 5633. The mean age of participants ranged from 8.8 to 17.6 years with an average of 13.2 years. The average proportion of female participants was 57%. Of the 38 trials, 23 were universal interventions and 15 were targeted interventions. The most commonly used depression measure was Child Depression Inventory (CDI; n = 22), followed by Reynolds Adolescent Depression Scale (RADS; n = 6), Center of Epidemiology Studies-Depression (CES-D; n = 5), and Beck Depression Inventory (BDI; n = 4). Homework was used in almost half of the studies (n = 18). Over half of the programs were led by professional interventionists (n = 21). The number of sessions ranged from 6 to 20 with an average of 11 sessions. The length of each session lasts 30 min 120 min with an average of one hour. PRP was used in 14 studies, RAP was used in 7 studies, and 17 studies used other resilience-oriented CBIs. 3.3. Risk of bias assessment Results of the risk of bias assessment are presented in Table 3. Of the 38 trials, only 11 (29%) met at least three of the four main criteria and were categorized as higher-quality studies. 17 trials (45%) described a random component such as a computer random number generator in the sequence generation process. 8 trials (21%) specified the allocation process was concealed. 9 trials (24%) stated blinding of assessors for the outcome measurement. 21 trials (55%) reported the procedure such as intent-to-treat analysis to deal with incomplete outcome data. 3.4. Qualitative synthesis

On the basis of previous meta-analyses (Dray et al., 2017; Brunwasser et al., 2009), to detect an average effect size of 0.15, for a significance level of 0.05 and a power level of 80%, a total sample size of 1102 participants would be needed. Assuming each study includes a conservative sample size of 100 participants (50 participants for each condition), approximately 11 studies would be needed. For a power of 90%, 15 or 16 studies would be needed. Although the statistical power to detect an average effect size of 0.15 with the overall sample could be high, it is likely that there was limited power to detect small moderator effects as we had only 38 effect sizes for the moderator analyses. Following the examples of Stice et al. (2009) and Brunwasser et al. (2009), we attempted to identify large moderator effects while null moderator effects were interpreted with subgroup and trend analyses.

3.5. Meta-analysis findings 3.5.1. Overall effect sizes at post-intervention A summary of all effect sizes at post-intervention is presented in Table 2. Effect sizes ranged from −0.43 to 0.68 with an overall mean effect size of 0.13. Only ten out of 38 studies reported negative effect sizes. The heterogeneity across these studies was substantial (I2 = 79.2%), indicating a need to subdivide studies. The funnel plot was somewhat asymmetric, indicating the presence of publication bias. After adjusting for possible publication bias with the trim-and-fill procedure, no hypothetical studies were incorporated and the mean effect size remained unchanged. We conducted additional influence analyses in which one or both of the smallest and largest effect size was excluded. The results of these meta-analyses are presented in Table 4. The overall effect size dropped to 0.11 (95%CI: 0.05–0.17; I2 = 75.5%) on exclusion of the largest effect size. The overall effect size remained 0.13 (95%CI: 0.07–0.20; I2 = 79.4%) after removal of the smallest effect size. And the overall effect size turned to 0.11 (95%CI: 0.05–0.17; I2 = 75.8%) by excluding both the largest and smallest effect sizes.

3. Results 3.1. Study identification and selection We identified 3341 relevant records through database searching and reference review. After exclusion of duplicates as well as screening of titles and abstracts, 147 articles were retrieved for detailed review. Of these, 109 were further excluded for the following reasons: 34 because the participants were not children and adolescents; 27 because the study did not include a relevant intervention condition; 15 because the study did not have a relevant control condition; 21 because the study did not report an outcome on depressive symptoms; 9 because they were study protocols; 3 because they were not written in English. Thus in total, 38 articles were eligible for inclusion in the present review and meta-analysis. A PRISMA flowchart for the inclusion of studies is presented in Fig. 1. 153

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

Fig. 1. Flowchart of study selection.

3.5.2. Moderator and subgroup analyses The results of moderator and subgroup analyses at post-intervention are presented in Table 4. Three characteristics relevant to participant demographics including age, gender, and risk status were assessed as potential moderators. None of these characteristics were found to be associated with the effect size. We conducted an additional subgroup analysis to examine the separate efficacy of such programs administered to universal and targeted samples. This analysis yielded significant effect sizes for both universal and targeted programs. Five characteristics of intervention conditions were assessed as potential moderators: program type, number of sessions, total intervention time, group leader type, and homework use. There were few indications for these characteristics to be associated with the effect size. Subgroup analyses revealed significant effect sizes for programs both with and without homework, as well as programs led by school personnel. In contrast, insignificant effect sizes were exhibited for programs based on PRP and RAP, as well as programs led by professional interventionists. Additionally, we assessed two study features as potential moderators: sample size and methodological quality. Neither of the characteristics was found to be associated with the effect size. In the

subgroup analysis, studies categorized as lower quality produced an insignificant effect size which was comparable to that of studies categorized as higher quality. 3.5.3. Overall effect sizes at follow-up For studies that conducted follow-up assessments, we calculated the effect sizes for each study at 6 months, 12 months, 18 months, 24 months, and the last follow-up. A summary of all effect sizes at 6 months and the last follow-up is presented in Table 2. The weighted effect size was still significant and stable at 6 months follow-up (n = 24; Hedges’g = 0.13; 95%CI: 0.05–0.22). However, this effect size faded at 12 months follow-up (n = 16; Hedges’g = 0.06; 95%CI: −0.01–0.13), 18 months follow-up (n = 9; Hedges’g = 0.07; 95%CI: 0.01–0.13), as well as 24 months follow-up (n = 8; Hedges’g = 0.11; 95%CI: −0.01–0.23). The results of these meta-analyses are presented in Table 5. The heterogeneity remained substantial and the funnel plots indicated asymmetry across these analyses, while the effect size did not change substantially in the influence analyses and after the trim-and-fill procedure.

154

Low-income minority middle school students

Early adolescent girls

11–12-year-old students beginning their secondary school enrolment 11–12-year-old students with elevated depressive symptoms

Cardemil et al., 2002

Chaplin et al., 2006

Challen et al., 2014

155

Adolescents in the eighth grade of secondary schools

Adolescent girls with elevated depressive symptoms

Girls with elevated depressive symptoms between 11 and 16 years old

Tak et al., 2015

Wijnhoven et al., 2014

Poppelaars et al., 2016

Kindt et al., 2014

Chinese children who were selected based on depressive symptoms and family conflict High-risk group of adolescents from low-income areas

Students from Year 5 and Year 6 classes

Pattison and Lynd-Stevenson 2001

Yu and Seligman 2002

At-risk 10–13-year-old school children

Jaycox et al., 1994

Preadolescent girls

Middle school students (ages 10–15)

Gillham et al., 2012

Quayle et al., 2001

Middle school students

Gillham et al., 2007

Gillham et al., 2006

Population

Study

OVK only, SPARX only (delivered at home, excluded from the metaanalysis), and OVK and SPARX combined

OVK

Participants of Op Volle Kracht (OVK, an adapted version of PRP) learn about associations among situations, cognitions, feelings, and behavior, learn to check the accuracy of their cognitions and to be flexible in finding alternative interpretations, and learn social and coping skills, including negotiating, assertiveness, and relaxation OVK

Two forms of PPP were evaluated: the Normal Penn Program involved the cognitive component followed by the social component, while the Reverse Penn Program involved the social component followed by the cognitive component Students in the Optimism and Lifeskills Program (OLP, adapted from PPP) were taught the link between thoughts and feelings, to evaluate and challenge negative thoughts, and to make more optimistic and realistic interpretation of day-to-day problems. They were also taught coping strategies for uncontrollable situations, social skills, decision making, assertiveness, and negotiation The Chinese version of the PRP

PRP-A (Penn Resiliency Program for adolescents), PRP-AP (in which adolescents participated in PRP-A and parents were invited to attend a parent intervention component) Penn Prevention Program (PPP, similar to PRP)

PRP

PRP

UK Resilience Programme, an adapted version of PRP

Participants in the Penn Resiliency Program (PRP) learn about the links between thoughts and emotions, learn how to generate a list of possible explanations for negative events in their lives, and learn how to use evidence to choose the most plausible explanations for these events. The program also helps children consider appropriate ways to handle conflict, set goals, and problem-solve social situations PRP

Intervention

Table 1 Descriptive information on population, intervention, measure, and findings of included studies.

RADS

CDI

CDI

CDI

CDI

CDI

CDI

CDI

CDI; RADS

CDI

CDI

CDI

CDI

CDI

Measure

(continued on next page)

No significant effects for depressive symptoms through 2-year follow-up Significant effects for depressive symptoms at posttest and 6 months follow-up No significant effects for depressive symptoms in all conditions at posttest and through 12 months follow-up

Significant effects for depressive symptoms at posttest, 3 months and 6 months follow-up No significant effects for depressive symptoms through 12 months follow-up

Significant effects for depressive symptoms at 6 months follow-up, but not at posttest

Significant effects for depressive symptoms in both girls-only and co-ed groups; no significant effects for depressive symptoms in neither groups at 12 months follow-up Significant effects for depressive symptoms at posttest, but not at 1year or 2-year follow-up No significant effects for depressive symptoms in the overall sample through 2-year follow-up; PRP significantly reduced depressive symptoms in girls, but not in boys No significant effects for depressive symptoms in the full sample at posttest and 3-year follow-up PRP-A significantly reduced depression symptoms at posttest, but not at the 6 months follow-up; PRP-AP was not more effective than PRP-A alone Significant effects for depressive symptoms at posttest and 6 months follow-up No significant effects for depressive symptoms at posttest and 8 months follow-up

A clearly beneficial effect for the Latino children up to 6 months after the conclusion of the depression prevention program (Study I), but no clear effect for the African American children (Study II)

Findings

L. Ma, et al.

Journal of Affective Disorders 270 (2020) 150–164

Female students from independent girls schools

Students enrolled in Grades 6 and 7 at secondary schools

Year 9 secondary school students

Young adolescents with Autism Spectrum Disorder

Students aged 13 to 15

Children and adolescents from two single-sex secondary public schools Adolescents aged 12–16 years

Harnett and Dadds 2004

Kirsten et al., 2014

Shochet et al., 2001

Mackay et al., 2017

Merry et al., 2004

Rivet-Duval et al., 2011

9–10 year-old children in low SES schools

Grade 7 students from disadvantaged schools

8–9 year-old children

Adolescents in the eighth grade of secondary schools

Children with elevated psychosocial distress

At-risk adolescents from secondary schools in vulnerable socioeconomic areas

Rooney et al., 2013

Roberts et al., 2010

Rooney et al., 2006

Sawyer et al., 2010

Jordans et al., 2010

Gaete et al., 2016

Stallard et al., 2012

Population

Study

Table 1 (continued)

156 The program is based on cognitive and behavioral principles, theories and strategies outlined by Seligman et al., (1995) and presents developmentally appropriate adaptations of activities used for older children in the Aussie Optimism Program The beyondblue Schools Research Initiative that aims to improve problem solving and social skills, resilient thinking style, and coping strategies; to build supportive environments; to facilitate adolescents’ access to support and professional services; to provide information to assist them to identify problems, and to seek help for themselves, and to help peers The Classroom-Based Intervention (CBI), based on concepts from creative-expressive and experiential therapy, cooperative play and cognitive behavioral therapy CBT-based program YPSA - I (Yo), Think (Pienso), Feel (Siento), Act (Actuo) that teaches thought restructuring and problem solving skills with interactive activities such as role-playing

Aussie Optimism Program (AOP) that focuses on identifying thoughts and feelings, exploring the connection between thoughts, feelings and behaviors, evaluating and challenging thoughts, learning to think more accurately and positively, learning about relaxation and distraction along with the scheduling of pleasurable events, and constructing a fear hierarchy. It also emphasizes learning areas relating to interpersonal and self-management skills AOP

RAP

RAP-A

RAP (Resourceful Adolescent Program) that teaches cognitivebehavioral therapy (CBT) approaches (e.g., Clarke et al., 1995) and addresses interpersonal risk and protective factors in adolescent development. It includes the following sessions: establishing rapport; affirmation of existing strengths; promoting selfmanagement and emotional regulation skills in the face of stress; cognitive restructuring; problem solving; building and accessing psychological support networks; interpersonal components designed to promote family harmony and avoid escalation of conflict Two RAP conditions were evaluated: (a) RAP followed by the Peer Interpersonal Relatedness (PIR) program (RAP-PIR); (b) RAP followed by a placebo program (RAP-placebo) Two RAP conditions were evaluated: (a) RAP-A, in which only adolescents participated in the program; (b) Resourceful Adolescent Program-Family (RAP–F), in which adolescents participated in the same RAP program and their parents were invited to attend the RAP program The Resourceful Adolescent Program-Autism Spectrum Disorder (RAP-A-ASD), an adapted version of RAP-A RAP-Kiwi, an adapted version of the RAP

Intervention

BDI

DSRS

(continued on next page)

No significant effects for depressive symptoms at posttest

Significant effects for depressive symptoms (crude change scores)

No significant effects for depressive symptoms through 2-year follow-up

CES-D

CDI

No significant effects for depressive symptoms through 18 months follow-up Significant effects for depressive symptoms at posttest, but not at 18 months follow-up

No significant effects for depressive symptoms at posttest and 6 months follow-up Significant effects for depressive symptoms (BDI) at posttest, but not at 18 months follow-up Significant effects for depressive symptoms at posttest, but not at 6 months follow-up No significant effects for depressive symptoms at posttest (at 12 months) Significant effects for depressive symptoms at posttest, but not at 18 months follow-up

Significant effects for depressive symptoms at posttest, but not at 10 months follow-up

CDI

CDI

SMFQ

RADS

BDI; RADS

CDI

CDI

No significant effects for depressive symptoms across the RAP intervention period

No significant effects for depressive symptoms at posttest and 3year follow-up

RADS

RADS; CDI

Findings

Measure

L. Ma, et al.

Journal of Affective Disorders 270 (2020) 150–164

157

Eighth-grade students

High school students from the mid-south of the United States 13–15-year-old adolescents

Grade 8 students, ranging in age from 12 to 14 years

Possel et al., 2013

Sheffield et al., 2006

Spence et al., 2005

No significant effects for depressive symptoms from preintervention to 4-year follow-up

No significant effects for depressive symptoms at posttest and 12 months follow-up

CDI; CES-D

BDI

Significant effects for depressive symptoms at 4 months follow-up

Positive intervention effects were found on girls’ depressive symptoms through the 12 months follow-up, while no such effects were found on boys’ depressive symptoms

Significant effects for depressive symptoms at post-intervention and 6 months follow-up but not at 18 months follow-up Significant effects for adolescents with minimal to mild depressive symptoms over a 6 months period; no significant effects for adolescents with clinically relevant depressive symptoms

Significant effects for depressive symptoms by posttest and 6 months follow-up No significant effects for depressive symptoms at posttest

CDI

SBB-DES

CES-D

MDI

BDI; KSADS SMFQ

K-SADS

Significant effects for depressive symptoms at posttest, but not at 6 months follow-up Significant effects for depressive symptoms at posttest but not over 2-year follow-up

Significant time by condition interaction effect from intake to postintervention but not from intake to 12 months follow-up (CESD); no significant interaction effect for either the intake to postintervention time frame, or the entire study period (HDRS)

CES-D; HDRS

CDI; CES-D

Findings

Measure

Note. BDI = Beck Depression Inventory, CDI = Child Depression Inventory, CES-D = Center for Epidemiology Studies Depression Index, DSRS = Depression Self-Rating Scale, RADS = Reynolds Adolescent Depression Scale, = SMFQ = Short Mood and Feelings Questionnaire, HDRS = Hamilton Depression Rating Scale, K-SADS = Schedule for Affective Disorders and Schizophrenia for School-Age Children, MDI = Major Depression Inventory, SBB-DES = Self-Report Questionnaire-Depression.

8th-grade students in the area of Tübingen (southwest Germany)

Possel et al., 2011

Perry and Werner-Seidler 2017 Possel et al., 2004

Kuosmanen et al., 2017

Problem Solving For Life (PSFL) program that includes cognitive restructuring and problem-solving skills training with a greater focus on the teaching of interpersonal skills (assertion, conflict resolution, and negotiation) and self-reward PSFL

Ease of Handling Social Aspects in Everyday Life Training (LISA-T) that includes the following topics: learning the relationship between cognition, emotion, and behavior; acknowledging their own automatic thoughts; confronting these thoughts with reality; and substituting them with functional thoughts. It also includes modules of assertiveness and social competence training LARS&LISA that focuses on understanding the relations among cognitions, emotions, and behaviors; identifying and challenging negative cognitions; and training assertiveness and social competence LARS&LISA

High-risk adolescents with elevated depressive symptoms Adolescents (age 15–20 years) who have left school early and are attending Youthreach, an alternative education (AE) program in Ireland

Stice et al., 2010

Final year secondary students

Students in wellness classes in three suburban/rural high schools Adolescents with elevated self-assessed depressive symptoms

Horowitz et al., 2007

Rohde et al., 2015

Coping with Stress Course program that focuses on identifying and challenging negative or irrational thoughts as well as improving self-esteem, coping skills, and frequency of pleasant events and activities. Cartoons, role-plays, and group discussions were oriented to the developmental level of the adolescent participants Interpersonal psychotherapy-adolescent skills training program (excluded from the meta-analysis), and Coping with Stress Course CBT intervention (modified from Clarke's Coping with Stress Course program) that focuses on building group rapport, increasing participant involvement in pleasant activities, and replacing negative cognitions with positive cognitions CBT intervention that was the same program evaluated in Rohde et al., 2015 SPARX-R (a revised version of SPARX) that teaches skills including psychoeducation, relaxation skills, activity scheduling, problem solving, cognitive restructuring, interpersonal skills, help seeking, and dealing with strong emotions SPARX-R

Adolescent offspring (aged 13–18 years) of depressed parents

Clarke et al., 1995

Intervention

Population

Study

Table 1 (continued)

L. Ma, et al.

Journal of Affective Disorders 270 (2020) 150–164

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

Table 2 Characteristics of included studies. Author

Sample Size

Age (mean)

Female%

Risk Status

Duration (n×min)

Group Leader

Howk

Program Type

Last Follow-up (months)

Effect Size (post)

Effect Size (6month)

Effect Size (last)

Cardemil et al., 2002 (Study I) Challen et al., 2014 Chaplin et al., 2006 Clarke et al., 1995 Gaete et al., 2016 Gillham et al., 2006 Gillham et al., 2007 Gillham et al., 2012 Harnett and Dadds 2004 Horowitz et al., 2007 Jaycox et al., 1994 (Gillham et al., 1999) Jordans et al., 2010 Kindt et al., 2014 Kirsten et al., 2014 Kuosmanend et al., 2017 Mackay et al., 2017 Merry et al., 2004 Pattison and LyndStevenson 2001 Perry and WernerSeidler 2017 Poppelaars et al., 2016 Possel et al., 2004 Possel et al., 2011 Possel et al., 2013 Quayle et al., 2001 Rivet-Duval et al., 2011 Roberts et al., 2010 Rohde et al., 2015 Rooney et al., 2006 Rooney et al., 2013 Sawyer et al., 2010 Sheffield et al., 2006 Shochet et al., 2001 Spence et al., 2005 Stallard et al., 2012 Stice et al., 2010 Tak et al., 2015 Wijnhoven et al., 2014 Yu and Seligman 2002

49

11.3

45

Targeted

12×90

PI

Yes

PRP

6

0.57

0.78

0.78

2844 68 150 342 271 466 408 212 380 142

11.5 12.2 15.3 15.9 NA 12.1 NA 13.6 14.4 11.4

49 100 70 50 53 46 48 100 54 46

Universal Universal Targeted Targeted Targeted Universal Universal Universal Universal Targeted

18×60 12×90 15×45 8 × 45 12×90 12×90 (10–12) × 90 11 × (40–50) 8 × 45 12×90

PI SP SP PI PI SP SP SP PI PI

No No Yes No Yes Yes No No No Yes

PRP PRP Others Others PRP PRP PRP RAP Others PRP

0.08 0.40 0.45 0.08 0.06 0.04 0.04 0.05 0.19 0.14

NA NA 0.15 NA 0.30 0.06 0.05 NA −0.12 0.24

325 1343 210 146 29 392 66

12.7 13.4 12.2 17.6 11.8 14.2 10.4

49 52 44 53.4 10 52 52

Targeted Targeted Universal Targeted Targeted Universal Universal

15×60 16×50 11 × (40–50) 7 × 30 11×50 11×60 10×120

PI SP PI SP PI SP PI

No Yes No Yes No No No

Others PRP RAP Others RAP RAP PRP

24 12 12 NA 24 36 6 36 6 6 (36) NA 12 12 NA 6 18 8

0.55 −0.02 0.00 0.11 0.01 0.20 −0.05

NA 0.15 NA NA −0.18 −0.09 NA

−0.04 NA 0.25 NA 0.16 0.15 0.05 −0.11 0.00 0.24 (0.07) NA −0.12 0.19 NA −0.18 −0.15 0.32

540

16.7

63

Universal

7 × 30

SP

Yes

Others

6

0.22

0.17

0.16

208 324 301 518 47 160 427 378 910 910 5633 1226 174 1500 1064 341 1341 102 220

13.4 13.8 13.7 15.1 11.5 14.0 12.0 15.5 9.1 8.8 13.1 14.3 13.5 12.8 14.2 15.6 13.9 13.3 11.8

100 44 47 62.7 100 50 55 68 43 49 53 54 53 52 65 56 47 100 45

Targeted Universal Universal Universal Universal Universal Universal Targeted Universal Universal Universal Universal Universal Universal Targeted Targeted Universal Targeted Targeted

8 × 50 10×90 9 × 90 10×90 8 × 80 11×60 20×60 6 × 60 8 × 60 10×60 10 × (40–45) 8 × 50 11×60 8 × (45–50) 12 × (50–60) 6 × 60 16×50 8 × 50 10×120

PI PI PI PI PI SP SP SP SP SP PI SP PI SP PI PI PI PI SP

Yes No No No Yes No Yes Yes No No No Yes No Yes No Yes Yes Yes No

PRP Others Others Others PRP RAP Others Others Others Others Others Others RAP Others RAP Others PRP PRP PRP

12 6 12 12 6 6 18 24 18 18 24 12 10 48 NA 24 24 6 6

−0.16 −0.18 0.11 −0.13 −0.43 0.45 −0.11 0.28 0.48 0.18 0.03 −0.02 0.35 0.37 −0.08 0.68 −0.18 0.24 0.33

−0.15 0.20 NA 0.13 1.42 0.15 −0.09 0.08 NA 0.02 NA −0.10 NA NA NA 0.73 0.01 0.36 0.45

−0.10 0.20 0.22 −0.08 1.42 0.15 0.00 0.08 0.01 0.01 0.06 −0.08 0.14 0.12 NA 0.43 −0.13 0.36 0.45

Note. Howk = Homework, PI = Professional Interventionists, SP = School Personnel, PRP = Penn Resiliency Program, RAP = Resourceful Adolescent Program, NA = Not Applicable.

3.5.4. Moderator and subgroup analyses We repeated the moderator and subgroup analyses for the effects at 6 months follow-up. The results of these analyses are presented in Table 5. None of the characteristics was found to be associated with the effect size. In the subgroup analyses, significant effect sizes were found for programs administered to targeted samples, programs based on PRP, programs with homework, and programs led by professional interventionists. In contrast, insignificant effect sizes were shown for programs administered to universal samples, programs based on RAP, programs without homework, programs led by school personnel, and studies categorized as both lower and higher quality.

on both the cognitive and behavioral components. This inclusive approach makes it difficult to draw concrete conclusions on the performance of specific subtypes of CBIs. In this meta-analytic review, we focus exclusively on resilience-oriented CBIs, which have to teach three types of skills (cognitive, problem-solving, and social). This restricted inclusion resulted in a total of 38 more comparable interventions in our analyses for this specific subtype of CBIs. The current study showed a wide range in the effect sizes of resilience-oriented CBIs. At post-intervention, 3 (8%) produced moderate to large effect sizes, 24 (63%) produced small effect sizes, while 11 (29%) produced zero or negative effect sizes. The weighted effect size at post-intervention was 0.13, which was considered small but comparable to those of other depression prevention programs (Horowitz and Garber, 2006; Merry et al., 2012; Stice et al., 2009). This effect size was quite robust in the influence analyses and was largely maintained at 6 months follow-up. These results reinforce the evidence that resilienceoriented CBIs could be indeed effective for the prevention of depressive symptoms. However, the wide distribution of effect sizes and overall small effect size highlight the need for future trials to replicate the effects of most successful programs (e.g., those producing the largest effect sizes) and enhance the current versions of those that worked suboptimally (e.g., those producing the negative effect sizes).

4. Discussion The primary purpose of this review was to determine whether resilience-oriented CBIs are effective for prevention of depressive symptoms in the school setting. Evidence from previous meta-analyses of CBIs commonly employed a broader definition, under which interventions based on isolated cognitive restructuring, behavioral activation, problem solving, or social skills training were considered as being CBIs. For instance, Hetrick et al. (2016) employed an inclusive approach to the 65 included CBIs but only 32 of these CBIs have an equal emphasis 158

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

Table 3 Risk of bias for included studies. Study

Random sequence

Allocation concealment

Assessment blinding

Incomplete outcome

Overall quality

Cardemil et al., 2002 Challen et al., 2014 Chaplin et al., 2006 Clarke et al., 1995 Gaete et al., 2016 Gillham et al., 2006 Gillham et al., 2007 Gillham et al., 2012 Harnett and Dadds 2004 Horowitz et al., 2007 Jaycox et al., 1994 Jordans et al., 2010 Kindt et al., 2014 Kirsten et al., 2014 Kuosmanend et al., 2017 Mackay et al., 2017 Merry et al., 2004 Pattison and Lynd-Stevenson 2001 Perry and Werner-Seidler 2017 Poppelaars et al., 2016 Possel et al., 2004 Possel et al., 2011 Possel et al., 2013 Quayle et al., 2001 Rivet-Duval et al., 2011 Roberts et al., 2010 Rohde et al., 2015 Rooney et al., 2006 Rooney et al., 2013 Sawyer et al., 2010 Sheffield et al., 2006 Shochet et al., 2001 Spence et al., 2005 Stallard et al., 2012 Stice et al., 2010 Tak et al., 2015 Wijnhoven et al., 2014 Yu and Seligman 2002

− − + − + + + + − + − + + − + + + − − + − − − − − − + − − − + − − − + + + −

− − − − + − − − − − − − + − − + + − + − − − − − − − − − − − + − − − − + + −

− − − − − + − − − − − − − − − + − − + − − − − − − + + − + + − − − + + − − −

− + − − + + + + − + − + + + − + + − + + − − − − + − + − − − + − + + + + + −

low low low low high high low low low low low low high low low high high low high low low low low low low low high low low low high low low low high high high low

Note. + = low risk, − = high risk.

The secondary purpose of this study was to assess potential moderators that could predict the magnitude of prevention effects specific to resilience-oriented CBIs. We conducted moderator analyses evaluating whether proposed characteristics were associated with the effect size. However, our ability to detect moderation was limited due to the relatively small number of studies (n = 38). None of the assessed moderators showed significant relations with the effect size. As research accumulates, further meta-analyses are needed to examine whether contextual factors moderate the intervention effects of such programs. Resilience-oriented CBIs tend to be more effective (although not significantly) when delivered to targeted samples than when delivered universally. This has been consistent with previous meta-analyses of depression prevention programs (Horowitz and Garber, 2006; Stice et al., 2009). The distress that characterizes individuals of targeted samples could motivate them to engage more effectively in the prevention programs, providing greater opportunities for symptom reduction (Stice et al., 2009). It is also possible that there is more room for an effect to emerge in targeted samples, whereas the lower levels of depression for the universal samples create a floor effect. While the effect size of targeted resilience-oriented CBIs may compare favorably to that of universal ones, universal resilience-oriented CBIs were still effective (at post-intervention) for reducing depressive symptoms relative to control conditions. Universal programs may have benefits beyond symptom reduction: they intervene with preventative inoculation for the whole population without identifying those at risk, they minimize the risk of stigmatizing factors that make individuals reluctant to participate in the intervention programs, and they could be embedded in current curriculum to be delivered in conjunction with

other educational programs (Barrett et al., 2006; Tugade et al., 2004). Such advantages could make universal programs more acceptable, available and cost-effective. Thus, both targeted and universal resilience-oriented CBIs may be appropriate for youth, depending on the context of particular participants. We found few indications for age of participants to be associated with the effect size. One interpretation for this may be resilience-oriented CBIs are equally effective for youth at different ages. However, this should be interpreted with caution as the majority of samples included in this review had a mean age ranging from 11 to 15 years. The lack of variability may result in the failure of detection for a statistical difference. Our analyses did not show significant indications of gender difference to be associated with the effect size. This finding is somewhat different from previous meta-analyses (Horowitz and Garber, 2006; Stice et al., 2009), which found depression prevention programs were more effective when delivered to samples containing a higher portion of female participants. Given that gender difference in depression becomes more pronounced during adolescence (Lewinsohn et al., 1994) and the majority of studies in this meta-analysis included youth in early adolescence, it is possible that the impact of gender difference would become significantly larger for late adolescence. Consistent with previous meta-analyses supporting relatively brief versions of depression prevention programs (Eng and Reime, 2014; Sockol et al., 2013; Stice et al., 2009), resilience-oriented CBIs with relatively fewer sessions appeared to be associated with greater (although not significantly) reductions in depressive symptoms. In school settings that commonly have a crowded agenda of educational and health-related programs, students may get bored with longer 159

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

Table 4 Meta-analyses of resilience-oriented CBIs: overall effect sizes and moderator analyses at post-intervention.

Overall effect sizes at post-intervention Es of all studies Es after removal of the largest Es the smallest Es the largest and smallest Es Moderator analyses Categorical characteristics Participant risk status Universal Targeted Program type PRP RAP Others Homework use Yes No Group leader type Professional interventionists School personnel Study quality High Low Continuous characteristics Sample size Female% Mean age No. of sessions Total intervention time

No.

Hedges’g (95%CI)

I2

38

0.13 (0.06, 0.19)

79.20%

37 37 36

0.11 (0.05, 0.17) 0.13 (0.07, 0.20) 0.11 (0.05, 0.17)

75.50% 79.40% 75.80%

23 15

0.09 (0.02, 0.16) 0.21 (0.06, 0.35)

79.10% 79.90%

0.12

14 7 17

0.05 (−0.04, 0.14) 0.13 (−0.02, 0.29) 0.18 (0.08, 0.28)

61.30% 69.40% 86.10%

0.42

18 20

0.13 (0.01, 0.25) 0.12 (0.05, 0.19)

85.20% 69.70%

0.91

21 17

0.09 (−0.00, 0.18) 0.17 (0.08, 0.26)

79.90% 76.60%

0.15

11 27

0.13 (0.00, 0.26) 0.13 (0.00, 0.27)

79.50% 85.10%

0.98

Slope

−0.00003 −0.001 0.0005 −0.02 −0.0001

P

0.32 0.64 0.98 0.16 0.30

Note. Es = Effect size, PRP = Penn Resiliency Program, RAP = Resourceful Adolescent Program, P value for categorical characteristics was assessed using ANOVA and P value for continuous characteristics was assessed using meta-regression.

Table 5 Meta-analyses of resilience-oriented CBIs: overall effect sizes and moderator analyses at follow-up.

Overall effect sizes at follow-up 6-month 12-month 18-month 24-month Moderator analyses based on 6 months follow-up Categorical characteristics Participant risk status Universal Targeted Program type PRP RAP Others Homework use Yes No Group leader type Professional interventionists School personnel Study quality High Low Continuous characteristics Sample size Female% Mean age No. of sessions Total intervention time

No.

Hedges’g (95%CI)

I2

24 16 9 8

0.13 0.06 0.07 0.11

(0.05, 0.22) (−0.01, 0.13) (0.01, 0.13) (−0.01, 0.23)

75.80% 66.00% 0.00% 78.80%

13 11

0.05 (−0.03, 0.13) 0.24 (0.10, 0.38)

60.40% 60.80%

0.49

11 3 14

0.22 (0.08, 0.35) 0.01 (−0.16, 0.18) 0.08 (−0.03, 0.19)

73.80% 0.00% 67.10%

0.38

17 8

0.14 (0.04, 0.25) 0.10 (−0.00, 0.21)

75.30% 39.20%

0.36

18 12

0.25 (0.08, 0.42) 0.06 (−0.01, 0.14)

76.40% 51.80%

0.11

10 14

0.13 (0.02, 0.24) 0.13 (0.01, 0.25)

72.10% 67.60%

0.59

Slope

−0.0002 0.003 −0.01 −0.02 0.0001

P

0.12 0.40 0.76 0.15 0.72

Note. PRP = Penn Resiliency Program, RAP = Resourceful Adolescent Program, P value for categorical characteristics was assessed using ANOVA and P value for continuous characteristics was assessed using meta-regression. 160

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

prevention programs. This accords our other findings that increased total intervention time and inclusion of homework did not result in larger effect sizes. However, it is noteworthy that the effect size for studies with homework remained similar and significant at follow-up, while the effect size for studies without homework turned smaller and insignificant at follow-up. A previous meta-analysis even suggested that use of homework is associated with significantly larger intervention effects at follow-up (Stice et al., 2009). The enduring effects of resilience-oriented CBIs with homework found in our analyses reinforce that it would be beneficial to include homework regularly in such prevention programs. We did not find indications for program type to be related with the effect size. Although this may be interpreted that these programs could be equally effective, subgroup analyses appeared to provide little support for this assumption. The effect size of PRP was insignificant at post-intervention but became stronger and turned significant at follow-up. The strengthening of PRP's effects at follow-up may be attributable to the possibility that control participants could have an increase in depressive symptoms following the study or that it may take time for students to begin applying the program skills in their everyday lives before an intervention effect emerges (Brunwasser et al., 2009). In contrast, the effect size of RAP was insignificant at post-intervention and continued to attenuate at follow-up, suggesting RAP in its current form may not be recommended at this time. The effect size of other resilience-oriented CBIs was significant at post-intervention. However, such intervention effects attenuated dramatically and turned insignificant over time, possibly due to the relapse of depressive symptoms in the intervention participants or the remission of depressive symptoms in the control participants. Given these divisive findings, future trials may compare directly the effects of PRP, RAP and other resilience-oriented CBIs. Larger effects (although not significantly) appeared to emerge for programs led by professional interventionists than those led by school personnel at follow-up. The mean effect size of programs led by professional interventionists turned significant and stronger at follow-up, while the effect size of programs led by school personnel turned insignificant and dropped sharply at follow-up. These findings were in accordance with the evidence of two previous meta-analyses. Stice and colleagues found prevention programs delivered by professional interventionists produced significantly stronger effect sizes than those delivered by endogenous providers (e.g., teachers) at follow-up. Brunwasser and colleagues found the mean effect size of PRP for studies with research team leaders tended to be larger (although not significantly) than those among studies with community leaders (e.g., teachers) at all assessments. Although it is encouraging that resilience-oriented CBIs led by school personnel produced favorable effects to those led by professional interventionists at post-intervention, the null intervention effects of programs led by school personnel at follow-up imply that more detailed training and supervision to endogenous providers would be needed. There were few indications of a difference between studies with lower quality and those with higher quality. However, studies with small sample size appeared to produce larger (although not significantly) effect sizes. This occurs in part because studies with small sample sizes are more likely to be published when they report significant results (Sterne et al., 2000; Thornton and Lee, 2000). This study has a number of limitations. First, our findings may be limited to the suboptimal methodological quality of included studies, most of which failed to meet our main criteria following Cochrane Collaboration's Tool. Second, it was rarely possible for the participants to be blinded to the intervention and control conditions as most studies employed an assessment-only or waitlist control condition instead of a placebo control condition. Participants would know whether they were assigned to the intervention or control conditions. Uncontrolled nonspecific factors (e.g., perceived group support or outcome expectancies) that would contribute in part to the beneficial prevention effects may result in distorted results of these studies. Third, we did not examine the effect sizes related to different intervention conditions when studies

included in their comparisons multiple variations of a resilience-oriented program. Effect sizes of such studies were computed based on the pooled means and standard deviations of the different intervention conditions so that no study provided multiple effect sizes at a given assessment. This may have mitigated the effect sizes of some promising versions of resilience-oriented CBIs from these studies. Finally, despite the correction of the trim-and-fill procedure, the possibility of publication bias from underreporting negative findings may not be excluded. Because of these limitations, findings of this meta-analysis should be interpreted with caution. This meta-analysis has several implications. First, it would be important to replicate the effects of the most promising studies as the majority of resilience-oriented CBIs produced small to moderate effects. Second, it would be helpful for future trials to experimentally manipulate potentially key effect size moderators such as number of sessions, use of homework, and type of group leader to examine if the moderator effects are real with a causal relation. Most of our results were based on subgroup and meta-regression analyses which only provided correlational associations. Third, it is worth further exploring how depression prevention programs can be enhanced to produce stronger and more enduring effects in the long term. Although it has been the case that post-intervention effects most often diminish after 6 12 months, it is encouraging that some studies found enduring or even strengthening intervention effects at follow-up. It is necessary for future trials to examine whether participants’ use of the program skills would impact their depressive symptoms over time. Our analyses also revealed that some study-specific characteristics (e.g., targeted studies, PRP, homework, and professional interventionists) appeared to be associated with more robust intervention effects at follow-up. It would be meaningful for future research to continue examining whether such features truly contribute to stronger effects in the long term. Another implication is that future trials should use more rigorous designs given that the majority of studies included in this meta-analysis were considered to have suboptimal quality. Furthermore, it would be useful to apply placebo control conditions rather than the commonly used assessment-only or waitlist control conditions to minimize the influence of non-specific factors. Finally, head-to-head trials would be necessary to establish the directly comparative effects of different resilience-oriented CBIs. It is also useful to examine the comparative effects of resilience-oriented CBIs and other psychological interventions. There are some practical concerns on the implementation of resilienceoriented CBIs. While resilience-oriented CBIs overall are associated with a reduction in depressive symptoms at up to 6 months follow-up, this finding alone is not sufficient evidence to support the larger implementation of resilience-oriented CBIs. Effective dissemination is contingent upon the success of these programs particularly when delivered universally and led by real-world personnel. This review suggests that universal resilience-oriented CBIs and those led by real-world personnel are effective in reducing symptoms among students in the short term. But these effects attenuated considerably and turned insignificant at 6 months follow-up. Given that depression prevention programs are intended to impart lasting skills that will reduce the risk for depression as youth enter late adolescence and early adulthood (Brunwasser et al., 2009), we have little confidence that resilience-oriented CBIs when delivered universally and led by real-world personnel could produce meaningful benefits in preventing the development of future depression. Furthermore, the lack of concrete effects on resilience as well as related protective factors also contributes to our concern on the practical benefits of these programs. We did not investigate the role of resilience in the effectiveness of the interventions, because of insufficient information provided by the included studies. A number of studies examined the impacts of these interventions on specific resilience factors, such as positive cognitive (or attributional) styles (Chaplin et al., 2006; Pattison and Lynd-Stevenson 2001; Roberts et al., 2010; Rooney et al., 2013, 2006; Sawyer et al., 2010), coping skills (Sawyer et al., 2010; Mackay et al., 2017), and social skills (Pattison and Lynd-Stevenson 2001; Roberts et al., 2010; Rooney et al., 161

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al.

2013, 2006; Sawyer et al., 2010). However, most of these studies did not find significant evidence in favor of the intervention conditions. There are several possible explanations for the failure of these studies to find significant improvements on the resilience factors. As stated earlier, these programs targeted largely internal protective factors for depression, but did not intervene with the larger family, school, or community factors. Parental depression, rejecting or controlling disciplinary styles, parentchild conflict, and school violence/bullying are also associated with depression (Roberts et al., 2010). Thus, future CBIs that target both internal and external protective factors for childhood depression may enhance the program efficacy as extant CBIs commonly focus on internal protective factors. Further, cognitive behavioral skills taught in these programs could be more appropriate when targeting individuals at late adolescence where they have better cognitive ability for abstract reasoning. Children of some study have just entered the operational stage of cognitive development (Rooney et al., 2013), where the program contents may be too difficult for them to comprehend. Intervention effects may become prominent in the longer-term follow-ups when the children have a greater understanding of abstract concepts. It is also possible that there was a lack of statistical power to detect the modest intervention effects as the sample size for each individual study was relatively small. Because of these concerns, we feel that the small effect on depressive symptoms found in this meta-analysis was insufficient evidence for resilience-oriented CBIs to be widely disseminated at this time.

References Alexopoulos, G.S., 2005. Depression in the elderly. Lancet 365, 1961–1970. Barrett, P., Turner, C., 2011. Prevention of anxiety symptoms in primary school children: preliminary results from a universal school-based trial. Br. J. Clin. Psychol. 40, 399–410. Barrett, P.M., Farrell, L.J., Ollendick, T.H., Dadds, M., 2006. Long-term outcomes of an Australian universal prevention trial of anxiety and depression symptoms in children and youth: an evaluation of the friends program. J. Clin. Child Adolesc. Psychol. 53 (35), 403–411. Bastounis, A., Callaghan, P., Banerjee, A., Michail, M., 2016. The effectiveness of the Penn Resiliency Programme (PRP) and its adapted versions in reducing depression and anxiety and improving explanatory style: a systematic review and meta-analysis. J. Adolesc. 52, 37–48. Beck, A.T., 1976. Cognitive Therapy and the Emotional Disorders. International Universities Press, New York. Brunwasser, S.M., Gillham, J.E., Kim, E.S., 2009. A meta-analytic review of the Penn Resiliency Program's effect on depressive symptoms. J. Consult. Clin. Psychol. 77, 1042–1054. Caldwell, D.M., Davies, S.R., Hetrick, S.E., Palmer, J.C., Caro, P., Lopez-Lopez, J.A., Gunnell, D., Kidger, J., Thomas, J., French, C., Stockings, E., Campbell, R., Welton, N.J., 2019. School-based interventions to prevent anxiety and depression in children and young people: a systematic review and network meta-analysis. Lancet Psychiatry 6, 1011–1020. Campbell-Sills, L., Cohan, S.L., Stein, M.B., 2006. Relationship of resilience to personality, coping, and psychiatric symptoms in young adults. Behav. Res. Ther. 44, 585–599. Cardemil, E.V., Reivich, K.J., Seligman, M.E.P., 2002. The prevention of depressive symptoms in low-income minority middle school students. Prevent. Treat. 5, 8a. Challen, A.R., Machin, S.J., Gillham, J.E., 2014. The UK Resilience Programme: a schoolbased universal nonrandomized pragmatic controlled trial. J. Consult. Clin. Psychol. 82, 75–89. Chaplin, T.M., Gillham, J.E., Reivich, K., Elkon, A.G., Samuels, B., Freres, D.R., Winder, B., Seligman, M.E., 2006. Depression prevention for early adolescent girls: a pilot study of all girls versus co-ed groups. J. Early Adolesc. 26, 110. Clarke, G.N., Hawkins, W., Murphy, M., Sheeber, L.B., Lewinsohn, P.M., Seeley, J.R., 1995. Targeted prevention of unipolar depressive disorder in an at-risk sample of high school adolescents: a randomized trial of a group cognitive intervention. J. Am. Acad. Child Adolesc. Psychiatry 34, 312–321. Cohen, J., 1977. Statistical Power Analysis for the Behavioral Sciences. Academic Press. Connor, K.M., Davidson, J.R.T., 2010. Development of a new resilience scale: the connordavidson resilience scale (CD-RISC). Depression Anxiety 18, 76–82. Cuijpers, P., Berking, M., Andersson, G., Quigley, L., Kleiboer, A., Dobson, K.S., 2013. A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Can. J. Psychiatry 58, 376–385. Cuijpers, P., van Straten, A., Bohlmeijer, E., Hollon, S.D., Andersson, G., 2010. The effects of psychotherapy for adult depression are overestimated: a meta-analysis of study quality and effect size. Psychol. Med. 40, 211–223. Dadds, M., Roth, J., 2008. Prevention of anxiety disorders: results of a Universal Trial with young children. J. Child Family Stud. 17, 320–335. Dane, A.V., Schneider, B.H., 1998. Program integrity in primary and early secondary prevention: are implementation effects out of control? Clin. Psychol. Rev. 18, 23–45. Dersimonian, R., Laird, N., 1986. Meta-analysis in clinical trials. Control. Clin. Trials 7, 177. Ding, H., Han, J., Zhang, M., Wang, K., Gong, J., Yang, S., 2017. Moderating and mediating effects of resilience between childhood trauma and depressive symptoms in Chinese children. J. Affect. Disord. 211, 130–135. Dray, J., Bowman, J., Campbell, E., Freund, M., Wolfenden, L., Hodder, R.K., McElwaine, K., Tremain, D., Bartlem, K., Bailey, J., Small, T., Palazzi, K., Oldmeadow, C., Wiggers, J., 2017. Systematic review of universal resilience-focused interventions targeting child and adolescent mental health in the school setting. J. Am. Acad. Child Adolesc. Psychiatry 56, 813–824. Dray, J., Bowman, J., Wolfenden, L., Campbell, E., Freund, M., Hodder, R., Wiggers, J., 2015. Systematic review of universal resilience interventions targeting child and adolescent mental health in the school setting: review protocol. Syst. Rev. 4, 186. Duval, S., Tweedie, R., 2000. Trim and fill: a simple funnel-plot-based method. Biometrics 56, 455–463 459. Eng, J.J., Reime, B., 2014. Exercise for depressive symptoms in stroke patients: a systematic review and meta-analysis. Clin. Rehabil. 28, 731–739. Gaete, J., Martinez, V., Fritsch, R., Rojas, G., Montgomery, A.A., Araya, R., 2016. Indicated school-based intervention to improve depressive symptoms among at risk Chilean adolescents: a randomized controlled trial. BMC Psychiatry 16, 276. Gelkopf, M., Berger, R., 2009. A school-based, teacher-mediated prevention program (ERASE-Stress) for reducing terror-related traumatic reactions in Israeli youth: a quasi-randomized controlled trial. J. Child Psychol. Psychiatry 50, 962–971. Gillham, J.E., Reivich, K.J., 1999. Prevention of depressive symptoms in school children: a research update. Psychol. Sci. Gillham, J.E., Hamilton, J., Freres, D.R., Patton, K., Gallop, R., 2006. Preventing depression among early adolescents in the primary care setting: a randomized controlled study of the Penn Resiliency Program. J. Abnorm. Child Psychol. 34, 203–219. Gillham, J.E., Reivich, K.J., Freres, D.R., Chaplin, T.M., Shatte, A.J., Samuels, B., Elkon, A.G., Litzinger, S., Lascher, M., Gallop, R., Seligman, M.E., 2007. School-based prevention of depressive symptoms: a randomized controlled study of the effectiveness and specificity of the Penn Resiliency Program. J. Consult. Clin. Psychol. 75, 9–19. Gillham, J.E., Reivich, K.J., Brunwasser, S.M., Freres, D.R., Chajon, N.D., KashMacdonald, V.M., Chaplin, T.M., Abenavoli, R.M., Matlin, S.L., Gallop, R.J.,

Contributors LM undertook the online searches and identified the initial records. LM and YZ collaborated in the process of literature reviewing, data coding, and statistical analysis. LM finished the initial version of the manuscript. CH and ZC were responsible for the final revision. All the authors have read and approved the final manuscript. Role of the funding source This study was sponsored by a grant from Liaoning Scientific Research Foundation (grant number: 2012006005) . Declaration of competing interest None Acknowledgments The authors would like to express gratitude to Dr. Bing Ma and Dr. Hao Sun of Department of Epidemiology of the First Hospital of China Medical University for their helpful comments. We would also like to thank Miss Zhaodi Wei for her assistance in the process of screening and preparation of this manuscript. Appendix The search strategy used in this meta-analysis is presented as follows: 1 Terms related to relevant participants: child* OR adolescent* 2 Terms related to relevant interventions: (cognitive OR problemsolving OR interpersonal) AND (intervention OR program OR training OR prevention) 3 Terms related to relevant outcomes: depression OR depressive OR depressed 4 Terms related to relevant design: random* AND control* Search strategy: #1 AND #2 AND #3 AND #4. 162

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al. Seligman, M.E., 2012. Evaluation of a group cognitive-behavioral depression prevention program for young adolescents: a randomized effectiveness trial. J. Clin. Child Adolesc. Psychol. 53 (41), 621–639. Gotlib, I.H., Lewinsohn, P.M., Seeley, J.R., 1995. Symptoms versus a diagnosis of depression: differences in psychosocial functioning. J. Consult. Clin. Psychol. 63, 90–100. Hankin, B.L., Abramson, L.Y., Moffitt, T.E., Silva, P.A., McGee, R., Angell, K.E., 1998. Development of depression from preadolescence to young adulthood: emerging gender differences in a 10-year longitudinal study. J. Abnorm. Psychol. 107, 128. Hetrick, S.E., Cox, G.R., Witt, K.G., Bir, J.J., Merry, S.N., 2016. Cognitive behavioural therapy (CBT), third-wave CBT and interpersonal therapy (IPT) based interventions for preventing depression in children and adolescents. Cochrane Database Syst. Rev. Harnett, P.H., Dadds, M.R., 2004. Training school personnel to implement a universal school-based prevention of depression program under real-world conditions. J. Sch. Psychol. 42, 343–357. Hawton, K., Casanas, I.C.C., Haw, C., Saunders, K., 2013. Risk factors for suicide in individuals with depression: a systematic review. J. Affect. Disord. 147, 17–28. Hazell, P., 2011. Depression in children and adolescents. BMJ Clin. Evid 2011. Higgins, J.P., Altman, D.G., Gøtzsche, P.C., Jüni, P., Moher, D., Oxman, A.D., Savovic, J., Schulz, K.F., Weeks, L., Sterne, J.A., 2011. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ Br. Med. J. 343, 889–893. Higgins, J.P.T., Thompson, S.G., Deeks, J.J., Altman, D.G., 2003. Measuring inconsistency in meta-analyses. Br. Med. J. 327, 557–560. Hjemdal, O., Aune, T., Reinfjell, T., Stiles, T.C., Friborg, O., 2007. Resilience as a predictor of depressive symptoms: a correlational study with young adolescents. Clin. Child Psychol. Psychiatry 12, 91–104. Horowitz, J.L., Garber, J., 2006. The prevention of depressive symptoms in children and adolescents: a meta-analytic review. J. Consult. Clin. Psychol. 74, 401–415. Horowitz, J.L., Garber, J., Ciesla, J.A., Young, J.F., Mufson, L., 2007. Prevention of depressive symptoms in adolescents: a randomized trial of cognitive-behavioral and interpersonal prevention programs. J. Consult. Clin. Psychol. 75, 693–706. Hovens, J., Giltay, E.J., Spinhoven, P., van Hemert, A.M., Penninx, B., 2015. Impact of childhood life events and childhood trauma on the onset and recurrence of depressive and anxiety disorders. J. Clin. Psychiatry 76, 931–938. Jaureguizar, J., Garaigordobil, M., Bernaras, E., 2018. Self-concept, social skills, and resilience as moderators of the relationship between stress and childhood depression. Sch. Ment. Health 1–12. Jaycox, L.H., Reivich, K.J., Gillham, J., Seligman, M.E.P., 1994. Prevention of depressive symptoms in school children. Behav. Res. Ther. 32, 801. Jefferis, B.J., Nazareth, I., Marston, L., Moreno-Kustner, B., Bellón, J.Á., Svab, I., Rotar, D., Geerlings, M.I., Xavier, M., Goncalves-Pereira, M., 2011. Associations between unemployment and major depressive disorder: evidence from an international, prospective study (the predict cohort). Soc. Sci. Med. 73, 1627–1634. Jordans, M.J., Komproe, I.H., Tol, W.A., Kohrt, B.A., Luitel, N.P., Macy, R.D., de Jong, J.T., 2010. Evaluation of a classroom-based psychosocial intervention in conflict-affected Nepal: a cluster randomized controlled trial. J. Child Psychol. Psychiatry 51, 818–826. Kindt, K.C., Kleinjan, M., Janssens, J.M., Scholte, R.H., 2014. Evaluation of a school-based depression prevention program among adolescents from low-income areas: a randomized controlled effectiveness trial. Int. J. Environ. Res. Public Health 11, 5273–5293. Kirsten, R., Hawes, D.J., Hunt, C.J., 2014. Randomized controlled trial of a friendship skills intervention on adolescent depressive symptoms. J. Consult. Clin. Psychol. 82, 510–520. Kleine-Budde, K., Muller, R., Kawohl, W., Bramesfeld, A., Moock, J., Rossler, W., 2013. The cost of depression - a cost analysis from a large database. J. Affect. Disord. 147, 137–143. Krishnan, K.R., 2002. Biological risk factors in late life depression. Biol. Psychiatry 52, 185–192. Kuosmanen, T., Fleming, T.M., Newell, J., Barry, M.M., 2017. A pilot evaluation of the SPARX-R gaming intervention for preventing depression and improving wellbeing among adolescents in alternative education. Internet Interv. 8, 40–47. Lewinsohn, P.M., Roberts, R.E., Seeley, J.R., Rohde, P., Gotlib, I.H., Hops, H., 1994. Adolescent psychopathology: II. Psychosocial risk factors for depression. J. Abnorm. Psychol. 103, 302–315. Lewinsohn, P.M., Rohde, P., Seeley, J.R., 1998. Major depressive disorder in older adolescents: prevalence, risk factors, and clinical implications. Clin. Psychol. Rev. 18, 765–794. Luppa, M., Heinrich, S., Angermeyer, M.C., Konig, H.H., Riedel-Heller, S.G., 2007. Costof-illness studies of depression: a systematic review. J. Affect. Disord. 98, 29–43. Mackay, B.A., Shochet, I.M., Orr, J.A., 2017. A pilot randomised controlled trial of a school-based resilience intervention to prevent depressive symptoms for young adolescents with Autism spectrum disorder: a mixed methods analysis. J. Autism Dev. Disord. 1–21. Mazure, C.M., 2010. Life stressors as risk factors in depression. Clin. Psychol. Sci. Pract. 5, 291–313. Merry, S., McDowell, H., Wild, C.J., Bir, J., Cunliffe, R., 2004. A randomized placebocontrolled trial of a school-based depression prevention program. J. Am. Acad. Child Adolesc. Psychiatry 43, 538–547. Merry, S.N., Hetrick, S.E., Cox, G.R., Brudevold‐Iversen, T., Bir, J.J., McDowell, H., 2012. Cochrane review: psychological and educational interventions for preventing depression in children and adolescents. Evidence‐Based Child Health 7, 1409–1685. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., 2009. Preferred reporting items for systematic reviews and meta-analyses: the Prisma statement. Revista Española De Nutrición Humana Y Dietética 18, e123. Perry, Y., Werner-Seidler, A., 2017. Preventing depression in final year secondary

students: school-Based randomized controlled trial. 19, e369. Moussavi, S., Chatterji, S., Verdes, E., Tandon, A., Patel, V., Ustun, B., 2007. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370, 851–858. Pattison, C., Lynd-Stevenson, R.M., 2001. The prevention of depressive symptoms in children: the immediate and long-term outcomes of a school-based program. Behav. Change 18, 92–102. Poppelaars, M., Tak, Y.R., Lichtwarck-Aschoff, A., Engels, R.C., Lobel, A., Merry, S.N., Lucassen, M.F., Granic, I., 2016. A randomized controlled trial comparing two cognitive-behavioral programs for adolescent girls with subclinical depression: a schoolbased program (Op Volle Kracht) and a computerized program (SPARX). Behav. Res. Ther. 80, 33–42. Possel, P., Horn, A.B., Groen, G., Hautzinger, M., 2004. School-Based prevention of depressive symptoms in adolescents: a 6-Month follow-up. J. Am. Acad. Child Adolesc. Psychiatry 43, 1003–1010. Possel, P., Adelson, J.L., Hautzinger, M., 2011. A randomized trial to evaluate the course of effects of a program to prevent adolescent depressive symptoms over 12 months. Behav. Res. Ther. 49, 838–851. Possel, P., Martin, N.C., Garber, J., Hautzinger, M., 2013. A randomized controlled trial of a cognitive-behavioral program for the prevention of depression in adolescents compared with nonspecific and no-intervention control conditions. J. Couns. Psychol. 60, 432–438. Quayle, D., Dziurawiec, S., Roberts, C., Kane, R., Ebsworthy, G., 2001. The effect of an optimism and lifeskills program on depressive symptoms in preadolescence. Behav. Change 18, 194–203. Reivich, K., Gillham, J.E., Chaplin, T.M., Seligman, M.E., 2013. From helplessness to optimism: the role of resilience in treating and preventing depression in youth. Handbook of Resilience in Children. Springer, pp. 201–214. Rivet-Duval, E., Heriot, S., Hunt, C., 2011. Preventing adolescent depression in mauritius: a universal school-based program. Child Adolesc. Ment. Health 16, 86–91. Roberts, C.M., Kane, R., Bishop, B., Cross, D., Fenton, J., Hart, B., 2010. The prevention of anxiety and depression in children from disadvantaged schools. Behav. Res. Ther. 48, 68–73. Roberts, R.E., Lewinsohn, P.M., Seeley, J.R., 1991. Screening for adolescent depression: a comparison of depression scales. J. Am. Acad. Child Adolesc. Psychiatry 30, 58–66. Rohde, P., Stice, E., Shaw, H., Gau, J.M., 2015. Effectiveness trial of an indicated cognitive-behavioral group adolescent depression prevention program versus bibliotherapy and brochure control at 1- and 2-year follow-up. J. Consult. Clin. Psychol. 83, 736–747. Rooney, R., Hassan, S., Kane, R., Roberts, C.M., Nesa, M., 2013. Reducing depression in 910 year old children in low SES schools: a longitudinal universal randomized controlled trial. Behav. Res. Ther. 51, 845–854. Rooney, R., Roberts, C., Kane, R., Pike, L., Winsor, A., White, J., Brown, A., 2006. The prevention of depression in 8- to 9-Year-Old children: a pilot study. J. Psychol. Couns. Schools 16, 76–90. Sandra Jo, W., Lipsey, M.W., Derzon, J.H., 2003. The effects of school-based intervention programs on aggressive behavior: a meta-analysis. J. Consult. Clin. Psychol. 71, 136–149. Sawyer, M.G., Harchak, T.F., Spence, S.H., Bond, L., Graetz, B., Kay, D., Patton, G., Sheffield, J., 2010. School-based prevention of depression: a 2-year follow-up of a randomized controlled trial of the beyondblue schools research initiative. J. Adolesc. Health 47, 297–304. Sheffield, J.K., Spence, S.H., Rapee, R.M., Kowalenko, N., Wignall, A., Davis, A., McLoone, J., 2006. Evaluation of universal, indicated, and combined cognitive-behavioral approaches to the prevention of depression among adolescents. J. Consult. Clin. Psychol. 74, 66–79. Shochet, I.M., Dadds, M.R., Holland, D., Whitefield, K., Harnett, P.H., Osgarby, S.M., 2001. The efficacy of a universal school-based program to prevent adolescent depression. J. Clin. Child Psychol. 30, 303–315. Shochet, I.M., Osgarby, S.M., 1999. The resourceful adolescent project: building psychological resilience in adolescents and their parents. Aust. Educ. Dev. Psychol. 16, 46–65. Smith, M., Calam, R., Bolton, C., 2009. Psychological factors linked to self-reported depression symptoms in late adolescence. Behav. Cogn. Psychother. 37, 73–85. Sockol, L.E., 2015. A systematic review of the efficacy of cognitive behavioral therapy for treating and preventing perinatal depression. J. Affect. Disord. 177, 7–21. Sockol, L.E., Epperson, C.N., Barber, J.P., 2013. Preventing postpartum depression: a meta-analytic review. Clin. Psychol. Rev. 33, 1205–1217. Southwick, S.M., Charney, D.S., 2012. The science of resilience: implications for the prevention and treatment of depression. Science 338, 79–82. Southwick, S.M., Vythilingam, M., Charney, D.S., 2005. The psychobiology of depression and resilience to stress: implications for prevention and treatment. Annu. Rev. Clin. Psychol. 1, 255–291. Spence, S.H., Sheffield, J.K., Donovan, C.L., 2005. Long-term outcome of a school-based, universal approach to prevention of depression in adolescents. J. Consult. Clin. Psychol. 73, 160–167. Stallard, P., Sayal, K., Phillips, R., Taylor, J.A., Spears, M., Anderson, R., Araya, R., Lewis, G., Millings, A., Montgomery, A.A., 2012. Classroom based cognitive behavioural therapy in reducing symptoms of depression in high risk adolescents: pragmatic cluster randomised controlled trial. BMJ 345, e6058 Clinical research. Sterne, J.A., Gavaghan, D., Egger, M., 2000. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J. Clin. Epidemiol. 53, 1119–1129. Stice, E., Shaw, H., Bohon, C., Marti, C.N., Rohde, P., 2009. A meta-analytic review of depression prevention programs for children and adolescents: factors that predict magnitude of intervention effects. J. Consult. Clin. Psychol. 77, 486–503.

163

Journal of Affective Disorders 270 (2020) 150–164

L. Ma, et al. Stice, E., Rohde, P., Gau, J.M., Wade, E., 2010. Efficacy trial of a brief cognitive-behavioral depression prevention program for high-risk adolescents: effects at 1- and 2year follow-up. J. Consult. Clin. Psychol. 78, 856–867. Tak, Y.R., Lichtwarck-Aschoff, A., Gillham, J.E., Van Zundert, R.M.P., Engels, R.C.M.E., 2015. Universal school-based depression prevention ‘Op volle Kracht’: a longitudinal cluster randomized controlled trial. J. Abnorm. Child Psychol. 44, 949–961. Thompson, S.G., Sharp, S.J., 1999. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat. Med. 18, 2693–2708. Thornton, A., Lee, P., 2000. Publication bias in meta-analysis: its causes and consequences. J. Clin. Epidemiol. 53, 207–216. Tugade, M.M., Fredrickson, B.L., Feldman Barrett, L., 2004. Psychological resilience and positive emotional granularity: examining the benefits of positive emotions on coping and health. J. Pers. 72, 1161–1190. Waugh, C.E., Koster, E.H., 2015. A resilience framework for promoting stable remission from depression. Clin. Psychol. Rev. 41, 49–60.

Wijnhoven, L.A., Creemers, D.H., Vermulst, A.A., Scholte, R.H., Engels, R.C., 2014. Randomized controlled trial testing the effectiveness of a depression prevention program ('Op Volle Kracht') among adolescent girls with elevated depressive symptoms. J. Abnorm. Child Psychol. 42, 217–228. Wingo, A.P., Wrenn, G., Pelletier, T., Gutman, A.R., Bradley, B., Ressler, K.J., 2010. Moderating effects of resilience on depression in individuals with a history of childhood abuse or trauma exposure. J. Affect. Disord. 126, 411–414. Wu, Y.L., Zhao, X., Ding, X.X., Yang, H.Y., Qian, Z.Z., Feng, F., Lu, S.S., Hu, C.Y., Gong, F.F., Sun, Y.H., 2017. A prospective study of psychological resilience and depression among left-behind children in China. J. Health Psychol. 22, 627–636. Yates, T.M., Masten, A.S., 2004. Fostering the Future: Resilience Theory and the Practice of Positive Psychology. John Wiley & Sons, Ltd. Yu, D.L., Seligman, M.E., 2002. Preventing depressive symptoms in Chinese children. Prevent. Treat. 5, 9a.

164