The Effects of Directive and Responsive Communication Intervention Strategies on Autistic Children’s Disruptive Behavior

Abstract

Naturalistic developmental behavior interventions (NDBIs) are commonly taught to caregivers to target social communication in autistic children. In addition to social communication challenges, 50% of autistic children engage in disruptive behaviors. This study aims to evaluate the impact of responsive and directive NDBI social communication strategies on autistic children’s disruptive behavior. At baseline, mother–child dyads were randomized to receive 8 weeks of a caregiver-mediated intervention focused on either responsive or directive NDBI communication strategies (n = 73, M_age = 33.49, standard deviation [SD] = 6.37, directive n = 36, responsive n = 37). Before and after intervention, 10-min mother–child interactions were video-recorded and coded using an event-based disruptive behavior code. A linear model revealed that children in the directive condition were more likely to display disruptive behavior as compared to the responsive condition. This study’s findings can be used by providers and caregivers when engaging in shared decision-making to identify NDBI strategies that best fit the family’s priorities. Additional research is needed to examine (a) disruptive behavior over time and across contexts and (b) the effects of directive and responsive strategies implemented in tandem on disruptive behavior. The original clinical trial was prospectively registered in a publicly accessible database (NCT02632773, clinicaltrials.gov).

Keywords

autism spectrum disorders disabilities and developmental delays parent training components of practice behavior problems and disorders language and communication child development

Introduction

Naturalistic developmental behavior interventions (NDBIs) are commonly taught to caregivers to target social communication in autistic children within naturalistic contexts (Sandbank et al., 2023; Schreibman et al., 2015). However, few studies have examined the extent to which NDBIs may impact multiple outcomes (e.g., social communication and disruptive behavior; Kushner et al., 2023). This is particularly important in light of recent research that suggests that implementation of interventions based on behavioral theory may negatively impact the well-being of autistic individuals (Leadbitter et al., 2021; Leaf et al., 2022). However, some autism researchers have discussed the potential for NDBIs to bridge the gap between behaviorally based intervention approaches and some of the concerns from the broader autism community (e.g., parents of autistic children, autistic adults, and the neurodiversity movement). Therefore, this study aims to evaluate the impact of two broad classes of NDBI communication intervention strategies on an outcome that was not the focus of these strategies (i.e., autistic children’s disruptive behaviors). Such an analysis is critical to determining how various intervention approaches may have different effects for different child outcomes.

Disruptive behavior is a particularly meaningful outcome of intervention for autistic children, as approximately 50% of autistic children present with disruptive behaviors (Kanne & Mazurek, 2011). Disruptive behavior is a cluster of behaviors, including aggression, self-injury, eloping, and behaviors observed during meltdowns that are typically a response to a distressing or challenging situation. Notably, disruptive behaviors do not include sensory-seeking or stimming behaviors, as the autistic community has advocated that researchers conceptualize these behaviors as distinct from disruptive behaviors, as they play a crucial role in managing sensory overload and emotional regulation (Leadbitter et al., 2021).

Caregivers of autistic children and therapy providers have expressed that children’s disruptive behavior is a primary developmental concern (Chow & Wehby, 2018; Ozonoff et al., 2009). Addressing disruptive behavior in early childhood may yield cascading benefits for later language and communication development, peer interactions, school readiness, and academic outcomes (Chow & Wehby, 2018; Fleury et al., 2014). At the same time, the presence of disruptive behaviors negatively affects caregivers’ self-efficacy, quality of life, mental health, and stress (Lecavalier et al., 2006; Rezendes & Scarpa, 2011). Despite the elevated prevalence of disruptive behaviors among autistic children and the concerns expressed by caregivers and providers, few studies have examined the extent to which NDBI communication intervention strategies may impact disruptive behaviors (Kushner et al., 2023). It may be that challenges in each domain (communication and disruptive behavior) have a reciprocal influence on each other, such that autistic children might (a) display disruptive behaviors when they are frustrated and unable to verbally express their needs with a communicative partner, and/or (b) have less frequent communication learning opportunities due to the higher presence of disruptive behavior (Chow & Wehby, 2018). Understanding how communication intervention strategies impact disruptive behaviors is critical to understanding how interventions may influence domains not specifically targeted by the intervention.

NDBIs include two broad classes of communication intervention strategies: responsive and directive. In responsive strategies, based on developmental theory, the caregiver follows the child’s lead and responds with contingent input to facilitate communication development (McDuffie & Yoder, 2010; Siller & Sigman, 2002). In contrast, directive strategies are based on behavioral theory as informed by the three-part contingency “ABC” model: antecedent, behavior, and consequence (Haebig et al., 2013). Directive strategies teach caregivers to (a) implement a communication prompt (which may include restricting access to an object or preferred activity), (b) wait for the child to communicate, and (c) reinforce their child’s communication attempt by expanding on their communication and providing access to preferred objects and activities. Caregiver expansion (e.g., the child says “ball” and the caregiver responds “here is your blue ball”) within directive strategies shapes and scaffolds children’s communication to facilitate further development (Schreibman et al., 2015). However, because directive strategies often require a communicative response from the child, this may prove particularly challenging for autistic children with communication difficulties. As such, implementing a directive prompt and requiring communication from an autistic child may be frustrating and lead to more disruptive behaviors. In contrast, responsive strategies may not elicit disruptive behaviors as there are no limits or demands set on the child’s communication. Responsive strategies may even reduce disruptive behaviors since reducing the use of questions and directions may minimize potential antecedents of disruptive behaviors. This study is a secondary data analysis of a randomized clinical trial (Jones et al., 2024). Within the original clinical trial, mother–child dyads were randomized to receive either responsive or directive communication intervention strategies. Children in the directive condition demonstrated significantly greater language improvement after intervention as compared to children in the responsive condition (Jones et al., 2024). However, children’s disruptive behaviors were not considered within the original clinical trial. Therefore, given the potential for various communication intervention strategies to reduce or elicit disruptive behaviors, it is important to extend the evaluation of various NDBI strategies beyond communication outcomes to include disruptive behaviors.

Although directive and responsive NDBI strategies are often implemented in tandem (Sandbank et al., 2023; Schreibman et al., 2015), this combined approach limits our ability to isolate the effects of each individual strategy. Clarifying each strategy’s distinct contribution is crucial for understanding its impact across developmental domains such as social communication, language, and disruptive behavior. This understanding will guide future research evaluating the optimal proportion of each strategy in NDBI implementation. For example, many NDBIs suggest using responsive strategies until the child is engaged and motivated, at which point a directive strategy may be introduced (Ingersoll & Wainer, 2013; Sandbank et al., 2023; Schreibman et al., 2015). While this approach is suggested across many NDBIs (Ingersoll & Wainer, 2013; Kaiser et al., 2000; Sandbank et al., 2023), there is currently no empirical evidence demonstrating how the proportion of each of these individual strategies impacts children’s outcomes. Therefore, this study, along with the original clinical trial (Jones et al., 2024), represents a first step toward evaluating the isolated effects of directive and responsive strategies across developmental domains, helping to inform future individualized clinical practice by identifying the optimal proportion of each strategy based on children’s specific needs. Despite the strong theoretical foundation as to why some communication intervention strategies may reduce or elicit disruptive behaviors in autistic toddlers, there is no empirical evidence of the effects of different communication intervention strategies on disruptive behavior in autistic toddlers (Sandbank et al., 2023). Addressing this gap in the literature is an important first step toward a more comprehensive evaluation of NDBI effects, which may lead to more individualized intervention planning. As such, this study is guided by the following research question:

1. To what extent is the effect of intervention strategy type (i.e., responsive or directive) associated with children’s frequency of disruptive behavior.

a. We hypothesized that directive strategies would be more likely to elicit disruptive behaviors in autistic children as compared to responsive strategies because of caregiver’s explicit restriction of an object or preferred activity. In contrast, responsive strategies may result in fewer disruptive behaviors because of the reduced communicative demands placed on the child and caregivers’ reinforcement of their child’s communication with their responsive verbal input.

Methods

Study Design

This study was a secondary analysis of data from a randomized clinical trial examining the effects of teaching mothers to use either directive or responsive communication facilitation strategies on the communication outcomes of autistic toddlers. The original clinical trial was prospectively registered in a publicly accessible database (NCT02632773, clinicaltrials.gov). This study included 95 mother–child dyads (M_age = 36.36 months, standard deviation [SD] = 6.15 months). Previous work with the same sample reported 96 mother–child dyads (Jones et al., 2024); however, after data collection, a participant requested that their data no longer be analyzed. Participants were randomized to the directive (n = 49) or responsive (n = 46) intervention condition. The randomization sequence was computer-generated by the senior project statistician and contained random block sizes with a 1:1 assignment ratio within blocks. Participants were recruited continuously between July 2015 and March 2020. After baseline data were collected for all participants, mothers in both intervention groups received weekly, 1-hr intervention sessions for 8 weeks. All participants were assessed before the intervention (i.e., T0 or baseline), immediately after the intervention (i.e., T1), and 3 months after the intervention (i.e., T2). Behavioral coding of disruptive behavior was conducted before and immediately after intervention (i.e., T0 and T1). Children in both intervention groups continued to receive community-based early intervention.

Participants

The study was conducted in the greater Chicagoland area. Participants were recruited from pediatricians, autism diagnostic programs, and Part C early intervention providers. Children were eligible for the study if they were between 24 and 48 months old, had a diagnosis of autism, or their mothers had concerns about autism, which were later confirmed by an in-person autism assessment, including the Autism Diagnostic Observation Schedule (ADOS-2; Lord et al., 2012). Due to the original clinical trial evaluating maternal characteristics (i.e., broader autism phenotype) on intervention strategy use, caregivers were eligible if they were the biological mother to the participating child, English was the mother’s first language or English was learned before the age of 10, and did not have a diagnosis of: Fragile X syndrome, cerebral palsy, schizophrenia, profound hearing loss, or brain or head injury where she lost consciousness. All mothers signed an informed written consent approved by Northwestern’s Institutional Review Board (STU00201708). In addition, the research team documented adverse events and protocol deviations, such as incomplete intervention sessions, sessions conducted outside the designated time window, sessions delivered out of order, or absence of the primary caregiver (see Supplemental Table 3).

The children within the sample were predominantly male (n = 71, 74.7%), 46.3% of children were preverbal (n = 44, defined as preverbal if communicating with vocalizations on the ADOS-2), with the remainder (n = 51, 53.7%) being verbal (i.e., single word or multiword speech on the ADOS-2; Lord et al., 2012). Groups did not significantly differ on any demographic data or primary dependent variables at baseline. See Table 1 for participant demographic characteristics and Figure 1 for the CONSORT chart.

Table 1.

Baseline Demographic Characteristics.

Child characteristics
		Intervention condition
Variable		Responsive n = 46	Directive n = 49	Effect size
Age	Months	32.8 (6.74)	33.6 (5.98)	d = 0.13
Biological sex	Male	30 (65%)	41 (84%)	RR = 0.78
Race	White	24 (52%)	30 (61%)	V = 0.22
	Asian	2 (4%)	1 (2%)
	Black	5 (11%)	6 (12%)
	More than one race	9 (20%)	11 (22%)
	Prefer not to answer	6 (13%)	1 (2%)
Ethnicity	Hispanic	14 (30%)	16 (33%)	RR = 0.93
	Not Hispanic or Latino	29 (63%)	32 (65%)
	Prefer not to answer	3 (7%)	1 (2%)
Disruptive behavior	Count of disruptive behavior during T0 MCX	2 (0–4)^a	1 (0–4)^a	d = 0.06
	Count of disruptive behavior during T1 MCX	1 (0–3)^a	1 (0–5)^a	d = 0.39
Languages spoken in the home other than English	Yes	16 (35%)	17 (35%)	RR = 0.99
Entry-communication level	ADOS-2 verbal (single word or multiword speech)	21 (46%)	23 (47%)	RR = 0.97
Mother characteristics
Variable
Age	Years	34.5 (5.23)	36.2 (5.46)	d = 0.32
Race	White	29 (63%)	31 (63%)	V = 0.19
	Asian	2 (4%)	3 (6%)
	Black	5 (11%)	7 (14%)
	Native Hawaiian or Other Pacific Islander	1 (2%)	0 (0%)
	American Indian or Alaska Native	0 (0%)	1 (2%)
	More than one race	4 (9%)	2 (4%)
	Prefer not to answer	5 (11%)	5 (10%)
Ethnicity	Hispanic	14 (39%)	11 (28%)	RR = 1
	Not Hispanic or Latino	30 (65%)	36 (73%)
	Prefer not to answer	2 (4%)	1 (2%)
Education	Without HS	1 (2%)	1 (2%)	V = 0.18
	HS graduate	2 (4%)	4 (8%)
	Some college	15 (33%)	15 (31%)
	College graduate from a 4-year college or more	28 (61%)	29 (60%)
Employment status	Not employed	3 (7%)	0 (0%)	V = 0.2
	Stay-at-home mother	15 (33%)	18 (37%)
	Part-time	12 (26%)	11 (22%)
	Full-time	15 (33%)	19 (39%)
	Second job	1 (2%)	1 (2%)
Income	<US$30,000	6 (13%)	4 (8%)	V = 0.17
	US$30,000–US$49,000	4 (9%)	6 (12%)
	US$50,000–US$100,000	7 (15%)	13 (27%)
	>US$100,000	22 (48%)	19 (39%)
	No response	7 (15%)	7 (14%)
Government assistance	Receiving assistance	11 (24%)	8 (16%)	RR = 0.91
Government assistance	Not receiving assistance	35 (76%)	41 (84%)	RR = 0.91

Note. Autism Diagnostic Observation Schedule–Second Edition; The Infant Toddler Social Emotional Assessment; d = Cohen’s d; RR = relative risk; V = Cramer’s V.

Median (IQR).

Figure 1.

CONSORT chart.

Intervention

For both intervention groups, the 8-week intervention included a workshop introducing the communication intervention strategies to the mother and seven coaching sessions with the mother and the child. All sessions were delivered in English. Coaching sessions were structured in accordance with the manualized format teach-model-coach-review (TMCR; Roberts et al., 2014). The TMCR model is based on adult learning theory and incorporates a variety of adult learning methods, including illustration, practice, and reflection (Roberts et al., 2014). In each session, clinicians observed the mother and child, provided direct instruction on the communication intervention strategies (“Teach”), demonstrated strategy use with the child (“Model”), provided feedback while the mother practiced with their child (“Coach”), and engaged in reflective discussion (“Review”).

Responsive and directive strategies are two broad classes of intervention strategies within NDBIs and are based on developmental and behavioral models (Sandbank et al., 2023; Schreibman et al., 2015). Responsive strategies are based on a developmental model in which caregivers provide linguistic input appropriate for their child’s developmental level and prioritize environmental arrangement to facilitate initiation and responding behaviors from the child (Schreibman et al., 2015). In the responsive condition, mothers were taught to engage with their child, notice and respond to nonverbal and verbal communication, interact by taking turns with their child, and follow their child’s lead. See the Supplemental Materials for responsive definitions. Directive strategies are rooted in behavioral learning theory and use the three-term “ABC” contingency to gain a specific response from the child (Schreibman et al., 2015). Directive strategies use prompting trials through either object/activity management in response to a spontaneous child request or in response to the child’s interest. As such, mothers in the directive condition were instructed to scaffold prompts to teach and reinforce communication. Mothers in the directive intervention group were taught four directive communication strategies: communication temptations/time delays, open prompts, choice prompts, and “say” prompts. See the Supplemental Materials for directive definitions. Jones et al. (2024) provide further information about responsive and directive intervention strategies.

Measures and Coding

Before and immediately after intervention was completed (i.e., T0 and T1), mother–child dyads were filmed during a 10-min naturalistic mother–child interaction (MCX) using a standard set of toys. Before filming, mothers were instructed to play with their child as they normally would. The video-recorded MCXs were coded using an event-based disruptive behavior code on Mangold Interact Software (Mangold, 2020); each video was 10 min and was coded in full. Disruptive behavior definitions were adapted from an observational disruptive behavior measure created specifically for autistic children (Palmer et al., 2020). A PhD and master’s student adapted the definitions to include examples and coding rules to differentiate between disruptive behaviors and stimming, self-regulatory, and motor behaviors (see Table 2). However, we did not use the standardized tasks (i.e., probes designed to elicit frustration or disruptive behavior) from the Palmer et al. (2020) observational disruptive behavior measure, as we wanted to see how, within a mother–child interaction, the specific NDBI strategies elicited disruptive behavior. The dependent variable of interest was the change in the frequency of disruptive behavior (i.e., subtracting baseline disruptive behavior count values from postintervention) and was calculated from the event-based disruptive behavior code (Castro-Schilo & Grimm, 2018). Furthermore, this disruptive behavior code was created in accordance with widely accepted recommendations for observational measures of behavior (Yoder et al., 2018). See Table 2 for disruptive behavior microcode definitions.

Table 2.

Disruptive Behavior Microcode Definitions.

Code item	Definition
Avoidance/Eloping	This code measures behaviors that involve the child avoiding or running away from a situation.Examples: pulling at the door handle, running away from their caregiver or the examiner, and hiding under the table or behind the door.
Destructive behavior	This code measures the child’s attempt to damage or destroy an object or property. The deliberateness and intent of the disruptive behavior should be considered when coding. Damage to an object or property should NOT be coded as destructive behavior if it results from (a) clumsiness or poor motor skills (e.g., trying to put puzzle pieces together but accidentally breaking a piece), or (b) self-regulating or stimming behavior (e.g., shaking a toy and accidentally breaking it).Examples: throwing, hitting, slapping, punching, kicking, or biting an object.
Frustrated vocalizations	This code captures vocalizations by the child that are disruptive or negative in nature. Vocalizations that appear to be related to ASD symptomatology, such as self-regulation or stimming, should NOT be coded here (e.g., humming, squealing, shrieking, or loud vocal noises without apparent frustration).Examples: whining, crying, yelling, shouting, and screaming.
Physical aggression toward others	This code captures the child’s behaviors that involve a physically aggressive act directed at their caregiver or the examiner. The child does NOT need to make contact with the individual for the act to be coded as a physically aggressive act (e.g. if the child goes to hit the caregiver but they move away, this should be coded as physical aggression). The deliberateness and intent of the disruptive behavior should be considered when coding. Physical aggression should NOT be coded if it results from clumsiness or poor motor skills (e.g., a child falling and accidentally kicking their caregiver as they fall).Examples: hitting, slapping, punching, kicking, or biting their caregiver or the examiner.
Physical aggression toward self	This code captures the child’s behaviors that involve a physically aggressive act directed toward themself. The deliberateness and intent of the disruptive behavior should be considered when coding. Physical aggression should NOT be coded if it results from clumsiness or poor motor skills (e.g., a child falling and accidentally hitting their head as they fall).Examples: hitting, slapping, punching, kicking, or biting themselves.

Coders were PhD and master’s students trained to 80% reliability across each code. In addition, 20% of all MCXs were coded by a second master’s student, and discrepancies were discussed during weekly coding meetings. All coders were naïve to the intervention condition. Reliability was completed for 20% of all MCX administrations (ICC = .99).

Statistical Methods

This study aimed to examine the effect of intervention strategy type (i.e., responsive or directive) on autistic children’s disruptive behavior. Therefore, we examined the change in the frequency of disruptive behavior between T1 (postintervention) and T0 (baseline). We used a linear regression model to test the effect of intervention condition on child disruptive behavior change score while controlling for child’s entry-communication level to align with the original clinical trial (Jones et al., 2024). Other covariates included child age, biological sex, mother’s employment status and education, as these variables were slightly different across conditions and among the children who never exhibited disruptive behavior (i.e., structural zeros) and thus were removed from the analysis (see Table 3). We dichotomized mothers’ education and employment status such that education was precollege or college (i.e., precollege included without high school, high school graduate, and some college), and employment was part-time or full-time (i.e., part-time included not employed, stay-at-home mother, and part-time). We chose to model disruptive behavior change scores (as opposed to the frequency of disruptive behaviors after intervention) because of the violation of statistical assumptions for traditional models, where postintervention is compared, controlling for baseline. The most frequent criticism of change scores is that they are not reliable, but their reliability is contingent on the reliability of the individual assessments, which was very high for coded behaviors, as noted earlier. Modifications to the model were considered to meet the necessary statistical assumptions.

Table 3.

Baseline Demographic Characteristics for Participants With Structural Zeros

Child characteristics
		Intervention condition
Variable		Responsive, n = 9 (9 removed from 46)	Directive, n = 13 (13 removed from 49)	Effect size
Age	Months	30.45 (5.31)	31.94 (5.03)	d = 0.29
Biological sex	Male	6 (66%)	10 (77%)	OR = 1.62
Race	White	5 (55%)	8 (62%)	V = 0.07
	Asian	1 (11%)	0 (0%)
	Black	0 (0%)	1 (8%)
	More than one race	3 (33%)	4 (30%)
	Prefer not to answer	0 (0%)	0 (0%)
Ethnicity	Hispanic	1 (11%)	2 (15%)	OR = 1.19
	Not Hispanic or Latino	7 (78%)	11 (85%)
	Prefer not to answer	1 (11%)	0 (0%)
Entry-communication level	ADOS-2 verbal (single word or multiword speech)	5 (56%)	7 (54%)	OR = 0.93
Languages spoken in the home other than English	Yes	2 (22%)	3 (23%)	OR = 0.97
Mother characteristics
Age	Years	34.49 (4.25)	39.5 (5.21)	d = 1.03
Race	White	5 (55%)	9 (69%)	V = 0.14
	Asian	1 (11%)	1 (8%)
	Black	1 (11%)	3 (23%)
	Native Hawaiian or Other Pacific Islander	0 (0%)	0 (0%)
	American Indian or Alaska Native	0 (0%)	0 (0%)
	More than one race	2 (22%)	0 (0%)
	Prefer not to answer	0 (0%)	0 (0%)
Ethnicity	Hispanic	2 (22%)	2 (15%)	OR = 0.65
	Not Hispanic or Latino	7 (78%)	11 (85%)
	Prefer not to answer	0 (0%)	0 (0%)
Education	Without HS	0 (0%)	0 (0%)	V = 0.33
	HS graduate	0 (0%)	1 (8%)
	Some college	0 (0%)	2 (15%)
	College graduate from a 4-year college or more	9 (100%)	10 (77%)
Employment status	Not employed	0 (0%)	0 (0%)	V = 0.32
	Stay-at-home mother	1 (11%)	5 (38%)
	Part-time	4 (44%)	3 (23%)
	Full-time	4 (44%)	5 (38%)
	Second job	0 (0%)	0 (0%)
Income	<US$30,000	0 (0%)	0 (0%)	V = 0.01
	US$30,000–US$49,000	2 (22%)	0 (0%)
	US$50,000–US$100,000	2 (22%)	5 (38%)
	>US$100,000	5 (55%)	6 (46%)
	No response	0 (0%)	2 (15%)
Government assistance	Receiving assistance	2 (22%)	1 (8%)	OR = 0.41
Government assistance	Not receiving assistance	7 (78%)	12 (92%)	OR = 0.41

Note. Autism Diagnostic Observation Schedule – 2nd Edition; The Infant Toddler Social Emotional Assessment; d = Cohen’s d; RR = relative risk; V = Cramer’s V.

Median (IQR).

Results

Initial analyses suggested our data would fail to meet statistical assumptions for linear regression modeling. We noticed there were children who had zero disruptive behaviors before and after intervention, resulting in a change score of “0” (i.e., structural zeros), so we calculated the kurtosis to evaluate the distribution’s “tailedness” (i.e., heavy- or light-tailed) as compared to a normal distribution (Chissom, 1970). The kurtosis of disruptive behavior change scores was highly leptokurtotic (25.15). Therefore, we removed the 22 children who never exhibited disruptive behavior (and thus, change was 0, not because of lack of improvement but because of never engaging in the behavior), which was affecting our sample’s kurtosis (after removal: n = 73, M_age = 33.49, SD = 6.37). Of the 22 participants removed due to structural zeros, 13 were removed from the directive condition and 9 were removed from the responsive condition. Therefore, our final sample used for our linear model was 36 participants in the directive condition and 37 participants in the responsive condition. See Table 3 for the full demographic characteristics data of the participants with structural zeros. We recalculated the distribution’s kurtosis without structural zeros and found that the kurtosis decreased to 18.23, which is better but still leptokurtotic compared with a normal distribution (Chissom, 1970). Overall, removing structural zeros is statistically advantageous as the residuals from the regression model now have a variance similar to that of a normal distribution, which better meets statistical assumptions for linear regression modeling. Clinically, we also removed structural zeros as these data represent the children who will likely never display disruptive behavior and not be affected by the intervention condition compared with the more clinically relevant subsample of children who displayed disruptive behavior during intervention (i.e., at T0 or T1). In addition, outliers were observed in the change scores. Therefore, we took a Winsorized approach to address these; extreme values were censored if the disruptive behavior change score was above or below the two-sided 95th percentile (Aguinis et al., 2013). As such, we changed two outliers to align with the two-sided 95th percentile (Aguinis et al., 2013). Both outliers were in the directive condition and had frequency of disruptive behavior values above the 95th percentile, indicating that they were the highest values of disruptive behavior in the sample.

A linear regression model was conducted to evaluate the effect of intervention condition on autistic children’s disruptive behavior change score while controlling for child’s entry-communication level, age, biological sex, mother’s education, and employment status. Results from the linear model showed that, while controlling for child entry-communication level, age, biological sex, mother’s education, and employment status, autistic children in the directive condition had a significant increase in disruptive behavior change score as compared to the responsive condition (B = 5.65, SE = 2.12, p = .01, d = 0.48 [95% CI: 0.01, 0.95]) such that children in the directive condition displayed a change in five additional instances of disruptive behaviors compared with the change for those in the responsive condition. Across the model coefficients, the responsive condition was expected to show a decrease in the number of disruptive behavior incidents, while the directive condition showed an increase. In addition, age was significantly associated with disruptive behavior change scores, such that a 1-month increase in age was associated with a 0.35 decrease in disruptive behavior change (B = −0.35, SE = 0.17, p = .05), suggesting that older autistic children exhibited greater decreases in disruptive behavior change compared with younger children. This final linear model met the statistical assumptions for regression, in particular normality of residuals and homoscedasticity. See Table 4 for linear model results.

Table 4.

Results From the Linear Regression Model of NDBI Condition Predicting Autistic Children’s Disruptive Behavior Change Score.

Linear model
Predictor	Change score of disruptive behavior frequency (T1–T0)
Predictor	B	SE	p
Intercept	9.61	5.68	.10
Intervention group^a	5.65	2.12	.01
Child’s entry level communication^b	0.28	2.06	.59
Child’s age	−0.35	0.17	.05
Child’s biological sex^c	3.81	2.46	.13
Mother’s education	−0.22	2.12	.92
Mother’s employment	−2.10	1.20	.34
R² = 0.06

Note. df = 66.

Directive condition was treatment coded as “1.” ^bADOS preverbal condition was treatment coded as “1.” ^cMale was treatment coded as “1.”

In addition, based on the final linear model, we calculated the estimated marginal means to evaluate changes in disruptive behavior scores for both the responsive and directive conditions. The change in scores for children in the responsive condition was −1.13 units (SE = 1.51, 95% CI [−4.14, 1.88], p = .46), indicating no significant change. In contrast, children in the directive condition showed a significant increase of 4.52 units (SE = 1.71, 95% CI [1.11, 7.94], p = .01) in their disruptive behavior scores.

Discussion

The goal of this study was to examine the effects of NDBI communication facilitation strategies on autistic children’s disruptive behavior. We found that directive NDBI strategies resulted in a significant increase in children’s disruptive behavior as compared to responsive strategies (see Table 4). This finding is in line with our hypothesis that directive strategies may be too challenging for some children, result in frustration, and inadvertently elicit disruptive behavior. Furthermore, our estimated marginal means results show that responsive NDBI strategies slightly reduce disruptive behaviors, albeit not statistically significantly. Finally, we saw that older autistic children had greater decreases in disruptive behavior change score as compared to younger children. This finding aligns with the literature, which shows that as children get older, they develop stronger behavior and emotion regulation skills, leading to larger reductions in disruptive behaviors (Baillargeon et al., 2012). These results highlight the effects of various NDBI strategies on child disruptive behavior outcomes.

We found that autistic children in the directive condition had a significant increase in the frequency of disruptive behavior as compared to children in the responsive condition. However, the original clinical trial found that directive strategies facilitate greater communication outcomes compared with responsive strategies (Jones et al., 2024). Together, these findings suggest that directive strategies may impact communication and disruptive behavior differently. Furthermore, this study and the original clinical trial provide critical insight into how each of the strategies independently affected children’s communication and disruptive behavior. While these findings can inform existing NDBIs, it is important to note that responsive and directive strategies are typically implemented in tandem (Schreibman et al., 2015). Within these existing NDBIs, caregivers are encouraged to predominantly implement responsive strategies and only use directive strategies when the child is regulated and engaged (Ingersoll & Wainer, 2013; Kaiser & Hester, 1994). However, the exact proportion of directive and responsive strategy use on child outcomes has yet to be evaluated (Schreibman et al., 2015). As such, future research should expand upon our findings and evaluate how varying the proportion of different NDBI strategies could improve social communication while limiting their effects on disruptive behavior (e.g., predominantly using responsive strategies and occasionally implementing directive strategies when the child is regulated and engaged). Given that directive strategies impact social communication and disruptive behavior differently, it is critical to engage caregivers within a shared decision-making framework by discussing their priorities and goals for intervention (Golnik et al., 2012). For example, if a clinician is teaching a caregiver NDBI strategies (i.e., responsive and directive in tandem), it is critical that they consider the effects of various strategies on caregiver’s goals for intervention. If a caregiver identifies social communication as their priority, the clinician should explain that the use of more directive strategies may result in an increase in disruptive behaviors yet may improve communication. If a caregiver’s primary priority is reducing disruptive behaviors, a clinician may choose to implement a higher proportion of responsive strategies as compared to directive strategies. Our findings can be used within a shared decision-making framework with families, such that caregivers are better able to choose NDBI communication strategies that align with their priorities for intervention.

This study has important implications for diagnosticians referring autistic children to early intervention (EI) providers. Given that directive strategies were found to have differential effects on autistic children’s social communication and disruptive behaviors, diagnosticians (e.g., developmental pediatricians, psychologists, or psychiatrists) should provide a comprehensive description of the child’s strengths and challenges across developmental domains when preparing reports and making EI referrals. This detailed information within a diagnostic report is essential for guiding the selection of appropriate intervention approaches and strategies by EI providers. For example, if a child engages in elevated rates of disruptive behavior, this should be noted in the diagnostic report, as it may influence an EI provider’s choice of NDBI strategies. Although both responsive and directive strategies are typically implemented together, the EI provider may prioritize responsive strategies and implement directive strategies once the child is emotionally regulated, motivated, and engaged. This approach aims to balance the use of both strategies to foster social communication development while minimizing disruptive behaviors (Ingersoll & Wainer, 2013; Kaiser & Hester, 1994).

Although both responsive and directive NDBI strategies show positive effects on children’s communication outcomes (Sandbank et al., 2023), the broader autism community encouraged intervention researchers to evaluate how intervention strategies impact environmental goodness-of-fit and autistic individuals’ experiences during intervention (Leadbitter et al., 2021). Within this study, we used disruptive behavior as a metric to evaluate the extent to which different communication strategies impacted children’s experiences during the intervention. We found that directive strategies exacerbate children’s disruptive behavior. This aligns with recent advocacy efforts, which highlight the negative impact of some behaviorally based strategies on autistic individuals (Leadbitter et al., 2021). However, additional research is needed to continue to evaluate the effects of directive NDBI strategies on autistic children and their families, while continuing to consider the broader autism community’s concerns. Furthermore, this study is one of the first to evaluate the effects of NDBI strategies on autistic children’s disruptive behavior; however, additional research is needed to replicate and extend these findings.

Limitations and Further Research

Limitations to this study include limited inclusion criteria (i.e., biological mothers) that were justified by the original study’s primary aims (R01DC014709, NCT02632773) but may limit the generalization of this study’s findings. An additional limitation is the collection of one 10-min mother–child interaction before and after intervention. Behavior is variable and context dependent; therefore, future studies should include additional observational samples of behavior both before and after intervention and in other contexts (Yoder et al., 2018). The collection of additional data points may provide a more robust profile of the child’s disruptive behavior. Another potential limitation is our methodological choice to eliminate structural zeros. While there are models appropriate for zero-inflated count data (e.g., zero-inflated negative binomial regression), similar models do not exist for continuous outcomes with both positive and negative real values. Removing structural zeros is similar to creating a “zero-class” of individuals who never exhibit disruptive behaviors, but future research should consider multiple observations and alternate models that allow inclusion of all participants. Finally, the current sample size limited our ability to examine sociodemographic characteristics as potential moderators of outcomes. Future research should consider how NDBI strategy use may vary based on children’s cultural backgrounds, as it may be that certain cultures have differing preferences for strategy type (Mandell & Novak, 2005).

Conclusion

The result of this study extends prior research examining the impact of NDBI strategies on child communication to disruptive behavior, an important first step in understanding the effects of NDBI strategies on different child outcomes. Providers and caregivers can use the findings of this study when engaging in shared decision-making to identify NDBI strategies that best fit the caregivers’ priorities. Further intervention research is needed to (a) examine disruptive behavior across contexts and over time, (b) evaluate how the simultaneous use of directive and responsive strategies may affect disruptive behavior, and (c) include the priorities and concerns of autistic individuals and the neurodiversity movement.

Supplemental Material

sj-docx-1-jei-10.1177_10538151261450183 – Supplemental material for The Effects of Directive and Responsive Communication Intervention Strategies on Autistic Children’s Disruptive Behavior

Supplemental material, sj-docx-1-jei-10.1177_10538151261450183 for The Effects of Directive and Responsive Communication Intervention Strategies on Autistic Children’s Disruptive Behavior by Hannah Fipp-Rosenfield, Rachel Levy, Maranda K. Jones, Jeffrey Grauzer, Aaron J. Kaat and Megan Y. Roberts in Journal of Early Intervention

Footnotes

ORCID iDs

Hannah Fipp-Rosenfield

Aaron J. Kaat

Megan Y. Roberts

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under award number R01DC014709. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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