A Preliminary Evaluation of the Utility of Sluggish Cognitive Tempo Symptoms in Predicting Behavioral Treatment Response in Children with Behavioral Difficulties

Abstract

Interest in symptoms of sluggish cognitive tempo (SCT) has led to a number of studies evaluating how these symptoms respond to treatment commonly utilized in youths with symptoms of ADHD. No study to date, however, has examined the extent to which symptoms of SCT predict behavioral treatment response in youths across multiple domains of functioning. The current preliminary investigation integrates a number of methodological (e.g., direct observations) and analytic (e.g., Poisson regression) refinements to evaluate the extent to which symptoms of SCT predict treatment responses across multiple domains including behavioral (e.g., interruptions, rule violations), social (e.g., social skills, negative verbalizations), and severe behavioral difficulties (e.g., intentional aggression) above and beyond other demographic characteristics (e.g., symptom severity, Full Scale Intelligence Quotient [FSIQ]). A relatively small sample of 37 children, aged six to 12 years (M = 8.03, SD = 1.83, 35 males: 2 females) attending an eight week multi-component intensive behavioral treatment program for youths with behavioral difficulties participated in the current study. Baseline parental perceptions of SCT were collected prior to the initiation of treatment. Results from this preliminary investigation revealed that pre-treatment SCT symptoms only predicted a less robust treatment response to time out which was associated also with parent’s perceptions of underlying working memory problems. Results revealed also that pre-treatment SCT symptoms failed to predict paraprofessional counselor’s and teacher’s improvement ratings of both rule following and social skills following treatment. Notably, other potential predictors (e.g., symptom severity, FSIQ) also largely failed to predict behavioral treatment response.

Keywords

Behavior problems sluggish cognitive tempo behavioral treatment observation

Introduction

Stimulant medications remain the most commonly recommended treatment for children with ADHD (Cortese et al., 2018); however, substantial research demonstrates that behavioral interventions are also effective in reducing symptoms of inattention, hyperactivity, and impulsivity as well as improving overall functioning across multiple domains (e.g., academic, social) in this population (Barkley et al., 2000; Chronis et al., 2004; Evans et al., 2014; Fabiano et al., 2009). Behavioral interventions involve recurrent sessions of behavioral treatment (e.g., repeated behavioral parent training meetings) lasting between eight and 12 weeks (Pelham & Fabiano, 2008) and changes in response to behavioral interventions may be observed immediately or over an extended period of time following the initiation of a treatment regimen. As a result, it is not always immediately evident whether a child is demonstrating response or non-response to a given behavioral intervention—which highlights a critical need to isolate what treatments work for whom – an approach described as “precision medicine” (Insel & Cuthbert, 2015). Unfortunately, to date, predictors of behavioral treatment response remain elusive and the extent to which these are immediately clinically applicable has not been firmly established. Specifically, only a few child characteristics including the presence of comorbid anxiety (Jensen et al., 2001; March et al., 2000; MTA Cooperative Group, 1999; van der Oord et al., 2008),¹ less severe ADHD symptoms (Owens et al., 2003), and higher IQ (Kazdin & Crowley, 1997; van der Oord et al., 2008) have been shown to predict or moderate greater improvements in symptoms of ADHD as a result of behavioral treatment.

Preliminary evidence that severity of ADHD symptoms moderates (but does not predict) treatment response points to a need for further inquiry into whether other sets of symptoms related to ADHD may provide useful information regarding those most likely to benefit from treatment. For example, substantial interest in a set of symptoms collectively referred to as sluggish cognitive tempo (SCT) has resulted in preliminary work on the implication of these symptoms for understanding existing treatments targeting inattentive, hyperactive/impulsive, and oppositional behavior (Froehlich et al., 2018).SCT represents a group of symptoms that are highly related but distinct from the inattentive presentation of ADHD (Becker et al., 2016, 2017) and include daydreaming, staring, mental fogginess and confusion, sluggishness or slow movement, sleepiness, lethargy, hypo-activity, and apathy (Carlson, 1986; Kofler et al., 2019; Servera et al., 2018; Smith et al., 2018). Only three studies to date have examined the impact of SCT symptomology on response to behavioral intervention in children with symptoms of ADHD, with most studies focusing exclusively on children presenting with only the predominantly inattentive presentation of ADHD.

Specifically, when comparing the impact of behavioral treatments designed to ameliorate many of the difficulties associated with the inattentive presentation of ADHD, similar reductions in both SCT symptoms and inattention symptoms are observed (Pfiffner et al., 2007). In contrast, examination of treatments targeting a broader array of behavioral dysfunction in children with greater variability in symptoms of ADHD reveals a more nuanced picture of the association of SCT symptoms with improvements across specific domains of dysfunction in youth with ADHD. For example, children with more SCT symptoms receive fewer time-outs and appear to exhibit fewer aggressive and hostile behaviors suggesting a reduced need for treatment focusing primarily on more severe forms of behavioral difficulties (Becker et al., 2014). However, these children appear to benefit the least from behavioral treatment with respect to social difficulties and internalizing problems suggesting that SCT symptoms may prove useful with respect to predicting treatment response across specific domains of functioning (Owens et al., 2016). Examinations of the potential impact of SCT symptomology in response to pharmacological treatment for ADHD has resulted in mixed findings as well (Firat et al., 2020; Froehlich et al., 2018; Ludwig et al., 2009; McBurnett et al., 2017).

While the emerging literature suggests that SCT symptoms may prove useful in predicting behavioral treatment response, there are three primary limitations of this literature. First, only children with the predominantly inattentive presentation of ADHD and/or those admitted to a psychiatric treatment facility have been examined in previous studies, which truncates the distribution of children with behavioral problems who also present with symptoms of SCT and/or comorbid psychopathology. Specifically, these populations represent two extremes of a continuous symptom presentation characterized by a normal distribution of inattentive, hyperactive, impulsive, and conduct-related problems. Examination of a population of children with a more diverse symptom expression may result in stronger associations between SCT and treatment response.

Further, the measures used to assess SCT in past studies are varied and based primarily on a limited number of items from scales that, although they are validated measures of evaluating behavioral functioning, were not initially intended to evaluate the SCT construct (e.g., a subset of items from the Child Behavior Checklist [CBCL]). Specifically, prior investigations utilized a method in which four or five SCT-related items from the CBCL (Achenbach, 1991) were used to derive an overall SCT score. More recently, a number of scales have been developed to specifically evaluate the SCT construct including: the 15-item Pfiffner Scale (Pfiffner et al., 2007), 14-item Penny Scale (Penny et al., 2009), and the 15-item Kiddie Sluggish Cognitive Tempo Rating Scale (KSCT; McBurnett et al., 2014). The KSCT represents a more recent measure that includes a variety of items related to SCT symptomology that each load on one of three subscales (daydreams, working memory, low energy/sleepy). Given the use of more limited and abbreviated scales in past studies, the current study utilizes the 15-item KSCT to assess the SCT construct prior to completion of a behavioral treatment program in hopes of fully capturing variance associated with the Sluggish Cognitive Tempo (SCT) construct.

Finally, nearly all of the studies to date (for an exception, see Becker et al., 2014) have relied exclusively on parent and teacher ratings of behavior as an indicator of response to treatment. Alternatively, the augmentation of ratings of behavior with direct observations of children’s behavior (e.g., counts of the number of times a child exhibits a given behavior) over the course of treatment may provide an additional assessment of the magnitude of change and potential predictors of these changes independent of biases that may be introduced by parents and/or teachers completing ratings of SCT symptoms and behavioral outcomes (e.g., mono-method bias, gender-related expectations of the child [Najman et al., 2001]). Critically, however, count data often violate the assumptions of traditional ordinary least squares (OLS)-regression-based approaches as counts of low frequency behaviors are typically non-normal and right skewed and have a lower bound of zero. Poisson regression (and related models) have been advocated as alternatives to traditional OLS-based regression when including count outcomes in studies as they are robust to these assumptions and provide better estimates of the underlying relations being evaluated (Coxe et al., 2009).

Current study

The current study seeks to extend the existing literature by examining the role of SCT symptomology in predicting response to an intensive eight week behavioral treatment program. This will be evaluated by assessing the frequency of directly observed behaviors during the final three weeks of behavioral treatment after controlling for the frequency of behaviors during the first three weeks of treatment. Specifically, this study focuses on well-defined and operationalized behaviors (e.g., intentional aggression, interrupting) spanning multiple domains in children with more diverse ADHD symptomology and greater SCT symptomology enrolled in a behavioral treatment program relative to past studies.

Given previous evidence that children with higher SCT scores exhibit less aggressive behavior as well as receive fewer time outs relative to those with lower SCT scores (Becker et al., 2014), we expected that SCT scores would be a significant predictor of fewer treatment-related changes in intentional aggression and the number of time outs received in the behavioral treatment program. Similarly, given past evidence that children with higher SCT symptoms exhibit a less robust treatment response in the area of social functioning, we expected that SCT scores would significantly predict fewer treatment-related changes in positive peer behaviors (i.e., helping, sharing), fewer reductions in negative verbalizations (e.g., teasing), and less counselor-rated improvement in social functioning. Finally, given past work demonstrating comparable improvements in inattention and hyperactive/impulsive symptoms regardless of severity of SCT symptoms, we expected that SCT symptoms would not be a significant predictor of a reduction in rule violations or interruptions.

Method

Participants

Thirty-seven children, aged six to 12 years (M = 8.03, SD = 1.83, 35 boys: 2 girls) attending an evidence-based Summer Treatment Program (STP; Pelham & Hoza, 1996) for children with disruptive behavior and related disorders participated in the current study (see Table 1). Participants were not randomly selected; however, all families participating in the STP were given the opportunity to participate in the study. Participants exhibited a range of symptom severity scores across the symptom domains of inattention (M = 17.95, SD = 6.57), hyperactive/impulsive (M = 16.46, SD = 6.75), and oppositional/defiant (M = 8.81, SD = 5.69) behaviors based on parent and teacher report on the Disruptive Behavior Disorders Rating Scale (DBD-RS, see description below; Pelham et al., 1992). Using a symptom count threshold while applying the OR rule to the DBD-RS ratings (i.e., either parent or teacher endorsed an item as occurring “pretty much” or “very much”) resulted in 24 participants meeting symptom count criteria for ADHD – Combined Presentation, four participants meeting symptom count criteria for ADHD – Hyperactive/Impulsive Presentation, and seven participants meeting symptom count criteria for ADHD – Inattentive Presentation. The remaining two participants exhibited subthreshold symptoms or were admitted into the STP due to symptom count scores related to Oppositional Defiant Disorder (ODD) exceeding four or more. Additionally, based on parent- or teacher-rated symptoms of oppositional and defiant behavior using similar scoring methodology, 21 children met symptom count criteria (i.e., > four symptoms endorsed as “pretty much” or “very much”) for comorbid ODD consistent with epidemiological estimates of ODD comorbidity in youth with ADHD (i.e., ∼ 57%; Wilens et al., 2002). Participants were primarily of Hispanic/Latino ethnicity (78.4%, n = 29) with the racial demographic composition of the remaining participants being 13.5% (n = 5) non-Hispanic/Latino Caucasian, 8.10% (n = 3) African American, and 2.70% (n = 1) Asian American. Annual family income ranged from $23,000 to $300,000 (Median = $82,000, SD = $81,524.53). During the study 35% (n = 13) of the sample was taking stimulant medication for ADHD.²

Table 1.

Descriptive statistics of sample.

	M	SD	Median	Min.	Max.
Age	8.35	1.88	7.60	6.10	12.90
Household income	$110,155.00	$81,524.53	$82,000.00	$23,000.00	$300,000.00
FSIQ-2	99.59	14.89	101.00	68.00	127.00
ADHD-H/I severity score	16.46	6.75	18.00	4.00	27.00
ADHD-I severity score	17.95	6.57	19.00	0.00	26.00
ODD severity score	8.81	5.69	8.00	0.00	22.00
CD severity score	2.08	2.37	1.00	0.00	9.00
Gender	Males (n = 35)		Females (n = 2)
Medication status	On medication (n = 13)		Off medication (n = 24)

Procedure

One hundred and forty parents of children participating in the STP were offered the opportunity to participate in this study via an online invitation providing using REDCap (Harris et al., 2009). If they agreed and provided informed consent, they were asked to complete the online survey regarding their child’s sluggish cognitive tempo symptoms. Thirty seven parents agreed and consented to have their child participate in this study. The current study was approved by the Institutional Review Board (IRB) prior to the onset of data collection and participants did not receive compensation.

Summer treatment program

Children attended the STP Monday through Friday from 8:00 AM to 5:00 PM for eight consecutive weeks. During the STP, one lead counselor and four to five undergraduate counselors delivered the behavioral treatment to children who were grouped by age (i.e., children were not randomly assigned to clinical groups) with no clinical group containing more than 15 children (M = 13.00, SD = 1.15). Each clinical group spent approximately two hours daily in classroom sessions led by special education teachers and one to two teacher aids as well as one hour in an art session. The remainder of each day consisted of recreationally based clinical group activities (e.g., soccer, basketball, and softball) during which a point system was implemented (see below). For the purposes of this manuscript, points earned and lost across each interval (e.g., every 15 minutes) were collapsed and evaluated as dependent variables. Children were present with their clinical group for a maximum of 28 intervals per day (i.e., 7 hours), given that counselors did not record children’s behavior during the classroom and art periods. This approach has been used in multiple treatment outcome studies to evaluate treatment response and additional details regarding the STP have been published (Pelham et al., 2010, 2012). During the activities in the STP, a reward/response cost contingency management system was utilized with participants via a point system. Specifically, children earned points for displaying and engaging in appropriate behaviors and lost points for displaying and engaging in inappropriate behaviors. Points were exchanged for prizes at a “point store” every Friday during the STP with the more reinforcing toys “costing” a greater number of points. Notably, for children participating in the current study, their data were analyzed at the individual level and these “clinical groups” were only used for clinical purposes in the provision of the intensive behavioral treatment program.

Measures

Parents completed the KSCT and DBD-RS rating scales as part of a clinical intake assessing their child’s eligibility and appropriateness for the Summer Treatment Program (STP) via an online survey prior to the onset of treatment. Parents were asked to rate their perceptions of their child’s behavior when their child was not taking medication. Children’s parents were not asked to withhold medication but were asked to rate their perception of their child based on their behaviors when the child is not receiving treatment (i.e., medication).

Kiddie Sluggish Cognitive Tempo Rating Scale

The Kiddie Sluggish Cognitive Tempo Rating Scale (KSCT) is a 15-item rating scale assessing symptoms associated with Sluggish Cognitive Tempo (SCT; McBurnett et al., 2014). Respondents were instructed to rate children’s behavior on a 4-point Likert scale (never or rarely = 0, sometimes = 1, often = 2, and very often = 3). The items on the KSCT were derived from previous work on SCT and are alternate descriptions of behaviors related to SCT or related clinical behaviors (e.g., stares into space, loses train of thought, moves around slowly, and has difficulty getting motivated). Each of the items belongs to one of three domains associated with SCT and encompasses a respective subscale. These include: (1) Daydreams (Cronbach’s alpha in the current sample = 0.95); (2) Working memory (Cronbach’s alpha in the current sample = 0.90); (3) Low energy/sleepy (Cronbach’s alpha in the current sample = 0.80). Reliability estimates in the current sample for the entire 15-item scale (Cronbach’s alpha = 0.93) were consistent with past work utilizing this measure (Cronbach’s alpha in past work ranged from 0.85 to 0.91; McBurnett et al., 2014). A pre-treatment KSCT score was derived by summing the endorsements using the Likert scale described above.

Disruptive Behavior Disorder Rating Scale

The Disruptive Behavior Disorder Rating Scale (DBD-RS) Parent-report Form is a 45-item questionnaire assessing varying behavioral symptoms of ADHD (18 items), Conduct Disorder (CD; 16 items), and Oppositional Defiant Disorder (ODD; 8 items) as defined by the DSM-IV. Parents were asked to rate their perceptions of their child’s behavior on a Likert scale with four options (not at all = 0, just a little = 1, pretty much = 2, or very much = 3). Each individual received four severity scores for the DBD-RS across the domains of conduct disorder, oppositional defiant disorder, ADHD-I, and ADHD-H/I. Reliability in the current sample across items was acceptable (Cronbach alpharanged from 0.93 to 0.94). Symptom severity scores for each domain were derived by summing endorsements using the Likert scale described above.

IQ

Full Scale IQ for participants was estimated using the Vocabulary and Matrix Reasoning subtests from the Wechsler Abbreviated Scale of Intelligence, 2^nd edition (WASI-II; Wechsler, 2011). The current sample had an average FSIQ-2 of 99.59 with a standard deviation of 14.89.

Treatment outcome measures

Staff Improvement Rating Form

The Staff Improvement Rating Form (SIRF) is a 35-item questionnaire that measures counselor’s and teacher’s perceptions of the extent to which the child improved over the course of the eight week treatment program across multiple domains. This was completed by each of the counselors, lead counselors, teachers, and teacher aides assigned to each child’s clinical group following the completion of the STP. Counselors and teachers were unaware of the child’s SCT ratings collected at the start of treatment and were blind to all study hypotheses for the current study. This provided a minimum of five ratings per child across ten potential areas of improvement: teasing/peer aggression, adult-directed defiance/noncompliance, problem-solving skills, self-esteem, happiness, responsibility, social skills, participation, rule following, and attention. Additionally, an overall global rating of improvement was completed for each child. Each domain was endorsed on a seven point Likert scale based on the counselor’s or teacher’s perception of how much the child’s behavior in that domain had changed over the course of the treatment program (“1” indicating “very much worse,” “4” indicating “unchanged,” and “7” indicating “very much improved”). Across all items, higher scores indicated greater perceived improvement demonstrated by the child. This measure was selected given its past use and sensitivity in studies evaluating treatment response (Pelham et al., 2000) and corresponding moderators/predictors of treatment response in children with callous/unemotional traits (e.g., Haas et al., 2011). No other reliability or validity data are available for this instrument.

A score was computed for each child by averaging the ratings obtained from each rater (i.e., counselors and lead counselors) for 1) following program rules/following class and school rules and 2) communication skills. Given that not every child demonstrated difficulty in every domain of functioning, raters were provided an option of “no problem” in order to minimize confusion regarding behavioral areas for which the child had no difficulty from those that were characterized by lack of improvement. In an effort to retain as much data as possible, items rated as “no problem” were coded as a “4” in the analyses given that the child did not get better or worse in relation to these items. This approach has been used in one study previously to evaluate perceptions of response to treatment by counselors and teachers while minimizing data loss (Haas et al., 2011; see Table 2). Collectively, ratings of “no problem” were provided a minority of the time by raters (between 23% and 45% depending on the scale item) with the most frequently occurring on items related to “following classroom rules.”

Table 2.

Descriptive statistics of behavioral outcomes.

	Time 1					Time 2
	M	Median	SD	Min.	Max.	M	Median	SD	Min.	Max.
Pre-treatment total KSCT score	11.70	9.00	10.06	0.00	42.00	–	–	–	–	–
Rule violations	222.65	162.00	218.64	14.00	1052.00	249.05	156.00	348.93	27.00	1930.00
Interruptions	98.27	82.00	85.34	1.00	370.00	91.22	67.00	95.87	5.00	495.00
Time outs	5.59	3.00	8.42	0.00	36.00	3.81	2.00	6.93	0.00	37.00
Positive peer behaviors	54.51	45.00	33.39	18.00	152.00	89.78	58.00	102.85	25.00	491.00
Intentional aggressions	3.16	0.00	6.18	0.00	31.00	4.59	0.00	15.20	0.00	89.00
Negative verbalizations	69.11	20.00	120.94	0.00	591.00	122.65	44.00	280.49	1.00	1515.00
Counselor rated rule following	–	–	–	–	–	5.12	5.20	0.75	3.20	6.20
Counselor rated social skills	–	–	–	–	–	5.16	5.33	0.73	3.00	7.00
Teacher rated rule following	–	–	–	–	–	4.78	4.50	1.22	1.50	7.00
Teacher rated social skills	–	–	–	–	–	4.95	4.50	0.86	3.50	7.00

Note: Time 1 = Sum of Weeks 1–3 of the STP and Time 2 = Sum of Weeks 6–8 of the STP.

Measures of treatment response

Behavior counts

Counselors participated in two weeks of intensive training during which they learned the operational definition of each of the behavioral codes used in the analyses. On the first day of the STP, children were introduced to the behavioral point system which reflected a basic reinforcement schedule. Subsequently, each child’s behavior was monitored closely by at least three trained undergraduate counselors and one trained graduate-level lead counselor during recreational periods throughout the day. Because of a modified schedule used on Fridays as part of the incentive system (i.e., “Fun Friday”), data included in the current study were collected Monday through Thursday.

Consistent with methods used in previous studies (Garcia et al., 2018), the sum of behavioral counts during the last three weeks of the STP (Time 2) after controlling for the sum of behavioral counts during the first three weeks of the STP (Time 1) was used as the dependent measure to examine the extent to which SCT symptoms predicted treatment-related changes across the relevant domains of interest. Behavioral point system data for the current study included the following behavior categories: (1) positive peer behaviors (sum of helping, sharing, and ignoring provocation), (2) negative verbalizations (sum of swearing, teasing peers, and verbal abuse directed towards adults), (3) intentional aggression (sum of intentional aggression towards staff and peers), (4) rule violations, (5) interruptions, and (6) time outs (see Pelham et al., 1998 for operational definitions of each behavior). See Table 2.

Data analytic strategy

The current study utilized ordinary least squares (OLS) regression as well as zero-inflated and negative binomial Poisson regression (described below) conducted in SAS 9.4 in order to evaluate the relations of interest. In all models, ADHD-I symptom severity, ADHD-H/I symptom severity, medication status, and FSIQ-2 were included as covariates. Notably, the pattern of findings remained largely unchanged when results were analyzed without covariates; however, we elected to retain and report the results with covariates in an effort to provide information regarding the extent to which the covariates also contributed to the prediction of treatment response. Additionally, FSIQ-2 was included in all models due to a significant relationship with other outcome variables (e.g., Teacher-rated Social Skills). Time 1 behavioral frequency counts were used as covariates in models utilizing frequency counts as outcome measures to control for the frequency at the beginning of treatment. Furthermore, the log of total intervals present for each child during Time 2 was used as an offset variable in models utilizing frequency counts as outcome measures to account for the fact that children accrued behavior counts over a different number intervals from day to day (i.e., a child may have left early for the day or been pulled out of the recreational activities for an hour to participate in other ongoing research studies).

KSCT and counselor and teacher ratings

Ratings of treatment response are one of the most commonly employed approaches to identifying potential moderators and/or predictors as well as the overall evaluation of treatment response. Consistent with past work using SIRF ratings (e.g., Hoza & Pelham, 1995), scores from the SIRF subscales of communication skills and rule following behaviors were used as primary outcome measures in linear regressions to examine the extent to which KSCT scores predict scores in these domains. Counselor and teacher outcomes were examined in separate models.

KSCT and behavior counts

Operationalized and directly observed counts of behaviors were utilized also in the current study to examine the extent to which results compare to more traditional measures of treatment response (i.e., ratings). The benefits of utilizing Poisson regression methods over ordinary least squares (OLS) regression models for count data have been previously documented (Coxe et al., 2009). Briefly, count or frequency variables involve discrete (i.e., whole number) values that must be greater than or equal to zero. Poisson regression relies upon a Poisson distribution, which is a discrete distribution that takes on a probability value for only nonnegative integers (i.e., whole numbers greater than or equal to 0). The effect size commonly examined in Poisson regression is the multiplicative change value, calculated as e^b. This represents the multiplicative change in the outcome variable when there are changes in the predictor variable; when there is no relation between the predictor and outcome, the value is closer to 1.00. When the multiplicative change value is less than 1.00, it means that the outcome decreases as the predictor increases whereas values greater than 1.00 indicate that the outcome increases as the predictor increases.

There are a variety of Poisson regression models that can be utilized to best fit the data. For example, count variables for infrequently occurring behaviors may contain a high number of zero values (i.e., zero-inflated) for different reasons. On one hand, it is possible that a child in the study never exhibited a particular behavior during the STP (resulting in a true count of 0 across all intervals); however, it is also possible that a child had a high number of 0s across most intervals but did exhibit this behavior, albeit infrequently, during the STP at other times. The utilization of zero-inflated Poisson regressions accounts for this by separately modeling true zeroes and zeroes that reflect behavior which has not yet occurred.

Another consideration when utilizing Poisson regression with count data is the occurrence of over-dispersion. Specifically, the Poisson regression utilizes a single parameter reflecting both the mean and variance of the distribution (for a review, see Coxe et al., 2009). As a result, the assumption that the mean and variance are equal is necessary. However, when there is greater variance in a distribution than this distribution assumes, the mean is not equal to the variance after accounting for the effect of the predictor variable and over-dispersion is present. In this situation, the use of the more flexible negative binomial distribution is necessary (Hilbe, 2011). Assumptions about dispersion are tested using a Pearson χ² dispersion statistic; values exceeding 1.00 suggest over-dispersion and indicate a need for the use of a negative binomial Poisson regression.

KSCT and total behavior frequencies

In addition to examining whether SCT symptoms predict treatment-related changes across the relevant behavioral domains described above, secondary data analyses were conducted to evaluate the extent to which greater levels of SCT symptoms were associated with the frequency of specific behavioral domains over the course of treatment (i.e., all eight weeks).

KSCT subscales

Given the relatively small sample size employed in the current study, a post-hoc exploratory analysis was conducted also to examine the extent to which specific subscales of the KSCT (i.e., Daydreams, Working Memory, and Low Energy/Sleepy) were associated with treatment response as measured by counselor and teacher ratings and frequency of behavior counts. Specifically, all analyses employed to estimate the predictive value of the overall KSCT score were repeated using specific subscales from the measure. In an effort to minimize Type I error rates, we only interpret significant associations that survived a Bonferroni correction (i.e., p < 0.002; Tables S1-S6).

Results

KSCT and counselor and teacher ratings

Linear regression was used to examine whether KSCT scores prior to treatment predicted counselor and teacher perceptions of improvement in rule following and social skills over the course of treatment after controlling for the covariates. KSCT scores at the beginning of treatment did not predict counselor perceptions of improvement in rule following (F [5, 36] = 0.50, p = 0.77) or social skills (F [5, 36] = 0.73, p = 0.60) based on examination of overall model statistics. Similarly, examination of the models for teacher ratings revealed that KSCT scores at the beginning of treatment were not associated with teacher perceptions of improvement in rule following (F [5, 36] = 0.78, p = 0.57) nor social skills (F [5, 36] = 0.83, p = 0.54; see Table 3).

Table 3.

KSCT and counselor and teacher ratings.

	B	SE	t	Sig. (p)
Counselor rated rule following
KSCT score	−0.003	0.01	−0.22	0.83
ADHD-H/I severity	0.02	0.03	0.69	0.50
ADHD-I severity	0.01	0.03	0.23	0.82
Medication status	0.33	0.28	1.19	0.24
FSIQ-2	−0.002	0.01	−0.22	0.82
Intercept	4.83	0.93	5.22	<0.001***
Counselor rated social skills
KSCT score	0.007	0.01	0.46	0.65
ADHD-H/I severity	−0.003	0.03	−0.12	0.91
ADHD-I severity	0.02	0.03	0.81	0.42
Medication status	0.02	0.26	0.07	0.94
FSIQ-2	−0.01	0.01	−1.34	0.19
Intercept	5.87	0.89	6.61	<0.001***
Teacher rated rule following
KSCT score	−0.03	0.02	−1.18	0.25
ADHD-H/I severity	−0.01	0.04	−0.31	0.76
ADHD-I severity	0.04	0.05	0.96	0.35
Medication status	−0.65	0.44	−1.47	0.15
FSIQ-2	−0.01	0.01	−0.77	0.45
Intercept	5.86	1.48	3.97	0.0004**
Teacher rated social skills
KSCT score	−0.002	0.02	−0.10	0.92
ADHD-H/I severity	−0.01	0.03	−0.34	0.74
ADHD-I severity	0.01	0.03	0.24	0.81
Medication status	−0.17	0.31	−0.56	0.58
FSIQ-2	−0.02	0.01	−1.83	0.08
Intercept	6.87	1.03	6.66	<0.001***

***p < 0.001, **p < 0.01, *p < 0.05.

KSCT and behavior counts

We assessed each of the variables for the possibility of excess zeroes and over-dispersion. Following assessment, zero-inflated negative binomial Poisson regressions were used to examine whether SCT symptoms predicted the frequency of intentional aggressions (Pearson χ² = 23.46) and time out (Pearson χ² = 33.34) at Time 2 (T2) after controlling for rates of these behaviors at Time 1 (T1) for each interval the child was present (Atkins & Gallop, 2007).

Negative binomial Poisson regressions were used to examine whether SCT symptoms predicted the frequency of positive peer behaviors (Pearson χ² = 37.93), rule violations (Pearson χ² = 36.91), negative verbalizations (Pearson χ² = 31.18), and interruptions (Pearson χ² = 32.69) at T2 after controlling for rates of these behaviors at T1 for each interval the child was present. This approach was utilized due to the poor fit of a normal Poisson regression for these outcome variables given over dispersion.

KSCT scores at the beginning of treatment were predictive of number of time outs (b = 0.05, e^b = 1.05, p = 0.05) at the end of treatment. Specifically, increases in KSCT symptoms were associated with a 5% increase in the likelihood of exhibiting a behavior that resulted in the assignment of a time out. In contrast, KSCT scores at the beginning of treatment were not predictive of the number of intentional aggressions (b = −0.06, e^b = 0.94, p = 0.12). Further, examination of the confidence interval (Table 4) for this effect revealed that the e^b for this behavior ranged from .86 to 1.02, indicating that increases in KSCT scores correspond to a change in intentional aggressions ranging from a 14% decrease to a 2% increase.

Table 4.

KSCT and behavior counts.

	b	e ^b	95% CI	Wald’s Χ²	Sig. (p)
Rule violations
KSCT score	−0.001	1.00	[−0.01, 0.01]	0.02	0.89
ADHD-H/I severity	0.01	1.01	[−0.02, 0.03]	0.59	0.44
ADHD-I severity	−0.01	0.99	[−0.03, 0.02]	0.37	0.54
Rule violations T1	0.83	2.29	[0.68, 0.99]	116.31	<0.0001***
Medication status	−0.31	0.73	[−0.55, −0.07]	6.57	0.01**
FSIQ-2	0.0001	1.00	[−0.01, 0.01]	0.00	0.98
Intercept	1.62	5.05	[0.82, 2.43]	15.53	<0.0001***
Dispersion	0.11	1.12	[0.07, 0.18]	–	–
Interruptions
KSCT score	0.001	1.00	[−0.01, 0.02]	0.02	0.90
ADHD-H/I severity	0.01	1.01	−[0.02, 0.05]	0.43	0.51
ADHD-I severity	−0.01	0.99	[−0.04, 0.03]	0.13	0.72
Interruptions T1	2.37	10.70	[1.68, 3.05]	46.19	<0.0001***
Medication status	−0.22	0.80	[−0.54, 0.10]	1.81	0.18
FSIQ-2	0.003	1.00	[−0.01, 0.01]	0.36	0.55
Intercept	0.20	1.22	[−0.88, 1.29]	0.13	0.71
Dispersion	0.20	1.22	[0.12, 0.33]	–	–
Time outs
KSCT score	0.05	1.05	[0.001, 0.10]	4.02	0.05*
ADHD-H/I severity	−0.10	0.90	[−0.18, −0.01]	4.95	0.03*
ADHD-I severity	0.08	1.08	[−0.04, 0.20]	1.71	0.19
Time outs T1	−0.49	0.61	[−10.95, 9.98]	0.01	0.93
Medication status	−0.45	0.64	[−1.56, 0.66]	0.63	0.43
FSIQ-2	0.03	1.03	[−0.01, 0.06]	2.41	0.12
Intercept	−4.64	0.01	[−9.15, −0.13]	4.06	0.04*
Dispersion	0.56	1.75	[0.17, 1.78]	–	–
Positive peer behaviors
KSCT score	−0.01	0.99	[−0.02, 0.01]	1.24	0.27
ADHD-H/I severity	0.00	1.00	[−0.02, 0.02]	0.00	1.00
ADHD-I severity	0.01	1.01	[−0.01, 0.03]	0.43	0.51
Positive peer behaviors T1	2.38	10.80	[2.01, 2.74]	165.45	<0.0001***
Medication status	0.16	1.17	[−0.04, 0.36]	2.44	0.12
FSIQ-2	0.01	1.01	[0.01, 0.02]	12.59	0.0004**
Intercept	−0.59	0.55	[−1.28, 0.10]	2.81	0.09
Dispersion	0.06	1.06	[0.03, 0.11]	–	–
Intentional aggressions
KSCT score	−0.06	0.94	[−0.15, 0.02]	2.40	0.12
ADHD-H/I severity	0.08	1.08	[−0.08, 0.24]	1.03	0.31
ADHD-I severity	−0.13	0.87	[−0.34, 0.08]	1.47	0.23
Intentional aggressions T1	38.78	6.95e¹⁶	[21.11, 56.45]	18.50	<0.0001**
Medication status	1.33	3.78	[0.05, 2.62]	4.11	0.04
FSIQ-2	−0.002	1.00	[−0.05, 0.05]	0.01	0.93
Intercept	−2.20	0.11	[−7.86, 3.47]	0.58	0.45
Dispersion	0.98	2.66	[0.32, 3.00]	–	–
Negative verbalizations
KSCT score	−0.02	0.98	[−0.04, 0.01]	1.10	0.29
ADHD-H/I severity	−0.001	1.00	[−0.05, 0.05]	0.00	0.96
ADHD-I severity	0.01	1.01	[−0.05, 0.07]	0.13	0.72
Negative verbalizations T1	1.88	6.55	[1.26, 2.51]	35.26	<0.0001***
Medication status	−0.36	0.70	[−0.86, 0.15]	1.93	0.16
FSIQ-2	0.01	1.01	[−0.01, 0.03]	1.26	0.26
Intercept	−0.41	0.66	[−2.29, 1.47]	0.18	0.67
Dispersion	0.52	1.68	[0.33, 0.82]	–	–

***p < 0.001, **p < 0.01, *p < 0.05.

The KSCT score at the beginning of treatment did not predict the number of positive peer behaviors (b = −0.01, e^b = 0.99, p = 0.27), rule violations (b =−0.001, e^b = 1.00, p = 0.89), negative verbalizations (b = −0.02, e^b = 0.98, p = 0.29), or interruptions (b = 0.001, e^b = 1.00, p = 0.90) at the end of treatment after controlling for their frequency at the beginning of treatment. The estimates of these effects were fairly precise given the examination of confidence intervals (Table 4) revealing that the e^b for these behaviors ranged from .96 to 1.02, indicating that increases in KSCT scores correspond to changes across these behaviors ranging from a 4% decrease to a 2% increase.

KSCT and total behavior frequencies

Examination of the association of pre-treatment KSCT scores with each of the behavioral categories described above revealed no significant associations (see Table 5). Additionally, visual examination of relevant scatterplots of these relationships did not suggest the presence of non-linear relationships across any of the associations of interest. Finally, the pattern of these results remained the same before and after correcting for outliers in the frequency of behavior counts.

Table 5.

Correlation between KSCT and total behavior frequencies.

	1	2	3	4	5	6
1. Pre-treatment total KSCT score	1.00
2. Rule violations	−0.19
3. Interruptions	−0.25	0.88**
4. Time outs	−0.12	0.93**	0.84**
5. Positive peer behaviors	−0.08	0.34*	0.30	0.29
6. Intentional aggressions	−0.08	0.66**	0.43**	0.68**	0.35*
7. Negative verbalizations	−0.07	0.76**	0.70**	0.77**	0.41*	0.76**

**p < 0.01, *p < 0.05

Note: behavior counts were summed across all 8 weeks of the STP and then averaged by days present per child.

KSCT subscales and counselor and teacher ratings/behavior counts

Examination of the association of pre-treatment KSCT subscale scores with the teacher/counselor ratings and each of the behavior categories revealed a significant association between the Working Memory Subscale of the KSCT and the assignment of a time out (b = 0.22, e^b = 1.25, p < .0001, (see Table S4). No other significant relationships were identified between the KSCT subscales and the variables of interest in the current study based on the Bonferroni correction (see Tables S1 – S6).

Discussion

The current study represents a preliminary step in examining whether parent-rated symptoms of sluggish cognitive tempo (SCT) have the potential to serve as a baseline predictor of behavioral treatment response in a sample of children receiving an eight week intensive behavioral intervention (i.e., the well-established Summer Treatment Program; STP). The emergence of substantial interest in SCT (Becker et al., 2016, 2017; Kofler et al., 2019) has given rise to a number of investigations examining the extent to which SCT symptoms respond to treatments for ADHD (Froehlich et al., 2018; Fırat et al., 2020) as well as whether children with greater levels of SCT symptoms respond similarly when compared to those with fewer symptoms of SCT. Given preliminary evidence of treatment response across some (Becker et al., 2014; Owens et al., 2016)—but not all (Pfiffner et al., 2007)—studies of children exhibiting SCT symptomology, we anticipated that SCT symptoms would be potentially useful in predicting behavioral treatment response in children with disruptive behavior problems. Furthermore, the current study extends the extant literature on symptoms of SCT and treatment response by incorporating a number of methodological and analytic advancements relative to past work in this area including a more diverse sample of children with symptoms of ADHD, vigorously operationalized and directly observed measures of behavioral treatment response, and a more optimal and appropriate approach to analysis of count data (i.e., Poisson regression).

Treatment related improvements in the frequency of most of the behaviors of interest were observed based on examination of median counts from T1 to T2 (for an exception, see negative verbalizations). While decreases in most of the behaviors of interest were observed, we did not expect to observe increases in negative verbalizations from T1 to T2. This finding may reflect the increasing familiarity children develop with one another over the course of treatment as well as improvements in the ability of paraprofessional staff to observe and record the behaviors over the eight weeks of treatment. In addition to clearly documented treatment-related improvements across most domains based on the frequency count data, examination of the counselor and teacher rated rule following and social skills domains on the SIRF revealed a similar pattern in that children were rated as somewhat improved at the end of treatment across these domains.

Notably, our primary aim was to examine whether pre-treatment symptoms of SCT predicted the frequency of behaviors exhibited across multiple domains (i.e., negative verbalizations, intentional aggressions, positive peer behaviors, rule violations, and interruptions) at the end of treatment after controlling for the frequency of these behaviors at the start of treatment. Additionally, we examined whether SCT symptoms predicted the frequency of the use of more intensive behavioral intervention approaches (e.g., time outs). These findings are described below).

Despite our expectation that higher SCT scores would significantly predict fewer treatment-related changes in intentional aggression, no association was observed. Further, with respect to the association between SCT symptoms and number of timeouts, our findings were inconsistent with our predictions as well as past work in this area (Becker et al., 2014) in that increases in SCT symptoms were actually associated with a less robust treatment response to time outs (i.e., a 5% increase in the likelihood of exhibiting a behavior that resulted in the assignment of a time out at the end of treatment). Further, while the specific subscales of the KSCT were not of primary interest in this study due to the relatively small sample utilized, post-hoc secondary analyses (see Online Appendix) revealed that even after corrections for multiple comparisons, this relation is likely driven primarily by the association between endorsements on the Working Memory Subscale of the KSCT and increases in behaviors that result in the assignment of a time out (b = 0.22, e^b = 1.25, p < .0001, see Table S4). Specifically, increases in endorsements on this subscale were associated with a 25% increase in the likelihood of being assigned a time out.

Notably, time outs were assigned for only one of three reasons (i.e., intentional aggression, intentional destruction of property, repeated noncompliance) and occur relatively infrequently in the program (median = 2 timeouts across three weeks at post-treatment; Table 2) highlighting that any increase in likelihood may still result in an overall low frequency of these behaviors (e.g., less than one additional timeout over the last three weeks). Despite this, however, given that increases in the severity of working memory problems on the KSCT were associated also with reductions in intentionally aggressive behavior (i.e., 17% decreased likelihood although not statistically significant after correcting for multiple comparisons; see Table S4), it is likely that this increase reflects time outs that resulted from failure to comply with instructions. This finding suggests that failure to comply with instructions for children with greater SCT symptoms may reflect parent’s perceptions of underlying difficulties in working memory (e.g., “loses train of thought”, “gets mixed up”, and/or “gets confused”) rather than intentionally oppositional and defiant behavior. Critically, given that the behavioral program is focused on reducing intentionally oppositional and defiant behavior (e.g., refusing to comply with an adult’s command) through contingency management (i.e., behavioral), these findings demonstrate that children with greater working memory problems associated with SCT symptoms may be more resistant to these behavioral techniques in reducing noncompliant behavior as the mechanism underlying these difficulties may be cognitive (e.g., forgot the instructions) rather than behavioral (e.g., decides not to comply with the instructions the child may recall). As noted above, this finding is inconsistent with past work (e.g., Becker et al., 2014) which may reflect a wider variety of symptoms of ADHD present within the current sample as well as the use of a more optimal measure of SCT symptoms in the current study. Furthermore, the incorporation of Poisson regression to examine the association of SCT symptoms with infrequently occurring, punishment procedures for certain behaviors, such as time out, may also account for some of the discrepancy between our findings and prior work in this area.

Substantial work on SCT, symptoms of ADHD, and associated social problems (e.g., Becker et al., 2014; Raiker et al., 2015) highlight the need to examine SCT symptoms as a potential predictor of change across domains related to social functioning over the course of treatment. Contrary to our expectations, results of this study revealed that higher SCT symptoms are not predictive of treatment-related improvements in positive peer behaviors (i.e., helping, sharing), a reduction in negative verbalizations (e.g., teasing), or counselor/teacher-rated improvements in social functioning. The discrepancy between our findings and prior work demonstrating that SCT symptoms are positively associated with social problems in children (Becker et al., 2014; Carlson & Mann, 2002) may reflect the types of measures used. For example, Becker et al. (2014) relied on parent-completed rating scales to assess both symptoms of SCT and treatment-related improvements in social problems. As a result, prior associations may overestimate these associations as a result of mono-informant and/or mono-method bias. The current study, in contrast, utilized parent ratings of SCT symptoms and direct observations and/or ratings provided by an informant other than the parent (e.g., counselors, teachers) to evaluate these associations.

Similarly, results revealed that KSCT pre-treatment scores did not predict treatment-related improvements in behaviors traditionally associated with symptoms of ADHD (e.g., rule violations, interruptions). In contrast to the results described above, however, this finding was consistent with predictions. Specifically, given past work demonstrating similar changes in symptoms of ADHD during treatment regardless of SCT symptom severity (Owens et al., 2016); the finding that behaviors characteristic of individuals with elevated symptoms of ADHD and disruptive behavior such as rule violations and interruptions were not predicted by severity of SCT symptoms is consistent with past work in this area. Notably, this finding replicates past studies in this area using methodological refinements such as operationally defined and directly observed behavioral indicators of disruptive behavior disorders over the course of treatment. Finally, while not the primary predictors of interest, it is notable that across most outcomes, the other covariates (e.g., ADHD symptom severity, FSIQ) of interest also failed to significantly predict treatment-related improvements. Collectively, these findings highlight that there remain a very limited number of characteristics that are likely to predict behavioral treatment response in children with symptoms of ADHD and future work is warranted to identify clinically useful predictors.

As a post-hoc evaluation of the extent to which directly observed, operationalized, and coded behaviors are associated with severity of SCT symptoms, we examined the associations between pre-treatment KSCT scores and each of the behaviors of interest across all eight weeks of treatment. Examination of these associations revealed that while the behaviors of interest were, for the most part, highly correlated with one another, parent-rated SCT scores were unrelated to each of these behaviors. This finding highlights the possibility that past associations between symptoms of SCT and specific domains of functioning in youth with symptoms of ADHD may reflect mono-informant and mono-method biases and points to a need for the incorporation of more diverse approaches to measurement (e.g., direct observation) into future studies in this area.

The current study incorporated a number of methodological and analytical refinements designed to overcome some of the limitations of past work in this area. For example, this study included a sample of children with a broader range of ADHD symptomology and the use of a well-established rating scale designed explicitly to evaluate the underlying symptom dimensions associated with SCT. Further, the incorporation of operationalized and directly observed behavioral categories as well as the utilization of Poisson regression procedures to overcome limitations of past approaches when applied to frequency data represent significant improvements designed to extend work in this area.

Despite these refinements, the results of this study should be interpreted with caution given the relatively small sample size and thereby preliminary nature of the current results. Although the sample size was small, it is important to note that the results are likely robust given the use of other analytic and design decisions such as direct observations of behaviors and inclusion of multiple time points (i.e., examining behaviors across intervals every day for eight weeks). In addition, although the response rate for participation in this study was lower than desired, the characteristics of the children participating in the current study (e.g., DBD-RS scores, age, gender) are largely consistent with children participating in past studies from this and similar treatment programs (Morris et al., 2020; Waxmonsky et al., 2008). Further, examination of confidence intervals revealed fairly precise estimates around an effect size of 1.00 (indicated no corresponding change in frequency of these behaviors with increases in SCT symptoms) for most outcomes. Nevertheless, the extent to which these findings generalize would benefit from future studies that include a larger and more diverse sample with a greater number of females and socioeconomic status that reflects broader population demographics (i.e., the current sample had a relatively high median income of $82,000 annually).

Additional methodological limitations concern some of the measures used and outcomes examined. Specifically, the rating scales of improvement as a function of treatment (which currently lack sufficient psychometric data) and the behavioral codes of interest were collected from individuals un-blinded to treatment status which has the potential to inflate estimates of overall improvements as a result of treatment (Karanicolas et al., 2010; Wood et al., 2008). Notably, however, given the range of scores obtained on the SIRF and the magnitude of improvement in this sample derived from the directly observed behaviors, it does not appear that this resulted in inflated estimates of improvement. Lack of a pre-treatment baseline in which the operationalized behavior codes were collected in the absence of ongoing behavioral intervention also reflects a limitation of this study as it is possible that children’s behaviors were improved within the first three weeks of the behavioral treatment program in response to the behavioral intervention thereby blunting potential treatment effects in the analyses. Despite this, however, examination of median treatment improvements (due to outliers) suggests that there was still room for improvement during the final three weeks of the program. It is also possible that SCT symptomology may predict treatment response in domains not currently examined. Finally, other factors, such as sleep, were not evaluated in the current study, but future work would benefit from incorporation of measures related to sleep given evidence for the association between sleep problems and symptoms similar to those observed in individuals with ADHD and SCT (e.g., Keshavarzi et al., 2014).

Supplemental Material

sj-pdf-1-prx-10.1177_0033294120957239 - Supplemental material for A Preliminary Evaluation of the Utility of Sluggish Cognitive Tempo Symptoms in Predicting Behavioral Treatment Response in Children with Behavioral Difficulties

Supplemental material, sj-pdf-1-prx-10.1177_0033294120957239 for A Preliminary Evaluation of the Utility of Sluggish Cognitive Tempo Symptoms in Predicting Behavioral Treatment Response in Children with Behavioral Difficulties by Kelcey Little, Joseph Raiker, Stefany Coxe, Mileini Campez, Morgan Jusko, Jessica Smith, Elizabeth Gnagy, Andrew Greiner, Miguel Villodas, Erika Coles and William E. Pelham in Psychological Reports

Footnotes

Acknowledgements

The authors would like to thank Dr. Whitney Fosco for her incredible feedback on this manuscript prior to submission for publication.

Notes

Supplemental material

Supplemental material for this article is available online.

Notes

ORCID iD

Joseph Raiker

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