For this assignment, your goal is to present what you have learned about your topic across all of the articles you gathered and read. If your thoughts have evolved since you turned into your outline, it is OK to deviate from what you submitted in your outline last week! You are encouraged to:
- Use headings within your literature review to guide the reader through your thoughts as you summarize the key points you want to make.
- Be scholarly, but don’t use overcomplicated language. Be direct. Do not use colloquial expressions, and do not use first person (e.g., “I,” “me,” “my”).
- Do not simply write a paper that brings the reader through a summary of what you read article-by-article (like mini article critiques). Instead, each paragraph should be about a different point you want to make about your topic and what the combined literature demonstrates about that point. Expect to use multiple citations in each paragraph.
- Take a look at the introduction for the article you critiqued in Week 3 if you are unsure how to write your literature review. Notice how their literature review is focused on topics and a synthesis of others’ research and theory. Model your structure, tone, and “flow of thoughts” after theirs. Also, note how the authors have used citations and where they are placed.
- Create a References page that only uses references (not annotations). Be sure that your page lists your references in the proper order and that formatting and punctuation are correct.
Parents’ and Children’s ADHD in a Family System
Published online: 8 February 2017 # Springer Science+Business Media New York 2017
Abstract ADHD symptoms Brun in families^. However, rel- atively little is known about the ways in which parents’ symp- toms might additively and interactively work with the parent- ing environment, to influence (and be influenced by) the de- velopmental trajectory of symptoms in children and adoles- cents. In this commentary on the special section addressing this gap in knowledge, emphasis is placed on the importance of replicating and extending family-wide studies of ADHD symptoms and etiology. The current papers exemplify the leading-edge of such efforts, demonstrating the feasibility and rigor with which studies are being conducted, utilizing longitudinal and experimental designs. Families and parenting environments operate as a system in which individuals influ- ence each other’s symptoms and functioning. In so doing, parents produce tremendous variability within (as well as be- tween) each family in individuals’ ADHD symptoms from childhood through adulthood, via gene-environment transac- tions that may even begin during prenatal development.
Keywords Attention deficit hyperactivity disorder .
Genetics . Parenting . Family systems . Intervention
ADHD Bruns in families^, is heritable, and is consistently associated with harsher and less positive parenting environ- ments. This conclusion is not news, and is probably regarded as a foregone conclusion by most readers of the journal. However, major gaps in empirical evidence still exist.
Nearly all of the evidence is based on small clinical studies or small-to-large behavioral genetic sibling (mostly twin) studies. There is a need for more research that empirically estimates the magnitude of effect sizes for the associations between parent ADHD and child ADHD, and that also exam- ines associations with variability in parent-child relationship processes. The current special section represents a rigorous and very important step toward addressing several of these gaps. The authors and editors are to be commended for com- piling an outstanding collection of review and empirical pa- pers that examine connections (and some surprising discon- nections) between parents’ and children’s ADHD symptoms, and parent-child relationship dynamics.
As a set, the special section is tackling some of the most basic and important questions and assumptions about familial transmission of ADHD and comorbid disorders, in an effort to better understand Bwhat works^ (and what does not work) when it comes to supporting parents in youth- and family- focused interventions. The special section situates the familial transmission and parenting topic in contemporary theories of family systems, genetic factors, and gene-environment inter- action. That is, the papers implicitly or explicitly articulate hypotheses regarding: (a) the complex (potentially indistin- guishable) causal influence that each family member has on other family members as part of systems of individuals and behaviors; and (b) parent-child genetic similarity operating alongside socialization influences via parenting behavior, reflecting complex gene-gene and gene-environment interac- tion processes.
Families and ADHD Symptoms as Systems
The collection of papers presents an important set of empirically-tested effects that reflect a potential causal role
* Kirby Deater-Deckard firstname.lastname@example.org
1 Department of Psychological and Brain Sciences, University of Massachusetts Amherst, 441 Tobin Hall, Amherst, MA 01003, USA
J Abnorm Child Psychol (2017) 45:519–525 DOI 10.1007/s10802-017-0276-7
for both child and parent ADHD symptoms in the etiology of problematic parent-child relationship processes. Because it is neither feasible nor wise to attempt to summarize all of the key pertinent findings, I have provided a Bbird’s eye view^ sum- mary (Table 1) of the key constructs that were assessed and tested in the empirical papers in the special section. This table also highlights whether different results were found within a study, as a function of parent gender or ADHD dimension (i.e., inattention, hyperactivity/impulsivity).
Two patterns in Table 1 stand out. First, in every study in which mothers and fathers were assessed and compared (Moroney et al. 2017, and Auerbach et al. 2017, had samples that were all or nearly all mothers only), there were significant gender differences in associations in the models (noted with a Bg^ for gender, in Table 1). Second, in the two studies in which the two major dimensions of parental ADHD symptoms were examined separately, dimension differences were found (noted with a Bd^ for dimensions in Table 1). Relatedly, inclusion of parental comorbid conditions had an impact on results in the two studies that examined these. In spite of the moderate to substantial covariation between adults’ dimensions of ADHD and comorbid conditions, inat- tentive and hyperactive-impulsive symptom scores showed distinct patterns of additive and interactive associations with the other variables being examined in those papers’ predictive models. As Chronis-Tuscano et al. (2017) have noted in their review of the treatment literature, there is already good reason to expect distinct additive and interactive effects for mothers and fathers and for particular dimensions of symptoms or comorbid conditions, when predicting trajectories of child ADHD symptoms and parent-child relationship processes. The current set of empirical findings further reinforce the im- portance of considering both parenting partners and distinct dimensions and comorbid conditions in mothers and fathers. It remains to be seen whether the distinct predictive patterns reported in the special section are replicated for mothers ver- sus fathers and inattentive versus hyperactive-impulsive symptoms. Still, there already is sufficient evidence that
studying only one parenting partner or one dimension of ADHD typically will not yield findings that generalize to the other parenting partner or dimension of ADHD. The same reasoning applies to inclusion of multiple dimensions of ADHD and comorbid conditions for children and adolescents in future family studies.
One of the methods for examining Bsystem^ concepts is to test higher-order interactive effects between both parents’ ADHD symptoms, as well as with child symptoms. Two of the papers did this (Williamson et al. 2017; Wymbs et al. 2017) and one other conducted the preliminary analyses for future testing of an interaction effect (Breaux et al. 2017, who tested additive effects of mother, father and child ADHD symptoms predicting subsequent child ADHD symptoms). As a set, these three papers make clear that significant additive and interactive effects of mother and father ADHD symptoms can be expected, with the papers delving into some detail in inferences regarding Bmatch/mismatch^ and Bcompensatory^ processes in couples’ parenting as part of the family system. However, it is just as noteworthy thatWymbs et al. found little evidence for significant parent-by-parent-by-child ADHD three-way interaction effects. Still, when considered on the whole, the evidence from these tests of potential additive and interactive effects in the prediction of parenting processes and growth in child ADHD symptoms indicates the need for future studies to be sufficiently large for detecting multiple higher-order family member interaction effects.
The challenge that our field faces for tackling this complex- ity is that the results from post-hoc probing of statistical inter- action effects are hard to replicate. This is especially true for three-way and higher-order interaction effects, and interaction effects that are detected using correlational designs. As most of the authors in the current section emphasize, such tests of parent-parent, parent-child, and family triad symptoms’ addi- tive and interactive effects require rigorous study designs and analytic methods. However, these standard approaches alone may not be sufficient for capturing robust replicable patterns at the family level, when it comes to identifying associations
Table 1 Special section papers’ statistically significant additive or interactive effects
Cross-sectional studies Longitudinal studies
Williamson Wymbs Nikolas Moroney Auerbach Breaux
Parent ADHD ✓g,d ✓g ✓ ✓ ✓d ✓g
Parent comorbidity na ✓g na na na ✓g
Family history or genetics na na ✓ na ✓ ✓g
Family stress/adversity na na na na ✓ ✓g
Parenting behavior ✓g,d ✓g ✓ ✓ ✓ ✓g
Child ADHD ✓ ✓ ✓ ✓ ✓d ✓
Child comorbidity na ✓ ✓ ✓ na ✓
g gender difference in effect, d ADHD dimension difference (inattentive vs. hyperactive/impulsive) in effect, na not assessed
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between ADHD symptoms, parent-child relationship process- es, and other constructs of interest. On this point, I offer two suggestions regardingmethodology (a two-part idea involving variable-centered and family-centered analyses), and a third suggestion regarding a family system conceptual framework. These are suggested not only as alternatives to traditional tests of variable-by-variable interaction effects (e.g., mother ADHD by father ADHD, etc.) and family systems theory concepts, but as additional complementary approaches.
Regarding methods and a variable-centered approach, in- vestigators can use family-level composite indices representing overall family history and symptom Bload^ for each family. This approach has been used widely in epidemi- ological studies, especially those striving to identify rare ge- netic variants of large effect size for discrete diseases and disorders (e.g., Hopper et al. 2005). The Nikolas and Momany (2017) paper in the current section provides a ver- sion of this kind of approach using examination of genotypes in multiple family members. As a general analytic approach, such measurement scaling and analytic methods might be useful for any family-based studies of dyads, triads or beyond. Within such a framework, competing hypotheses can be tested regarding dyad, triad, and family-level linear (i.e., additive) and nonlinear (i.e., interactive) effects. Researchers can com- pute (either separately for each dimension of ADHD and co- morbid disorders, or for overall ADHD symptoms) an overall symptom load (e.g., sum or count score) for each dyad, a triad, or whole family.
The family-wide index of the continuum of ADHD symp- toms can then be examined for linear and nonlinear associa- tions with other constructs of interest, such as the parent-child relationship. An example is provided in Fig. 1, in which the potential statistical prediction of harsh, negative family rela- tionship processes (inter-partner, parent-child, or a composite of these) from between-family variation in family-wide ADHD symptom load is estimated. Competing hypotheses can be tested for linear and nonlinear effects or functions. In this example, one would test for an Benhancing^ pattern (ac- celerating effect at higher levels of family-wide symptom load) versus a Bswamping^ pattern (decelerating effect at
higher levels of family-wide symptom load). Estimating and testing for such competing hypothesized functions in no way prevents researchers from also using traditional tests of inter- action effects. However, the addition of such family-level function estimation might yield more replicable findings that also lead to insights about Bthreshold effects^ in the family system that could be very useful for clinical prevention and intervention or social, medical and educational policy (e.g., May and Bigelow 2005). What may matter just as much (or even more) than a specific two-way or three-way interaction effect in family members’ ADHD symptoms, is that a certain threshold is exceeded in family-wide ADHD symptoms.
Another complementary methodological toolbox involves identification of replicable and clinically meaningful qualita- tive sub-groups of dyads, triads or families based on symp- toms. This identification can be done in a fully exploratory way (using cluster, latent class, or latent profile analysis methods; e.g., McCutcheon 1987), or confirmatory algorithms can be used to specify a priori anticipated sub-groups of fam- ilies (e.g., comparing a group of families for which mother- father, parent-child, or triad ADHD symptom types and levels are very similar, to a group of families with average differ- ences in symptom profiles and a group with notable symptom discrepancies). These groups can then be compared on a host of other constructs of interest. Such an approach could be used, for instance, to test for some of the proposed family- system compensatory and match/mismatch concepts highlighted in several of the papers in the special section.
Turning to conceptualizations of the family system, the special section papers also inspire us as readers to continu- ally reconsider the value of applying longstanding theo- rized family transactional models (e.g., Minuchin et al. 1975; Olson 2000) as heuristics for understanding ADHD symptoms, parenting, and intergenerational transmission. There are multiple examples of this already in the family ADHD literature, as demonstrated in most of the special section empirical papers and summarized in the review pa- per by Chronis-Tuscano et al. (2017). As another example of a family systems lens, my colleagues and I recently de- scribed a dyadic transactional model of each partner’s
Fig. 1 ADHD symptoms and harsh reactive family relationship processes. This figure illustrates examples of two competing hypotheses regarding the family-wide level of ADHD (and possibly comorbid
disorder) symptoms and distinct associations with variance in distressed, conflicted parent-parent, parent-child, and family triad relationships
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noxious behavior as an elicitor of stress reactivity and self- regulation (including cognitive-affective, behavioral, and physiological) in the other. These effects work together to serve an ongoing relationship process that reinforces adap- tive or maladaptive functioning (including symptoms of psychopathology) in both relationship partners (Deater- Deckard et al. 2016). Accordingly, ADHD symptoms them- selves and their underlying endophenotypes (e.g., execu- tive function deficits, physiological hypo- or hyper-reactiv- ity) in parents and children alike play crucial roles in the etiology of chronic harsh reactive and insensitive parenting behavior, and growth over time in both dyad partners’ symptoms. This kind of family process theorizing is evident in several of the current special section papers. Also of relevance regarding ADHD endophenotypes, a coinciding complementary two-issue special section at Journal of Family Psychology (2017, Volume 31, Issues 1–2) includes 11 studies of parental neurobiological and neurocognitive factors (e.g., inhibitory control, working memory, effortful control, heart rate reactivity as well as variability [vagal tone]) and their role in family processes and children’s functioning. Although none examined ADHD symptoms specifically, most of those studies’ results align with the studies in the current special section. Just like the child, the parent’s ADHD symptoms and underlying deficits in executive function and physiological self-regulation influ- ence each other in the parent-child dyad (and probably the parenting partner dyad as well). It is apparent and wel- comed, that a Bnew norm^ for family systems studies of ADHD and key neurobiological vulnerability factors, is for studies that place equal emphasis on individual and dy- adic parent and youth symptoms, functioning, and relation- ships. Yet there is a caveat. The findings from multi-level family-wide empirical approaches present new challenges for translation in clinical contexts. It remains to be seen whether such approaches can be applied to improve the efficacy of existing individual and family-level intervention and prevention approaches.
Family Diversification of ADHD Symptoms: Gene-Environment Processes
Although it may not have been a stated goal of the authors in all of the papers, the empirical papers as a group (and a few of the papers quite intentionally) provide critically important data on the effect sizes pertaining to familial transmission in ADHD symptoms. Families are generators of diversity in ADHD and related symptoms—like most aspects of human variation, which arises from within (as well as between) each family. What is the evidence for this diversification? To ex- amine this question, correlations between family members’ ADHD symptoms from the largest relevant study to date
(see Table 4 of Boomsma et al. 2010) along with relevant correlations from the current special section set of papers, are summarized in Fig. 2. The figure shows unweighted aver- ages of correlations reported in each paper that I computed (often, averaged across dimensions of parent or child ADHD symptoms, across both parents, and across negativity and pos- itivity scales in the parent-child relationship). Because they are distinct in various ways, twin-pair correlations (in Boomsma et al.) were not included in the estimates. The com- puted average correlations in Fig. 2 can be thought of as effect sizes, with larger positive or negative correlations approaching 1.0 indicating stronger effects, and correlations approaching 0.00 indicating weaker effects.
First, consider path BA^ in Fig. 2—parenting partner sim- ilarity, estimated as the correlation between symptoms in mothers and fathers; a higher positive correlation would indi- cate greater partner similarity in symptoms, and a negative correlation would indicate partner differentiation. These data suggest that there is only modest selective pairing (i.e., assor- tative mating) of partners based on ADHD symptoms—either selecting someone more alike or more different from oneself. These potential selection effects must be taken into account when evaluating evidence of intergenerational transmission of symptoms, because selection on symptoms can systematically bias estimates of effect sizes in family studies (e.g., Loehlin et al. 2009). Fortunately, with an overall averaged effect size of 0.10, this does not appear to be a major concern for studies of family processes and family member ADHD symptoms. As many of the papers in the special section make clear, it is critically important to take into consideration both parents’
Fig. 2 Effect sizes for links between parent-parent-child ADHD symp- toms and family relationship processes. This figure illustrates effect sizes (correlations or beta weights [ϯ]) from a Boomsma et al. (2010) and current special section papers (all 2017): b Breaux et al., c Wymbs et al., d
Williamson et al., e Auerbach et al., f Moroney et al., gNikolas & Momany, and AVE unweighted average effect size. A = mother-father symptom similarity; B = parent-child symptom similarity; C = link be- tween parenting environment and child symptoms; D = link between par- enting environment and parent symptoms
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ADHD symptoms, because on average, one parent’s symp- toms tell us little about the other’s—and both contribute as part of a family system. I return to this crucial point later.
That partner selection is probably negligible for ADHD symptoms is not too surprising. Studies of related tempera- ment and personality facets (e.g., activity, impulsivity, dis- tractibility) tend to show similarly modest partner correlations (Watson et al. 2004). Perhaps far more surprising is the modest effect size found for parent-child similarity in ADHD symp- toms—shown as path BB^ in Fig. 2. Like path BA^ (partner similarity in symptoms), a higher positive correlation would indicate greater parent-child similarity in symptoms, and a negative correlation would point to greater parent-child differ- entiation. These studies had an overall averaged correlation of 0.23. This effect size fits with existing studies of full siblings. For example, Boomsma et al. (2010) reported sibling similar- ity (excluding twin pairs) of 0.09 averaged across gender com- binations. In Nikolas and Burt (2010), a meta-analysis of twin and adoptive sibling studies of ADHD reportedmodal sibling- similarity correlations in the 0.20 range for fraternal twins (with the range of similarity correlations in some studies in- cluding 0.00 and even slightly below 0.00). Indeed, some of the smallest (and sometimes negative) correlations for frater- nal twin and full-sibling similarity are seen in parents’ ratings of ADHD-related facets of temperament including attention, impulsivity, and activity level (Mullineaux et al. 2009).
How can this be? Heritability estimates for ADHD are not small—but this is because identical twin similarity is much more substantial. Importantly, this heritability effect includes additive and interactive effects of many genes and environ- mental inputs, reflecting gene-by-gene interaction effects and possibly unidentified gene-environment interaction effects as well (Mullineaux et al. 2009; Nikolas and Burt 2010). As several papers in the current section make clear, unique geno- types and unique experiences within the same family can con- tribute to very substantial differences between family mem- bers. This is also apparent when symptoms in extended-family members (e.g., brothers and sisters of parents, cousins of chil- dren) are considered; this information on extended family net- works adds critical information for examining how the disor- der and its comorbid conditions may Bmove through^ and operate within family systems (Breaux et al. 2017). The emerging picture points to families as Bdifferentiation generators^—through additive and interactive genetic and en- vironmental mechanisms, including robust effects of differen- tial parenting behavior toward sibling children that reinforces existing differences in behavior and functioning (Dunn and Plomin 1990).
Another major contribution of the special section is its ex- amination of the links between parent and child ADHD, and the parenting environment. In the commentary, I refer globally to Bparent-child relationship processes^ indicated as higher levels of negative affect, conflict, and harsh control—and
lower levels of sensitivity, warmth, and positive control. As reported in the literature reviews of all of the papers in the section, this overall pattern of parent-child relationship dy- namics in families of youth with ADHD is well documented (represented as path BC^ in Fig. 2). The current studies pro- vide further evidence for that pattern, but also offer a crucial expansion of the literature by addressing parent ADHD symp- toms and their potential role in these family processes (repre- sented as path BD^ in Fig. 2). The correlations shown in the figure again are the most global averaged estimates—as be- fore, averaged across both parents and multiple dimensions of parent or child ADHD (when applicable), and dimensions of parenting (scaled such that a higher score corresponds with more negative and less positive parenting). As with other paths, a higher positive correlation would indicate that more ADHD symptoms are associated with more negative and less positive parent-child relationship processes; a larger negative correlation would indicate that more ADHD symptoms are linked with less negative and more positive parenting. In the current set of studies, the overall averaged correlation was 0.18 (for child ADHD and parent ADHD symptoms alike) with parent-child relationship processes, indicating a consis- tent and usually modest (depending on the study in question) association betweenmore symptoms, and greater negativity or less positivity in the parent-child relationship.
It is worthwhile to pause and consider a potentially major problem for interpretation of the effects in paths C and D in Fig. 2. Specifically, because the effects are based almost en- tirely on correlational studies of genetically related parents and youth, it is plausible that the effects simply reflect the Bbackground^ genetic similarity of the parents and children (referred to as Bpassive gene-environment correlation), rather than reflecting social relationship and learning processes. Considering passive gene-environment correlation is impor- tant. Ignoring it leaves open the possibility that intervening on parenting and parent-child relationship processes only ad- dresses another aspect of symptoms of family risk for ADHD, rather than addressing an actual social-relational cause of growth or decreases in symptoms over time and be- tween generations.
Few of the current section’s studies tested for passive gene- environment correlation explicitly, but there are several pieces of evidence suggesting that passive gene-environment corre- lation is not an issue. First, in the multiple current studies that found significant associations between parenting behavior and the parent’s or child’s ADHD symptoms, those associations usually were not substantially affected by statistically control- ling for the child’s or parent’s (respectively) ADHD symp- toms. If passive genetic correlation effects were explaining the results, statistically controlling for the other family mem- bers’ symptoms would fully account for the observed associ- ations between ADHD symptoms and parent-child relation- ship variables. Second, one of the longitudinal studies
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(Moroney et al. 2017) found that parental negativity toward the child statistically mediated or accounted for the association between parent and child ADHD symptoms over time, while controlling for both individuals’ prior symptoms. Again, if passive genetic correlation was explaining the results, this longitudinal predictive pattern would not be seen. Third, in a direct test for passive gene-environment correlation, Nikolas and Momany (2017) found no associations of variations in a specific candidate gene for ADHD symptoms (i.e., dopamine receptor 4), and variation in parenting behaviors. Fourth, Wymbs et al. (2017) used an experiment involving Bconfederate^ child actors who played the role of a child with or without ADHD symptoms during observed interaction with couples (Wymbs et al. 2017). Their findings showed the ex- pected link between child challenging behaviors and negative adult behavior toward the child (path C in Fig. 2), and then some. The average correlation was 0.40 between ADHD sta- tus of the confederate child actor, and greater negative or less positive parenting behavior across mothers and fathers. Because the adults and children in the observed interactions were genetically unrelated, the association between observed child symptoms and observed parenting behavior reflected social-behavioral influences between child and adults, rather than underlying passive gene-environment correlation effects. Similar results also have been reported in analyses of geneti- cally unrelated children and teachers (Greene et al. 2002; Mejia and Hoglund 2016).
The special section also caused me to reconsider my prior collaborative work examining genetically related and unrelat- ed (i.e., adoptive, foster) parent-child dyads (summarized in Deater-Deckard 2009). Those studies showed similar effects overall, for genetically related and unrelated (i.e., adoptive) dyad types. Furthermore, we found that the same mother’s differential positive parenting toward her genetically identical twins was correlated with identical twin differences in levels of attentional control (with effect sizes in the 0.20 range, similar to effect sizes for path C in Fig. 2). Taken together— the modest parent-child similarity in ADHD symptoms (path B in Fig. 2), the similar effect sizes for associations between child symptoms and parenting in genetically related and unre- lated dyads (or even larger effects for unrelated dyads), and the similar effect size for identical twin within-family differ- entiation in attention problems —the evidence suggests that passive gene-environment correlation effects are negligible.
Two of the special section’s papers show examples of po- tential additive and interactive effects of measured genetic variations in dopamine transporter and receptor-4 genes (Auerbach et al. 2017; Nikolas and Momany 2017). As these and several other papers in the section emphasize, the future of family research on parent and child ADHD and comorbid conditions will need to include adequately powered studies of gene-by-gene and gene-by-environment interaction effects. Similarly, there is a growing need for studies of the very
earliest intergenerational transmission processes that begin prior to conception, and that influence fetal and post-natal child development. In an adult romantic relationship in which one or both partners have ADHD, some aspects of their indi- vidual and dyadic functioning that affect parenting after a child is born or is adopted, may have existed prior to the child’s arrival. For example, consider the family context dur- ing pregnancy. ADHD and comorbid symptoms in family members can contribute to stress in the pregnant mother, and that stress can have major consequences for the neurological development of the fetus. This is because exposure to high levels of stress hormones in utero can lead to Bepigenetic^ modifications of DNA (i.e., molecular alterations to the struc- tures surrounding the DNA molecule that influence its func- tioning). These epigenetic changes can have lasting effects on gene expression in ways that modify the developing structures and functional effectiveness of the young child’s nervous sys- tem (Nigg 2016). Epigenetic alterations can increase risk for hypo- or hyper-reactivity, deficits in self-regulation, and psy- chopathology (Mulder et al. in press; Neuenschwander and Oberlander in press). Over time after the arrival of the child, the stress from problems in the couple’s and parent-child re- lationships in the family can further exacerbate prenatal and post-natal epigenetic alterations of the child’s developing neu- robiology. In future research, it will be increasingly important to also include pre-conception and prenatal time periods and family relationship contexts, as our field strives to better un- derstand families and ADHD as interweaving systems.
Although translating empirical findings from family-wide studies of ADHD to clinical practice will be a major chal- lenge, testing competing hypotheses about familial trans- mission matters. In practice, when working with families while holding incorrect but testable and refutable assump- tions unnecessarily increases the odds of failure in preven- tion and intervention. Elucidating all of the many complex bioecological family system processes will be arduous, but it is necessary. For instance, the monoamine neurotransmit- ter system includes dopamine, serotonin, and epinephrine and norepinephrine (among other molecules). There are nu- merous candidate genes in relevant regions of DNA, with many common and rare variations that are still being dis- covered. Many of these variations are implicated in the etiology of ADHD and relevant underlying risk factors (e.g., executive function deficit, hyperactivity, impulsivity; for examples of meta-analyses see Barnes et al. 2011; Neale et al. 2010). In future, the family system of environments and genetic risks increasingly will be operationalized with greater precision, and with an eye toward supporting meta- analytic statistical approaches that integrate findings across
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many studies, as well as community and clinical contexts (Le Novère 2015; Lee 2015). In the face of this complexity, the theory and empirically tested models presented in the current special section move us another step forward as we strive to continually improve the rigor and replicability of the evidence in our developmental clinical science.
Compliance with Ethical Standards
Conflict of Interest The author declares that he has no financial conflict of interest.
Auerbach, J. G., Zilberman-Hayun, Y., Atzaba-Poria, N., & Berger, A. (2017). The contribution of maternal ADHD symptomatology, ma- ternal DAT1, and home atmosphere to child ADHD symptomatol- ogy at 7 years of age. Journal of Abnormal Child Psychology. doi:10.1007/s10802-016-0230-0.
Barnes, J. J., Dean, A. J., Nandam, L. S., O’Connell, R. G., & Bellgrove, M. A. (2011). The molecular genetics of executive function: role of monoamine system genes. Biological Psychiatry, 69, e127–e143.
Boomsma, D. I., Saviouk, V., Hottenga, J. J., Distel, M. A., De Moor, M. H., Vink, J. M., et al. (2010). Genetic epidemiology of attention deficit hyperactivity disorder (ADHD index) in adults. PloS One, 5, e10621.
Breaux, R. P., Brown, H. R., & Harvey, E. A. (2017). Mediators and moderators of the relation between parental ADHD symptomatolo- gy and the early development of child ADHD and ODD symptoms. Journal of Abnormal Child Psychology. doi:10.1007/s10802-016- 0213-1.
Chronis-Tuscano, A., Wang, C. H., Woods, K. E., Strickland, J., & Stein, M. A. (2017). Parent ADHD and evidence-based treatment for their children: review and directions for future research. Journal of Abnormal Child Psychology. doi:10.1007/s10802-016-0238-5.
Deater-Deckard, K. (2009). Parenting the genotype. In K. McCartney & R.Weinberg (Eds.), Experience and development: A festschrift in honor of Sandra Wood Scarr (pp. 141–161). New York: Taylor and Francis.
Deater-Deckard, K., Chen, N., & El Mallah, S. (2016). Gene- environment interplay in coercion. In T. Dishion & J. Snyder (Eds.),Oxford handbook of coercive dynamics in close relationships (pp. 23–38). New York, NY: Oxford University Press.
Dunn, J., & Plomin, R. (1990). Separate lives: why siblings are so different. New York: Basic Books.
Greene, R. W., Beszterczey, S. K., Katzenstein, T., Park, K., & Goring, J. (2002). Are students with ADHDmore stressful to teach? Patterns of teacher stress in an elementary school sample. Journal of Emotional and Behavioral Disorders, 10, 79–89.
Hopper, J. L., Bishop, D. T., & Easton, D. F. (2005). Population- based family studies in genetic epidemiology. The Lancet, 366, 1397–1406.
Lee, Y. H. (2015). Meta-analysis of genetic association studies. Annals of Laboratory Medicine, 35, 283–287.
Le Novère, N. (2015). Quantitative and logic modelling of molecular and gene networks. Nature Reviews Genetics, 16, 146–158.
Loehlin, J. C., Harden, K. P., & Turkheimer, E. (2009). The effect of assumptions about parental assortative mating and genotype–
income correlation on estimates of genotype–environment interac- tion in the national merit twin study. Behavior Genetics, 39, 165– 169.
May, S., & Bigelow, C. (2005). Modeling nonlinear dose-response rela- tionships in epidemiologic studies: statistical approaches and prac- tical challenges. Dose-Response, 3, 474–490.
McCutcheon, A. L. (1987). Latent class analysis. Thousand Oaks: Sage. Mejia, T. M., & Hoglund, W. L. (2016). Do children’s adjustment
problems contribute to teacher–child relationship quality? Support for a child-driven model. Early Childhood Research Quarterly, 34, 13–26.
Minuchin, S., Baker, L., Rosman, B. L., Liebman, R., Milman, L., & Todd, T. C. (1975). A conceptual model of psychosomatic illness in children: family organization and family therapy. Archives of General Psychiatry, 32, 1031–1038.
Moroney, E., Tung, I., Brammer, W. A., Peris, T. S., & Lee, S. S. (2017). Externalizing outcomes of youth with and without ADHD: time- varying prediction by parental ADHD and mediated effects. Journal of Abnormal Child Psychology. doi:10.1007/s10802-016- 0215-z.
Mulder, R. H., Rijlaarsdam, J. & Van IJzendoorn, M. H. (in press). DNA methylation: A mediator between parenting stress and adverse child development? To appear in K. Deater-Deckard & R. Panneton (Eds.), Parenting stress and early child development: Adaptive and maladaptive outcomes. New York: Springer.
Mullineaux, P. Y., Deater-Deckard, K., Petrill, S. A., Thompson, L. A., & DeThorne, L. S. (2009). Temperament in middle childhood: a be- havioral genetic analysis of fathers’ and mothers’ reports. Journal of Research in Personality, 43, 737–746.
Neale, B. M., Medland, S. E., Ripke, S., Asherson, P., Franke, B., Lesch, K. P., et al. (2010). Meta-analysis of genome-wide association stud- ies of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 884–897.
Neuenschwander, R., & Oberlander, T. F. (in press). Developmental ori- gins of self-regulation: Prenatal maternal stress and psychobiologi- cal development during childhood. To appear in K. Deater-Deckard &R. Panneton (Eds.),Parenting stress and early child development: Adaptive and maladaptive outcomes. New York: Springer.
Nigg, J. T. (2016). Where do epigenetics and developmental origins take the field of developmental psychopathology? Journal of Abnormal Child Psychology, 44, 405–419.
Nikolas, M. A., & Burt, S. A. (2010). Genetic and environmental influ- ences on ADHD symptom dimensions of inattention and hyperac- tivity: a meta-analysis. Journal of Abnormal Psychology, 119, 1–17.
Nikolas, M., & Momany, A. (2017). DRD4 variants moderate the impact of parental characteristics on child attention-deficit hyperactivity disorder: exploratory evidence from a multiplex family design. Journal of Abnormal Child Psychology. doi:10.1007/s10802-017- 0264-y.
Olson, D. H. (2000). Circumplex model of marital and family sytems. Journal of Family Therapy, 22, 144–167.
Watson, D., Klohnen, E. C., Casillas, A., Nus Simms, E., Haig, J., & Berry, D. S. (2004). Match makers and deal breakers: analyses of assortative mating in newlywed couples. Journal of Personality, 72, 1029–1068.
Williamson, D., Johnston, C., Noyes, A., Stewart, K., & Weiss, M. (2017). Attention-deficit/hyperactivity disorder symptoms in mothers and fathers: family level interactions in relation to parent- ing. Journal of Abnormal Child Psychology. doi:10.1007/s10802- 016-0235-8.
Wymbs, B. T., Dawson, A. E., Egan, T. E., Sacchetti, G. M., Tams, S. T., &Wymbs, F. A. (2017). ADHD and depression symptoms in parent couples predict response to child ADHD and ODD behavior. Journal of Abnormal Child Psychology. doi:10.1007/s10802-016- 0220-2.
J Abnorm Child Psychol (2017) 45:519–525 525
- Parents’ and Children’s ADHD in a Family System
- Families and ADHD Symptoms as Systems
- Family Diversification of ADHD Symptoms: Gene-Environment Processes
Mechanisms of Behavioral and Affective Treatment Outcomes in a Cognitive Behavioral Intervention for Boys
Jeffrey D. Burke & Rolf Loeber
Published online: 27 January 2015 # Springer Science+Business Media New York 2015
Abstract Evidence for effective treatment for behavioral problems continues to grow, yet evidence about the effective mechanisms underlying those interventions has lagged be- hind. The Stop Now and Plan (SNAP) program is a multicom- ponent intervention for boys between 6 and 11. This study tested putative treatment mechanisms using data from 252 boys in a randomized controlled trial of SNAP versus treat- ment as usual. SNAP includes a 3 month group treatment period followed by individualized intervention, which persisted through the 15 month study period. Measures were administered in four waves: at baseline and at 3, 9 and 15 months after baseline. A hierarchical linear modeling strat- egy was used. SNAP was associated with improved problem- solving skills, prosocial behavior, emotion regulation skills, and reduced parental stress. Prosocial behavior, emotion reg- ulation skills and reduced parental stress partially mediated improvements in child aggression. Improved emotion regula- tion skills partially mediated treatment-related child anxious- depressed outcomes. Improvements in parenting behaviors did not differ between treatment conditions. The results sug- gest that independent processes may drive affective and be- havioral outcomes, with some specificity regarding the mech- anisms related to differing treatment outcomes.
Keywords Cognitive behavioral treatment . Mechanisms .
Aggression . Anxiety . Depression
The establishment of sound and replicable models of interven- tion for children’s behavioral problems should remain a prior- ity. Practitioners and administrators benefit from being able to select from an array of evidence-based treatment models to meet varying needs within their community. Additionally, demonstrating that broad forms of intervention (e.g., cognitive behavioral therapy, or CBT) are more effective than others helps to choose between possible alternatives. At the same time, it is crucial to understand the mechanisms within established models that are most associated with desirable outcomes. Doing so helps to make intervention efforts more efficient, and refinements of intervention models can be made with some knowledge about integral elements. Understanding which mechanisms are fundamental to treatment may also provide information about key etiological factors involved in the onset or maintenance of disorders.
Interest in the identification of mechanisms of treatment is not novel. Clinical researchers and theorists have called for a greater focus on this subject for several decades, and it re- mains the case that the literature base contains insufficient evidence to determine which processes are activated by ther- apies in order to effect change (e.g., Kazdin 2011; La Greca et al. 2009). For example, a meta-analysis of interventions in juvenile justice settings (Landenberger and Lipsey 2005) found that, among other factors, interventions that included CBT-based components designed to enhance interpersonal problem solving and to promote anger regulation skill devel- opment were particularly associated with improved outcomes. In fact, accounting for the individual factors or putative mech- anisms of treatment resulted in there being no remaining dif- ferences between part icular intervent ion models (Landenberger and Lipsey 2005).
The SNAP (Stop Now and Plan) intervention model (Augimeri et al. 2007) was developed to capitalize on empir- ically established treatment elements. It is a structured and manualized multi-component intervention model that
J. D. Burke (*) : R. Loeber Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA e-mail: email@example.com
J. D. Burke Department of Psychology, University of Connecticut, 406 Babbidge Road U-1020, Storrs, CT 06269, USA
J Abnorm Child Psychol (2016) 44:179–189 DOI 10.1007/s10802-015-9975-0
incorporates CBT principles throughout. It includes compo- nents of in-group treatment activities for children and parents, and addresses individualized needs through components such as individual mentoring, family therapy sessions, homework help, and other similarly targeted treatment components. The program was originally developed with a primary focus on reducing antisocial behavior, but has also shown effectiveness in reducing children’s affective difficulties (Augimeri et al. 2006, 2007; Koegl et al. 2008).
The SNAP logic model identifies, among the presumed mechanisms of treatment effect, problem solving skills train- ing, emotion regulation skills training, and social skills train- ing. Regarding problem solving, for example, children are taught to consider potential behaviors in response to a partic- ular dilemma, and are taught to evaluate whether a given so- lution might make their problems bigger or smaller. They are instructed to develop solutions that do not hurt others. Chil- dren are also exposed to the problem solving strategies of others. Using role plays, vignette discussions and videotape reviews, children critique one another’s solutions to problems and have the opportunity to observe and reflect upon their own problem-solving efforts.
SNAP teaches emotion regulation skills, including a focus on recognizing cues related to negative affect and working to interrupt the process before responding out of anger or frus- tration. Children are provided with techniques to help redirect themselves, relax and calm themselves, and engage in more mindful solutions. The SNAP model targets social skills de- velopment by capitalizing on the group-based format. Under careful guidance from group leaders, children engage in role plays, provide critiques and feedback to one another, help one another to develop productive solutions to problems, and en- gage in modeling of desirable behavior.
In addition, parent management training is a common com- ponent of effective treatment models for behavioral disorders in youth (Chorpita et al. 2011; Eyberg et al. 2008). The SNAP model provides parenting skills development activities through the use of group sessions with other parents, in which parents discuss dilemmas and concerns and are led in the use of SNAP techniques to address challenging child behavior. Parents not only learn through observation and modeling, but they have the opportunity to discuss their frustrations with other parents who may be experiencing similar difficulties with their children. After group sessions are completed, par- ents may be provided with additional SNAP parenting booster sessions on an individualized, as-needed basis. These compo- nents of the SNAP program are intended to both convey in- formation about appropriate and productive parenting behav- iors to employ with children as well as to provide parents with resources to manage their own affective experiences and help parents cope with the stress of problematic child behaviors.
As noted, these treatment components are not unique to SNAP. Parent behavioral training is a core component of
many effective intervention strategies, such as parent manage- ment training (PMT). These strategies typically focus on the use of behavioral principles to reinforce desirable and compli- ant behavior and to eliminate undesirable and antisocial be- haviors. PMT is often paired with another well-established intervention, problem-solving skills training, or PSST, and problem-solving skills development is a common element in many other treatment models. As with SNAP, problem- solving skills intervention components typically involve working with the child to evaluate multiple solutions to prob- lems and to consider the consequences of varying options. Efforts to improve interpersonal skills are also often included in treatment models (Chorpita and Daleiden 2009; Spence 2003), with the recognition that many of children’s antisocial behaviors emerge from problematic interactions with peers. Landenberger and Lipsey (2005) meta-analysis, as noted pre- viously, also found support for the use of problem-solving skills and anger control components of behavioral interven- tions in juvenile justice populations. However, they did not find support for the effectiveness of social skills training components.
Prior studies have examined similar constructs as putative mediators of treatment outcome. For example, Lochman and Wells (2002) identified treatment related changes in parenting practices and in social cognition, particularly hostile attribu- tional bias, and found some evidence suggesting that they mediated outcomes. However, they also demonstrated that therapeutic mechanisms may vary in their effects on differing outcomes (e.g., delinquency versus school behavior). Chang- es in parenting behaviors have been identified in several stud- ies as mediators of treatment outcomes (e.g., Dishion et al. 1992; Henggeler et al. 2009; Patterson and Forgatch 1995). In a randomized controlled trial for treatment of attention- deficit hyperactivity disorder (ADHD), Hinshaw (2002) found evidence for the effects of parenting behaviors on behavioral outcomes, but did not find differences between treatment con- ditions on parenting behaviors.
Efforts to identify mechanisms of treatment effects for chil- dren with behavioral problems are complicated by two issues. First, behavioral problems are heterogeneous and multiply determined (e.g., Burke et al. 2002), even within distinct di- agnostic constructs such as oppositional defiant disorder (ODD) and conduct disorder (CD). Relatedly, behavioral problems show a high level of comorbidity with depression and anxiety (Angold et al. 1999). In particular, evidence sup- ports separate affective and behavioral dimensions of symp- toms within ODD (Burke et al. 2014). Given this, it should perhaps not be surprising to find that behaviorally-oriented interventions like SNAP influence affective functioning as well. Additionally, it does not seem likely that any single mechanism will sufficiently explain the outcomes associated with interventions for these problems. There remains a great level of need regarding evidence as to how these interventions
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affect outcomes, and whether there are general treatment ef- fects across outcomes, or specific treatment effects on partic- ular mechanisms, which in turn influence particular outcomes.
As part of a structure set of planned analyses, the present paper uses the same sample and data as a prior publication that detailed treatment-related outcomes associated with SNAP (Burke and Loeber in press). This project was the first large- scale, random controlled trial of SNAP conducted indepen- dently of the developers of the SNAP intervention model. Data was collected from one of a small number of implementations of the SNAP progroutside of Canada. Partic- ipants (n=252) were randomly assigned to receive SNAP ser- vices or to receive standard treatment as usual in the community.
The first set of analyses (Burke and Loeber in press) pro- vided a comprehensive assessment of outcomes on measures of ODD, CD, ADHD, depression and anxiety, including both continuous (Child Behavior Checklist (CBCL); Achenbach and Rescorla 2001) and symptom count (Child Symptom In- ventory (CSI); Gadow and Sprafkin 1994) measures. Signifi- cant effects favoring SNAP were identified for CBCL out- comes of Aggressive Behavior, Conduct Problems, External- izing, Internalizing, Withdrawn-Depressed and Anxious De- pressed. Additionally, significant CSI-based symptom count reductions favoring SNAP were found for ADHD, ODD, de- pression and separation anxiety.
As noted above, the literature on treatments for behavioral problems has identified problem-solving skills, emotional reg- ulation skills, social skills development, and parenting skills as particularly relevant treatment components. Since these are also described as key components of the SNAP model, the present analyses were planned during the development of the project in order to test putative treatment mechanisms as identified by the developers of the SNAP program. Specifi- cally, these analyses will test four mechanisms that follow from the literature base: 1) problem solving skills, 2) prosocial behaviors, 3) emotion regulation skills, 4) parenting behav- iors, and 5) parental stress.
We selected two CBCL-measured outcomes from among the available outcomes (see Burke and Loeber in press) to be tested in mediational models. This selection was driven by a desire to present a focused analysis rather than a comprehen- sive and potentially unwieldy consideration of many out- comes. We also selected outcomes with the intent of testing whether putative mechanisms of treatment would be equally associated with changes in markedly varying types of out- comes. Finally, we were interested in considering compara- tively circumscribed outcomes rather than the broader exter- nalizing or internalizing constructs. A primary focus of the SNAP program is to reduce serious behavioral problems; we selected the Aggressive Behavior subscale over Rule Break- ing as an index of serious behavioral problems. To consider a contrasting alternative from among the internalizing
subscales, we chose to test the Anxious-Depressed subscale over Somatic Complaints or Withdrawn-Depressed.
We hypothesized that problem solving skills, prosocial be- haviors, emotion regulation skills, parenting behaviors and parental stress will be enhanced by SNAP treatment partici- pation relative to standard services as usual in the community (STND), and that these in turn would predict improvements across both types of outcomes. We predicted that tests of me- diation will reveal significant mediation of treatment group effects on outcomes for each of the aforementioned mecha- nisms. However, due to the heterogeneous and multiply deter- mined nature of these outcomes, we did not anticipate observ- ing any instances of full mediation.
Parents calling for services at the two SNAP-providing agen- cies in the region were informed about the study (N=481). Any parents expressing interest were informed that study par- ticipation would involve a random chance of participating in SNAP or standard services (STND). After being given basic information about the study, approximately 30 % of parents (n=144) declined further contact regarding the study. The most common reasons for doing so were an unwillingness to be randomly assigned to treatment other than SNAP, or al- ready being involved in other behavioral health services for the child. Of those interested in learning more about the study, 34 declined participation, 25 were not eligible, and 26 were lost to further contact. Of the 252 enrolled into the study, randomization resulted in 130 boys participating in SNAP and 122 in standard services. Study participation at the 3, 9 and 15 month interview waves was 89.2 % (n=116), 80.0 % (n=104), and 84.6 % (n=110), respectively, in the SNAP condition, and 89.3 % (n=122), 83.6 % (n=109) and 82.8 % (n=101), respectively, in the STND condition.
In order to avoid interfering with the clinical needs of chil- dren in the study, the only restriction placed on participants regarding services, subsequent to assignment to condition, was that children in SNAP could not receive the most inten- sive community-based service (wraparound) and conversely, those in the STND service condition could not participate in SNAP.
Eligibility Children had to have a qualifying behavioral score via parent report (CBCL) or teacher report (Teacher Report Form; Achenbach and Rescorla 2001) of Aggressive Behavior (Tscore greater than or equal to 70), Rule Breaking (70), DSM Conduct Problems (70) or Externalizing Behavior (T score greater than or equal to 64).
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Random Assignment Upon signing consent and meeting eli- gibility requirements, participants were randomly assigned to study condition. Randomization was performed by the study investigators independently of the treatment providers using a random number generating computer program. As an intent- to-treat study, once assigned to condition, participants remained in the study, regardless of their actual level of par- ticipation in the treatment to which they were assigned.
At the time of the initiation of the study, only the SNAP for Boys version of the SNAP program was provided in the re- gion. As a result, only boys could be enrolled in the study. Half the sample (50 %) reported a household income of $14, 999 or less; 14 % reported an income above $33,201. Three- quarters of parents identified their child as African American, 13 % asWhite, and 10 % using more than one racial category. Parent participants were almost exclusively female; in only 14 cases was the parental informant male. The mean age of the boys was 8.5 (SD=1.8) years of age; boys ranged from 6.0 to 12.8 years at baseline. Estimated IQ ranged from 60 to 128; the average was 91.6 (SD=12.5). There were 38 participants (14.7 %) who had parent-reported police contact due to the youth’s behavior. Of the total sample, 82.9 % (n=175) were non-siblings, and 17.1 % (n=77) were siblings, in 36 sibling clusters. There were 41 siblings in SNAP and 36 siblings in STND; the difference was not significant (χ2=0.12, p=0.72). Analyses accounted for nested observations among siblings.
SNAP includes several distinct treatment components. First, during the initial 12 week period, separate parent and child group-based modules are provided on a weekly basis. Groups for parents and children are conducted simultaneously. SNAP children’s groups adhere closely to a manual in order to pro- vide consistent and structured content. Each group session moves through a sequence of activities, and addresses a spe- cific topic for the week, such as stealing, coping with anger, and managing group pressure. Children are taught cognitive and behavioral skills and are given structured practice experi- ences, observation of others and rehearsal to apply these skills to specific circumstances. Each group session makes signifi- cant use of structured elements of role-play, problem-solving and peer feedback to evaluate alternative solutions. Children are helped to evaluate whether the solutions that they generate for various problems will lead to improved or to poorer out- comes. During parent groups, parents are led in psychoeducational content, and discuss with other parents their use of parenting strategies and their efforts at coping with their own emotional reactions. Parent group content is also manualized and incorporates SNAP principles.
Upon completion of group sessions, after 12 weeks, each child is re-assessed to determine where clinical needs contin- ue. SNAP treatment at this point is individualized; SNAP
providers select from established treatment modules to ad- dress a child and family’s specific needs. Modules include individualized SNAP family counseling sessions, SNAP booster sessions, a mentoring component, school advocacy, academic tutoring, a victim restitution module, crisis counsel- ing and a fire-setting component. A leadership module pro- vides children who have been successful in the program an opportunity to provide peer mentoring. There is no set dura- tion for this portion of SNAP treatment; it is provided as long as clinical needs remain for a particular child or family.
SNAP Service Use Children in the SNAP condition attended an average of 6.25 (SD=4.3) of the 12 child sessions, and parents attended an average of 5.02 (SD=4.2) of the 12 parent sessions. Of the 130 children assigned to SNAP, there were 30 children (23.1 %) who attended no child SNAP groups and 37 parents (28.5 %) who attended no parent SNAP groups.
Subsequent to participating in the group treatment compo- nent of SNAP, youth received individual SNAP components as determined by protocol. Of all families in SNAP, 70.0 % received at least one component; the maximum number of different components used was four. The most common was individualized SNAP family counseling sessions, which was provided to 68 participants (52 % of the SNAP group), who received an average of 4.12 (SD=6.9) sessions. The mentoring component was provided to 64 participants (49 %), who received a mean of 2.5 (SD=4.2) units. School advocacy was used by 23 (18 %) participants, who received between 0 and 11 units, mean=0.43 (SD=1.35). Other SNAP components used by fewer than 10 % of the SNAP families included academic tutoring, crisis counseling, and the leader- ship module.
Standard Services Treatment
Children assigned to the STND condition were provided with referral information for treatment providers in their vicinity. For each child in standard services, study staff worked to coordinate contact with wraparound service providers in order to ensure that each participant in the STND condition had the opportunity to receive the most intensive level of community- based services for severe behavioral problems available in the region. However, families were not required to participate in wraparound services; nor was it guaranteed that community providers would agree that the requisite level of need existed for that level of service.
Approximately half (53 %) of those in the STND group engaged in behavioral health services, either fromwraparound providers, specialty behavioral health service providers or school-based behavioral or emotional services. Regarding wraparound specifically, 43 standard service participants (35 %) participated in wrap services at some point. At base- line, 17 % of youth in each condition were involved in other
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behavioral health services, and during waves 2 through 4, 23 % of youth in each condition were involved in any such services. Regarding school-based services for behavioral or emotional problems, more youth in STND than SNAP were involved in such services at baseline (40 % vs. 21 %). Over waves 2 through 4, 36% of youth in SNAP and 31% of youth in STND were involved in school-based services.
Parents and children were interviewed in 4 waves: at baseline, prior to randomization, and then again at 3 months, 9 months and 15 months after baseline. An assessment wave was con- ducted at 3 months because the SNAP program begins with 3 months of group sessions prior to delivering individualized treatment options. Subsequent assessment waves occurred at 9 and 15months past baseline to reflect periods of 6 months and 1 year after the end of the group session portion of SNAP. Interviews were administered using a laptop computer by trained research interviewers. Participants were compensated for participating. Interviews were usually conducted in family homes, although office interviews and alternate locations were employed at family request. Informed consent to participate was obtained from all parents, and assent was obtained from all children in the study. All study procedures were approved and monitored by the University of Pittsburgh Institutional Review Board.
Outcomes Parent report on the CBCL was obtained at each wave. Outcomes used in this study were the T-scores on the Aggression and Anxious-Depressed subscales. The Aggres- sion subscale consists of 18 items relating to aggressive be- havior, such as being mean, destroying others things, and get- ting in fights. Reliability alpha at baseline was 0.77 for the Aggression scale. There was no baseline difference on Ag- gression between SNAP (M=79.1, SD=9.6), and STND (M=79.3, SD=9.5). Items on the Anxious-Depressed subscale tap behaviors such as crying a lot, being fearful or nervous, and feeling unloved or worthless. At baseline, reliability alpha was 0.71 for Anxious-Depressed, and there was no significant difference at baseline between SNAP (M=62.7, SD=8.6) and STND (M=62.9, SD=9.1), on the scale.
Problem Solving Children were administered the Outcome Expectations Questionnaire (OEQ; Pardini et al. 2003) as an index of problem solving. This version of the OEQ (Perry et al. 1986) includes eight brief vignettes which elicit chil- dren’s expectations about the outcomes of aggressive behavior against a peer. In response to each vignette, participants are asked to rate the likelihood that various outcomes will occur on a four-point scale (from 1, or very sure the outcomewill not
occur to 4, or very sure that the outcome will occur. For the purposes of the current study, only the items measuring ex- pectations for Remorse, Punishment, and Victim Suffering due to aggressive behaviors were used. The mean scores at baseline for these constructs were 12.18 (SD=8.5), 16.31 (SD=6.9), and 17.49 (SD=6.1), respectively.
Prosocial Behaviors and Emotion Regulation Skills The So- cial Competence Scale – Parent Version is a 12-item measure created for the Fast Track Project (Conduct Problems Preven- tion Research Group 1995). The scale includes two subscales; one assesses the child’s prosocial behaviors and the second measures emotional regulation skills. Response options were on a five point scale from not at all to very well. The Prosocial Behaviors construct consisted of six items measuring how well the child resolved problems on his own, listened to others point of view, or was helpful to others. Reliability alpha at baseline for this construct was 0.79; the mean score at baseline was 8.38 (SD=4.2). The six Emotion Regulation Skills items measured how well the child did at things like coping with failure or controlling temper during disagreements. Reliability alpha at baseline was 0.71, and the mean was 5.12 (SD=3.3).
Parenting Behaviors The Parenting Practices Inventory (PPI; Webster-Stratton et al. 2001) is a 72-item questionnaire used to assess the disciplinary style of a parent or caregiver, with parents responding to items rated on a 7 point scale ranging from 1 (never) to 7 (always). Four subscales from the measure were used in this study: Harsh Discipline, Inconsistent Disci- pline, Positive Parenting, and Clear Expectations.
Harsh Discipline included 15 items measuring how often parents scold or yell, or spank, slap or hit the child for misbe- havior. At baseline in the present study, the scale showed good reliability (alpha=0.91), with a mean of 28.51 (SD =13.4). In- consistent Discipline consists of 6 items measuring how often the parent initiates but gives up on disciplinary efforts, how often the child gets away with misbehavior, or how often disciplinary efforts depend on the parent’s mood. At baseline, reliability was 0.87 in the present study (M=11.2, SD=5.4). Positive Parenting includes 15 items relating to the frequency with which parents praise the child, given hugs, or give extra privileges for desirable behavior. In the present study, the reliability of the scale was 0.93 at baseline (M=48.79, SD=11.6). Clear Expectations in- clude 3 items measuring the parents’ estimation of how clear are the rules or expectations they have set for the child. Reliability at baseline was 0.88 (M=14.07, SD=4.3).
Parental Stress The Parenting Stress Index- Short Form (PSI- SF; Abidin 1995) was used to measure self-reported parental stress. This 36-itemmeasure yields a total index measuring the amount of stress the parent is feeling, and includes three sub- scales. In the present study, constructs reflecting the three subscales were used as potential mechanisms of treatment
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outcomes. Parental Distress included 12 items reflecting stress due to personal factors, including impaired parenting compe- tence. At baseline, reliability for this scale was 0.94 (M=30.5, SD=10.1). Parent–child Dysfunctional Interaction includes 12 items measuring the parent’s perception of their interac- tions with the child, and whether those are reinforcing or neg- ative, rejecting experiences for the parent. Reliability at base- line was 0.95 for this scale (M=27.08, SD=9.4). The Difficult Child scale included 12 items reflecting stress due to difficulty managing the child’s behaviors, non-compliance or defiance. This subscale had a reliability alpha of 0.91 at baseline (M= 36.0, SD=8.6).
Since participants in the SNAP treatment condition participat- ed in groups, while those in STND participated in individual- ized treatment, the study design is partially nested (Bauer et al. 2008). However, there was no significant variation associated with participation in a specific treatment group; the ICC for treatment group for Aggression was 0.006, 95 % confidence interval (CI) [0.000, 0.065], and for Anxious-Depressed scores was 0.000, 95 % CI [0.000, 0.020]. As a result, the present analyses do not include clustering at the level of groups within treatment condition.
In the present data, youth were also nested in sibling clus- ters, and observations by wave were nested within individual participants. A hierarchical linear modeling strategy was used to model clustering at these two levels. Wave was coded as 0, 3, 9, and 15 to represent months from baseline, in order to account for the shorter duration between baseline and the first assessment point (3 months) relative to the remaining assess- ment points. It should also be noted that the SNAP group treatment occurred between the baseline and 3 month time point, while individualized treatment occurred for those in the SNAP group for the remainder of the study. Normal dis- tributions of outcomesweremodeled. Analyses were conduct- ed using Stata (StataCorp 2009).
Figure 1 shows a simple mediation model. Whether a par- ticular time-varying factor (e.g., emotion regulation) might be
considered a mechanism of treatment would depend on whether it was influenced by the treatment (the independent variable; path a in Fig. 1) and whether it in turn influenced a particular outcome of interest (e.g., aggressive behavior; path b in Fig. 1). We used a strategy for testing mediation (Krull andMacKinnon 2001) adapted for longitudinal data. Figures 2 and 3 illustrate the modeling strategy that was used to obtain the parameter values to estimate paths a, b and c’ in these analyses. We lagged values for the mediating variable by one observation period, so that prediction from the mediating variable to the outcome (path b) represented the relationship over time. In addition, we introduced the value of the outcome variable at the preceding time point as a control so that the prediction from the putative mediator to the outcome repre- sented change in the outcome from one time point to the next (e.g., Duckworth et al. 2010). Since consistent mediation re- quires that the predictor significantly predicts both the out- come (path c) and the mediator (path a), we began by identi- fying those potential mediators that were significantly differ- entiated (path a) by treatment group membership.
Missing Data Multilevel modeling is flexible regarding miss- ing data, and the present analyses used maximum likelihood estimators for each model, which provide advantages in han- dling missing data (Allison 2012). To further evaluate the effect of missing data on estimates in the present analyses, multiple imputation models were tested imputing values for Aggression (StataCorp 2009). There were no meaningful differences in comparison to models with missing data. As a result, the anal- yses presented here did not employ multiple imputation.
Aggression and Anxious-Depressed Outcomes
The effect of SNAP treatment on behavioral and affective outcomes as measured by the CBCL has been previously re- ported (Burke and Loeber in press). SNAP was associated
Fig. 1 The simple mediational model
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with significantly better outcomes in comparison to STND across multiple indicators of child behavioral and affective problems, including Aggression and Anxious-Depressed be- haviors. The modeling strategy employed in the prior analyses of treatment outcomes (Burke and Loeber in press) was slight- ly different than that employed here. In those analyses, a ran- dom effect for individual child’s slope was modeled. The present analyses include the prior wave measurement of each outcome as a fixed effect. This modeling strategy yielded the following for SNAP treatment in contrast to STND for child Aggression, B=−2.24, SE=0.84, p=0.008, 95 % CI [−3.89, −0.58], and for child Anxious-Depressed scores, B=−1.50, SE=0.50, p=0.003, 95 % CI [−2.49, −0.52], which vary slightly from the previous report due to the aforementioned modeling strategy differences.
To examine whether changes in children’s Aggression and Anxious-Depressed scores were predicted by each of the other
outcomes being tested here, separate models were tested for each as a predictor of each other outcome at waves 2 through 4, controlling for the outcome itself in the preceding wave. These outcomes changed independently of one another: Ag- gression did not predict changes in Anxious-Depressed scores, B=0.04, SE=0.02, p=0.15, 95 % CI [−0.01, 0.08. Similarly, Anxious-Depressed scores did not predict changes in Aggression, B=−0.01, SE=0.05, p=0.91, 95 % CI [−0.10, 0.09].
Potential Mechanisms of Treatment
To most efficiently evaluate the selected variables as potential mechanisms of SNAP treatment, we first identified those that significantly differed by treatment condition. In each model, the value of the outcome at the preceding wave was included as a predictor. The results are shown in Table 1. Youth in SNAP showed higher levels of each indicator of problem solving skills: they anticipated higher levels of punishment, remorse and victim suffering as a result of engaging in unde- sirable behavior. They showed higher levels of social compe- tence in terms of both prosocial behaviors and in emotion regulation skills. There were no observed differences between SNAP and STND youth on measures of parenting behaviors, but there was a trend towards higher levels of positive parent- ing behaviors for SNAP. As a result, this variable was retained for further testing as a potential mechanism, while harsh par- enting, inconsistent parenting, and consistent use of clear ex- pectations were not. Among indicators of parental stress, only the difficult child subscale differed between SNAP and STND children, while the subscales of parental distress and parent– child dysfunctional interaction did not.
M0 M3 M9 M15
X0 X3 X9
Fig. 2 Modeling mediational path a using the fixed effect for treatment condition (X) predicting eachmeasure of themediator (M) in a longitudinal multilevel model. Subscripts represent the month at which measures were taken from baseline at month 0. The stability and cross-lagged paths are represented by a single parameter estimate each in themodels. Fixed effects for age and assessment month were also included in each model
M0 M3 M9
Y0 Y3 Y9 Y15
X0 X3 X9
Fig. 3 Modeling mediational paths b and c’ in a longitudinal multilevel model. The coefficient for the mediational path b reflects the fixed effect of the prediction from each measurement of the mediator (M) to each outcome (Y). c’ is the fixed effect of treatment condition (X) predicting outcome (Y). Subscripts represent the month at which measures were taken from baseline at month 0. The stability and cross-lagged paths are represented by a single parameter estimate each in the models. Fixed effects for age and assessment month were also included in each model
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Mechanisms as Predictors of Aggression Those variables identified as significantly different between SNAP and STND children were each tested as predictors of Aggression in the following assessment wave. Age, wave, treatment condition and level of Aggression in the preceding wave were all includ- ed as predictors in each model. Table 2 shows the coefficient associated with the potential mechanism of treatment as a predictor of Aggression in each model.
As is evident, none of the indicators of problem-solving skills predicted changes in aggressive behavior. Each of the scales related to social competence, on the other hand, namely prosocial behaviors and emotion regulation skills, significant- ly predicted changes in Aggression. Changes in Aggression were also predicted by positive parenting, and by parental stress associated with difficult child behavior.
Four constructs were potential mediators of the effect of SNAP on Aggression, given that the treatment condition sig- nificantly differentiated aggressive behavior scores over time (path a) and that the variable predicted change in aggressive behavior (path b). The mediation effect was tested by multi- plying the mediator coefficient (path b) and the coefficient for the independent variable (path a). Using the joint significance test (MacKinnon 2008), the presence of both a significant path a and path b indicates a significant indirect effect. This means that significant indirect effects predicting Aggression include prosocial behavior, emotional regulation skills, and parental
stress: difficult child. In each case, treatment group remained a significant predictor of aggressive behavior, suggesting that the mediating effect in each case was partial, rather than full.
Mechanisms as Predictors of Anxious-Depressed Scores Each of the putative mechanisms that had been shown to differ by treatment group were tested as predictors of Anxious-Depressed scores. Age, wave, treatment condition and level of Anxious-Depressed score in the preceding wave were all included as predictors in each model. In only one instance, that of emotion regulation skills, did the putative mechanism predict changes in Anxious-Depressed behaviors, B=−0.13, SE=0.06, p=0.04, 95 % CI [−0.25, −0.01]. Table 3 shows the model estimates for each potential mechanism as a predictor of Anxious-Depressed scores.
Given the significant path a and path b involving emotion regulation skills, the joint significance test suggests a signifi- cant indirect effect in the prediction of Anxious-Depressed scores. As with the prediction of aggressive behavior, the ef- fect was partially mediating, since treatment condition remained a significant predictor.1
Table 1 Testing for treatment group differences in potential mechanism at waves 2 through 4
Mechanism B SE p 95 % Confidence interval
Problem solving skills
Punishment 2.42 0.64 <0.001 1.17 3.66
Remorse 2.73 0.91 0.003 0.95 4.52
Victim suffering 1.89 0.70 0.007 0.52 3.26
Prosocial behaviors 1.56 0.56 0.005 0.46 2.66
Emotion regulation skills 1.28 0.48 0.007 0.34 2.22
Harsh −2.38 1.53 0.12 −5.38 0.62 Inconsistent −0.66 0.62 0.28 −1.87 0.55 Positive 2.42 1.44 0.09 −0.40 5.24 Clear expectations 0.45 0.46 0.32 −0.45 1.35
Difficult child −2.40 1.13 0.03 −4.61 −0.18 Parental distress −1.30 1.30 0.32 −3.86 1.25 Parent–child dysfunctional interaction
−1.15 1.17 0.33 −3.44 1.15
Values of each outcome at the precedingwavewere included as predictors in each model, as were values for wave and age. The beta coefficient for each outcome is the difference between SNAP and treatment as usual on scores for the specified putative mechanism. These represent tests of path a in Fig. 1
Table 2 Selected putative SNAP mechanisms predicting Aggression
Mechanism B SE p 95 % Confidence interval
Problem solving skills
Punishment −0.01 0.06 0.92 −0.13 0.12 Remorse −0.03 0.05 0.53 −0.12 0.06 Victim suffering −0.00 0.06 0.92 −0.11 0.12
Prosocial behaviors −0.23 0.09 0.01 −0.41 −0.05 Emotion regulation skills −0.35 0.11 0.001 −0.56 −0.14 Parenting behaviors
Positive −0.08 0.03 0.015 −0.15 −0.02 Parental stress
Difficult child 0.11 0.05 0.031 0.01 0.21
Values of each outcome at the precedingwavewere included as predictors in each model, as were values for wave and age. The beta coefficient for each row is the value of each unit increase in aggressive behavior for each unit increase in the specified putative mechanism. These represent tests of path b in Fig. 1
1 Our a prior selection of outcomes was driven by an intention to test mechanisms potentially related to specific outcomes. However, it may have been of interest to consider broader constructs as outcomes. We therefore ran models testing the CBCL Externalizing and Internalizing scales as outcomes. The Externalizing scale includes Aggression and Rule Breaking behavior. The pattern of results – that is, which mecha- nisms were significantly predictive andwhich were not – was the same for Externalizing as was observed for Aggression as an outcome. Prediction to the Internalizing scale (which includes Somatic Complaints, Withdrawn-Depressed and Anxious-Depressed subscales) did vary from the results described above for the Anxious-Depressed outcome. Specif- ically, emotion regulation skills did not predict changes in Internalizing scores. Details on these analyses are available upon request from the first author.
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The present study tested four sets of putative mechanisms of treatment selected to represent key theoretical mechanisms of effect targeted by the SNAP treatment program: problem solv- ing skills training, emotion regulation skills training, social skills training and changes in parenting behaviors and parental stress. Previous research has demonstrated that SNAP influ- ences positive change on measures of behavioral problems as well as affective problems (Burke and Loeber in press). The present analyses were designed to test specific putative mech- anisms of the SNAP treatment effect on aggressive behavior and anxious-depressed scores. A transitional modeling strate- gy, in which prior levels of each outcome were included as predictors, led to a robust test of the effects described in the results. Formal tests of mediation were used to assess apparent mechanisms of treatment.
The results support the theoretical model described for the intervention on several domains. Mediating roles were found for social skills and emotion regulation skills, where both were enhanced in the SNAP treatment condition, and both predict- ed improvements on a measure of aggressive behavior. Emo- tion regulation skills were also found to predict improvements in anxious-depressed scores. Parenting stress associated with difficult child behavior was also reduced in the SNAP treat- ment condition, and was also predictive of improvements in boys’ aggression. A marginal effect for changes in positive parenting skills in the SNAP treatment condition, along with prediction from positive parenting to reduced aggressive be- havior, was not supported as a mediated relationship in statis- tical testing. No effects for the selected problem solving skills
measures of expectations of punishment, remorse or victim suffering on either outcome were found.
It is important to keep in mind that the structure of the present analyses was specific to tests of mechanisms associat- ed with the specific treatment model of interest in this study, the SNAP program. Because potential mechanisms were ini- tially assessed to identify those that differed by treatment group, this strategy would not identify general mechanisms of treatment that did not differ between SNAP and STND. For example, improvement was evident on parenting practices for children in both SNAP and STND, and testing not reported here showed that parenting practices were strong predictors of changes in aggressive behavior and anxious-depressed behav- ior. However, because the differences between treatment groups were not significant in these data, indicators of parent- ing behaviors were largely not examined further in the report- ed results. These results are similar to Hinshaw (2002), who found evidence that parenting behaviors were predictive of be- havioral outcomes, but did not find differences between com- parison treatment conditions on parenting behaviors. It would be a mistake to interpret these results to suggest that in general, treatments should not attempt to change parenting behaviors.
In addition, it is also useful to observe that the SNAP treat- ment group showed significantly greater scores on each of the measures of problem solving, even though these scores did not themselves predict changes in either aggression or anxious- depressed scores. The SNAP treatment program focuses exten- sively on problem solving, including direct instruction, model- ing and role-playing to enhance children’s ability to anticipate potential outcomes of various solutions to problems. Children are taught to anticipate which solutions might make a given problem smaller or bigger. As a result, their increased expecta- tions for a higher likelihood of punishment, remorse or victim suffering, relative to children in the treatment as usual group are not surprising. Nevertheless, the present results do not support a connection between greater expectations for such outcomes due to undesirable behavior and the outcomes studied here.
The results suggest the possibility that different processes may be underpinning change in behavioral and affective out- comes. First, neither aggressive behavior nor anxious- depressed scores predicted subsequent changes in the other outcome. Thus, it is not the case that children were feeling less anxious or depressed as a function of improvements in their aggression, or vice versa. Furthermore, the results iden- tified prosocial behavior, emotion regulation skills and paren- tal distress as mechanisms of change in aggressive behavior, whereas only emotion regulation skills were mediators of anxious-depressed scores. This suggests some discrimination in the nature of the mechanisms associated with either out- come, excepting that improved emotional regulation skills were common partial mediators for each outcome.
Why would prosocial behavior and parental stress predict aggression scores and not anxious-depressed scores? From the
Table 3 Selected putative SNAP mechanisms predicting Anxious- Depressed scores
Mechanism B SE p 95 % Confidence interval
Problem solving skills
Punishment 0.06 0.04 0.15 −0.02 0.14 Remorse 0.04 0.03 0.18 −0.02 0.10 Victim suffering 0.003 0.04 0.92 −0.07 0.08
Prosocial behaviors −0.03 0.05 0.57 −0.13 0.08 Emotion regulation skills −0.13 0.06 0.04 −0.25 −0.01 Parenting
Positive −0.02 0.02 0.46 0.06 0.03 Parental stress
difficult child 0.05 0.03 0.11 −0.01 0.11
Values of each outcome and the mediator at the preceding wave were included as predictors in each model, as were values for wave and age. The beta coefficient for each row is the value of each unit increase in aggressive behavior for each unit increase in the specified putative mech- anism. These represent tests of path b in Fig. 1
J Abnorm Child Psychol (2016) 44:179–189 187
perspective of the SNAP treatment model in particular, the SNAP group sessions reinforce prosocial behavior both with- in the treatment groups and through the content on specific topics. Individualized treatment components continue to ad- dress individual needs as well. This focus persistently encour- ages, models and reinforces using prosocial and non-violent behaviors. Some individuals might ultimately have positive feelings when reflecting upon the successful use of prosocial skills, but for children in this sample, levels of aggressive behaviors themselves did not predict changes in anxious- depressed scores, nor did levels of prosocial behavior.
Parental stress due to difficult child behavior seems more directly related to aggression and thus perhaps not surprising as a mediator. However, general parental stress and parental stress associated specifically with difficult parent–child interactions were not mediators. The aspects of parental stress associated with having a difficult child would seem to be an effect of problem behaviors like aggression, not a predictor of such. It may be that the measure of this aspect of parental stress picked up on some aspect of the parent–child relationship that was not captured by the other dimensions of parenting or parental stress included in these analyses. Alternatively, it may have picked up on changing child behaviors that were independent of concurrent aggression but were neverthe- less predictive of future aggression.
An implication of this result might be that, while there may be some generalization across different dimensions of out- comes for a given treatment, exposure to specific elements of a treatment may act upon different treatment mechanisms. These results will need to be replicated, and in particular, nuanced studies will be needed to confirm such specificity between treatment components and mechanisms. Treatment providers may nevertheless be encouraged to think carefully about the interventions they are employing, the processes they are aiming to alter, and the specific goals they are aiming to achieve.
These results should be interpreted with several limitations in mind. The measures of putative mechanisms of treatment used in this study each only represented one aspect of a given mechanism. For instance, problem solving skills do include thinking more fully about the potential outcomes for one’s behavior, and as noted in the case of SNAP treatment, partic- ularly focus on estimating when possible solutions might worsen a problem. Thus, anticipating a higher likelihood of punishment makes sense as one aspect of problem solving. However, problem solving also involvesmany other elements, such as the ability to recognize the problem, to generate mul- tiple solutions, to generate a variety of different types of solu- tions, and so on. Representing possible mechanisms of treat- ment using a measure of only one aspect of the mechanism may not fully reveal which mechanisms are truly associated with outcomes.
A related issue is the concern that the investigation of mechanisms of treatment might have an exploratory and post hoc quality. On the one hand, given the paucity of evidence for treatment mechanisms, there may be some utility in explor- atory endeavors. On the other hand, where research involves the post hoc inclusion of many variables to identify potential mediators, spurious associations might lead investigators and treatment providers to erroneous conclusions about how treat- ments work. In the present study, this concern is mitigated somewhat by the fact that these specific mechanisms were identified for testing in an a priori fashion.
An additional related limitation of this study was that our modeling strategy accounted for putative mediators individu- ally, in separate models. An alternate method, using structural equation modeling, tests multiple mediators simultaneously (Preacher and Hayes 2008). We opted to examine each of these putative mechanisms of change separately, controlling for lagged effects in an HLM model, but we recognize the possibility that identified mechanisms in one model might have influenced the relationships involving mechanisms in other models as well.
The present study involved only boys. The mechanisms associated with improvements for girls may differ greatly. For instance, girls may benefit differently from changes in interpersonal or communication skills than boys. The present study was specific to a program that has developed gender specific treatment models, and could thus only include boys. However, it will be worthwhile to more fully understand which treatment mechanisms may be more or less relevant for outcomes for boys versus girls.
Acknowledgments This work was supported by a grant (07-365-01) from the Department of Health of the Commonwealth of Pennsylvania to Drs. Loeber and Burke, and by a grant to Dr. Burke (MH 074148) from the National Institute of Mental Health.
Conflict of Interest Drs. Burke and Loeber have no conflicts of interest to report.
Abidin, R. R. (1995). Parenting stress index, third edition: professional manual. Odessa: Psychological Assessment Resources, Inc.
Achenbach, T. M., & Rescorla, L. (2001).Manual for the ASEBA school- age forms & profiles. Burlington: University of Vermont.
Allison, P. D. (2012). Handling missing data by maximum likelihood. Unpublished manuscript . Retr ieved from http: / /www. statisticalhorizons.com/wp-content/uploads/MissingDataByML. pdf.
Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87.
Augimeri, L. K., Jiang, D., Kogel, C. J., & Carey, J. (2006). The Under 12 Outreach Project: effect of a community-based program for children with conduct problems. Toronto: Center for Child Committing Offences, Child Development Institute.
188 J Abnorm Child Psychol (2016) 44:179–189
Augimeri, L. K., Farrington, D. P., Koegl, C. J., &Day, D.M. (2007). The SNAP™ Under 12 Outreach Project: effects of a community based program for children with conduct problems. Journal of Child and Family Studies, 16, 799–807.
Bauer, D. J., Sterba, S. K., & Hallfors, D. D. (2008). Evaluating group- based interventions when control participants are ungrouped. Multivariate Behavior Research, 43, 210–236. doi:10.1080/ 00273170802034810.
Burke, J. D., & Loeber, R. (in press). The effectiveness of the Stop Now and Plan (SNAP) Program for boys at risk for violence and delin- quency. Prevention Science.
Burke, J. D., Loeber, R., & Birmaher, B. (2002). Oppositional defiant disorder and conduct disorder: a review of the past 10 years, part II. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1275–1293.
Burke, J. D., Boylan, K., Rowe, R., Duku, E., Stepp, S. D., Hipwell, A. E., &Waldman, I. D. (2014). Identifying the irritability dimension of ODD: application of a modified bifactor model across five large community samples of children. Journal of Abnormal Psychology, 123, 841–851.
Chorpita, B. F., & Daleiden, E. L. (2009). Mapping evidence-based treat- ments for children and adolescents: application of the distillation and matching model to 615 treatments from 322 randomized trials. Journal of Consulting and Clinical Psychology, 77, 566–579. doi: 10.1037/A0014565.
Chorpita, B. F., Daleiden, E. L., Ebesutani, C., Young, J., Becker, K. D., Nakamura, B. J., & Starace, N. (2011). Evidence-based treatments for children and adolescents: an updated review of indicators of efficacy and effectiveness. Clinical Psychology-Science and Practice, 18, 154–172.
Conduct Problems Prevention Research Group. (1995). Psychometric properties of the social competence scale – teacher and parent rat- ings. (fast track project technical report). University Park: Pennsylvania State University.
Dishion, T. J., Patterson, G. R., & Kavanagh, K. A. (1992). An experi- mental test of the coercionmodel: Linking theory, measurement, and intervention. In J. McCord & R. E. Tremblay (Eds.), Preventing antisocial behavior: Interventions from birth through adolescence (pp. 253–282). New York: Guilford.
Duckworth, A. L., Tsukayama, E., & May, H. (2010). Establishing cau- sality using longitudinal hierarchical linear modeling: an illustration predicting achievement from self-control. Social Psychological and Personality Science, 1, 311–317. doi:10.1177/1948550609359707.
Eyberg, S. M., Nelson, M. M., & Boggs, S. R. (2008). Evidence-based psychosocial treatments for children and adolescents with disruptive behavior. Journal of Clinical Child and Adolescent Psychology, 37, 215–237. doi:10.1080/15374410701820117.
Gadow, K. D., & Sprafkin, J. (1994). Child symptom inventories manual. Stony Brook: Checkmate Plus.
Henggeler, S. W., Letourneau, E. J., Chapman, J. E., Borduin, C. M., Schewe, P. A., & McCart, M. R. (2009). Mediators of change for
multisystemic therapy with juvenile sexual offenders. Journal of Consulting and Clinical Psychology, 77, 451.
Hinshaw, S. P. (2002). Intervention research, theoretical mechanisms, and causal processes related to externalizing behavior patterns. Development and Psychopathology, 14, 789–818.
Kazdin, A. E. (2011). Evidence-based treatment research: advances, lim- itations, and next steps. American Psychologist, 66, 685–698.
Koegl, C. J., Farrington, D. P., Augimeri, L. K., & Day, D. M. (2008). Evaluation of a targeted cognitive behavioural program for children with conduct problems – the SNAP™ Under 12 Outreach Project: service intensity, age and gender effects on short and long term outcomes. Clinical Child Psychology and Psychiatry, 13, 441–456.
Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individ- ual and group level mediated effects. Multivariate Behavioral Research, 36, 249–277. doi:10.1207/S15327906mbr3602_06.
La Greca, A. M., Silverman, W. K., & Lochman, J. E. (2009). Moving beyond efficacy and effectiveness in child and adolescent interven- tion research. Journal of Consulting and Clinical Psychology, 77, 373.
Landenberger, N., & Lipsey,M. (2005). The positive effects of cognitive– behavioral programs for offenders: a meta-analysis of factors asso- ciated with effective treatment. Journal of Experimental Criminology, 1, 451–476. doi:10.1007/s11292-005-3541-7.
Lochman, J. E., & Wells, K. C. (2002). Contextual social–cognitive me- diators and child outcome: a test of the theoretical model in the Coping Power program. Development and Psychopathology, 14, 945–967.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum.
Pardini, D. A., Lochman, J. E., & Frick, P. J. (2003). Callous/unemotional traits and social-cognitive processes in adjudicated youths. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 364–371. doi:10.1097/01.Chi.0000037027.04952.Df.
Patterson, G. R., & Forgatch, M. S. (1995). Predicting future clinical adjustment from treatment outcome and process variables. Psychological Assessment, 7, 275.
Perry, D. G., Perry, L. C., & Rasmussen, P. (1986). Cognitive social learning mediators of aggression. Child Development, 57, 700–711.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strat- egies for assessing and comparing indirect effects in multiple medi- ator models. Behavior Research Methods, 40, 879–891.
Spence, S. H. (2003). Social skills training with children and young people: theory, evidence and practice. Child and Adolescent Mental Health, 8, 84–96.
StataCorp. (2009). Stata statistical software: release 11. College Station: StataCorp LP.
Webster-Stratton, C., Reid, M. J., & Hammond, M. (2001). Preventing conduct problems, promoting social competence: a parent and teach- er training partnership in Head Start. Journal of Clinical Child Psychology, 30, 283–302.
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- Mechanisms of Behavioral and Affective Treatment Outcomes in a Cognitive Behavioral Intervention for Boys
- SNAP Treatment
- Standard Services Treatment
- Data Collection
- Statistical Analyses
- Aggression and Anxious-Depressed Outcomes
- Potential Mechanisms of Treatment
- Mechanisms of Behavioral and Affective Treatment Outcomes in a Cognitive Behavioral Intervention for Boys
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